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Virtual Discover Brigham, November 12, 11AM EST – 3:45PM EST

 

2020 AGENDA

http://www.discoverbrigham.org/

11:00 – 11:45 AM EST

Poster Session & Demos

11:45 AM – 12:15 PM EST

Rock the Mic: Postdoc Fast Pitch

12:30 – 1:05 PM EST

What We Know About COVID-19

1:10 – 1:40 PM EST

Hey Briggie: The Use of Artificial Intelligence to Improve Patient Safety and Experience

1:45 – 2:15 PM EST

Committing to Diversity, Inclusion, and Equity in Clinical Research: The Time is Now

2:25 – 2:55 PM EST

Getting Ahead: Advances in Food and Drug Allergy

3:00 – 3:30 PM EST

What’s Sex Got to Do with It: Risk and Management of Autoimmune Disease

3:35 – 3:45 PM EST

Closing Remarks

“Kicking off with a keynote, the day-long event featured seven interactive demos, eight scientific sessions, 99 poster presentations, 37 speakers and the announcement of the winner of the $100,000 BRIght Futures Prize.”READ MORE

SESSION
SPOTLIGHTS

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Open Data Science Conference, Virtual and In-Person | October 27th – 30th, 2020, Natural Language Processing Track

Virtual and In-Person | October 27th – 30th, 2020

Natural Language Processing Track

Learn the latest models, advancements, and trends from the top practitioners and researchers behind NLP

Conference Website

AGENDA

https://live.odsc.com/

Thursday – 10/29/2020

09:00 AM – 10:30 AM – ODSC Keynotes

10:30 AM – 5:30 PM – ODSC Hands-on Trainings and Workshops

10:00 AM – 4:30 PM – Partner Demo Talks

10:30 AM – 5:00 PM – Breakout Talk Sessions

09:30 AM – 4:30 PM – Applied AI Free Virtual Event

12:00 PM – 2:00 PM – Woman Ignite Session

1:00 PM – 1:45 PM – Virtual Networking Event

4:00 PM – 5:30 PM         – AI Investors Reverse Pitch

3:30 PM – 4:30 PM – Meet the Expert

 

Friday – 10/30/2020 

09:00 AM – 10:30 AM – ODSC Keynotes

10:30 AM – 5:30 PM – ODSC Hands-on Trainings and Workshops

10:30 AM – 5:00 PM – Breakout Talk Sessions

10:30 AM – 5:00 PM – Career Mentor Talks

11:30 AM – 12:00 PM – Meet the Speaker

4:00 PM – 5:30 PM –  Learning from Failure

Are We Ready for the Era of Analytics Heterogeneity? Maybe… but the Data Says No

 

Wed, October 28, 9:00 AM
(PDT)

Marinela Profi | Global Strategist AI & Model Management | Data Science Evangelist | SAS | WOMEN TECH NETWORK

 

Type: Keynote

 

Session Details & Prerequisites Q&A Slack Channel

Keynote Session – Suchi Saria

 

Wed, October 28, 9:30 AM
(PDT)

Suchi Saria, PhD | Director, Machine Learning & Healthcare Lab | Johns Hopkins University

 

Type: Keynote

 

Q&A Slack Channel

A Secure Collaborative Learning Platform

 

Wed, October 28, 10:00 AM

Raluca Ada Popa, PhD | Assistant Professor | Co-Founder | Berkeley | PreVeil

 

Type: Keynote

 

Session Details & Prerequisites Q&A Slack Channel

OCTOBER 29TH

Data for Good: Ensuring the Responsible Use of Data to Benefit Society

 

Thu, October 29, 9:00 AM
(PDT)

Jeannette M. Wing, PhD | Avanessians Director of the Data Science Institute and Professor of Computer Science | Columbia University

  • Causal INFERENCE Effects – estimate effects
  • Over and under estimation of instrumental variables
  • Confounders: Model assigned causes – Over and under estimation
  • De-Confounder: Estimate substitute confounders – Over and under estimation
  • Convolutional Neuro-networks model
  • Economics: Monopsony, Robo-Advising
  • History: Topic modeling with NLP,
  • Trustworthy Computing vs Trustworthy AI: Safety, Fairness, Robustness
  • Classifiers: Fair/Unfair make then more robust to a class of distributions
  • Image recognition system: DeepXplore: Semantic perturbation
  • DP and ML: PixelDP – STOP sign vs Yield sign
  • HealthCare @Columbia University: 600 Million EHR
  1. The Medical De-confounder: Treatment Effects on A1c DM2

Type: Keynote, Level: All Levels, Focus Area: AI for Good, Machine Learning

Session Details & Prerequisites Q&A Slack Channel

Keynote Session – Ben Taylor

Thu, October 29, 9:30 AM
(PDT)

Ben Taylor, PhD | Chief AI Evangelist | DataRobot

  • Convolution NN – Clustering of Countries: Latin America, Asia
  • Story telling
  • Acceleration:
  1. GPT-3 from OpenAI – Q&A, Translation, grammar
  2. Image GPT
  • Can AI Predict

Type: Keynote, Level: All Levels, Focus Area: Data Science Track

Q&A Slack Channel

Applying AI to Real World Use Cases

Thu, October 29, 10:00 AM
(PDT)

John Montgomery | Corporate Vice President, Program Management, AI Platform | Microsoft

Type: Keynote

  • Machine comprehension
  • Massive ML Models: Vision Model – Reznet
  • Alternative to Azure, OpenAI (Partner of Microsoft) released –>>>>> GPT-3 1758
  • AZURE ML: create models, operationalize models, build models responsibly
  • Model interpretability – Data Science, gov’t regulation: Features importance dashdourd
  • USE CASES
  • Building accurate models
  1. Little Ceasar’s Pizza: “Hot N-Ready” – Demand forecasting of Pizza Supply by combination of ingredients

Predict: X Quantity by Auto ML

  • Deploy and Manage Many Models: MMM Accelerator: Ten Models at AGL – Australia renewal energy

Model for Responsible ML: Fairness & Interpretability

  • EY – Bank denies a LOAN
  • Mitigation of Bias detection for Men and Women in Loan Applications

Loan Approval

  • Explanation dashboard – Aggregate model: Top feature in loan approval: Education Level
  • Fairness – Hazard performance for Accuracy: Disparity in prediction by Gender

ML is part of AZURE Platform

Bonsai – is Reinforcement Learning: Simulation Scenarios

AutoML – do know standard algorithms vs when you do not know

Session Details & Prerequisites Q&A Slack Channel

TALKS on 10/29/2020

NLP

Thu, October 29, 10:30 AM
(PDT)

Join

Tian Zheng, PhD | Chair, Department of Statistics | Associate Director | Columbia University | Data Science Institute

Type: Track Keynote, Level: Intermediate, Focus Area: NLP

  • Stochastic variability inference
  • Case-control likelihood approximation
  • Sampling node system

TEXT

  • LDA – Latent Distribution Modeling Dirichlet

Probability distribution over the vocabulary of words: Topic assignment

LINKS

  • MMSB – Mixed Membership

Detect communities in networks

blockmodel – profile of social interaction in different nodes

  • LMV – Pairwise-Link-LDA – same topic proportions have equal % for citing

Pair-wise-Link-LDA

  1. Draw topic
  2. Draw Beta
  3. For each document
  4. For each document pair

Variational Inference – fully factored model

  • article visibility

Stochastic Variation Inference

  • local (specific to each node) & global (across nodes)
  • At each iteration minibatch of nodes

Sampling Document pairs

  • Stratified sampling scheme – shorter link
  • Informative set sampling [informative vs non-imformative sets]
  • these scheme – Mean estimation problem: Inclusion probability: All links are included
  • Stochastic gradient updates for global parameters
  • Comparison with alternative Approaches
  1. LDA + Regression
  2. Relational topic model
  3. Pairwise-Link-LDA combine LDA and MMB [Same priors]
  • Predictive ranks (random guessing) and Runtimes (compact id distinct no overalp)
  1. evaluate model fit: average predictive rank of held-out documents – Top articles

Cora dataset

LMVS – better predictive performance than

KDD Dataset

Citation trends in HEP: Relevance of Topics vs Visibility

Article recommendation by Rank Topic Proportions

Visibility as a topic-adjusted measure

More recent are more visible

CItation is not a strong indicator for visibility

Visibility as a topic-adjusted measure

Making Deep Learning Efficient

Thu, October 29, 11:20 AM
(PDT)

Join

Kurt Keutzer, PhD | Professor, Co-founder, Investor | UC Berkeley, DeepScale

Type: Track Keynote

  • ML – SubSets
  1. Deep Learning – TRAINING for Clssification – Neuralnets – LeNet vs AlexNet – 7 layers 140x flops – using parallelism
  2. Shallow learning – deterministic and linear classifier used
  3. ML algorithms: Core ML, Audio analysis (Speech and audio recognition) , Multimedia
  4. NLP: translation,
  5. McKinsey & Co. – AI as a Service (AIasS)

PROBLEMS to Solve

Image Classification

  • Object Detection
  • Semantic Segmentation
  • Convolutional NN

Audio Enhancement at BabbleLabs 

Video Sentiment Analysis – Recommendations to Watch or to search

Natural Language Processing & Speech

  • Translation
  • Document understanding
  • Question answering
  • general language understanding evaluation (GLUE)

BerkeleyDeepDrive (BDD)

BERT – Transformer – 7 seconds per sentence

  • BERT-base
  • Q-BERT
  • Transformer

Computational Patterns of Deep NN (DNN) – TRAINING required for DNN

PLATFORMS OF CLOUD

  • GRADIANT DESCENT (GD)
  • Stochastic GRADIANT DESCENT (SGD)

Recommendation Models – DNN – Parallelism

  • Facebook – 80% is recommendation = Advertisement
  • No sharing of data by Collector: Alibaba, Facebook, twitter

 Considerations

  • Latency – NETWORK WIFI
  • Energy
  • Computation power
  • Privacy
  • Quantization: Fewer Memory Accesses
  • Lower Precision implies higher
  • Flat Loss Landscape – Precision Layer by Layer
  • Move computation to the EDGE

 

Language Complexity and Volatility in Financial Markets: Using NLP to Further our Understanding of Information Processing

Thu, October 29, 12:10 PM
(PDT)

Join

Ahmet K. Karagozoglu, Ph.D. | C.V. Starr Distinguished Professor of Finance | Visiting Scholar, Volatility and Risk Institute | Hofstra University | New York University Stern School of Business

Type: Track Keynote, Level: All Levels, Focus Area: NLP

 

Intelligibility Throughout the Machine Learning Life Cycle

Thu, October 29, 2:00 PM
(PDT)

Join

Jenn Wortman Vaughan, PhD | Senior Principal Researcher | Microsoft Research

Type: Talk, Level: Beginner-Intermediate, Focus Area: Machine Learning

  • A Human-centered Agenda for Intelligibility
  • Beyond the model: Data, objectives, performance metrics
  • context of relevant stakeholders
  • Properties of system design vs Properties of Human behavior

Learning with Limited Labels

Thu, October 29, 3:05 PM
(PDT)

Join

Shanghang Zhang, PhD | Postdoc Researcher | University of California, Berkeley

Type: Talk, Level: Intermediate-Advanced, Focus Area: Deep Learning, Research frontiers

 

How AI is Changing the Shopping Experience

Thu, October 29, 3:05 PM
(PDT)

Join

Sveta Kostinsky | Director of Sales Engineering | Samasource
Marcelo Benedetti | Senior Account Executive | Samasource

Type: Talk, Level: Intermediate, Focus Area: Machine Learning, Deep Learning

  • quality rubric
  • Internal QA Sampling
  • Client QA Sampling
  • Auto QA

Transfer Learning in NLP

Thu, October 29, 3:40 PM
(PDT)

00:
03:
30

Joan Xiao, PhD | Principal Data Scientist | Linc Global

Type: Talk, Level: Intermediate, Focus Area: NLP, Deep Learning

Transfer learning enables leveraging knowledge acquired from related data to improve performance on a target task. The advancement of deep learning and large amount of labelled data such as ImageNet has made high performing pre-trained computer vision models possible. Transfer learning, in particular, fine-tuning a pre-trained model on a target task, has been a far more common practice than training from scratch in computer vision.

In NLP, starting from 2018, thanks to the various large language models (ULMFiT, OpenAI GPT, BERT family, etc) pre-trained on large corpus, transfer learning has become a new paradigm and new state of the art results on many NLP tasks have been achieved.

In this session we’ll learn the different types of transfer learning, the architecture of these pre-trained language models, and how different transfer learning techniques can be used to solve various NLP tasks. In addition, we’ll also show a variety of problems that can be solved using these language models and transfer learning.

  •  Transfer learning: Computer Vision – ImageNet Classification
  •  ResNet, GoogleNet, ILSVRC – VGG, ILSVRC’12 – AlexNet
  •   Feature Extrator vs Fine-tune
  •  Transfer learning: NLP
  • Transfer Transformer: Text-to-Text Transfer Transformer 
  1. Word embeddings: No context is taken into account – Word2vec, Glove
  2. ELMo – embedding from language models: Contextual,
  3. BERT – Bi-directional Encoder Representations fro Transformers
  4. MLM – Masked Language Model: Forward, Backward, Masked
  5. Next Sentence Prediction
  6. Achieved SOTA – 11 tasks: GLUE, SQuAD 1.0
  • Predisction models;
  • Input
  • Label – IsNext vs NotNext

 GLUE Test score

BERT BASE vs BERT LARGE

  • Featured-based approach

BERT Variants – TinyBert, Albert, RoBETa, DistilBert

Multi-lingual BERT, BERT other languages

A Primer in BERTology: How BERT Works

 OpenAI built a text generator – too dangerous to release

OpenAI GPT-3 – Trained on 300B tokens – THREE models:

  1. Zero-shot – English to French – no training
  2. one-shots
  3. Few-shot – the GOAL – GPT-3
  4. GRT-3 is large scale NLP

Examples – Feature extraction

  • English to SQL
  • English to CSS
  • English to LaTex

Semantic textual similarity

NL inference 

ULMFiT – Fine tuning – the larger the # of Training examples – the better the performance 

  1. LM pre-training – start from scratch: BART, Big Bird, ELECTRA, Longformer
  2. LM fine-tuning
  3. Classifier fine-tuning

Data augmentation

Contextual Augmentation

  1. Original sentence
  2. masked
  3. augmented

Test generation

  1. boolean questions
  2. from structured data, i.e.,  RDF – Resource Description Framework

OCTOBER 30TH

Generalized Deep Reinforcement Learning for Solving Combinatorial Optimization Problems

 

Fri, October 30, 9:00 AM
(PDT)

Azalia Mirhoseini, PhD | Senior Research Scientist | Google Brain

Type: Keynote

Abstract: 

Many problems in systems and chip design are in the form of combinatorial optimization on graph structured data. In this talk, I will motivate taking a learning based approach to combinatorial optimization problems with a focus on deep reinforcement learning (RL) agents that generalize. I will discuss our work on a new domain-transferable reinforcement learning methodology for optimizing chip placement, a long pole in hardware design. Our approach is capable of learning from past experience and improving over time, resulting in more optimized placements on unseen chip blocks as the RL agent is exposed to a larger volume of data. Our objective is to minimize PPA (power, performance, and area), and we show that, in under 6 hours, our method can generate placements that are superhuman or comparable on modern accelerator chips, whereas existing baselines require human experts in the loop and can take several weeks.

Bio: 

Azalia Mirhoseini is a Senior Research Scientist at Google Brain. She is the co-founder/tech-lead of the Machine Learning for Systems Team in Google Brain where they focus on deep reinforcement learning based approaches to solve problems in computer systems and metal earning. She has a Ph.D. in Electrical and Computer Engineering from Rice University. She has received a number of awards, including the MIT Technology Review 35 under 35 award, the Best Ph.D. Thesis Award at Rice and a Gold Medal in the National Math Olympiad in Iran. Her work has been covered in various media outlets including MIT Technology Review, IEEE Spectrum, and Wired.

Session Details & Prerequisites Q&A Slack Channel
  • Learning Based Approaches vs branch & Bound, Hill climbing, ILP
  • scale on distributed platforms
  • Device Placement – too big to fit – PARTITION among multiple devices – evaluate run time per alternative placements
  • Learn Placement on NMT – Profiling Placement on NMT
  • CPU + layers encoder and decoders – overhead tradeoffs – parallelization for work balancing
  • RL-based placement vs Expert placement
  • Memory copying task
  • Generalization to be achieved forr Device Placement Architecture
  • Embeddings that transfer knowledge across graphs
  • Graph Partitioning: Normalized cuts objective: Volume , Cuts,
  • Learning based approach Train NN on nodes of graph assign Probability of node belonging to a given partition
  • Continuous relaxation of Normalized cuts
  • Optimize expected normalized Cuts
  • Generalized Graph Partitioning Framework
Chip Placement Problem (Floor planning) – Chip Design – resource optimization, canonical reimforcement learning
  • Placement Optimmization using AGENTS to place the nodes
  • Train Policy to be using for placement of ALL chips
  • Compiling a Dataset of Chip Placements
  • Policy/Value Model Architecture to save wire length used
  • RISC-V: Placement Visualization: Training from Scratch (Human) 6-8 weeks vs Pre-Trained 24 hours

Keynote Session – Zoubin Ghahramani

Fri, October 30, 9:30 AM
(PDT)

Zoubin Ghahramani, PhD | Distinguished Scientist and Sr Research Director | Professor of Information Engineering | ex-Chief Scientist and VP of AI | Google | University of Cambridge | Uber

Type: Keynote

Q&A Slack Channel

  • Data- models predictiona decisions Understanding
  • AI & Games
  • AI + ML
  • Deep Learning! (DL)
  1. NN  – tunable nonlinear functions with many parameters
  2. Parameters are weights of NN
  3. Optimization + Statistics
  4. DL – New-branding of NN
  5. Many layers – ReLUs attention
  6. Cloud resources
  7. SW – TensorFloe, JAX
  8. Industry investment in DL

DL – very successful

  • non-parametric statistics
  • use huge data – simulated data
  • automatic differentiation
  • stay close to identity – makes models deeps ReLU, LSTMs GRUs, ResNets
  • Symmentry parameter tieying

Limitations of DL

  • data hungry
  • adversarial examples
  • black-boxes – difficult to trust
  • uncertainty – not easily incorporated

Beyond DL

  • ML as Probabilistic Modeling: Data observed from a system
  • uncertainty
  • inverse probability
  • Bayes rule Priors from measured quantities inference for posterior
  • learning and predicting can be seen as forms of inference – likelihood
  • approximations from estimation of Likelihoods
  1. Learning
  2. Prediction
  3. Model Comparison
  4. Sum rule: Product rule

Why do probabilities matter in AI and DS?

  • COmplexity control and structure learning
  • exploration-exlpoitations trade-offs
  • Building prior knowledge algorithms for small and large data sets
  • BDP – Bayesian DL
  • Gaussian Processes – Linear and logistics regressions SVMs
  • BDL – Baysian NN/ GP Hybrids
  • Deep Sum=Product Networks – deescrimitive programming

Probabilistic Programming Languagues

Languages: Tensors, Turing,

Automatic Statistician –

  • model discovery from data and explain the results

Probabilistic ML

  • Learn from Data  decision theory Prob AI BDL, Prob Prog,

Zoubin Ghahramani, 2015, Probabilistic machine learning and AI, Nature 521; 452-459

 

The Future of Computing is Distributed

Fri, October 30, 10:00 AM

(PDT)
Ion Stoica, PhD | Professor of Computer Science Division | Co-Founder | Berkeley | Anyscale | Databricks | Conviva Networks
  • 1970 – ARPA net 1970 – distributed
  • 1980 – High performance computing – HPC 1980s
  • 1990 – WEB – Amazon
  • 2000 – Big data – Google

Distributed computing – Few courses at universities

  • Rise of deep learning (DL)
  • Application becomes AI centered: Healthcare, FIN, Manufacturing
  • Morse law – is dead: Memory and Processors
  • Specialized hardware: CPU, GPU, TPU
  • Memory dwarfed by demand
  • Memory: Tutring Project 17B
  • GPT-2 8.3B
  • GPT-1
  • Micro-services: Clusters of clouds – integrating with distributed workloads
  • AI is overlapping with HPC
  • AI and Big Data

AI Applications

  • MPI,
  • Stitching several existing systems

RAY riselab @Berkeley – Universal framework for distributed computing (Python and JAVA) across different Libraries

  • Asynchronous execution enable parallelism
  • Function -> Task (API)
  • Object ID – every task scheduled
  • Library Ecosystem – Native Libraries 3rd Party Libraries
  • Amazon and AZURE SPARK, MARS (Tensor)

ADOPTIONS

  • Number of contributors increase fast N=300

 

TALKS on 10/30/2020

 

Advances and Frontiers in Auto AI & Machine Learning – Lisa Amini



Lisa Amini, Director | IBM Research – Cambridge
  • Auto AI – holistic approach
  • Auto ML – Models: Feature creation, modeling, training & testing

AI AUTOmation for Enterprise

  • Feature Preprocessor ->>Feature Transformer Feature selector Estimator
  • Joint-optimization problem
  1. Method selection
  2. Hyper-parameter Optimization
  3. Black-box constraints
  • Bias Mitigation Algorithms
  1. Pre-processing algo
  2. In-processing Algo
  3. Post-processing algo
  • Automation for Data – READINESS for ML
  • relational data –
  • knowledge augmentation
  • Data readiness reporting
  • Labeling Automation: Enhance

Knowledge augmentation – Federated Learning

  • External data sources
  • existing data
  • documents containing domain knowledge
  • Automating Augmenting Data with knowledge: feature-concept mapping

Modeling

  • Time Series Forecasting

AI to decision Optimization

  • Demand forecasting from Standard AutoAI by ADDING Historical Decisions and Historical Business Impact__>> reinforced learning – Automatically created model from past and Auto AI

Validation

  • Meta-learning for performance prediction
  • Train the META data
  • Score production data with AI

Deployment

  • staged deployment with contextual bandits

Monitoring

  • Performance prediction meta model applied over windows of production traffic

INNOVATIONS;

  • End-to-end AI life cycle
  • expanding scope of automation; Domain knowledge and decision optimization

 

The State of Serverless and Applications to AI

 

Fri, October 30, 11:20 AM
(PDT)

Joe Hellerstein, PhD | Chief Strategy Officer, Professor of Computer Science | Trifacta, Berkeley

The Cloud and practical AI have evolved hand-in-hand over the last decade. Looking forward to the next decade, both of these technologies are moving toward increased democratization, enabling the broad majority of developers to gain access to the technology.

Serverless computing is a relatively new abstraction for democratizing the task of programming the cloud at scale. In this talk I will discuss the limitations of first-generation serverless computing from the major cloud vendors, and ongoing research at Berkeley’s RISELab to push forward toward “”””stateful”””” serverless computing. In addition to system infrastructure, I will discuss and demonstrate applications including data science, model serving for machine learning, and cloud-bursted computing for robotics.

Bio: 

Joseph M. Hellerstein is the Jim Gray Professor of Computer Science at the University of California, Berkeley, whose work focuses on data-centric systems and the way they drive computing. He is an ACM Fellow, an Alfred P. Sloan Research Fellow and the recipient of three ACM-SIGMOD “Test of Time” awards for his research. Fortune Magazine has included him in their list of 50 smartest people in technology , and MIT’s Technology Review magazine included his work on their TR10 list of the 10 technologies “most likely to change our world”. Hellerstein is the co-founder and Chief Strategy Officer of Trifacta, a software vendor providing intelligent interactive solutions to the messy problem of wrangling data. He has served on the technical advisory boards of a number of computing and Internet companies including Dell EMC, SurveyMonkey, Captricity, and Datometry, and previously served as the Director of Intel Research, Berkeley.

Type: Talk, Level: Intermediate, Focus Area: AI for Good, Machine Learning

Session Details & Prerequisites Q&A Slack Channel
  • What happened with the Cloud – no app
  • Parallelism – distributed computers – scale up or down, consistency and partial failure
  • Serverless Computing: Functions-as-a-Service (FaaS)
  • Developers OUTSIDE AWS, AZURE< Google to program the CLoud
  • Python for the Cloud
  • AutoScaling – yes
  • Limitations of FaaS: AWS Lambda: I/O Bottlenecks, lifetine 15 min, No Inbound Network COmmunication
  • Program State: local data – managed across invocations
  • Data Gravity – expensive to move

Distributed consistency – data replication: Agree on data  value mutable variable x [undate took place]

  • Two-Phase commit [ Consensus – Paxos]
  • coordination avoidance: waiting for control TALL LATENCY- DISTRIBUTION OF PERFORMANCE
  • Slowdown cascades: I/O
  • Application semantics: Programs requires coordination
  • Program must have property of Monotonic
  • MONOTONICITY: Input grows/output grows – wait on information not on Coordination

CALM – infinitely-scalable systems – no coordination ->> parallelism and smooth scalability

Monotonicity syntactically in a logic language

Hydro: a Platform for Programming the Cloud

Anna Serverless KVS – Hydro Project

  • shared-nothing at all scales (even across Threads)
  • Fast under contention: 90% request handling

Cloudburst: A stateful Serverless Platform: CACHE close to compute: Cache consistency

Latency Python, Cloudburst, AWS, AWS Lambda:

  • AWS Lambda is SLOW for AI vs Python, Cloudburst

Scalable AWS Lambda simultaneously

  • Motion planning compute
  • Cloudburst + Anna requirement

@joe_hellerstein

Bloom Lab

RiseLab

Hydro

 

Just Machine Learning

Fri, October 30, 1:10 PM
(PDT)

Join

Tina Eliassi-Rad, PhD | Professor | Core Faculty | Northeastern University | Network Science Institute

Type: Talk, Level: All Levels, Focus Area: Machine Learning

In 1997, Tom Mitchell defined the well-posed learning problem as follows: “A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.” In this talk, I will discuss current tasks, experiences, and performance measures as they pertain to fairness in machine learning. The most popular task thus far has been risk assessment. We know this task comes with impossibility results (e.g., see Kleinberg et al. 2016, Chouldechova 2016). I will highlight new findings in terms of these impossibility results. In addition, most human decision-makers seem to use risk estimates for efficiency purposes and not to make fairer decisions. I will present an alternative task definition whose goal is to provide more context to the human decision-maker. The problems surrounding experience have received the most attention. Joy Buolamwini (MIT Media Lab) refers to these as the “under-sampled majority” problem. The majority of the population is non-white, non-male; however, white males are overrepresented in the training data. Not being properly represented in the training data comes at a cost to the under-sampled majority when machine learning algorithms are used to aid human decision-makers. In terms of performance measures, a variety of definitions exist from group- to individual- to procedural-fairness. I will discuss our null model for fairness and demonstrate how to use deviations from this null model to measure favoritism and prejudice in the data.

Tasks:

  • Assessing risk
  • Ranking
  • Statistical parity: among classifier

PARITY vs imperfect classifier – can’t satisfy all the three conditions

  • Precision
  • Tru positive
  • False parity

All classifier do not consider context or allow for uncertainty

  • Learning to Place within existing cases
  • Incentives/values of Human decision maker which incorporate in the decision external factors
  • Game-theoretical framework
  • How human exemplars make decision
  • Are algorithms value free?

Computational Ethics

  • Logically consistent principle
  • Camouflage – machine did not learn on the task but on the cloudiness of the sky
  • Model Cards for Model Reporting
  • The “undersampled majority”
  • Experience: Demonstration: Should we learn from demonstrations or from simulations?
  • Complex networks: guilt by association vs privilege and prejudice, individual fairness
  • Datasheets for Datasets
  • Algorithms are like prescription drug: Adverse events

Human vs Machine judgement

  • Performance measure – FAIRNESS: Group, individual
  • Normativity throughout the entire well-posed learning problem
  • Incentive/values
  • Human or machines to make decisions?
  • Laws are needed if algorithms are used as expert witness

 

Machine Learning for Biology and Medicine

Fri, October 30, 2:00 PM

Sriram Sankararaman, PhD | Professor, Computer Science | University of California – Los Angeles

Type: Talk, Focus Area: Machine Learning

Abstract: 

Biology and medicine are deluged with data so that techniques from machine learning and statistics will increasingly play a key role in extracting insights from the vast quantities of data being generated. I will provide an overview of the modeling and inferential challenges that arise in these domains.

In the first part of my talk, I will focus on machine learning problems arising in the field of genomics. The cost of genome sequencing has decreased by over 100,000 fold over the last decade. Availability of genetic variation data from millions of individuals has opened up the possibility of using genetic information to identifying the cause of diseases, developing effective drugs, predicting disease risk and personalizing treatment. While genome-wide association studies offer a powerful paradigm to discovering disease-causing genes, the hidden genetic structure of human populations can confound these studies. I will describe statistical models that can infer this hidden structure and show how these inferences lead to novel insights into the genetic basis of diseases.

In the second part of my talk, I will discuss how the availability of large-scale electronic medical records is opening up the possibility of using machine learning in clinical settings. These electronic medical records are designed to capture a wide range of data associated with a patient including demographic information, laboratory tests, images, medications and clinical notes. Using electronic records from around 60,000 surgeries over five years in the UCLA hospital, I will describe efforts to use machine learning algorithms to predict mortality after surgery. Our results reveal that these algorithms can accurately predict mortality from information available prior to surgery indicating that automated predictive systems have great potential to augment clinical care.

Bio: 

Sriram Sankararaman is an assistant professor in the Departments of Computer Science, Human Genetics, and Computational Medicine at UCLA where he leads the machine learning and genomic lab. His research interests lie at the interface of computer science, statistics and biology and is interested in developing statistical machine learning algorithms to make sense of large-scale biomedical data and in using these tools to understand the interplay between evolution, our genomes and traits. He received a B.Tech. in Computer Science from the Indian Institute of Technology, Madras, a Ph.D. in Computer Science from UC Berkeley and was a post-doctoral fellow in Harvard Medical School before joining UCLA. He is a recipient of the Alfred P. Sloan Foundation fellowship (2017), Okawa Foundation grant (2017), the UCLA Hellman fellowship (2017), the NIH Pathway to Independence Award (2014), a Simons Research fellowship (2014), and a Harvard Science of the Human Past fellowship (2012) as well as the Northrop-Grumman Excellence in Teaching Award at UCLA (2019).

  • ML & BioMedicine

BioMedical data: high D, heterogeneous, noisy data

  1. Clinical Data & DL
  • Predict death after surgery – 1000 dealth complication, sepsis acout kidney injury
  • Mortality during and after surgery
  • collaboration: Anesthesiology, PeriOps, UCLA Health
  • Data warehouse – EMR 4/2013 – 12/2018
  • 60,000 patients in data: Age, height, weight, gender,ASA Status- input from physician

Pre-operative mortality risk prediction – False positive, missing data: Lab data was collected, what were the values

2% of admission associate with mortality

SMOTE: over-sampling of associate with risk

Learning setup: Temporal training-testing split, hyper parameter

Models: Logistics, Random forest, gradient-boosted trees

Feature sets: ASA status, surrugate-ASA

  • ASA Status – did not contribute  with it and without it the same
  • Lab values and timing of lab – is the most important festure.
  • RANDOM FOREST model was selected
  • Precision/recall curve
  • The model reduced number of patients flagged by around 20x

Open problemsL Interoperability, Learning over private data

2. Epidemiological dat and ML – Social distancing in COVID-19 Pandemic

  • Effectiveness of social distancing
  • SEIR
  • Average duration of infection
  • Susceptible-Exposed-Infectious-Removed (SEIR) model
  • R-naught applied to social distancing the ratio of Susceptible /Exposed is compared to Infectious/Removed the lowe the better
  • Social distancing-relaxation – Relaxation in 2022
  • COVID spread – estimate when SOcial distabcing need to END
  • UK, NY, Spain, France, Germany, Denmark
  • Hierarchical Bayesian model: Shared Global parameters, Location-specific, Observations
  • Hierarchical Bayesian model SEIR Model: Data generation process
  • Empirical Bayes: Maximize likelihood of the global parameters
  • Trajectory based on Model Fit
  • Estimation of uncertainty
  • End of Social distancing – time distribution around a mean
  • No seasonality, no infinite immunity, No vaccine
  • Quantify Uncertainty
  • Work with domain knowledge experts is great

The Bayesians are Coming! The Bayesians are Coming, to Time Series – Aric LaBarr


Fri, Oct 30, 2020 5:50 PM – 6:35 PM EDT


Aric LaBarr, Associate Professor of Analytics | Institute for Advanced Analytics at NC State University
With the computational advances over the past few decades, Bayesian analysis approaches are starting to be fully appreciated. Forecasting and time series also have Bayesian approaches and techniques, but most people are unfamiliar with them due to the immense popularity of Exponential Smoothing and autoregressive integrated moving average (ARIMA) classes of models. However, Bayesian modeling and time series analysis have a lot in common! Both are based on using historical information to help inform future modeling and decisions. Using past information is key to any time series analysis because the data typically evolves over time in a correlated way. Bayesian techniques rely on new data updating their models from previous instances for better estimates of posterior distributions. This talk will briefly introduce the differences between classical frequentist approaches of statistics to their Bayesian counterparts as well as the difference between time series data made for forecasting compared to traditional cross-sectional data. From there, it will compare the classical Exponential Smoothing and ARIMA class models of time series to Bayesian models with autoregressive components. Comparing the results of these models across the same data set allows the audience to see the potential benefits and disadvantages of using each of the techniques. This talk aims to allow people to update their own skill set in forecasting with these potentially Bayesian techniques. At the end, the talk explores the technique of model ensembling in a time series context. From these ensembles, the benefits of all types of models are potentially blended together. These models and their respective outputs will be displayed in R
  • Single Exponential Smoothing
  • ARIMA – long-memory models – Autoregressive AR
  • Moving Average (MA) model – short memory
  • Intergrated AR+MA = ARIMA

Learning Intended Reward Functions: Extracting all the Right Information from All the Right Places

Fri, October 30, 3:45 PM

(PDT)

00:04:42
Anca Dragan, PhD | Assistant Professor, EECS | Head | UC Berkeley | InterACT lab

Type: Talk, Focus Area: Deep Learning

Learning Intended Reward Functions: Extracting all the Right Information from All the Right Places

Abstract: 

Content: AI work tends to focus on how to optimize a specified reward function, but rewards that lead to the desired behavior consistently are not so easy to specify. Rather than optimizing specified reward, which is already hard, robots have the much harder job of optimizing intended reward. While the specified reward does not have as much information as we make our robots pretend, the good news is that humans constantly leak information about what the robot should optimize. In this talk, we will explore how to read the right amount of information from different types of human behavior — and even the lack thereof.
Learning outcomes: After participating, you should be able to articulate the common pitfalls we face in defining an AI reward, loos, or objective function. You should also develop a basic understanding of the main algorithmic tools we have for avoiding these pitfalls.

Target audience: Participants with some AI experience, be in supervised or reinforcement learning.

Bio: 

Anca Dragan is an Assistant Professor in EECS at UC Berkeley, where she runs the InterACT lab. Her goal is to enable robots to work with, around, and in support of people. She works on algorithms that enable robots to a) coordinate with people in shared spaces, and b) learn what people want them to do. Anca did her PhD in the Robotics Institute at Carnegie Mellon University on legible motion planning. At Berkeley, she helped found the Berkeley AI Research Lab, is a co-PI for the Center for Human-Compatible AI, and has been honored by the Presidential Early Career Award for Scientists and Engineers (PECASE), the Sloan fellowship, the NSF CAREER award, the Okawa award, MIT’s TR35, and an IJCAI Early Career Spotlight.

  • Sequential decision making
  • defining what robots goal is
  • Autonomous car
  • AI = optimize intended rewards vs specified reward
  • parametrization of the reward function
  • Agent over-learn from specified rewards but under-learn from other sources
  • observing feedback and express the human feedback in observation (human) model
  • How can we model reward design/specification as a noisy and suboptiman process
  • Development vs deployment environment
  • Robot trust the development environment
  • good behavior incentivized reward
  • maximize winning, maximizing score, minimize winning, minimize score
  • model the demo as a reward-rational implicit
  • Human feedback as a reward-rational implicit choice
  • The state of the environment as a reward-rational implicit choice
  • task specification –>> reward

 

KEYNOTE SPEAKERS

ODSC West Keynotes

Suchi Saria, PhD
Suchi Saria, PhD

Director Of The Machine Learning And Healthcare Lab, John C. Malone Endowed Chair, Founder Of Bayesian Health, MIT Technology Review’s 35 Innovators Under 35, And A World Economic Forum Young Global Leader

Johns Hopkins University

Jeannette M. Wing, PhD
Jeannette M. Wing, PhD

Avanessians Director Of The Data Science Institute, Professor Of Computer Science Columbia University, Former Corporate Vice President Microsoft, Former Assistant Director, National Science Foundation

Columbia University

Ion Stoica, PhD
Ion Stoica, PhD

Professor Of Computer Science, Head Of RISELab. Co-Founder Of Anyscale, Databricks, And Conviva Networks, ACM Fellow, SIGOPS Hall Of Fame Award (2015), SIGCOMM Test Of Time Award (2011)

UC Berkeley

Raluca Ada Popa, PhD
Raluca Ada Popa, PhD

Cybersecurity & Applied Cryptography Professor, MIT Technology Review’s 35 Under 35, Recipient Of Intel Early Career Faculty Honor Award, George M. Sprowls Award For Best MIT CS Doctoral Thesis, Co-Founder Of PreVeil

UC Berkeley

Zoubin Ghahramani, PhD
Zoubin Ghahramani, PhD

Chief Scientist, Founding Director Of The AlanTuring Institute, Prof. Of Information Engineering & Deputy Director Of The Leverhulme Centre For The Future Of Intelligence, Fellow Of St John’s College Cambridge And Of The Royal Society

Uber | The University of Cambridge

Azalia Mirhoseini, PhD
Azalia Mirhoseini, PhD

Senior Research Scientist At Google Brain. Advisor At Cmorq. Co-Founder Machine Learning For Systems Moonshot At Brain Focusing On Deep RL. MIT Technology Review 35 Under 35 Award

Google Brain

Marinela Profi
Marinela Profi

Global Strategist For AI, Global Ambassador For The Women Tech Network, Author Of “Mastering Model Lifecycle Orchestration: An Interactive Guide”

SAS

John Montgomery
John Montgomery

Corporate Vice President, Visual Studio, Microsoft Azure AI Lead, Former Chief Information Office At Imagine Publishing, Author At Visual Studio

Microsoft

Ben Taylor,PhD
Ben Taylor,PhD

Chief AI Evangelist, Deep Learning & HPC Expert, Co-Founder & Chief Scientist At Zeff.Ai, Former Chief Scientist At HireVue, ProductCraft Contributor

DataRobot

SCHEDULE

Open Data Science

TUESDAY, OCTOBER 27TH

Pre-conference Day

ODSC BootCamp

BOOTCAMP KICKOFF WEST VIRTUAL
10:00 am

Fundamentals | Morning Sessions

 – 

Choose from 6 foundation sessions in Programming, Mathematics for Data Science, and Statistics

Virtual break

 – 

11:00 am
12:00 pm
1:00 pm
2:00 pm

Fundamentals | Afternoon Sessions

 – 

Choose from 6 foundation sessions in Programming, Mathematics for Data Science, and Statistics

3:00 pm
4:00 pm
5:00 pm

 

Open Data Science

WEDNESDAY, OCTOBER 28TH

Day 1

ODSC Trainings, Workshops & AI Expo, Ai x and Ai x Keynotes

VIRTUAL HANDS-ON TRAINING WEST VIRTUAL VIRTUAL AI X EXPO & DEMO HALL WEST VIRTUAL EVENTS WEST VIRTUAL
10:00 am

Hands-on Training and Workshops

 – 

Choose from Five 3.5 hours Training Sessions and Six 90 minute Workshop Sessions

Networking break

 – 

Morning Partners Demo Talks

 – 

Choose from 12 Partners Sessions

11:00 am

Virtual Exhibitor Showcase

 – 

Visit 30+ Virtual Partners booth

12:00 pm
1:00 pm

Networking break

 – 

2:00 pm

Hands-on Training and Workshops

 – 

Choose from Five 3.5 hours Training Sessions and Six 90 minute Workshop Sessions

Afternoon Partners Demo Talks

 – 

Choose from 12 Partners Sessions

3:00 pm
4:00 pm
5:00 pm

 

Open Data Science

THURSDAY, OCTOBER 29TH

Day 2

ODSC Keynotes, Talks, Trainings, Workshops, AI Expo & Events

VIRTUAL HANDS-ON TRAINING WEST VIRTUAL VIRTUAL AI X EXPO & DEMO HALL WEST VIRTUAL VIRTUAL PRESENTATIONS WEST VIRTUAL
9:00 am

ODSC Keynote

 – 

10:00 am

Morning Hands-on Training and Workshops

 – 

Choose from Five 3.5 hours Training Sessions and Six 90 minute Workshop Sessions

Networking break

 – 

Virtual Exhibitor Showcase & Partners Demo Talks

 – 

Choose from 12 Morning Partners Sessions & Visit 25+ Virtual Partners booth

11:00 am

Breakout Talk Sessions

 – 

Choose from 7 talk presentations

12:00 pm
1:00 pm

Networking break

 – 

2:00 pm

Afternoon Hands-on Training and Workshops

 – 

Choose from Five 3.5 hours Training Sessions and Six 90 minute Workshop Sessions

Virtual Exhibitor Showcase & Partners Demo Talks

 – 

Choose from 12 Afternoon Partners Sessions & Visit 25+ Virtual Partners booth

Breakout Talk Sessions

 – 

Choose from 7 talk presentations

3:00 pm
4:00 pm
5:00 pm

 

Open Data Science

FRIDAY, OCTOBER 30TH

Day 3

ODSC Keynotes, Talks, Trainings, Workshops, Events, & Career Expo

VIRTUAL HANDS-ON TRAINING WEST VIRTUAL VIRTUAL PRESENTATIONS WEST VIRTUAL CAREER LAB AND EXPO & POSTER SESSIONS WEST VIRTUAL
9:00 am

ODSC Keynote

 – 

10:00 am

Morning Hands-on Training and Workshops

 – 

Choose from Five 3.5 hours Training Sessions and Six 90 minute Workshop Sessions

Virtual Lunch & Networking break

 – 

Virtual Career Expo

 – 

Get n touch with 30+ Hiring Partners and choose from 12 Mentor Talks

11:00 am

Breakout Talk Sessions

 – 

Choose from 7 talk presentations

12:00 pm
1:00 pm

Virtual Lunch & Networking break

 – 

2:00 pm

Afternoon Hands-on Training and Workshops

 – 

Choose from Five 3.5 hours Training Sessions and Six 90 minute Workshop Sessions

Breakout Talk Sessions

 – 

Choose from 7 talk presentations

3:00 pm
4:00 pm
5:00 pm

 


SPEAKERS

Click for
more info

Nadja Herger, PhD

DATA SCIENTISTTHOMSON REUTERS

Click for
more info

Viktoriia Samatova

HEAD OF TECHNOLOGY & INNOVATIONTHOMSON REUTERS

Click for
more info

Nina Hristozova

JUNIOR DATA SCIENTISTTHOMSON REUTERS

Click for
more info

Daniel Whitenack, PhD

INSTRUCTOR, DATA SCIENTISTDATA DAN

David Talby: NLP for healthcare
Click for
more info

David Talby, PhD

CTOPACIFIC AI, JOHN SNOW LABS

Click for
more info

Tian Zheng, PhD

CHAIR, DEPARTMENT OF STATISTICSCOLUMBIA UNIVERSITY

Click for
more info

Phoebe Liu

SENIOR DATA SCIENTISTAPPEN

Click for
more info

Frank Zhao

SENIOR DIRECTOR, QUANTAMENTAL RESEARCHS&P GLOBAL MARKET INTELLIGENCE

TOPICS – trends in NLP, including pre-trained models, with use-cases focusing on deep learning, speech-to text, and semantic search.

  • Natural Language Processing
  • NLP Transformers
  • Pre-trained Models
  • Text Analytics
  • Natural Language Understanding
  • Sentiment Analysis
  • Natural Language Generation
  • Speech Recognition
  • Named Entity Extraction

MODELS

  • BERT
  • XLNet
  • GPT-2
  • Transformers
  • Word2Vec
  • Deep Learning Models
  • RNN & LSTM
  • Machine Learning Models
  • ULMFiT
  • Transfer Learning

TOOLS

  • Tensorflow 2.0
  • Hugging Face Transformers
  • PyTorch
  • Theano
  • SpaCy
  • NLTK
  • AllenNLP
  • Stanford CoreNLP
  • Keras
  • FLAIR

Read Full Post »


Tweets & Retweets by @pharma_BI and @AVIVA1950 at #BioIT20, 19th Annual Bio-IT World 2020 Conference, October 6-8, 2020 in Boston

 

Virtual Conference coverage in Real Time: Aviva Lev-Ari, PhD, RN

 

Amazing conference ended at 2PM on October 8, 2020

e-Proceedings 19th Annual Bio-IT World 2020 Conference, October 6-8, 2020 Boston

Virtual Conference coverage in Real Time: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2020/03/26/19th-annual-bio-it-world-2020-conference-october-6-8-2020-in-boston/

Review Tweets and Retweets

and 2 others liked your Tweet

#BioIT20 Plenary Keynote: cutting innovative approach to #Science #Game On: How #AI, #CitizenScience #HumanComputation are facilitating the next leap forward in #Genomics and in #Biology may be in #PrecisionMedicine in the Future @pharma_BI @AVIVA1950 pic.twitter.com/L52qktkeYc

Retweeted your Tweet
#BioIT20 Plenary Keynote: cutting innovative approach to #Science #Game On: How #AI, #CitizenScience #HumanComputation are facilitating the next leap forward in #Genomics and in #Biology may be in #PrecisionMedicine in the Future @pharma_BI @AVIVA1950 pic.twitter.com/L52qktkeYc

and

liked your Tweet

#BioIT20 Plenary Keynote: cutting innovative approach to #Science #Game On: How #AI, #CitizenScience #HumanComputation are facilitating the next leap forward in #Genomics and in #Biology may be in #PrecisionMedicine in the Future @pharma_BI @AVIVA1950 pic.twitter.com/L52qktkeYc

NIH Office of Data Science Strategy
@NIHDataScience

We’ve made progress with #FAIRData, but we still have a ways to go and our future is bright. #BioIT20 #NIHData

Image

3

Aviva Lev-Ari
@AVIVA1950

#BioIT20

Driving Scientific Discovery with Data Digitization great ideas shared by moderator Timothy Gardner

#CEO Inspiration from History Total Quality Implementation is key for BioScience Data #AI won’t solve the problem #Data #Quality will

Image

Rob Lalonde
@HPC_Cloud_Rob

My #BioIT20 talk, “#Bioinformatics in the #Cloud Age,” is tomorrow at 3:30pm. I discuss cloud migration trends in life sciences and #HPC. Join us! A panel with

and

follows the talk.

1
16

Jean Marois
@JeanMarois

My team is participating in Bio-IT World Virtual 2020, October 6-8. Join me! Use discount code 20NUA to save 20%! invt.io/1tdbae9s8lp

#BioIT20

I’m going to Bio-IT World 2020, Oct 6-8, from home! Its a virtual event. Join me!
My team is participating in Bio-IT World Virtual 2020, October 6-8. Join me! Use discount code 20NUA to save 20%! @bioitworld #BioIT20
invt.io
2

NIH Office of Data Science Strategy
@NIHDataScience

One of the challenges we face today: we need an algorithm that can search across the 36+ PB of Sequence Read Archive (SRA) data now in the cloud. Imagine what we could do! #BioIT20 #NIHdata #SRAdata

Image

2

NCBI Staff
@NCBI

NCBI’s virtual #BioIT20 booth will open in 15 minutes. There, you can watch videos, grab some flyers and even speak with an expert! bio-itworld.pathable.co/organizations/ The booth will close at 4:15 PM, but we’ll be back tomorrow, Oct 7 and Thursday, Oct 8 at 9AM.
Bio-IT World
Welcome to Bio-IT World Virtual
bio-itworld.pathable.co
1
6
Show this thread

PERCAYAI
@percayai

Happening soon at #BioIT20: Join our faculty inventor Professor Rich Head’s invited talk “CompBio: An Augmented Intelligence System for Comprehensive Interpretation of Biological Data.”
4

Wendy Anne Warr
@WendyAnneWarr

This was a good discussion
Quote Tweet
Cambridge Innovation
@CIInstitute
·
RT percayai: We’ve put together what’s sure to be a thought-provoking discussion group for #BioIT20 “Why Current Approaches Using #AI in #…
1
2

Cambridge Innovation
@CIInstitute

RT VishakhaSharma_: Excited to speak and moderate a panel on Emerging #AI technologies bioitworld #BioIT20
1

Titian Software
@TitianSoftware

Meet Titian at #BioIT20 on 6-8th October and discover the latest research, science and solutions for exploring the world of precision medicine and the technologies that are powering it: bit.ly/2GjCj4B

Image

1

PERCAYAI
@percayai

Thanks for joining us, Wendy! You’ve done a great job summing up key points from the discussion. #BioIT20
1

Aviva Lev-Ari
@AVIVA1950

#NIHhealthInitiative #BioItWorld20

Out standing Plenary Keynote on #DataScience

CONNECTED DATA ECOSYSTEM FAIR Foundable, Accessible, Interoperable, reusable

Image

2

Read Full Post »


Tweet Collection by @pharma_BI and @AVIVA1950 and Re-Tweets for e-Proceedings 14th Annual BioPharma &amp; Healthcare Summit, Friday, September 4, 2020, 8 AM EST to 3-30 PM EST – Virtual Edition

Real Time Press Coverage: Aviva Lev-Ari, PhD, RN

 

e-Proceedings 14th Annual BioPharma & Healthcare Summit, Friday, September 4, 2020, 8 AM EST to 3-30 PM EST – Virtual Edition

Real Time Press Coverage: Aviva Lev-Ari, PhD, RN

Founder & Director, LPBI Group

https://pharmaceuticalintelligence.com/2020/07/28/14th-annual-biopharma-healthcare-summit-friday-september-4-2020-8-am-est-to-3-30-pm-est-virtual-edition/

 

Aviva Lev-Ari
@AVIVA1950

#USAIC20 Dr. Hal Barron, Chief Scientific Officer and President R&D, GlaxoSmithKline GWAS not easy to find which gene drives the association  Functional Genomics gene by gene with phenotypes using machine learning significant help

Aviva Lev-Ari
@AVIVA1950

#USAIC20 Dr. Hal Barron, Chief Scientific Officer and President R&D, GSK GWAS not easy to find which gene drives the association  Functional Genomics gene by gene with phenotypes using machine learning significant help

Srihari Gopal
@sgopal2

Enjoyed hearing enthusiasm for Neuroscience R&D by Roy Vagelos at #USAIC20. Wonderful interview by Mathai Mammen

Image

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Aviva Lev-Ari
@AVIVA1950

#USAIC20 Nina Kjellson, General Partner, Canaan Data science is a winner in Healthcare Women – Data Science is an excellent match

Aviva Lev-Ari
@AVIVA1950

#USAIC20 Arpa Garay, President, Global Pharmaceuticals, Commercial Analytics, Merck & Co. Data on Patients and identification who will benefit fro which therapy  cultural bias risk aversion

Aviva Lev-Ari
@AVIVA1950

#USAIC20 Dr. Najat Khan, Chief Operating Officer, Janssen R&D Data Sciences, Johnson & Johnson Data Validation  Deployment of algorithms embed data by type early on in the crisis to understand the disease

Aviva Lev-Ari
@AVIVA1950

#USAIC20 Sastry Chilukuri, President, Acorn AI- Medidata Opportunities in Data Science in Paharma COVID-19 and Data Science

Aviva Lev-Ari
@AVIVA1950

#USAIC20 Dr. Maya Said, Chief Executive Officer, Outcomes4Me Cancer patients taking change of their care Digital Health – consumerization of Health, patient demand to be part of the decision, part the information FDA launched a Program Project Patient Voice

USAIC
@USAIC

We’re taking a quick break at #USAIC20 before our next panel on rare diseases starts at 12:20pm EDT. USAIC would like to thank our Sponsors and Partners for supporting this year’s digital event.

Image

Aviva Lev-Ari
@AVIVA1950

#USAIC20 Dr. Roy Vagelos, Chairman of the Board, Regeneron HIV-AIDS: reverse transcriptase converted a lethal disease to a chronic disease, tried hard to make vaccine – the science was not there

Aviva Lev-Ari
@AVIVA1950

#USAIC20 Dr. Roy Vagelos, Chairman of the Board, Regeneron Pharmaceuticals Congratulates Big Pharma for taking the challenge on COVID-19 Vaccine, Antibody and anti-viral Government funding Merck was independent from Government – to be able to set the price

1

Dr Kapil Khambholja
@kapilmk

Christopher Viehbacher, Gurnet Point Capital touches very sensitive topic at #USAIC20 He claims that we are never going to have real innovation out of big pharma! Well this isn’t new but not entirely true either… any more thoughts?
1
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Aviva Lev-Ari
@AVIVA1950

#USAIC20 Daphne Zohar, Founder & CEO, PureTech Health Disease focus, best science is the decision factors

1

Aviva Lev-Ari
@AVIVA1950

#USAIC20 Christopher Viehbacher, Managing Partner, Gurnet Point Capital Dream of every Biotech – get Big Pharma coming to acquire and pay a lot Morph and adapt

anju ghangurde
@scripanjug

Biogen’s chair Papadopoulos big co mergers is an attempt to solve problems; typically driven by patent expirations.. #usaic20

2

anju ghangurde
@scripanjug

Chris Viehbacher/Gurnet Point Capital on US election: industry will work with whoever wins; we’ll have to ‘morph & adapt’ #usaic20

1

Dr Kapil Khambholja
@kapilmk

of

talks about various philosophies and key reasons why certain projects/molecules are killed early. My counter questions- What are chances of losing hope little early? Do small #biopharma publish negative results to aid to the knowledge pool? #USAIC20

Image

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Aviva Lev-Ari
@AVIVA1950

#USAIC20 Dr. Laurie Glimcher, President & CEO, Dana-Farber Cancer Institute DNA repair and epignetics are the future of medicine

Aviva Lev-Ari
@AVIVA1950

#USAIC20 Dr. Laurie Glimcher, President & CEO, Dana-Farber Cancer Institute COlonorectal cancer is increasing immuno therapy 5 drugs marketed 30% cancer patients are treated early detection key vs metastatic 10% of cancer are inherited treatment early

Aviva Lev-Ari
@AVIVA1950

#USAIC20 Rehan Verjee, President, EMD Serono Charities funding cancer research – were impacted and resources will come later and in decreased amount New opportunities support access to Medicine improve investment across the board

Aviva Lev-Ari
@AVIVA1950

#USAIC20 Dr. Philip Larsen, Global Head of Research, Bayer AG Repurposing drugs as antiviral from drug screening innovating methods Cytokine storm in OCVID-19 – kinase inhibitors may be antiviral data of tested positive allows research of pathway in new ways

Aviva Lev-Ari
@AVIVA1950

#USAIC20 Dr. Laurie Glimcher, President & CEO, Dana-Farber 3,000 Telemedicine session in the first week of the Pandemic vs 300 before – patient come back visits patient happy with Telemedicine team virtually need be reimbursed same rate working remotely

Aviva Lev-Ari
@AVIVA1950

#USAIC20 Dr. Raju Kucherlapati, Professor of Genetics, Harvard Medical School New normal as a result of the pandemic role of personalized medicine

Aviva Lev-Ari
@AVIVA1950

#USAIC20 Rehan Verjee, President, EMD Serono entire volume of clinical trials at Roche went down same at EMD delay of 6 month, some were to be initiated but was put on hold Charities funding cancer research were impacted and resources will come later smaller

Aviva Lev-Ari
@AVIVA1950

#USAIC20 Dr. Laurie Glimcher, President & CEO, Dana-Farber Cancer Institute Dana Farber saw impact of COVID-19 on immunosuppressed patients coming in for Cancer Tx – switch from IV Tx to Oral 96% decrease in screenings due to Pandemic – increase with Cancer

Aviva Lev-Ari
@AVIVA1950

#USAIC20 Kenneth Frazier, Chairman of the Board and Chief Executive Officer, Merck & Co. Pharma’s obligation for next generations requires investment in R&D vs Politicians running for 4 years Patients must come first vs shareholders vs R&D investment in 2011

Aviva Lev-Ari
@AVIVA1950

#USAIC20 Kenneth Frazier, Chairman of the Board and Chief Executive Officer, Merck & Co. Antibiotic research at Merck – no market incentives on pricing for Merck to invest in antibiotics people will die from bacterial resistance next pandemic be bacterial

Aviva Lev-Ari
@AVIVA1950

#USAIC20 Kenneth Frazier, Chairman of the Board and Chief Executive Officer, Merck & Co. Strategies of Merck = “Medicine is for the People not for Profit” – Ketruda in India is not reembureable in India and million are in need it Partnership are encouraged

Dr Kapil Khambholja
@kapilmk

Chairman Stelios Papadopoulos asks #KennethFrazier if wealthy nations will try to secure large proportion of #COVID19 drugs/vaccines. #KennethFrazie rightly mentions: pharma industry’s responsibility to balance the access to diff countries during pandemic. #USAIC20

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Dr Kapil Khambholja
@kapilmk

Almost 60% participants at #USAIC20 feel that MNCs are more likely to run their #clinicalTrials in #INDIA seeing changing environment here, reveals the poll. Exciting time ahead for scientific fraternity as this can substantially increase the speed of #DrugDevelopment globally

Clapping hands sign

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Aviva Lev-Ari
@AVIVA1950

#USAIC20 Dr. Barry Bloom, Professor & former Dean, Harvard School of Public Health Vaccine in clinical trials, public need to return for 2nd shot, hesitancy Who will get the Vaccine first in the US  most vulnerable of those causing transmission Pharma’s risk

4

Aviva Lev-Ari
@AVIVA1950

#USAIC20 Dr. Barry Bloom, Professor & former Dean, Harvard School of Public Health Testing – PCR expensive does not enable quick testing is expensive result come transmission occurred Antibody testing CRISPR test based Vaccine in clinical trials

1

Aviva Lev-Ari
@AVIVA1950

#USAIC20 Dr Andrew Plump, President of R&D, Takeda Pharmaceuticals COllaboration effort around the Globe in the Pandemic therapy solutions including Vaccines

Read Full Post »


Tweets by @pharma_BI and @AVIVA1950 @ 2020 State of Possible Conference, MassBio’s VIRTUAL Annual Meeting, August 26 – 27, 2020

Real Time press coverage: Aviva Lev-Ari, PhD, RN

 

2020 State of Possible Conference, MassBio’s VIRTUAL Annual Meeting, August 26 – 27, 2020

Real Time press coverage: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2020/02/21/2020-state-of-possible-conference-massbios-annual-meeting-march-25-26-2020-sonesta-hotel-cambridge-ma/

 

Aviva Lev-Ari
@AVIVA1950

2020 State of Possible Conference, MassBio’s VIRTUAL Annual Meeting, August 26 – 27, 2020
2020 State of Possible Conference, MassBio’s VIRTUAL Annual Meeting, August 26 – 27, 2020 Leaders in Pharmaceutical Business Intelligence (LPBI) Group will cover this event in REAL TIME…
pharmaceuticalintelligence.com
2

Aviva Lev-Ari
@AVIVA1950

Thomas McCourt President Ironwood Pharmaceuticals, Inc. Clinical trials many are STUCK – solve problems calls for adoption of all companies to digital platforms Entrepreneurial spirit in Kendall square took away the prime position of CA Biotech

Aviva Lev-Ari
@AVIVA1950

Thomas McCourt President Ironwood Pharmaceuticals, Inc. GI disease in Patients – My Gi Health started in NIH – symptoms of GI diseases GI entrepreneurs to build a smart e-Tool to analyze the GI Symptoms  few thousand Patients

Aviva Lev-Ari
@AVIVA1950

Nick Dougherty Managing Director MassChallenge HealthTech Around the World communities, MA Biotech infrastructure  MassChallenge HealthTech: In Mexico, in Israel in Switzerland Becoming virtual instantly in MARCH 2020 More locations pick up scale up

1

Aviva Lev-Ari
@AVIVA1950

Naomi Fried Founder CEO Health Innovation Strategies MA best Hospitals: MGH-BWH, Beth Israel-Lahey Clinic, Stuart BC/BS Medical Schools Clusters in Biotech & Digital Health Counsel  Definition of Community changed in the COVID-19 Era

Aviva Lev-Ari
@AVIVA1950

Stephen Bernstein McDermott Will & Emery LLP Virtual Bench science Life sciences Products; Deploying a compound, provider responsible for the cost or how the Reimbursement will work Consumer & Patients: Specialty Pharmacy  collaborations by planning

Aviva Lev-Ari
@AVIVA1950

Stephen Bernstein McDermott Will & Emery LLP Webinars and Zooms allows communication we will see more innovations – Flatten the World Greater isolation US is expected to lead collaborate is NOW home based no travel creative  Virtual Clinical Trials

Aviva Lev-Ari
@AVIVA1950

Renée Deehan-Kenney PhD, VP, QuartzBio Precision Medicine Group Network biology modeling – helps inform chemistry models: Cell line in Mice translation to Human in pancreatic cancer cell line  Pessimistic view: Long way to go end point most time

Aviva Lev-Ari
@AVIVA1950

Rachel Hodos Senior AI Scientist BenevolentAI Panelist Pick Targets AI is a Possible dream trusted a drug in human

Aviva Lev-Ari
@AVIVA1950

Nora Khaldi Founder and CSO Nuritas AI biological data is early while relying on that knowledge identify drug safe for human is possible I believe AI – take a molecule to humans 99% working in humans AI teated and validated in vitro

Aviva Lev-Ari
@AVIVA1950

Juan Alvarez AVP, Merck High dimensional spaces sample comparison Find therapies for Humans in the absence of having Humans participating, data on human is BIASED by drugs history Drugable identify interventions translatable to Humans pathway-based

Aviva Lev-Ari
@AVIVA1950

Renée Deehan-Kenney PhD, VP, QuartzBio, Precision Medicine Group Chemistry in developing drugs is complex Network biology modeling – helps inform chemistry models: Cell line in Mice translation to Human in pancreatic cancer cell line

Aviva Lev-Ari
@AVIVA1950

Renée Deehan-Kenney PhD, VP, QuartzBio Precision Medicine Group technology enable company clinical data analysis of data clinical trials ML prior knowledge network biology drive inside MOA prioritize indications Chemistry in developing drugs complex

Aviva Lev-Ari
@AVIVA1950

Juan Alvarez AVP, Computational and Structural Chemistry Merck metabolomics evolving proteins analyzing data access to compute power data acquisition and storage – High dimensional spaces sample comparison

1

Aviva Lev-Ari
@AVIVA1950

Juan Alvarez AVP, Computational and Structural Chemistry Merck Drug Target identification Drug Discovery – ML since 1980s Identify molecules syntesis prediction physico space – physiological systems Transcriptomics, single cell biomarkers proteomics

Aviva Lev-Ari
@AVIVA1950

Renée Deehan-Kenney PhD, VP, QuartzBio Precision Medicine Group Network biology modeling – helps inform chemistry models: Cell line in Mice translation to Human in pancreatic cancer cell line  Pessimistic view: Long way to go end point most time

Aviva Lev-Ari
@AVIVA1950

Rachel Hodos Senior AI Scientist BenevolentAI Panelist Pick Targets AI is a Possible dream trusted a drug in human

Aviva Lev-Ari
@AVIVA1950

Nora Khaldi Founder and CSO Nuritas AI biological data is early while relying on that knowledge identify drug safe for human is possible I believe AI – take a molecule to humans 99% working in humans AI teated and validated in vitro

Aviva Lev-Ari
@AVIVA1950

Thomas McCourt President Ironwood Pharmaceuticals, Inc. Clinical trials many are STUCK – solve problems calls for adoption of all companies to digital platforms Entrepreneurial spirit in Kendall square took away the prime position of CA Biotech

Aviva Lev-Ari
@AVIVA1950

Thomas McCourt President Ironwood Pharmaceuticals, Inc. GI disease in Patients – My Gi Health started in NIH – symptoms of GI diseases GI entrepreneurs to build a smart e-Tool to analyze the GI Symptoms  few thousand Patients

Aviva Lev-Ari
@AVIVA1950

Nick Dougherty Managing Director MassChallenge HealthTech Around the World communities, MA Biotech infrastructure  MassChallenge HealthTech: In Mexico, in Israel in Switzerland Becoming virtual instantly in MARCH 2020 More locations pick up scale up

1

Aviva Lev-Ari
@AVIVA1950

Naomi Fried Founder CEO Health Innovation Strategies MA best Hospitals: MGH-BWH, Beth Israel-Lahey Clinic, Stuart BC/BS Medical Schools Clusters in Biotech & Digital Health Counsel  Definition of Community changed in the COVID-19 Era

Aviva Lev-Ari
@AVIVA1950

Stephen Bernstein McDermott Will & Emery LLP Virtual Bench science Life sciences Products; Deploying a compound, provider responsible for the cost or how the Reimbursement will work Consumer & Patients: Specialty Pharmacy  collaborations by planning

Aviva Lev-Ari
@AVIVA1950

Stephen Bernstein McDermott Will & Emery LLP Webinars and Zooms allows communication we will see more innovations – Flatten the World Greater isolation US is expected to lead collaborate is NOW home based no travel creative  Virtual Clinical Trials

Aviva Lev-Ari
@AVIVA1950

Kenneth Anderson Director, Multiple Myeloma Center Dana-Farber Cancer Institute Hematologic Division – African Americans  Change paradigm of clinical trials Geraldine Feraro was patient at DFCI Tom Bracow patient at DFCI  STEM for girls 6-12 grades

Aviva Lev-Ari
@AVIVA1950

Kenneth Anderson Director, Multiple Myeloma Center Dana-Farber Cancer Institute Multiple Myeloma – 23 drugs approved by FDA Dana-Farber Cancer Institute with Sanofi collaboration Foundations stepped forward to study Multiple Myeloma FDA motivated

Aviva Lev-Ari
@AVIVA1950

Juan Alvarez AVP, Merck High dimensional spaces sample comparison Find therapies for Humans in the absence of having Humans participating, data on human is BIASED by drugs history Drugable identify interventions translatable to Humans pathway-based

Aviva Lev-Ari
@AVIVA1950

Renée Deehan-Kenney PhD, VP, QuartzBio, Precision Medicine Group Chemistry in developing drugs is complex Network biology modeling – helps inform chemistry models: Cell line in Mice translation to Human in pancreatic cancer cell line

Aviva Lev-Ari
@AVIVA1950

Renée Deehan-Kenney PhD, VP, QuartzBio Precision Medicine Group technology enable company clinical data analysis of data clinical trials ML prior knowledge network biology drive inside MOA prioritize indications Chemistry in developing drugs complex

Aviva Lev-Ari
@AVIVA1950

Juan Alvarez AVP, Computational and Structural Chemistry Merck metabolomics evolving proteins analyzing data access to compute power data acquisition and storage – High dimensional spaces sample comparison

1

Aviva Lev-Ari
@AVIVA1950

Juan Alvarez AVP, Computational and Structural Chemistry Merck Drug Target identification Drug Discovery – ML since 1980s Identify molecules syntesis prediction physico space – physiological systems Transcriptomics, single cell biomarkers proteomics

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14th Annual BioPharma & Healthcare Summit, Friday, September 4, 2020, 8 AM EST to 3-30 PM EST – Virtual Edition

Real Time Press Coverage: Aviva Lev-Ari, PhD, RN

Founder & Director, LPBI Group

 

Tweet Collection by @pharma_BI and @AVIVA1950 and Re-Tweets for e-Proceedings 14th Annual BioPharma &amp; Healthcare Summit, Friday, September 4, 2020, 8 AM EST to 3-30 PM EST – Virtual Edition

Real Time Press Coverage: Aviva Lev-Ari, PhD, RN

https://pharmaceuticalintelligence.com/2020/09/04/tweet-collection-by-pharma_bi-and-aviva1950-and-re-tweets-for-e-proceedings-14th-annual-biopharma-healthcare-summit-friday-september-4-2020-8-am-est-to-3-30-pm-est-virtual-editio/

 

 

 

http://www.usaindiachamber.org

 

 2021 summit- June 22. Marriott Cambridge, Massachusetts, USA

 

LPBI’s 2020 VISION

@pharma_BI

@AVIVA1950

#USAIC20

 

 

USAIC has created an ecosystem committed to driving a global dialogue on BioPharma & Healthcare innovation, attracting a diverse mix of senior industry professionals and catalyzing partnerships, new ideas, networks and regulatory reform. This unique platform creates mutually beneficial opportunities and relationships for the global Life Sciences & Healthcare industry.

14th Annual BioPharma & Healthcare Summit, Friday, September 4, 2020, 8 AM EST to 3-30 PM EST – Virtual Edition

 

Speakers


Kenneth Frazier
Chairman of the Board & CEO
Merck & Co.

Dr. Andrew Plump
President of R&D
Takeda Pharmaceuticals

Dr. Laurie Glimcher
President & CEO
Dana-Farber Cancer Institute

Dr. Roy Vagelos
Chairman of the Board
Regeneron

Dr. Stelios Papadopoulos
Chairman of the Board
Biogen

Dr. Mathai Mammen
Global Head of Janssen R&D
Johnson & Johnson

Christopher Viehbacher
Managing Partner
Gurnet Point Capital

Hari Bhartia
Founder & Co-Chairman
Jubilant Bhartia Group

Dr. Hal Barron
President, R&D and CSO
GlaxoSmithKline

Prof. K. Vijay Raghavan
Principal Scientific Advisor
Government of India

Sanat Chattopadhyay
President- Merck Manufacturing Division
Merck & Co.

Dr. George Yancopoulos
Co-Founder, President & CSO
Regeneron

Kiran Mazumdar Shaw
Executive Chairperson
Biocon

Dr. Elias Zerhouni
Professor Emeritus
Johns Hopkins University

Dr. David Reese
Executive Vice President- R&D
Amgen

Dr. Alfred Sandrock
Executive Vice President, R&D
Biogen

Dr. Naresh Trehan
Chairman
Medanta – the Medicity

Dr. Najat Khan
Chief Operating Officer, Data Sciences
Janssen- Johnson & Johnson

Dr. Richard Hatchett
Chief Executive Officer
CEPI

Amitabh Kant
Chief Executive Officer
NITI Aayog

Dr. Martin Mackay
Co-Founder
Rallybio

Dr. Daniel Curran
Head of the Rare Diseases TA
Takeda Pharmaceuticals

Daphne Zohar
Founder & CEO
PureTech Health

Dr. David Meeker
Chairman & CEO
Rhythm Pharmaceuticals

Dr. John Orloff
EVP and Head of R&D
Alexion

Dr. Mandeep Bhandari
Joint Secretary
Ministry of Health, India

Dr. Barry Bloom
Professor & former Dean
Harvard School of Public Health

Dr. Anne Heatherington
Head of Data Sciences Institute
Takeda Pharmaceuticals

Dr. Philip Larsen
Global Head of Research
Bayer AG

Dr. Timothy Yu
Assistant Professor in Pediatrics
Harvard Medical School

Rehan Verjee
President
EMD Serono

Sastry Chilukuri
Executive Vice President
Medidata

Arpa Garay
President, Commercial Analytics
Merck & Co.

Dr. William Chin
Professor of Medicine, Emeritus
Harvard Medical School

Dr. V G Somani
Drugs Controller General of India
Government of India

Dr. Rajeev Venkayya
President-Global Vaccines
Takeda

Dr. Steve Uden
Co-Founder
Rallybio

Muna Bhanji
SVP, Global Market Access
Merck & Co.

Dr. Maya Said
Chief Executive Officer
Outcomes4Me

Dr. Raju Kucherlapati
Professor of Genetics
Harvard Medical School

Dr. Tony Ho
Head of R&D
CRISPR Therapeutics

Dr. Sanjeev Sinha
Professor of Medicine
All India Institute of Medical Sciences

Nina Kjellson
General Partner
Canaan

Dr. Michael Rosenblatt
Chief Medical Officer
Flagship Pioneering

Dr. Shiv Kumar Sarin
Director
Institute of Liver & Biliary Sciences

Matt Wilsey
Co-Founder & Chairman
Grace Science Foundation

Dr. Samuel Waksal
Founder
Meira GTx

Dr. Alise Reicin
Former President, Global Clinical Dev.
Celgene

Dr. Toni Choueiri
Director
Lank Center for Genitourinary Oncology
Dana-Farber Cancer Institute

Dr. Dhaval Patel
EVP & Chief Scientific Officer
UCB

Dr. Nirmal Kumar Ganguly
Former Director General
Indian Council of Medical Research

Dr. Peter Mueller
President
The Muller Health Foundation

Dr. Timothy Clackson
President & CTO
Xilio Therapeutics
 

 

14th Annual BioPharma & Healthcare Summit, Friday, September 4, 2020,

8 AM EST to 3-30 PM EST – Virtual Edition

 

Chair and Master of Ceremonies (Emcee)– Dr. Andrew Plump, President of R&D, Takeda Pharmaceuticals

Timings are Eastern Standard Time (EST)

Time Topic
8 AM – 8-10 AM Welcome addressKarun Rishi, President, USAIC

  • COVID-19 Pandemic is a Global crisis
  • India can play a special role in R&D and in Manufacturing including Vaccine development

Opening commentsDr Andrew Plump, President of R&D, Takeda Pharmaceuticals

  • Global Summit around the World – JP Morgan of the East as we were called – it is Now a Global Conference vs East Coast
  • Record number of Drugs approved as New Drugs with special quality
  • explosion of modality of therapies to include Gene Therapy
  • Billion underserved vs N-of-One drug
  • India’s President Modi allow healthcare access to 1/2Billion
  • collaboration across the World COVID Alliance in vaccine development
  • Global effort, China recovery is remarkable
  • India battle the infection and it is growing – Public Health
  • Remarkable Speakers
8-10 AM – 8-50 AM Panel Discussion- COVID-19: Where are we now? Where are we going?

Panelists:
Dr. Barry Bloom, Professor & former Dean, Harvard School of Public Health

  • Testing – PCR expensive does not enable quick testing is expensive result come transmission occurred
  • Antibody testing
  • CRISPR test based
  • Vaccine in clinical trials, public need to return for 2nd shot, hesitancy
  • Who will get the Vaccine first? in the US  most vulnerable of those causing transmission
  • Pharma takes risk when efficacious level is unknown
    Dr. George Yancopoulos, Co-Founder, President & CSO, Regeneron
  • Repurpose – be careful
  • Ebola vaccine development approach is been REUSED for COVID-19
  • Existential threat by Disease – preparedness is ridiculous as size of investment – far where we need to be
  • Untreatable disease burden COVID-19 cost of healthcare calls massive increases as a society and Private sector Moderna invested in new technology from Academe to the Industry
  • Universal HealthCare will cripple the the healthcare systems
    Kiran Mazumdar-Shaw, Executive Chairperson, Biocon
  • Safety in proof of concept
  • Children focus for emergency use
  • validation of repurpose drugs
  • oral vaccine involve sequential processing, approval and TRUST,
  • concerns about risks
  • accelerate the process is the opportunity
    Dr. Rajeev Venkayya, President of the Global Vaccine Business Unit, Takeda
  • Public confidence in COVID-19 Vaccine
  • The Group with concerns at present is larger than 15 years ago due to the accelerate process od the development process
  • political influences on CDC emergency authorization given prior to election
  • hesitancy – influence of social media, conspiracies
  • Transparency by Pharma and by Regulatory Agencies
  • Independent reviews
    Dr. Richard Hatchett, CEO, Coalition for Epidemic Preparedness Innovations (CEPI)
  • 78 countries ready to participate, Healthcare workers priority to be ready end of next year

 

Moderator:
Dr. William Chin, Professor of Medicine, Emeritus, Harvard Medical School

8-50 AM – 8-55 AM Break + Polling
8-55 AM – 9-10 AM India Regulatory update

Dr. Mandeep Bhandari, Joint Secretary, Ministry of Health & Family Welfare, India

  • COVID related – support for Clinical Trials support to the Industry, innovators, processes and infrastructure is in place

Dr. V G Somani, Drug Controller General of India, Central Drug Control Organization

  • partnership, time line, transparency
  • interaction online with regulators
  • 30 days approval pre and post approval – progress achieved
  • Online presubmission very useful to both sides
  • Ecosystems on early development: Gene therapy

Moderator:
Muna Bhanji, Senior Vice President,  Merck & Co.

  • India’s preparedness
9-10 AM – 9-15 AM Break + Polling
9-15 AM – 9-55 AM Fireside Chat

Kenneth Frazier, Chairman of the Board and Chief Executive Officer, Merck & Co.

Strategies of Merck = “Medicine is for the People not for Profit”

  • AntiViral – nucleocide – orally bioavailable
  • Vaccine in early development – BSV Vaccine used in EBOLA – attenuated virus vector platform experience – 1 single doze, deployed Globally
  • Vaccine modified Measles Vaccine, novel platform – out patient and Hospital
  • Antibiotic research at Merck – no market incentives on pricing for Merck to invest in antibiotics
  • people will die from bacterial resistance infection and next pandemic will be bacterial not viral

Moderator:
Dr. Stelios Papadopoulos, Chairman of the Board, Biogen

  • Most important comments on urgency in investment in drug development by multiple constituencies made by
  • Dr. George Yancopoulos, Co-Founder, President & CSO, Regeneron
  • Access to therapy
9-55 AM – 10 AM Break + Polling
10 AM – 10-40 AM India Innovation Landscape

Panelists:
Amitabh Kant, Chief Executive Officer, National Institution for Transforming India (NITI)

  • Innovation in drug discovery collaboration for clinical trial infrastructure
  • BioEconomy BioSimilar the largest number approved anywhere
  • Incentives for size and scale
  • Ingredients manufacturing to become India’s priority
  • Investment in R&D and Human Capital in the BioEconomy

Hari Bhartia, Founder & Co-Chairman, Jubilant Bhartia Group

  • US history of innovations cluster and infrastructure: Academe, VC, small medium Biopharma, Government involvement
  • India: Contract research – 20 years history, lagging the ability to take risk
  • Changing, pricing of drug increased, innovating drug for local consumption, and it can be taken to US for a better price
  • Cancer immunology in India under development
  • India was Leading Chemistry Research – China’s government invested and took the market
  • Indian companies bigger in size – free on requirement imposed on China
  • India will be a great supplier to US Market to build high capacity raw materials

Dr. K. Vijay Raghavan, Principal Scientific Advisor, Government of India

Resources are necessary 30% from Industry vs Government and Academe with great students and labs

Indian context – Personalized Medicine – Telemedicine and IT infrastructure allowing innovation in a 1Billion Population- sheer volume of quality professional

Dr. Naresh Trehan, Chairman, Medanta – the Medicity

  • Ecosystem ready for Government to promote innovations to conduct clinical trial with global acceptance standard
  • diverse gene pool in population to innovate for new molecule to market
  • Vaccine under development on Phase 1,2,3 – regulatory mechanism is in place
  • genetic drugs, BioSimilar dominance in the market – biotech can do clinical trials in India vs abroad

Moderator:
Sanat Chattopadhyay, President, Merck Manufacturing Division; Merck & Co.

  • Largest producer of generic drugs
  • antiretroviral drug produced by Indian Pharma
  • Biotech innovations growing middle class – how innovation , infrastructure and shift to research
  • Diversify and become self reliance
10-40 AM – 10-45 AM Break + Polling
10-45 AM – 11-25 AM Panel Discussion- Oncology: Changing landscape- COVID learnings and the promise of new technologies

Panelists:
Dr. Alise Reicin, Former President, Global Clinical Development, Celgene

  • Clinical trial were impacted by association of patients to trials
  • anti bacterial resistance requires investment – needs will be greater for antibiotics in the future
  • Cancer mutation next therapy biomarkers for mutations to be developed

Dr. Laurie Glimcher, President & CEO, Dana-Farber Cancer Institute

  • Dana Farber saw impact of COVID-19 on immunosuppressant population of patients coming in for Cancer Tx – switch from IV Tx to Oral
  • 96% decrease in screenings due to Pandemic – increase with Cancer diagnosis in coming years
  • No clinical Trials in Cancer were suspended – all continued
  • Telemedicine and working at home very efficient
  • Genomics of COVID-19 studies at Dana Farber same pathway identifies
  • safety and efficacy must be achieved – not to approve drugs without phase I & Phase II endpoints

Dr. Philip Larsen, Global Head of Research, Bayer AG

  • Repurposing drugs as antiviral from drug screening innovating methods
  • Cytokine storm in OCVID-19 – kinase inhibitors may be antiviral  – dat of tested positive allows research of pathway in new ways
  • Regulatory agencies in US and Europe for types of drugs vs single patient drugs

Rehan Verjee, President, EMD Serono

  • entire volume of clinical trials at Roche went down same at EMD
  • delay of 6 month, some were to be initiated but was put on hold
  • Charities funding cancer research – were impacted and resources will come later and in decreased amount
  • New opportunities support access to Medicine
  • improve investment across the board
  • Antibody cytotoxic with precision

Dr. Tony Ho, Head of Research and Development, CRISPR Therapeutics

  • challenges overcome by testing at home

Moderator:
Dr. Raju Kucherlapati, Professor of Genetics, Harvard Medical School

  • New normal as a result of the pandemic role of personalized medicine
  • Cancer cure – what are the prospects
11-25 AM – 11-30 AM Break + Polling
11-30 AM – 12-10 PM Panel Discussion- Industry & Investment Outlook

Panelists:
Christopher Viehbacher, Managing Partner, Gurnet Point Capital

  • IPOs can have advantages in Pandemics – Travel curtails all deals done virtually in greater efficiency
  • Drug pricing is a target by White house
  • Dream of every Biotech – get Big Pharma coming to acquire and pay a lot
  • Morph and adapt

Daphne Zohar, Founder & CEO, PureTech Health

  • kill project early financial incentive not in line in the industry
  • incentive to move resources among project and kill early project experiments to find which project to kill
  • Innovations – pattern recognition, fast followers academic translation
  • Disease focus, best science is the decision factors

Dr. Elias Zerhouni, Professor Emeritus, Johns Hopkins University

  • Digital Health
  • CVS opens clinics
  • R&D – Capital is low
  • Network of global innovation hubs vc investor channel like in the past
  • Value of company driven by hits blockbusters

 

Dr. Stelios Papadopoulos, Chairman, Biogen

  • Worst pandemic in our lifetime
  • stock market if hot – in balance in supply and demand, interest rates low, excess supply of equities in entertainment, Travel, hospitality
  • Healthcare was defensive therapeutics needed – opportunity to innovate in HC – shift money from entertainment, Travel hospitality to HC
  • Recovery will shift money away from Healthcare
  • IP Protection and patent expiration – biotech are cases not trends

Moderator:

Dr. Andrew Plump,

President of Research & Development, Takeda Pharmaceuticals

Moderator Presenter: Dr. Michael Rosenblatt, CEO

12-10 PM – 12-20 PM Break + Polling
12-20 PM – 1 PM Panel Discussion- Rare Diseases: No longer forgotten; but more to be achieved

ROI is not there, regulatory requirements reduced, Registry

Panelists:
Dr. Alfred Sandrock, Executive Vice President, Research & Development, Biogen

  • Multiple Sclerosis therapy
  • cost effectiveness is not there vs save a life
  • Appeal opportunity is there and regulators are people

Dr. Daniel Curran, Head of the Rare Diseases Therapeutic Area Unit, Takeda

  • Takeda collaborates with Grace Science Foundation

Dr. David Meeker, Chairman & CEO , Rhythm Pharmaceuticals

  • Cystic Fibrosis 

Dr. John Orloff, Head of Research & Development, Alexion

  • ALS
  • Duchenne Muscular Destrophy
  • HUS
  • ASO
  • gene therapy – one time therapy: Valuation for the industry of long term therapy: US (long term non existence) vs Europe and Japan (much appreciated

Matt Wilsey, Co-Founder & Chairman, Grace Science Foundation

  • Ultra-rare (500 Patients) vs Ultra Ultra-rare (50 Patients)
  • 70 patients in the World, Grace disease, Parent drive the search for drug
  • Manufacturing cost comes down
  • Price is dynamic

Moderator:
Dr. Steve Uden, Co-Founder, Rallybio

  • Regulators are people

 

1 PM – 1-05 PM Break + Polling
1-05 PM – 1-50 PM Fireside Chat

Dr. Roy Vagelos, Chairman of the Board, Regeneron Pharmaceuticals

  • Congratulate Big Pharma for taking the challenge on COVID-19
  • Vaccine, Antibody and anti-viral
  • Government funding
  • Merck was independent from Government – to be independent and be able to set the price
  • HIV-AIDS: reverse transcriptase converted a lethal disease to a chronic disease, tried hard to make vaccine – the science was not there
  • Industry role: Competition of drug discovery capacity is been built, global needs, price need be low for global reach
  • Government is a already a player hoping without a control on pricing
  • 300Million people were treated FREE by Merck’s Family Program HepC
  • 9% in China immunize the newborn with HepB 1994 100% babies immunized – no profit to Merck – eradication of HepB in China
  • Neuro degeneration – science supports drug development
  • Role of R&D Scientists in Drug discovery?

Moderator:
Dr. Mathai Mammen, Global Head of Janssen Research & Development, Johnson & Johnson

  • COVID-19 drug development: Response by Big Pharma
  • Industry role in Access to medicines, biologics, antibodies, vaccines
  • Role of R&D Scientists in Drug discovery?
  • PAHTN – use Machine Learning on top of data collected routinely,

 

1-50 PM – 1-55 PM Break + Polling
1-55 PM – 2-35 PM Panel Discussion- Digital & Data Science in Healthcare: Pragmatic Insights from the Real-World

Panelists:
Dr. Anne Heatherington, Head of Data Sciences Institute, Takeda Pharmaceuticals

  • Reliance on Data – AI and Data in Pharma alliance with MIT
  • collaboration of Data for COVID-19
  • Women need education in STEM and in Data Science

Arpa Garay, President, Global Pharmaceuticals, Commercial Analytics, Merck & Co.

  • Data on Patients and identification who will benefit fro which therapy
  •  cultural bias risk aversion
  • Invest early on in STEM

Dr. Maya Said, Chief Executive Officer, Outcomes4Me

  • Cancer patients taking change of their care
  • Digital Health – consumerization of Health, patient demand to be part of the decision, part of the information
  • FDA launched a Program Project Patient Voice

https://www.fda.gov/about-fda/oncology-center-excellence/project-patient-voice

  • Women should not undersell themselves

Dr. Najat Khan, Chief Operating Officer, Janssen R&D Data Sciences, Johnson & Johnson

  • Validation
  • Deployment of algorithms
  • embed data by type early on in the crisis to understand the disease
  • Compare the Big IT-Data and Pharma where are the barriers?
  • STEM and Women in Pharma – the opportunity must be right

Nina Kjellson, General Partner, Canaan

  • Data science is a winner in Healthcare
  • Women – Data Science is an excellent match

Moderator:
Sastry Chilukuri, President, Acorn AI- Medidata

  • Opportunities in Data Science in Pharma
  • COVID-19 and Data Science
  • STEM and Women in Pharma

 

2-35 PM – 2-40 PM Break + Polling
2-40 PM – 3-20 PM Panel Discussion- R&D Strategies and Trends: Innovation – The Big I

Panelists:
Dr. Andrew Plump, President of Research & Development, Takeda Pharmaceuticals

  • Enter for Plasma and for manufacturing vs discovery
  • Change how pharma behaved inefficiently in the past – with COVID-19 new behaviors in the industry
  • End of Century most diseases could be cured

Dr. David Reese, Executive Vice President, Research and Development, Amgen

  • Interaction with regulator was most favorable

Dr. Hal Barron, Chief Scientific Officer and President R&D, GlaxoSmithKline

  • Cytokine storm – few approaches
  • Control molecule GSK owned
  • GWAS not easy to find which gene drives the association
  • Functional Genomics gene by gene with phenotypes using machine learning significant help

Dr. Mathai Mammen, Global Head of Janssen Research & Development, Johnson & Johnson

  • Neuro-modulation: Symptomology Outcomes – no correlation
  • Vaccine platform used in the past for several vaccines: Selection process from several candidates, cell line enter Clinical waiting for data
  • Using same platform with several proteins – great communality in the development
  • Regulator deepen trust relationship which will carry for the future
  • Pulmonologists and cardiologist in the COVIS-19 Patients – remove drugs monitoring on drugs

Moderator:
Duval Patel presented the Moderator

Moderator:

Martin Mackay, Co-Founder, RallyBio

 

3-20 PM – 3-30 PM Closing Remarks

  • Every year it is getting better
  • India – innovate and make drugs for every country and for India
  • Diversity and inclusion
  • Leadership in Pharma Industry in all Panels
  • Massive impact can be made

 

Poll Questions for September 4

Polling Time (EST) Polling Topic
8-50 AM COVID-19 PanelQuestion 1: What do you foresee as the most likely outcome of the race to develop a vaccine?

  • Heightened international tensions due to inequities in distribution
  • Use of the vaccine as an instrument of geopolitics
  • Collaboration between governments to use vaccine to end the pandemic
  • All of the above

Question 2: What minimum criteria would you like to see for approval of COVID19 vaccines, assuming adequate efficacy?

  • Immune response in people over 60 years
  • Durability of response
  • Antibody plus T-cell response
  • Emergency Use Authorization with caveats followed by final approval
9-10 AM India Regulatory UpdateHow will MNCs respond to the recent regulatory changes for BioPharmas in India? They are _____ to run clinical trials there:

  • More likely
  • Less likely
  • Equally likely
9-55 AM Fireside Chat: Ken Frazier

The BioPharma industry this year has publicly committed itself to greater diversity. What specific measures do you expect to see?

  • Increasing diversity in clinical trials
  • Increasing diversity at the C-suite and board level
  • Increasing diversity throughout the company
  • All of the above
  • None of the above
10-40 AM India Innovation LandscapeWhat is the most important step India could take to become a global leader in life sciences innovation?”

  • Implement government policies to incentivize innovative drug development
  • Increase availability of financing for BioPharmas
  • Improve clinical trial infrastructure
  • Increase IP protection
11-25 AM Oncology PanelQuestion 1:

Changes in policy and reimbursement over the next five years will impact innovation in cancer therapeutics

  • Not at all
  • Slightly
  • Moderately
  • Significantly

Question 2: What therapeutic innovation do you think will have the biggest impact on cancer in the next five years?

  • Cell-based immunotherapies
  • Antibody-based immunotherapies
  • Bispecific / multi-specific antibodies
  • Antibody drug conjugates
12-10 PM Industry & Investment Outlook PanelMore and more funding has been going into preclinical companies — do you expect this trend to continue?

  • Yes
  • No

R&D Strategies and Trends Panel

COVID-19 has led to an unprecedented level of collaboration among stakeholders in the biopharma industry. Where do you expect to see the biggest increase in collaborations post-pandemic?

  • Discovery/preclinical research
  • Clinical development
  • Manufacturing
  • Commercialization
1 PM Rare Diseases PanelWhat is the biggest barrier to access to Orphan drugs in low-income countries?

  • Price, Access and Availability
  • Disease recognition and diagnosis
  • Lack of patient education regarding new therapies
  • Ultra-rarity of certain diseases creates barriers for BioPharma companies to pursue therapeutic
1-50 PM Fireside Chat: Roy VagelosQuestion 1:

Will pharma’s reputation continue its positive trend or return to negative base line beyond the pandemic

  • Yes
  • No

Question 2:

COVID-19 has put the spotlight on BioPharma as an essential player in the return to normalcy. What primary action do you think the industry needs to take to maintain a positive reputation beyond the pandemic?

  • Continue developing innovative drug pricing models
  • Increase drug pricing transparency
  • Increase data sharing and transparency
  • Improving availability and access in low income countries
2-35 PM Digital & Data Sciences PanelWhere has COVID-19 had the biggest impact on your adoption and use of digital health technologies?

  • Remote clinical trials and patient monitoring
  • Real-world data collection and analysis
  • Virtual drug launches

 

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In these unprecedented times due to COVID-19, USAIC is offering Free Registration for its annual summit.

Click for free registration

 

AGENDA & SPEAKERS

Chair and Master of Ceremonies (Emcee)– Dr. Andrew Plump, President of R&D, Takeda Pharmaceuticals
Summit Theme: “From N of One to N of a Billion”

  • Moderated Fireside Chat- Kenneth Frazier, Chairman of the Board and Chief Executive Officer, Merck & Co. and Stelios Papadopoulos, Chairman of the Board, Biogen
  • Moderated Fireside Chat- Roy Vagelos, Chairman of the Board, Regeneron Pharmaceuticals and Mathai Mammen, Global Head of R&D, Janssen Pharmaceutical Companies of Johnson & Johnson
  • Moderated Fireside Chat- K. VijayRaghavan, Principal Scientific Advisor, Government of India and Amitabh Kant, CEO, National Institution for Transforming India (NITI)

Panel Discussions:

  • Covid-19: Where are we now? Where are we going?
  • Oncology: A never ending tunnel?
  • Rare Diseases: Breaking Barriers for a Healthy Brain
  • Digital & Data Sciences: Leveraging data and digital to achieve healthcare solutions
  • Industry & Investment Outlook
  • R&D Strategies and Trends: Innovation – The Big I

Program and speakers subject to change*

14th Annual BioPharma & Healthcare Summit, Friday, September 4, 2020, 8 AM EST to 3-30 PM EST – Virtual Edition

Speakers


Kenneth Frazier
Chairman of the Board & CEO
Merck & Co.

Dr. Andrew Plump
President of R&D
Takeda Pharmaceuticals

Dr. Laurie Glimcher
President & CEO
Dana-Farber Cancer Institute

Dr. Roy Vagelos
Chairman of the Board
Regeneron

Dr. Stelios Papadopoulos
Chairman of the Board
Biogen

Christopher Viehbacher
Managing Partner
Gurnet Point Capital

Dr. Mathai Mammen
Global Head of R&D
Janssen- Johnson & Johnson

Kiran Mazumdar Shaw
Chairperson & Managing Director
Biocon

Dr. Hal Barron
President, R&D and CSO
GlaxoSmithKline

Prof. K. Vijay Raghavan
Principal Scientific Advisor
Government of India

Dr. George Yancopoulos
Co-Founder, President & CSO
Regeneron

Dr. Elias Zerhouni
Professor Emeritus
Johns Hopkins University

Daphne Zohar
Founder & CEO
PureTech Health

Sanat Chattopadhyay
President- Merck Manufacturing Division
Merck & Co.

Dr. David Reese
Executive Vice President- R&D
Amgen

Hari Bhartia
Founder & Co-Chairman
Jubilant Bhartia Group

Dr. Alfred Sandrock
Exe Vice President R&D & CMO
Biogen

Dr. Najat Khan
Chief Operating Officer, Data Sciences
Janssen- Johnson & Johnson

Dr. Richard Hatchett
Chief Executive Officer
CEPI

Amitabh Kant
Chief Executive Officer
NITI Aayog

Dr. Martin Mackay
Co-Founder
Rallybio

Dr. Daniel Curran
Head of the Rare Diseases TA
Takeda Pharmaceuticals

Dr. Alise Reicin
Former President, Global Clinical Dev.
Celgene

Dr. David Meeker
Chairman & CEO
Rhythm Pharmaceuticals

Dr. John Orloff
EVP and Head of R&D
Alexion

Dr. Barry Bloom
Professor & former Dean
Harvard School of Public Health

Dr. Mandeep Bhandari
Joint Secretary
Ministry of Health, India

Arpa Garay
President, Commercial Analytics
Merck & Co.

Dr. Steve Uden
Co-Founder
Rallybio

Dr. Philip Larsen
Global Head of Research
Bayer AG

Sastry Chilukuri
Executive Vice President
Medidata

Dr. William Chin
Professor of Medicine, Emeritus
Harvard Medical School

Dr. Anne Heatherington
Head of Data Sciences Institute
Takeda Pharmaceuticals

Dr. V G Somani
Drugs Controller General of India
Government of India

Dr. Rajeev Venkayya
President-Global Vaccines
Takeda

Dr. Raju Kucherlapati
Professor of Genetics
Harvard Medical School

Matt Wilsey
Co-Founder & Chairman
Grace Science Foundation

Muna Bhanji
SVP, Global Market Access
Merck & Co.

Dr. Maya Said
Chief Executive Officer
Outcomes4Me

Rehan Verjee
President
EMD Serono
Pharmasia News Biospectrum India Online

SOURCE:

https://usaindiachamber.org/speaker.php

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Live Conference Coverage AACR 2020 in Real Time: Monday June 22, 2020 Mid Day Sessions

Reporter: Stephen J. Williams, PhD

This post will be UPDATED during the next two days with notes from recordings from other talks

Follow Live in Real Time using

#AACR20

@pharma_BI

@AACR

 

 

 

 

 

 

 

Register for FREE at https://www.aacr.org/

 

AACR VIRTUAL ANNUAL MEETING II

 

June 22-24: Free Registration for AACR Members, the Cancer Community, and the Public
This virtual meeting will feature more than 120 sessions and 4,000 e-posters, including sessions on cancer health disparities and the impact of COVID-19 on clinical trials

 

This Virtual Meeting is Part II of the AACR Annual Meeting.  Part I was held online in April and was centered only on clinical findings.  This Part II of the virtual meeting will contain all the Sessions and Abstracts pertaining to basic and translational cancer research as well as clinical trial findings.

 

REGISTER NOW

 

Pezcoller Foundation-AACR International Award for Extraordinary Achievement in Cancer Research

The prestigious Pezcoller Foundation-AACR International Award for Extraordinary Achievement in Cancer Research was established in 1997 to annually recognize a scientist of international renown who has made a major scientific discovery in basic cancer research OR who has made significant contributions to translational cancer research; who continues to be active in cancer research and has a record of recent, noteworthy publications; and whose ongoing work holds promise for continued substantive contributions to progress in the field of cancer. For more information regarding the 2020 award recipient go to aacr.org/awards.

John E. Dick, Enzo Galligioni, David A Tuveson

DETAILS

Awardee: John E. Dick
Princess Anne Margaret Cancer Center, Toronto, Ontario
For determining how stem cells contribute to normal and leukemic hematopoeisis
  • not every cancer cell equal in their Cancer Hallmarks
  • how do we monitor and measure clonal dynamics
  • Barnie Clarkson did pivotal work on this
  • most cancer cells are post mitotic but minor populations of cells were dormant and survive chemotherapy
  •  only one cell is 1 in a million can regenerate and transplantable in mice and experiments with flow cytometry resolved the question of potency and repopulation of only small percentage of cells and undergo long term clonal population
  • so instead of going to cell lines and using thousands of shRNA looked at clinical data and deconvoluted the genetic information (RNASeq data) to determine progenitor and mature populations (how much is stem and how much is mature populations)
  • in leukemic patients they have seen massive expansion of a single stem cell population so only need one cell in AML if the stem cells have the mutational hits early on in their development
  • finding the “seeds of relapse”: finding the small subpopulation of stem cells that will relapse
  • they looked in BALL;;  there are cells resistant to l-aspariginase, dexamethasone, and vincristine
  • a lot of OXPHOS related genes (in DRIs) that may be the genes involved in this resistance
  • it a wonderful note of acknowledgement he dedicated this award to all of his past and present trainees who were the ones, as he said, made this field into what it is and for taking it into directions none of them could forsee

Monday, June 22

1:30 PM – 3:30 PM EDT

Virtual Educational Session

Experimental and Molecular Therapeutics, Drug Development, Cancer Chemistry

Chemistry to the Clinic: Part 1: Lead Optimization Case Studies in Cancer Drug Discovery

How can one continue to deliver innovative medicines to patients when biological targets are becoming ever scarcer and less amenable to therapeutic intervention? Are there sound strategies in place that can clear the path to targets previously considered “undruggable”? Recent advances in lead finding methods and novel technologies such as covalent screening and targeted protein degradation have enriched the toolbox at the disposal of drug discovery scientists to expand the druggable ta

Stefan N Gradl, Elena S Koltun, Scott D Edmondson, Matthew A. Marx, Joachim Rudolph

DETAILS

Monday, June 22

1:30 PM – 3:30 PM EDT

Virtual Educational Session

Bioinformatics and Systems Biology, Molecular and Cellular Biology/Genetics

Informatics Technologies for Cancer Research

Cancer researchers are faced with a deluge of high-throughput data. Using these data to advance understanding of cancer biology and improve clinical outcomes increasingly requires effective use of computational and informatics tools. This session will introduce informatics resources that support the data management, analysis, visualization, and interpretation. The primary focus will be on high-throughput genomic data and imaging data. Participants will be introduced to fundamental concepts

Rachel Karchin, Daniel Marcus, Andriy Fedorov, Obi Lee Griffith

DETAILS

  • Variant analysis is the big bottleneck, especially interpretation of variants
  • CIVIC resource is a network for curation, interpretation of genetic variants
  • CIVIC curators go through multiple rounds of editors review
  • gene summaries, variant summaries
  • curation follows ACSME guidelines
  • evidences are accumulated, categories by various ontologies and is the heart of the reports
  • as this is a network of curators the knowledgebase expands
  • CIVIC is linked to multiple external informatic, clinical, and genetic databases
  • they have curated 7017 clinical interpretations, 2527 variants, using 2578 papers, and over 1000 curators
  • they are currently integrating with COSMIC ClinVar, and UniProt
  • they are partnering with ClinGen to expand network of curators and their curation effort
  • CIVIC uses a Python interface; available on website

https://civicdb.org/home

The Precision Medicine Revolution

Precision medicine refers to the use of prevention and treatment strategies that are tailored to the unique features of each individual and their disease. In the context of cancer this might involve the identification of specific mutations shown to predict response to a targeted therapy. The biomedical literature describing these associations is large and growing rapidly. Currently these interpretations exist largely in private or encumbered databases resulting in extensive repetition of effort.

CIViC’s Role in Precision Medicine

Realizing precision medicine will require this information to be centralized, debated and interpreted for application in the clinic. CIViC is an open access, open source, community-driven web resource for Clinical Interpretation of Variants in Cancer. Our goal is to enable precision medicine by providing an educational forum for dissemination of knowledge and active discussion of the clinical significance of cancer genome alterations. For more details refer to the 2017 CIViC publication in Nature Genetics.

U24 funding announced: We are excited to announce that the Informatics Technology for Cancer Research (ICTR) program of the National Cancer Institute (NCI) has awarded funding to the CIViC team! Starting this year, a five-year, $3.7 million U24 award (CA237719), will support CIViC to develop Standardized and Genome-Wide Clinical Interpretation of Complex Genotypes for Cancer Precision Medicine.

Informatics tools for high-throughput analysis of cancer mutations

Rachel Karchin
  • CRAVAT is a platform to determine, categorize, and curate cancer mutations and cancer related variants
  • adding new tools used to be hard but having an open architecture allows for modular growth and easy integration of other tools
  • so they are actively making an open network using social media

Towards FAIR data in cancer imaging research

Andriy Fedorov, PhD

Towards the FAIR principles

While LOD has had some uptake across the web, the number of databases using this protocol compared to the other technologies is still modest. But whether or not we use LOD, we do need to ensure that databases are designed specifically for the web and for reuse by humans and machines. To provide guidance for creating such databases independent of the technology used, the FAIR principles were issued through FORCE11: the Future of Research Communications and e-Scholarship. The FAIR principles put forth characteristics that contemporary data resources, tools, vocabularies and infrastructures should exhibit to assist discovery and reuse by third-parties through the web. Wilkinson et al.,2016. FAIR stands for: Findable, Accessible, Interoperable and Re-usable. The definition of FAIR is provided in Table 1:

Number Principle
F Findable
F1 (meta)data are assigned a globally unique and persistent identifier
F2 data are described with rich metadata
F3 metadata clearly and explicitly include the identifier of the data it describes
F4 (meta)data are registered or indexed in a searchable resource
A Accessible
A1 (meta)data are retrievable by their identifier using a standardized communications protocol
A1.1 the protocol is open, free, and universally implementable
A1.2 the protocol allows for an authentication and authorization procedure, where necessary
A2 metadata are accessible, even when the data are no longer available
I Interoperable
I1 (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.
I2 (meta)data use vocabularies that follow FAIR principles
I3 (meta)data include qualified references to other (meta)data
R Reusable
R1 meta(data) are richly described with a plurality of accurate and relevant attributes
R1.1 (meta)data are released with a clear and accessible data usage license
R1.2 (meta)data are associated with detailed provenance
R1.3 (meta)data meet domain-relevant community standards

A detailed explanation of each of these is included in the Wilkinson et al., 2016 article, and the Dutch Techcenter for Life Sciences has a set of excellent tutorials, so we won’t go into too much detail here.

  • for outside vendors to access their data, vendors would need a signed Material Transfer Agreement but NCI had formulated a framework to facilitate sharing of data using a DIACOM standard for imaging data

Monday, June 22

1:30 PM – 3:01 PM EDT

Virtual Educational Session

Experimental and Molecular Therapeutics, Cancer Chemistry, Drug Development, Immunology

Engineering and Physical Sciences Approaches in Cancer Research, Diagnosis, and Therapy

The engineering and physical science disciplines have been increasingly involved in the development of new approaches to investigate, diagnose, and treat cancer. This session will address many of these efforts, including therapeutic methods such as improvements in drug delivery/targeting, new drugs and devices to effect immunomodulation and to synergize with immunotherapies, and intraoperative probes to improve surgical interventions. Imaging technologies and probes, sensors, and bioma

Claudia Fischbach, Ronit Satchi-Fainaro, Daniel A Heller

DETAILS

Monday, June 22

1:30 PM – 3:30 PM EDT

Virtual Educational Session

Survivorship

Exceptional Responders and Long-Term Survivors

How should we think about exceptional and super responders to cancer therapy? What biologic insights might ensue from considering these cases? What are ways in which considering super responders may lead to misleading conclusions? What are the pros and cons of the quest to locate exceptional and super responders?

Alice P Chen, Vinay K Prasad, Celeste Leigh Pearce

DETAILS

Monday, June 22

1:30 PM – 3:30 PM EDT

Virtual Educational Session

Tumor Biology, Immunology

Exploiting Metabolic Vulnerabilities in Cancer

The reprogramming of cellular metabolism is a hallmark feature observed across cancers. Contemporary research in this area has led to the discovery of tumor-specific metabolic mechanisms and illustrated ways that these can serve as selective, exploitable vulnerabilities. In this session, four international experts in tumor metabolism will discuss new findings concerning the rewiring of metabolic programs in cancer that support metabolic fitness, biosynthesis, redox balance, and the reg

Costas Andreas Lyssiotis, Gina M DeNicola, Ayelet Erez, Oliver Maddocks

DETAILS

Monday, June 22

1:30 PM – 3:30 PM EDT

Virtual Educational Session

Other Articles on this Open Access  Online Journal on Cancer Conferences and Conference Coverage in Real Time Include

Press Coverage

Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Symposium: New Drugs on the Horizon Part 3 12:30-1:25 PM

Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Session on NCI Activities: COVID-19 and Cancer Research 5:20 PM

Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Session on Evaluating Cancer Genomics from Normal Tissues Through Metastatic Disease 3:50 PM

Live Notes, Real Time Conference Coverage 2020 AACR Virtual Meeting April 28, 2020 Session on Novel Targets and Therapies 2:35 PM

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Live Conference Coverage AACR 2020 in Real Time: Monday June 22, 2020 8AM-Noon Sessions

Reporter: Stephen J. Williams, PhD

Follow Live in Real Time using

#AACR20

@pharma_BI

@AACR

 

Register for FREE at https://www.aacr.org/