Natural Language Processing Track

Keynote Session – Suchi Saria

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

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

Type: Keynote

A Secure Collaborative Learning Platform

Wed, October 28, 10:00 AM

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

Type: Keynote

OCTOBER 29^TH

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

The Medical De-confounder: Treatment Effects on A1c DM2

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

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:

GPT-3 from OpenAI – Q&A, Translation, grammar
Image GPT

Can AI Predict

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

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

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

TALKS on 10/29/2020

NLP

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

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

Draw topic
Draw Beta
For each document
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

LDA + Regression
Relational topic model
Pairwise-Link-LDA combine LDA and MMB [Same priors]

Predictive ranks (random guessing) and Runtimes (compact id distinct no overalp)

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)

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

Type: Track Keynote

ML – SubSets

Deep Learning – TRAINING for Clssification – Neuralnets – LeNet vs AlexNet – 7 layers 140x flops – using parallelism
Shallow learning – deterministic and linear classifier used
ML algorithms: Core ML, Audio analysis (Speech and audio recognition) , Multimedia
NLP: translation,
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)

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)

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)

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)

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:

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

Word embeddings: No context is taken into account – Word2vec, Glove
ELMo – embedding from language models: Contextual,
BERT – Bi-directional Encoder Representations fro Transformers
MLM – Masked Language Model: Forward, Backward, Masked
Next Sentence Prediction
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:

Zero-shot – English to French – no training
one-shots
Few-shot – the GOAL – GPT-3
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

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

Data augmentation

Contextual Augmentation

Original sentence
masked
augmented

Test generation

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

OCTOBER 30^TH

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.

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)

Type: Keynote

Data- models predictiona decisions Understanding
AI & Games
AI + ML
Deep Learning! (DL)

NN – tunable nonlinear functions with many parameters
Parameters are weights of NN
Optimization + Statistics
DL – New-branding of NN
Many layers – ReLUs attention
Cloud resources
SW – TensorFloe, JAX
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

Learning
Prediction
Model Comparison
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)

Type: Keynote

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

Fri, Oct 30, 2020 1:30 PM – 2:15 PM EDT

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

Method selection
Hyper-parameter Optimization
Black-box constraints

Bias Mitigation Algorithms

Pre-processing algo
In-processing Algo
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

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)

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

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

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

Q&A Link – https://odscwestvirtual.slack.com/archives/fri-ariclabarr-the-bayesians-are-coming_-the-bayesians-are-coming_-to-time-serie

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

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

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

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

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

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

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

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

SAS

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

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

DataRobot

SCHEDULE

TUESDAY, OCTOBER 27TH

Pre-conference Day

ODSC BootCamp

	BOOTCAMP KICKOFF WEST VIRTUAL
10:00 am	Fundamentals \| Morning Sessions 10:00 am – 1:30 pm Choose from 6 foundation sessions in Programming, Mathematics for Data Science, and Statistics Virtual break 1:00 pm – 1:30 pm
11:00 am
12:00 pm
1:00 pm
2:00 pm	Fundamentals \| Afternoon Sessions 2:00 pm – 5:30 pm Choose from 6 foundation sessions in Programming, Mathematics for Data Science, and Statistics
3:00 pm
4:00 pm
5:00 pm

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 10:00 am – 1:30 pm Choose from Five 3.5 hours Training Sessions and Six 90 minute Workshop Sessions Networking break 1:00 pm – 1:30 pm	Morning Partners Demo Talks 10:00 am – 1:00 pm Choose from 12 Partners Sessions
11:00 am			Virtual Exhibitor Showcase 11:00 am – 5:00 pm Visit 30+ Virtual Partners booth
12:00 pm
1:00 pm		Networking break 1:00 pm – 1:30 pm
2:00 pm	Hands-on Training and Workshops 2:00 pm – 5:30 pm Choose from Five 3.5 hours Training Sessions and Six 90 minute Workshop Sessions	Afternoon Partners Demo Talks 2:00 pm – 5:00 pm Choose from 12 Partners Sessions
3:00 pm
4:00 pm
5:00 pm

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 9:00 am – 10:00 am
10:00 am	Morning Hands-on Training and Workshops 10:00 am – 1:30 pm Choose from Five 3.5 hours Training Sessions and Six 90 minute Workshop Sessions Networking break 1:00 pm – 1:30 pm	Virtual Exhibitor Showcase & Partners Demo Talks 10:00 am – 1:00 pm Choose from 12 Morning Partners Sessions & Visit 25+ Virtual Partners booth
11:00 am			Breakout Talk Sessions 11:00 am – 1:00 pm Choose from 7 talk presentations
12:00 pm
1:00 pm		Networking break 1:00 pm – 1:30 pm
2:00 pm	Afternoon Hands-on Training and Workshops 2:00 pm – 5:30 pm Choose from Five 3.5 hours Training Sessions and Six 90 minute Workshop Sessions	Virtual Exhibitor Showcase & Partners Demo Talks 2:00 pm – 5:00 pm Choose from 12 Afternoon Partners Sessions & Visit 25+ Virtual Partners booth	Breakout Talk Sessions 2:00 pm – 5:00 pm Choose from 7 talk presentations
3:00 pm
4:00 pm
5:00 pm

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 9:00 am – 10:00 am
10:00 am	Morning Hands-on Training and Workshops 10:00 am – 1:30 pm Choose from Five 3.5 hours Training Sessions and Six 90 minute Workshop Sessions Virtual Lunch & Networking break 1:00 pm – 1:30 pm		Virtual Career Expo 10:30 am – 5:00 pm Get n touch with 30+ Hiring Partners and choose from 12 Mentor Talks
11:00 am		Breakout Talk Sessions 11:00 am – 1:00 pm Choose from 7 talk presentations
12:00 pm
1:00 pm		Virtual Lunch & Networking break 1:00 pm – 1:30 pm
2:00 pm	Afternoon Hands-on Training and Workshops 2:00 pm – 5:30 pm Choose from Five 3.5 hours Training Sessions and Six 90 minute Workshop Sessions	Breakout Talk Sessions 2:00 pm – 5:00 pm Choose from 7 talk presentations
3:00 pm
4:00 pm
5:00 pm

SPEAKERS

Nadja Herger, PhD

DATA SCIENTISTTHOMSON REUTERS

Viktoriia Samatova

HEAD OF TECHNOLOGY & INNOVATIONTHOMSON REUTERS

Nina Hristozova

JUNIOR DATA SCIENTISTTHOMSON REUTERS

Daniel Whitenack, PhD

INSTRUCTOR, DATA SCIENTISTDATA DAN

David Talby: NLP for healthcare — Click for
more info

David Talby, PhD

CTOPACIFIC AI, JOHN SNOW LABS

Tian Zheng, PhD

CHAIR, DEPARTMENT OF STATISTICSCOLUMBIA UNIVERSITY

Phoebe Liu

SENIOR DATA SCIENTISTAPPEN

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

Approaches and Solutions for Management of the COVID Pandemic

Posted in COVID-19, Population Health Management, Population Health Management, Genetics & Pharmaceutical on October 22, 2020| Leave a Comment »

Approaches and Solutions for Management of the COVID Pandemic

Reporter: Aviva Lev- Ari, PhD, RN and Stephen J. Williams, PhD

October 8, 2020 N Engl J Med 2020; 383:1479-1480 DOI: 10.1056/NEJMe2029812

Dying in a Leadership Vacuum

CONTINUE TO READ AT THE SOURCE N Engl J Med 2020; 383:1479-1480 DOI: 10.1056/NEJMe2029812 Janice Hopkins Tanne. (2020) Covid 19: NEJM and former CDC director launch stinging attacks on US response. BMJ, m3925. BMJ 2020;371:m3925

Covid 19: NEJM and former CDC director launch stinging attacks on US response

Janice Hopkins Tanne Author affiliations

The US is “dying in a leadership vacuum,” in responding to the covid-19 pandemic, the New England Journal of Medicine has said in an editorial.

“Our leaders have failed. They have taken a crisis and turned it into a tragedy,” the NEJM editors said. US leaders are “dangerously incompetent,” have undercut trust in science and in government,” and should be voted out,1 the journal said.

The intervention came as a former director of the Centers for Disease Control and Prevention (CDC) suggested the current CDC director should update staff in writing about the agency’s failings, apologise, and resign.2 3

The US leads the world in the death rate from covid-19, which is far higher than larger countries and those with less sophisticated technology and health services, the editors said.

“We have failed at almost every step,” they wrote, describing problems with supplies of personal protective equipment, delays in testing, and failure to employ quarantine, isolation, and social distancing appropriately and quickly. Government inaction has led to business losses and unemployment.

Earlier, William Foege, former director of the CDC and a leader in smallpox eradication, criticised the US response and the failure of the CDC. He sent a letter to Robert Redfield, the current CDC director, asking him to write to CDC employees describing the White House’s failure to put the CDC in charge of the covid-19 pandemic and then resign. A letter, he wrote, would be on the record.

Foege called the US response to the pandemic “a slaughter and not just a political dispute” that had turned the CDC’s reputation from “gold to tarnished brass.”

Foege is emeritus presidential distinguished professor of international health at Emory University. He was director of the Carter Center’s Task Force for Child Survival and senior medical advisor to the Bill and Melinda Gates Foundation. President Barack Obama awarded him the Presidential Medal of Freedom, the nation’s highest civilian honour, in 2012. His private letter, written on 23 September, was published by USA Today on 7 October.

Redfield, a virologist with expertise in HIV/AIDS and a clinician, served in the US Army’s medical corps. He co-founded the University of Maryland’s Institute of Human Virology and was chief of infectious diseases at the university’s medical school.

Foege wrote, “You don’t want to be seen, in the future, as forsaking your role as servant to the public in order to become a servant to a corrupt president. You could send a letter to all CDC employees (a letter leaves a record and avoids the chance of making a mistake with a speech) laying out the facts. The White House will, of course, respond with fury. But you will have right on your side. Like Martin Luther, you can say, ‘Here I stand, I cannot do otherwise.’”

Among the truths that need to be faced, Foege said, are that, despite White House spin attempts, the failure of the US public health system is because of “the incompetence and illogic of the White House programme.”

The White House failed to put the CDC in charge of the pandemic, violating rules of public health so that “people and the media go to the academic community for truth, rather than to CDC,” Foege’s letter says. Unlike former responses to health crises, there has been no federal plan, “resulting in 50 states developing their own plans, often in competition.”

The need to form coalitions to fight the pandemic “has been ignored as the president thrives instead on creating divisions, and the need for global cooperation has been squandered by an ‘America first’ policy. The best decisions are based on the best science while the best results are based on the best management. The White House has rejected both science and good management,” Foege wrote.

Foege, the CDC, Redfield, and the White House have not publicly commented on the letter.

References

↵

Editors. Dying in a leadership vacuum. N Engl J Med2020;383:1479–80. doi:10.1056/NEJMe2029812 pmid:33027574

CrossRef PubMed Google Scholar
↵

Foego W. Foege letter to Robert Redfield. 23 September 2020. www.documentcloud.org/documents/7222830-Foege-Letter.html
↵

Murphy B, Stein L. “It is a slaughter”: Public health champion asks CDC director to expose White House, orchestrate his own firing. USA Today. 7 October 2020. www.usatoday.com/story/news/investigations/2020/10/06/expert-cdcs-redfield-should-expose-trump-covid-failures-leave-post/5899724002.

SOURCES for the NEJM https://www.nejm.org/doi/full/10.1056/NEJMe2029812?query=recirc_mostViewed_railB_article https://www.nejm.org/doi/full/10.1056/NEJMe2029812#.X39d2y9tN84.twitter Janice Hopkins Tanne. (2020) Covid 19: NEJM and former CDC director launch stinging attacks on US response. BMJ, m3925. BMJ 2020;371:m3925

Covid 19: NEJM and former CDC director launch stinging attacks on US response

BMJ 2020; 371 doi: https://doi.org/10.1136/bmj.m3925 (Published 08 October 2020) Cite this as: BMJ 2020;371:m3925 References

Johns Hopkins University Coronavirus Resource Center. COVID-19 dashboard by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University (https://coronavirus.jhu.edu/map.html. opens in new tab).
Google Scholar. opens in new tab
Total number of COVID-19 tests per confirmed case, September 14, 2020. Our World in Data (https://ourworldindata.org/grapher/number-of-covid-19-tests-per-confirmed-case. opens in new tab).
Google Scholar. opens in new tab
McGinley L, Abutaleb L, Johnson CY. Inside Trump’s pressure campaign on federal scientists over a Covid-19 treatment. Washington Post. August 30, 2020 (https://www.washingtonpost.com/health/convalescent-plasma-treatment-covid19-fda/2020/08/29/e39a75ec-e935-11ea-bc79-834454439a44_story.html. opens in new tab).
Google Scholar. opens in new tab
Haberman M. Trump admits downplaying the virus knowing it was ‘deadly stuff.’ New York Times. September 9, 2020 (https://www.nytimes.com/2020/09/09/us/politics/woodward-trump-book-virus.html. opens in new tab).
Google Scholar

Related Articles

https://pharmaceuticalintelligence.com/coronavirus-portal/

Eight COVID-19 Topics Covered and Lead Curators are:

Versions of LPBI Group’s Elevator Pitch: 2.0 LPBI Group’s Team – In Our Own Words

Posted in LPBI Group, e-Scientific Media, DFP, R&D-M3DP, R&D-Drug Discovery, US Patents: SOPs and Team Management on October 20, 2020| Leave a Comment »

Versions of LPBI Group’s Elevator Pitch: 2.0 LPBI Group’s Team – In Our Own Words

Updated on 3/29/2026 with Grok deliberation

2026 Positioning: Three-focus

Core Narrative (use in every pitch):

“LPBI Group offers a complete, production-ready ecosystem for domain-aware AI in Health. It combines

(1) a proprietary 14-part Tool Factory with the world’s first autonomous journal-updating system (AJAUS + multi-agent orchestration),

(2) a unique 9 GB private multimodal training corpus, and

(3) 14 years of expert human curation and guidance. The three components work together synergistically: the human signal ensures quality, the data provides depth and causality, and the Tool Factory (especially AJAUS) makes it continuously fresh and scalable.”

Then, tailor the emphasis in the first 2–3 minutes of each conversation:

Buyer focused on AI infrastructure / agentic systems → Lead with COM Part 14 (AJAUS) as the flagship, then show how it is powered by the data + human signal.
Buyer focused on high-quality training data → Lead with the 9 GB corpus + 13 Gems + 15 SLM collections, then show how AJAUS keeps it fresh and how human experts ensure trustworthiness.
Buyer focused on clinical-grade / trusted output → Lead with the Human Signal (Aviva expertise + Dr. Williams + domain KOLs), then show how it is amplified by the data and made scalable by AJAUS.

Practical Steps to Learn and Execute This Balanced Positioning

Create a Master “Three-Pillar Story” Slide One clean slide that visually shows the three components as interlocking parts of a single moat. This slide becomes the anchor for every pitch.
Prepare Three Tailored One-Pagers (or slide versions)
- Version A: Emphasis on AJAUS / Tool Factory
- Version B: Emphasis on Trainable Data
- Version C: Emphasis on Human Signal + Expert Curation
Research Each Target Deeply Before any outreach, understand what the buyer cares about most right now (their recent announcements, press, job postings, etc.). Then choose the right emphasis.

Updated on 4/12/2022

Final Approved Version

The complex and rapid deluge of scientific information, absence of a collaborative, open environment to produce transformative innovation, and dearth of alternative ways to disseminate scientific findings has led to major operational inefficiencies in the biomedical and pharma industries, to the tune of billions of dollars per year. These issues have driven the need for a more context-aware architecture for knowledge and scientific discourse within the biomedical arena. LPBI’s proprietary curation method and body of content decreases the time invested in the assessment of current trends, adding multiple insights. The LPBI method, delivers insights from a major body of the scientific community on multiple WEB 2.0 platforms, and makes use of new computational and Web-based tools to provide interoperability of data.

Our Ideas are Products of our Environment

Leaders in Pharmaceutical Business Intelligence (LPBI) Group has been developing a strategy for the facilitation of Global access to Biomedical knowledge rather than the access to sheer search results on Scientific subject matters in the Life Sciences and Medicine. According to the founder and Chief Editor Aviva Lev-Ari, PhD, RN , “for the methodology to attain this complex goal the task at hand requires popularizing ORIGINAL Scientific Research via Curation of the Scientific Research Results by Experts, Authors, Writers providing expert interpretation of the original primary research results to deliver a sum greater than its parts.

LPBI’s copyrighted curation process includes synthesis, analysis, and interpretation of complex medical and scientific areas performed by our highly trained and well-regarded staff. It creates vast, universally accessible scientific content, over multiple platforms within the Life Sciences, Medical, and Allied Health Care professional fields. This curative process of a new formulation of clinical interpretations is establishing new conceptual connections across publications and produces new transformative ideas.

Using our curation methodology, LPBI has produced an open-access online scientific journal, a series of 18 BioMed e-books, and real-time press coverage of scientific conferences, all of which are optimally designed for novel Text Analysis methods using leading AI, ML and NLP algorithms from 3rd parties’ software, customized for classification and clustering of the Semantics in LPBI’s contents: Journal articles (6,100+) e-Books (18) Conferences content (100+). We are fundamentally a media system integrator, platform developer and platform customizer; an innovative and creative scientific content creator. We function as a fully vertically integrated BioMed creator and generator of knowledge for health information markets via our own Journal articles, BioMed e-Series of Books, Conference e-Proceedings, Podcasts, and additional five strategies https://pharmaceuticalintelligence.com/vision/

We are seeking investors looking for transfer of ownership of our Intellectual Property Assets, consisting of ~2MM e-Readers and offers +6,000 of the best interpretive articles in five specialties of Medicine and Life Sciences. These offerings are available in English and Spanish, with the opportunity for seamless translation to over 18 languages.

Through use of AI, ML, NLP, biological images with text legends, Audio Podcasts and additional modern content processing technology, we are postulating revenues on the order of $50MM to the acquirer by exploiting our content.

Versions by members of the Team

Aviva Lev-Ari, PhD, RN, Founder, LPBI Group: 1.0 & 2.0

My proposed Elevator Pitch

For the first time in the ten years of our private ownership, the opportunity to acquire the Inventor of Scientific curation has become a reality, Available for Transfer of ownership.

You can own a portfolio of Intellectual Property Assets that commands ~2MM e-Readers and offers +6,000 of the best interpretive articles in five specialties of Medicine and Life Sciences. Pages of our 18 books have been downloaded ~135,000 times and over 100 of the top biotech and medical conferences were covered in real time and recorded in writing and Tweets. New strategies in AI and Blockchain are now applied on LPBI’s content for INSIGHT searches and pattern recognition by automated Machine Learning algorithms for use in drug discovery and drug repurposing. All of LPBI’s content was created by our Experts, Authors, Writers (EAWs).

Bold vision for the coming five years includes: All content will be converted by Machine Learning algorithms to search for all hyper-graphs and their expression in WordClouds.
From text we will convert content to Audio. From English Text we will translate to foreign languages like Japanese, Spanish and Russian.
From Open Access we will transition to Blockchain transaction networks.
From Digital Cloud-based biographies we will create audio and video Podcasts
From a sole owner-operator status we will transition to Joint-Ventures to M&A and Partnerships

Our Transformational transition is two dimensional:

Our deep expertise and innovations in media platforms and content creation will have new directions: we will focus on other Countries (x,y,z) and Geographical regions: i.e., EU and South-East Asia. Currently the Table of Contents of 18 books is being translated into Spanish for the 22 Countries speaking Spanish.
Our created content will become the basis of our content mining and the subject of managed computerized text analysis under supervised learning guided by our own team of experts.

We are fundamentally a media system integrator, platform developer and platform customizer; an innovative and creative scientific content creator. We function as a fully vertically integrated BioMed creator and generator of knowledge for health information markets via our own Journal articles, BioMed e-Series of Books, Conference e-Proceedings, Podcasts, and additional five strategies https://pharmaceuticalintelligence.com/vision/

Marcus Feldman, PhD, Member of the Board, Scientific Counsel: Life Sciences

My proposed Elevator Pitch

1. Strong scientific curation makes basic research readily available to wide range of scholars, practitioners and students in biomedical science

2. The books are convenient and comprehensive compendia of the latest scholarship.

3. The updating methodology keeps material current

4. Reporting on conferences and meetings gives the audience early access to the latest technologies in biomedicine

5. Curation methodology is transferable across disciplines and languages allowing for big international and interdisciplinary markets

6. The pricing analysis has been carefully researched across multimedia platforms

Stephen J. Williams, Member of the Board, Chief Scientific Officer, Senior Editor, PhD Pharmacology, BSc Toxicology

My proposed Elevator Pitch

The complex and rapid deluge of scientific information, absence of a collaborative, open environment to produce transformative innovation, and dearth of alternative ways to disseminate scientific findings has led to major operational inefficiencies in the biomedical and pharma industries, to the tune of millions of dollars per year. These issues have driven the need for a more context-driven architecture for knowledge and discourse within the biomedical arena. The process of curation decreases time for assessment of current trends adding multiple insights, analyses WITH an underlying METHODOLOGY, provides insights from WHOLE scientific community on multiple WEB 2.0 platforms, and makes use of new computational and Web-based tools to provide interoperability of data, reporting of findings.

Our Ideas are Products of our Environment

At Leaders in Pharmaceutical Business Intelligence (LPBI) Group website owner and Editor-in-Chief, Aviva Lev-Ari, PhD, RN has been developing a strategy for the

facilitation of Global access to Biomedical knowledge rather than the access to sheer search results on Scientific subject matters in the Life Sciences and Medicine”. According to Aviva, “for the methodology to attain this complex goal it is to be dealing with popularization of ORIGINAL Scientific Research via Content Curation of Scientific Research Results by Experts, Authors, Writers using the critical thinking process of expert interpretation of the original research results.

LPBI’s copyrighted curation process includes synthesis, analysis, and interpretation of complex medical and scientific areas by our highly trained and well-regarded staff. It creates vast, universally accessible scientific content, over multiple platforms within the Life Sciences, Medical, and Allied Health Care professional fields. This curative process establishes new connections and produces new and transformative ideas.

Using our curation methodology, LPBI has produced an open-access online scientific journal, a series of 16 BioMed e-books, and Real-time press coverage of scientific conferences, all of which are optimally designed for novel Text Analysis methods using leading AI, ML and NLP algorithms from 3rd parties software applied on LPBI’s own content customizing the applications for the needs developed by LPBI in the classification and clustering of the Semantics in our contents: Journal articles (6,000) e-Books (16) Conferences content (100).

Through use of AI, ML, NLP, Audio Podcasts and additional modern technology, we are postulating revenues on the order of $50MM to the acquirer by exploiting our content.

Joel Shertok, PhD, Member of The Board, Business Development and Process Designer of a Global AI-based External Relations Prospecting System for LPBI Group

My proposed Elevator Pitch

Currently, there is a digital information explosion in both the Life Sciences and Medical arenas. Tracking new information and discoveries, while guarding against obsolescence, is a major challenge for scientists working in these fields.

To overcome these challenges, the Leaders in Pharmaceutical Business Intelligence (LPBI) Group was created in 2012 by Dr. Aviva Lev-Ari.

Through use of AI, NLP, Audio Podcasts and additional advanced Machine Learning technologies, we are postulating revenues on the order of $50MM to the acquirer by exploiting our content

LPBI’s copyrighted curation process includes synthesis, analysis, and interpretation of complex medical and scientific areas by our highly trained and well-regarded staff. It creates vast, universally accessible scientific content, over multiple platforms within the Life Sciences, Medical, and Allied Health Care professional fields.

This enables the LPBI Group to respond to the needs of our scientific audiences, guarding against information obsolescence and overload, through innovative digital technologies and solutions, via:

An open-access online scientific journal
A series of 16 BioMed e-books.
Real-time press coverage with real-time Twitter posting of speakers quotes during conferences.

These have been made widely available to the scientific and non-research community by the Open Access feature of LPBI’s Journal’s website PharmaceuticalIntelligence.com.

Justin D. Pearlman, MD, PhD, FACC – Scientific Expert & Key Opinion Leader on Cardiovascular Diseases, Cardiac Imaging & Complex Diagnosis in Cardiology: Senior Editor & Author

My proposed Elevator Pitch

The Leaders in Pharmaceutical Business Intelligence (LPBI) Group was created in 2012 by Dr. Aviva Lev-Ari.

The effort filled a major need for information technology by the development, refinement and fulfillment of CURATION, which combines literature review, experience, and expertise into a guided-tour of accomplishments and pathways for understanding current issues in biology, chemistry, medicine and therapeutics, identifying opportunities and seizing leadership in the development and utilization of therapeutic opportunities. Through practice, recruitment, and refinement of methods, a large body of information and information management technology was amassed, along with a large following. This body of informatics and methodology is now available for commercial use to own, nurture and grow a valuable audience.

The available material includes:

An open-access online scientific journal
A series of 16 BioMed e-books.
Unique coverage of speakers quotes during conferences.
Curated cumulative knowledge organized as a path to leadership

The reach includes video and audio podcasts as well as a unique annotated chain of web links that expedites comprehensive mastery of biologic, medical, and pharmaceutical curriculum. The curation overcomes limitations of publication bias to bring the viewers to the leading edge of capabilities pointing to opportunities ripe for the exciting future.

Sudipta Saha, PhD, Expert, Author, Writer, Co-Editor, Series D, Volume 2: Infectious Diseases

My proposed Elevator Pitch

Irina Robu, PhD – 3D BioPrinting, Tissue Engineering, Biomaterials, Nanotechnology & Drug Delivery

My proposed Elevator Pitch

Our world is full of device screens, we keep them in our hands (hand-held devices) and surround ourselves with them using IOT. What if there was a way this new network brought world’s best resources and knowledge, as a normal part of life ?

To overcome the digital information explosion in both life sciences and medical field, the Leaders in Pharmaceutical Business Intelligence (LPBI) Group was created in 2012 by Dr. Aviva Lev-Ari.

LPBI (previously an equity sharing, non-profit entity) is now making its intellectual and digital intellectual properties available for sale to outside entities that are willing to take over and extend our reach. Tracking new information and discoveries, while creating new paths for current issues in biology, chemistry, medicine and therapeutics.

Aided by artificial intelligence and neurolinguistic programming LPBI has created its own curation process which includes synthesis, analysis, and interpretation of complex medical and scientific areas as well as various audio podcasts of Key Opinion Leaders.

The symbiotic nature of LPBI group allows us to respond to the needs of our scientific audiences, guarding against information obsolescence and overload, through innovative digital technologies and solutions, via:

An open-access online scientific journal
A series of 16 BioMed e-books.
Real-time press coverage with real-time Twitter posting of speakers quotes during conferences.

Our resources have been widely accessible to the scientific and non-research community by the Open Access feature of LPBI’s Journal’s website PharmaceuticalIntelligence.com.

Gail S. Thornton, PhD(c), Contributing Editor, Author and Medical Writer, Marketing Communications

My proposed Elevator Pitch

About LPBI Group

Leaders in Pharmaceutical Business Intelligence Group (“LPBI Group”) is a leading, global pharmaceutical news source that provides scientific and medical curated and general-reporting content to a wide range of thought leader audiences.

There are three interrelated areas — an open-access online scientific journal, including a selection of timely podcasts and newsletters, a series of 16 BioMed e-books and real-time press coverage of medical and scientific conferences. The Group also synthesizes, analyzes and interprets therapeutic and disease information in various disciplines within biomedicine and life sciences through electronic publishing in the cloud with the goal of advancing knowledge and research efforts of the scientific and business community. Additionally, the Group applies existing software algorithms, i.e., NLP, ML, AI on its own content as they develop expert-driven interpretation of the medical text analysis outcomes.

The Group, comprised of a highly experienced team in science, medicine and business, was created in 2012 by Dr. Aviva Lev-Ari, Ph.D., R.N. This team reports on currently available medical and scientific information on a variety of subjects in the domains of BioMed, Biotech, MedTech, BioScience, Medicine, Pharmaceuticals, Life Sciences and Health care. These subjects are classified under 700+ research categories forming the ontology of the Journal of PharmaceuticalIntelligence.com.

Today, there is a considerable amount of digital information in the fields of medicine and life sciences. The Group is actively responding to the scientific and business community with technologies and solutions that help advance the world of research by using the methodology of curations of scientific findings from the perspective of clinical interpretation of the experimental results to communicate science.

Ofer Markman, PhD – Advisor to LPBI Group’s Founder on the Biotech Ecosystem in Israel and Domain Expert on Text Analysis with ML

My proposed Elevator Pitch

We are in the higher digital-healthcare / electronic-healthcare arena a growing market of multiple unicorns, and a field that is in the rise. Our niche is trendy, high valued field, current trends are well known also to us. We understand the market as well as the scientific arena
We bring to the market the second opinion concept that is working in heath care for so many years to the scientific research for pharmaceutical R&D, medical and life science education.
We also offer a synthetic approach for the presented science and current knowledge as known and understood by the expert in his subject matter.
LPBI’s future will be in AI, ML, NLP – contextual Medical Text Analysis, digital-medicine and digital education
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The company is a multimedia publishing company, specializing in making available unique, curated, peer reviewed life science, medical and pharmaceutical research information of interest to whom?. Its unique repository of data includes: X, Y, and Z, via print, e-articles, e-books, podcasts, etc.

mHealth market growth in America, Europe, & APAC

Posted in Health Care System by Country, Health Economics and Outcomes Research, Hospital-based Medical Innovations, Investment in Technological Breakthrough, Medical Devices R&D Investment on October 19, 2020| Leave a Comment »

mHealth market growth in America, Europe, & APAC

Reporter on this Industry News: Aviva Lev-Ari, PhD, RN

An industry news titled ‘Pivotal trends propelling mHealth market growth in America, Europe, & APAC’ by Graphical Research released on 10/19/2020

Pivotal trends propelling mHealth market growth in America, Europe, & APAC

Rapid expansion of digital healthcare for the provision of delivery, medical support, and intervention through mobile technologies is likely to augment mHealth market expansion through the coming years. Active involvement of patients toward bettering their own health will further contribute to mHealth market growth over the forecast period.

The recent years have witnessed an upsurge in government initiatives in the mHealth technology sector in turn prompting major market players to get involved in product development and promotion programs at both regional and global level.

Prominent trends likely to propel the regional expansion of mHealth market:

Rising internet penetration to push North America mHealth revenue share

Surging internet and mobile phone penetration coupled with a rise in the usage of healthcare mobile applications has been instrumental in creating a high demand for mobile health devices in the region. North America mHealth market will surpass USD 113 bn by 2026, with an estimated CAGR of 39.5%, having registered a valuation of 11,364.1 million in 2019.

Surging demand for fitness apps for the maintenance of healthy body in Canada and the U.S. has been instrumental in impelling the growth of mHealth apps segment in the region. Mobile apps contributed a revenue of USD 7,877.2 million holding the largest revenue share in 2019.

In terms of the end-use spectrum, physicians’ segment was worth USD 3,431.1 million in 2019. The segment in fact, accounted for the largest revenue share in the year. The growth can be aptly credited to the rising adoption of digitization in medical care facilities, in tandem with the increasing healthcare spending in the region.

Around 2,000 healthcare providers in San Francisco presently utilize mHealth wearables for temperature monitoring for the identification of people who have been infected with COVID-19, cites study. Increasing use of healthcare wearables will thus propel North America mHealth industry outlook over the coming years.

Rising technological advancements in Europe mHealth market

Increasing adoption of leading-edge technology for the minimization of extra bulk devices usage for blood glucose level monitoring will add to industry expansion in the region.

Europe mhealth market size will exceed USD 137.5 billion valuation by 2026 with a targeted CAGR of 39%, having registered a revenue of USD 14,162.0 million in 2019.

The International Diabetes Foundation (IDF) has stated that about 9.1 per cent of the people in Europe suffered from diabetes in 2017. Scientists are on the path of developing skin-based glucose monitor for the purpose of detecting glucose levels in sweat, opening up avenues for Europe mHealth market expansion in the near future.

Reports state that Germany accounted for 20 per cent of the overall market share in 2019 and is poised to witness commendable growth in the coming years, driven by the rising advancements in the ehealth technology sector in the region. The hardware segment pertaining to the use of medical devices and mobile sensors will augment Europe mHealth market size over the estimated period. What’s more, the region has been manifesting proliferating trends pertaining to health and fitness consciousness as well as healthcare digitalization that’ll further boost the regional growth.

Prominent players in the Europe mHealth industry comprise Masimo Corporation, Allscripts Healthcare Solutions, Cardionet, AT&T, Qualcomm, Apple, Philips Healthcare, Boston Scientific, and others.

Latin America mHealth market to gain massive proceeds from remote data collection

Remote data collection in Latin America accounted for a valuation of USD 523.6 million in 2019 and is estimated to account for a remarkable revenue share over the forecast period. Latin America mHealth industry is slated to depict a commendable CAGR of 40.7 per cent over 2020 to 2026.

The largest segmental share can be attributed to the transmission and collection of data through mobile phones. The system has been designed for sending messages or e-mails given the data is aggregated in a centralized database and the symptoms are recorded.

Based on application, Latin America mHealth market has been segmented into disease and epidemic outbreak tracking, communication and training, remote data collection, education and awareness, diagnostics and treatment, remote monitoring, and others.

According to a 2017 study, over 40 million patients in Mexico and Brazil were treated through mobile health services. Patients segment in the Latin America mHealth market will witness lucrative growth at a CAGR of 41.6 per cent over the estimated timeframe. This will also create remarkable mHealth deployments and lucrative job opportunities, in turn adding to mHealth product adoption over the estimated period.

Rising government intervention to bolster Asia Pacific mHealth market over the forecast period

Surging consumer awareness is likely to bolster regional mHealth product demand over the forecast period. The Asia Pacific mHealth industry will register an appreciable CAGR of 41.1 per cent from 2020 to 2026.

The rise is primarily attributed to the surging government interventions coupled with the substantial growth in developing economies. As per the National Center for Biotechnology Information, highest number of mHealth program initiatives have been undertaken owing to considerable government investments in healthcare sector across the region.

Various limitations pertaining to availability and the access to healthcare services in addition to inaccurate results emerging from discrepancies in mHealth devices will, however, hinder mHealth industry growth in the Asia Pacific region.

Improving global access pertaining to point-of-care tools for supporting enhanced patient outcomes and better clinical decision making will, thus, improve and bolster mHealth business landscape over the coming years. Rising focus of industry players on application strategies for the purpose of fighting chronic diseases will further spur industry expansion.

SOURCE

From: <pradip.s@graphicalresearch.com>

Date: Monday, October 19, 2020 at 12:39 PM

To: “Aviva Lev-Ari, PhD, RN” <AvivaLev-Ari@alum.berkeley.edu>

Subject: Exclusive Article On “mHealth market”

Dear Editor,

An industry news titled ‘Pivotal trends propelling mHealth market growth in America, Europe, & APAC’ by Graphical Research is relevant to your esteemed website https://pharmaceuticalintelligence.com/ . This email is a suggestion to publish this news (content attached in word format) on your website with an objective to share the information with your audiences.

Looking forward to hear from you.

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https://www.statnews.com/2020/09/10/anthony-fauci-lessons-learned-covid19-pandemic

The lessons from the Covid-19 response, according to Anthony Fauci

Posted in COVID-19, Population Health Management, Population Health Management, Genetics & Pharmaceutical on October 18, 2020| Leave a Comment »

The lessons from the Covid-19 response, according to Anthony Fauci

Reporter : Irina Robu, PhD

UPDATED on 10/18/2020

Since COVID-19 was declared an international pandemic, the world has learned difficult lessons according to Dr. Anthony Fauci. They are as follows:

Don’t understand the impact of the pandemic. Don’t ever estimate [an outbreak] as it evolves and don’t try to look at the rosy side of things.
Always do scientifically sound research.
Adapt to new information. If you look at what we knew in February compared to what we know now [about Covid-19], there really are a lot of differences. The role of masks, the role of aerosol, the role of indoor vs. outdoors, closed spaces. You’ve just got to be humble enough to realize that we don’t know it all from the get-go and even as we get into it.
Address existing health care disparities. There is a high number of hospitalizations with COVID within African-American and Latin community.

SOURCE

https://dewpointx.com/news/

Biomolecular Condensates: A new approach to biology originated @MIT – Drug Discovery at DewPoint Therapeutics, Cambridge, MA gets new leaders, Ameet Nathwani, MD (ex-Sanofi, ex-Novartis) as CEO and Arie Belldegrun, PhD on R&D

Posted in Bio Instrumentation in Experimental Life Sciences Research, Biological Engineering, Biological Networks, Gene Regulation and Evolution, Biomedical Measurement Science, BioTechnology - Venture Creation, BioTechnology - Venture Creation, Venture Capital, Gene Regulation, Genetics & Innovations in Treatment, Genetics & Pharmaceutical, Genome Biology, Genomics Pharmacy on October 15, 2020| Leave a Comment »

Biomolecular Condensates: A new approach to biology originated @MIT – Drug Discovery at DewPoint Therapeutics, Cambridge, MA gets new leaders, Ameet Nathwani, MD (ex-Sanofi, ex-Novartis) as Chief Executive Officer and Arie Belldegrun, PhD (ex-Kite Therapeutics) on R&D

Curator & Reporter: Aviva Lev-Ari, PhD, RN

Hooked by the science, Arie Belldegrun joins a group of influentials who believe Dewpoint may have the key to the next big thing in biotech

A new approach to biology

“The real voyage of discovery consists, not in seeking new landscapes, but in having new eyes.” Marcel Proust

Starting with the study of P granules in C.elegans embryos in 2009, Tony Hyman, working with his collaborators like Frank Julicher, Cliff Brangwynne, Simon Alberti, Mike Rosen, and Rohit Pappu, began to unravel the mysteries of biomolecular condensates. These scientists realized that P granules behave like liquid droplets that form by phase separation (think of oil droplets in salad dressing) and called them condensates.

In subsequent studies, they found to their surprise that many compartments inside cells had the behavior of condensates: they are liquid-like and form by phase separation.

Inspired by the work of Tony and his colleagues, Richard Young, Phillip Sharp, and Arup Chakraborty at MIT applied these approaches to the study of gene expression, similarly shedding light on many important questions in gene control.

Rick Young on condensates and gene control.

Tony Hyman on how cells form compartments. (From LatestThinking.org via CC BY 4.0; trimmed for length.)

Foundational Papers in Biomolecular Condensates

Germline P granules are liquid droplets that localize by controlled dissolution/condensation (Science, June 2009)

Liquid-Liquid Phase Separation in Biology (Annual Review of Cell & Developmental Biology, Oct 2014)

A Liquid-to-Solid Phase Transition of the ALS Protein FUS Accelerated by Disease Mutation (Cell, Aug 2015)

Biomolecular condensates: organizers of cellular biochemistry (Nature Review Cell Biology, Feb 2017)

A Phase Separation Model for Transcriptional Control. (Cell, March 2017)

Transcription Factors Activate Genes through the Phase-Separation Capacity of Their Activation Domains (Cell, Nov 2018)

SOURCE

https://dewpointx.com/

Press releases and Dewpoint in the news

Dewpoint Therapeutics Appoints Ameet Nathwani as Chief Executive Officer

October 13, 2020

Dewpoint

New York Times interviews Rick Young and Amy Gladfelter on the role of condensate “droplets” in COVID-19

October 9, 2020

New York Times

Dewpoint Therapeutics raises $77 million to go after ‘undruggable’ diseases

September 29, 2020

Boston Globe

Hooked by the science, Arie Belldegrun joins a group of influentials who believe Dewpoint may have the key to the next big thing in biotech

September 29, 2020

Endpoint News

Dewpoint Therapeutics to put ‘pedal to the metal’ with $77M round

September 29, 2020

FierceBiotech

Dewpoint Therapeutics Raises $77M Series B Financing to Advance the Development of Drugs That Target Biomolecular Condensates

September 29, 2020

Dewpoint

21 biotech startups that are set to take off, according to top VCs

August 13, 2020

Business Insider

Proteins — and labs — coming together to prevent Rett Syndrome

July 22, 2020

Whitehead Institute

Dewpoint Therapeutics Collaborates with Merck to Evaluate Novel Approach for the Treatment of HIV

July 13, 2020

Dewpoint

Discovery of how cancer drugs find their targets could lead to a new toolset for drug development

June 18, 2020

Whitehead Institute

SOURCE

Danny Bar-Zohar, MD – New R&D Leader for new pipelines at Merck KGaA as Luciano Rossetti steps out

Posted in Drug Development Process, drug repurposing, Pharmaceutical Discovery, Pharmaceutical Drug Discovery, Pharmaceutical Industry Competitive Intelligence, Pharmaceutical R&D Investment, Pharmacogenomics, Scientist: Career considerations on October 15, 2020| Leave a Comment »

Danny Bar-Zohar, MD – New R&D Leader for new pipelines at Merck KGaA as Luciano Rossetti steps out

Reporter: Aviva Lev-Ari, PhD, RN

Danny Bar-Zohar, MD – A Pharmaceutical Executive Profile in R&D: Ex-Novastis, Ex-Teva

Experience

Partner

Company Name

Syncona Limited Full-time

Dates EmployedApr 2020 – Present

Employment Duration7 mos

LocationLondon, England, United Kingdom

Company NameNovartis

Total Duration6 yrs 9 mos

TitleGlobal Head, Clinical Development & Analytics

Dates EmployedMay 2019 – Apr 2020

Employment Duration1 yr

LocationBasel Area, Switzerland

TitleGlobal Head, Neuroscience Development

Dates EmployedMar 2017 – Sep 2019

Employment Duration2 yrs 7 mos

LocationBasel Area, Switzerland

Show 2 more roles

Company NameTeva Pharmaceuticals

Total Duration6 yrs

TitleHead of CNS & Pain Therapeutic Area, Global Branded Products

Dates EmployedOct 2010 – Dec 2012

Employment Duration2 yrs 3 mos

LocationIsrael

TitleGlobal Medical Director, Multiple Sclerosis

Dates EmployedOct 2009 – Nov 2010

Employment Duration1 yr 2 mos

TitleAssociate Director, Global Clinical Program Leader, MS Products

Dates Employed2006 – Oct 2009

Employment Duration3 yrs

Responsible for the clinical development of the innovative MS franchise in Teva Pharmaceutical Industries

Show fewer roles

Search and Rescue Airborne Surgeon

Company Name

Israeli Air Force

Dates Employed 2004 – Dec 2009

Employment Duration5 yrs

Education

SOURCE

https://www.linkedin.com/in/danny-bar-zohar-513904a/

Novartis vet Danny Bar-Zohar leaps back into R&D, taking over the development team at Merck KGaA as Luciano Rossetti steps out

John Carroll
Editor & Founder

After a brief stint as a biotech investor at Syncona, Novartis vet Danny Bar-Zohar is back in R&D, and he’s taking the lead position at Merck KGaA’s drug division.

Bar-Zohar had led late-stage clinical development across a variety of areas — neuroscience, immunology, oncology and ophthalmology, among others — before joining the migration of talent out of the Basel-based multinational. He had been at Novartis for 7 years, which followed an earlier chapter in research at Teva.

Luciano Rossetti
The scientist is taking the lead on development at Merck KGaA, in place of Luciano Rossetti, who had a mixed record in R&D that nevertheless marked a big improvement over the dismal run the company had endured earlier. Joern-Peter Halle will continue on as global head of research. Rossetti is retiring after 6 years of running the research group, which has extensive operations in Germany as well as Massachusetts.

Their PD-L1 Bavencio — allied with Pfizer — has had a few successes, and a whole slate of failures. Sprifermin was touted as a big potential advance in osteoarthritis, but Merck KGaA is now auctioning off that part of the portfolio. One of the few late-stage bright spots has been their MET inhibitor tepotinib, which won breakthrough status and now is under priority review. That drug faces a rival at Novartis — capmatinib — that won an accelerated OK at the FDA in May.

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There’s also a BTK inhibitor, evobrutinib, that’s being developed for MS. But that’s a very crowded field, and Sanofi has been bullish about its prospects in the same research niche after buying out Principia.

Moving back into mid-stage development, there’s a major program underway for bintrafusp alfa, a bifunctional fusion protein targeting TGF-β and PD-L1, which Merck KGaA has high hopes for.

That all marks some bright, though limited, prospects for Merck KGaA, highlighting the need to find something new to beef up the pipeline. Bar-Zohar will get a say in that.

AUTHOR
John Carroll

SOURCE

https://endpts.com/novartis-vet-danny-bar-zohar-leaps-back-into-rd-taking-over-the-team-at-merck-kgaa-as-luciano-rossetti-steps-out/

https://www.fortebio.com/covid19research19research

Detecting SARS-COV-2 antibodies in serum and plasma samples

Posted in COVID-19, Diagnostics and Lab Tests, FDA, Population Health Management, Genetics & Pharmaceutical, SAR-Cov-2 a vasculotropic (blood vessels) RNA Virus, SARS-CoV-2, Serology tests for coronavirus antibodies, Treatment Protocols for COVID-19 on October 13, 2020| Leave a Comment »

Detecting SARS-COV-2 antibodies in serum and plasma samples

Reporter: Irina Robu, PhD

Convalescent plasma therapy is a possible treatment under investigation where antibodies from recovered patients are transfused to current COVID-19 patients with the intent to help them fight the infection and buy time until their immune system can produce antibodies. Yet, not all recovered patients have the same quantity of antibody titers suitable for such transfusions. In some patients it will minimize the severity of the disease length.

The U.S. Food and Drug Administration authorized convalescent plasma therapy for patients with coronavirus disease 2019 and it permitted to be used during the pandemic because there is no approved treatment for COVID-19. The donated blood is processed to remove cells, leaving behind liquid and antibody.

Companies like Forte Bío are developing instruments such as Octet HTX Instrument, Octet RED384 Octet RED96e Instrument and Octet K2 Instrument to detect SARS-COV-2 antibodies in serum and plasma samples. The Octet technology allows quantification with high resolution comparable to an HPLC . The instrument utilizes BLI enabling label-free detection for protein quantitation and kinetic characterization at unmatched speed and throughput. The instrument can measure up to 96 samples simultaneously allowing both unlimited characterization capacity for various applications and custom assay tailoring to maximize analytical throughput or sensitivity and preventing bottlenecks.

How are antibodies tested ?

Immobilize a virus protein such as the receptor binding domain (RBD) of the SARS CoV-2 spike protein.
Dip the coronavirus biosensor into diluted patient plasma or serum samples.
Block the biosensor with non-relevant serum or blocking buffer if needed to prevent non-specific binding.

Even the researchers believe that the risk to donors is low, there are additional risks such as allergic reactions, lung damage, difficulty breathing or infections such as HIV, hepatitis B and Donated blood must be tested for safety prior to administering to patients.

What to expect ? It is up to the doctor treating the patient, if convalescent plasma therapy is an option. Even though data from clinical trials suggest that convalescent plasma may diminish the severity or duration of the COVID19, more research is needed to determine if convalescent plasma therapy is an effective treatment.

SOURCE

https://www.medrxiv.org/content/10.1101/2020.07.17.20156281v1

Connecting the Immune Response to Amyloid-β Aggregation in Alzheimer’s Disease via IFITM3

Posted in Alzheimer's Disease, Immunology, Neurological Diseases, Systemic Inflammatory Response Related Disorders, tagged Alzheimer Disease, Beta amyloid, IFITM3, immune response, Tau protein on October 13, 2020| Leave a Comment »

Connecting the Immune Response to Amyloid-β Aggregation in Alzheimer’s Disease via IFITM3

Reporter : Irina Robu, PhD

Alzheimer’s disease is a complex condition and it begins with slow aggregation of amyloid-β deposits over the course of years. This produces a mild cognitive impairment and a state of chronic inflammation enough to trigger harmful aggregation of the altered tau protein. Clearing amyloid-β from the brain hasn’t produced telling benefits to patients suggesting that it is not the key process in the development of the condition.

Recent research indicates that beta-amyloid has antiviral and antimicrobial properties, indicating a possible link between the immune response against infections and development of Alzheimer’s disease. Scientists have discovered evidence that protein interferon-induced transmembrane protein 3 (IFITM3) is involved in immune response to pathogens and play a key role in the accumulation of beta amyloid in plaques. IFITM3 is able to alter the activity of gamma-secretase enzyme, which breaks down the precursor proteins into fragments of beta-amyloid that make up plaques.

Yet it was determined that the production of IFITM3 starts in reply to activation of the immune system by invading viruses and bacteria. Indeed, researchers found that the level of IFITM3 in human brain samples correlated with levels of certain viral infections as well as with gamma-secretase activity and beta-amyloid production. Age is the number one risk factor for Alzheimer’s and the levels of both inflammatory markers and IFITM3 increased with advancing age in mice.

Innate immunity is also correlated with Alzheimer’s disease1, but the influence of immune activation on the production of amyloid beta is unknown. They were able to identify IFITM3 as γ-secretase modulatory protein, and establish a mechanism by which inflammation affects the generation of amyloid-β.

According to the current research, inflammatory cytokines induce the expression of IFITM3 in neurons and astrocytes, which binds to γ-secretase and upregulates its activity, thereby increasing the production of amyloid-β. The expression of IFITM3 is increased with ageing and in mouse models that express Alzheimer’s disease genes. IFITM3 protein is upregulated in tissue samples from a subset of patients with late-onset Alzheimer’s disease that exhibit higher γ-secretase activity. The amount of IFITM3 in the γ-secretase complex has a strong and positive correlation with γ-secretase activity in samples from patients with late-onset Alzheimer’s disease. These conclusions disclose a mechanism in which γ-secretase is controlled by neuroinflammation via IFITM3 and the risk of Alzheimer’s disease is thus amplified

SOURCE

https://www.nature.com/articles/s41586-020-2681-2

CAR T-Cell Therapy Market: 2020 – 2027 – Global Market Analysis and Industry Forecast

Posted in Cancer and Current Therapeutics, Cancer Vaccines: Targeting Cancer Genes for Immunotherapy, CAR-T, combination immunotherapies., Immune Engineering, Immune Modulatory, Immunotherapy, Modulating Macrophages in Cancer Immunotherapy, Pharmaceutical Industry Competitive Intelligence, Pharmacogenomics, Synergistic Innate and Adaptive Immunotherapy on October 12, 2020| Leave a Comment »

CAR T-CELL THERAPY MARKET: 2020 – 2027

G L O B A L M A R K E T A N A L Y S I S A N D

I N D U S T R Y F O R E C A S T

DISCLAIMER

LPBI Group’s decision to publish the Table of Contents of this Report does not imply endorsement of the Report

Aviva Lev-Ari, PhD, RN, Founder 1.0 & 2.0 LPBI Group

Guest Reporter: MIKE WOOD

Marketing Executive
BIOTECH FORECASTS

ABOUT BIOTECH FORECASTS

BIOTECH FORECASTS is a full-service market research and business- consulting firm primarily focusing on healthcare, pharmaceutical, and biotechnology industries. BIOTECH FORECASTS provides global as well as medium and small Pharmaceutical and Biotechnology businesses with unmatched quality of “Market Research Reports” and “Business Intelligence Solutions”. BIOTECH FORECASTS has a targeted view to provide business insights and consulting to assist its clients to make strategic business decisions, and achieve sustainable growth in their respective market domain.

UPDATED on 10/13/2020

CAR T-CELL THERAPY MARKET

Published on October 13, 2020

Mike Wood

Marketing Executive at Biotech Forecasts

1 article

CAR T-cell therapy as a part of adoptive cell therapy (ACT), has become one of the most rapidly growing and promising fields in the Immuno-oncology. As compared to the conventional cancer therapies, CAR T-cell therapy is the single-dose solution for the treatment of various cancers, significantly for some lethal forms of hematological malignancies.

CAR T-cell therapy mainly involves the use of engineered T-cells, the process starts with the extraction of T-cells through leukapheresis, either from the patient (autologous) or a healthy donor (allogeneic). After the expression of a synthetic receptor (Chimeric Antigen Receptor) in the lab, the altered T-cells are expanded to the right dose and administered into the patient’s body. where they target and attach to a specific antigen on the tumor surface, to kill the cancerous cells by igniting the apoptosis.

The global CAR T-cell therapy market was valued at $734 million in 2019 and is estimated to reach $4,078 million by 2027, registering a CAGR of 23.91% from 2020 to 2027.

Factors that drive the market growth involve, (1) Increased in funding for R&D activities pertaining to cell and gene therapy. By H1 2020 cell and gene therapy companies set new records in the fundraising despite the pandemic crisis. For Instance, by June 2020 totaled $1,452 Million raised in Five IPOs including, Legend Biotech ($487M), Passage Bio ($284M), Akouos ($244M), Generation Bio ($230M), and Beam Therapeutics ($207M), which is 2.5 times the total IPO of 2019.

Moreover, in 2019 cell therapy companies specifically have raised $560 million of venture capital, including Century Therapeutics ($250M), Achilles Therapeutics Ltd. ($121M in series B), NKarta Therapeutics Inc. ($114M), and Tmunity Therapeutics ($75M in Series B).

(2) Increased in No. of Approved Products, By July 2020, there are a total of 03 approved CAR T-cell therapy products, including KYMRIAH®, YESCARTA®, and the most recently approved TECARTUS™ (formerly KTE-X19). Furthermore, two CAR T-cell therapies BB2121, and JCAR017 are expected to get the market approval by the end of 2020 or in early 2021.

Other factors that boost the market growth involves; (3) increase in government support, (4) ethical acceptance of Cell and Gene therapy for cancer treatment, (5) rise in the prevalence of cancer, and (6) an increase in awareness regarding the CAR T-cell therapy.

However, high costs associated with the treatment (KYMRIAH® cost around $475,000, and YESCARTA® costs $373,000 per infusion), long production hours, obstacles in treating solid tumors, and unwanted immune responses & potential side effects might hamper the market growth.

The report also presents a detailed quantitative analysis of the current market trends and future estimations from 2020 to 2027.

The forecasts cover 2 Approach Types, 5 Antigen Types, 5 Application Types, 4 Regions, and 14 Countries.

The report comes with an associated file covering quantitative data from all numeric forecasts presented in the report, as well as with a Clinical Trials Data File.

KEY FINDINGS

The report has the following key findings:

The global CAR T-cell therapy market accounted for $734 million in 2019 and is estimated to reach $4,078 million by 2027, registering a CAGR of 23.91% from 2020 to 2027.

By approach type the autologous segment was valued at $655.26 million in 2019 and is estimated to reach $ 3,324.52 million by 2027, registering a CAGR of 22.51% from 2020 to 2027.

By approach type, the allogeneic segment exhibits the highest CAGR of 32.63%.

Based on the Antigen segment CD19 was the largest contributor among the other segments in 2019.

The Acute lymphocytic leukemia (ALL) segment generated the highest revenue and is expected to continue its dominance in the future, followed by the Diffuse large B-cell lymphoma (DLBCL) segment.

North America dominated the global CAR T-cell therapy market in 2019 and is projected to continue its dominance in the future.

China is expected to grow the highest in the Asia-Pacific region during the forecast period.

TOPICS COVERED

The report covers the following topics:

Market Drivers, Restraints, and Opportunities

Porters Five Forces Analysis

CAR T-Cell Structure, Generations, Manufacturing, and Pricing Models

Top Winning Strategies, Top Investment Pockets

Analysis of by Approach Type, Antigen Type, Application, and Region

51 Company Profiles, Product Portfolio, and Key Strategies

Approved Products Profiles, and list of Expected Approvals

COVID-19 Impact on the Cell and Gene Therapy Industry

CAR T-cell therapy clinical trials analysis from 1997 to 2019

Market analysis and forecasts from 2020 to 2027

FORECAST SEGMENTATION

By Approach Type

Autologous

Allogeneic

By Antigen Type

CD19

CD20

BCMA

MSLN

Others

By Application

Acute lymphoblastic leukemia (ALL)

Diffuse large B-Cell lymphoma (DLBCL)

Multiple Myeloma (MM)

Acute Myeloid Leukemia (AML)

Other Cancer Indications

By Region

North America: USA, Canada, Mexico

Europe: UK, Germany, France, Spain, Italy, Rest of Europe

Asia-Pacific: China, Japan, India, South Korea, Rest of Asia-Pacific

LAMEA: Brazil, South Africa, Rest of LAMEA

Contact at info@biotechforecasts.com for any Queries or Free Report Sample

Report this

Published by

Mike Wood

Marketing Executive at Biotech Forecasts

Published • 10h

1 article

The global CAR T-cell therapy market was valued at $734 million in 2019 and is estimated to reach $4,078 million by 2027, registering a CAGR of 23.91% from 2020 to 2027. hashtagcelltherapy hashtaggenetherapy hashtagimmunotherapy hashtagcancertreatment hashtagcartcell hashtagregenerativemedicine hashtagbiotech hashtagcancer

CHAPTER 1: INTRODUCTION

1.1 REPORT DESCRIPTION 17

1.2 TOPICS COVERED 19

1.3 KEY MARKET SEGMENTS 20

1.4 KEY BENEFITS 21

1.5 RESEARCH METHODOLOGY 21

1.6 TARGET AUDIENCE 22

1.7 COMPANIES MENTIONED 23

CHAPTER 2: EXECUTIVE SUMMARY

2.1 EXECUTIVE SUMMARY 26

2.2 CXO PROSPECTIVE 29

CHAPTER 3: MARKET OVERVIEW

3.1 MARKET DEFINITION AND SCOPE 30

3.2 KEY FINDINGS 31

3.3 TOP INVESTMENT POCKETS 32

3.4 TOP WINNING STRATEGIES 33

3.4.1.Top winning strategies, by year, 2017-2019* 34

3.4.2.Top winning strategies, by development, 2017-2019*(%) 34

3.4.3.Top winning strategies, by company, 2017-2019* 35

3.5 TOP PLAYER POSITIONING, BY PIPELINE VOLUME, 2019 38

3.6 PORTERS FIVE FORCES ANALYSIS 39

3.7 COVID19 IMPACT ON CELL AND GENE THERAPY (CGT) INDUSTRY 41

3.8 MARKET DYNAMICS 46

3.8.1    Drivers 46

3.8.1.1 Increase in funding for R&D activities of CAR T-cell therapy 46

3.8.1.2 The rise in the prevalence of cancer 47

3.8.1.3 Increase in awareness regarding CAR T-cell therapy 47

3.8.2    Restrains 48

3.8.2.1 The high cost of CAR T-cell therapy treatment 48

3.8.2.2 Unwanted immune responses and side effects 48

3.8.2.3 Long production time 48

3.8.2.4 Obstacles in treating solid tumors 49

3.8.3    Opportunities 49

3.8.3.1 Untapped potential for emerging markets 49

CHAPTER 4: CAR T-CELL THERAPY, A BRIEF INTRODUCTION

4.1 OVERVIEW 50

4.2 SIXTY YEARS HISTORY OF CAR T-CELL THERAPY 51

4.3 CAR T-CELL STRUCTURE AND GENERATIONS 53

4.4 CAR T-CELL MANUFACTURING PROCESSES 56

4.5 PRICING AND PAYMENT MODELS FOR CAR T-CELL THERAPIES 59

CHAPTER 5: CAR T-CELL THERAPY MARKET, BY APPROACH TYPE

5.1 OVERVIEW 61

5.1.1    Market size and forecast 62

5.2 AUTOLOGOUS 63

5.2.1    Key market trends 63

5.2.2    Key growth factors and opportunities 64

5.2.3    Market size and forecast 64

5.2.4    Market size and forecast by country 65

5.3 ALLOGENEIC 66

5.3.1    Key market trends 67

5.3.2    Key growth factors and opportunities 68

5.3.3    Market size and forecast 68

5.3.4    Market size and forecast by country 69

CHAPTER 6: CAR T-CELL THERAPY MARKET, BY ANTIGEN TYPE

6.1 OVERVIEW 70

6.1.1         Market size and forecast 71

6.2 CD19 72

6.2.1         Market size and forecast 73

6.2.2         Market size and forecast by country 74

6.3 CD20 75

6.3.1 Market size and forecast 76

6.3.2 Market size and forecast by country 77

6.4 BCMA 78

6.4.1 Market size and forecast 79

6.4.2 Market size and forecast by country 80

6.5 MSLN 81

6.5.1 Market size and forecast 82

6.5.2 Market size and forecast by country 83

6.6 OTHERS 84

6.6.1 Market size and forecast 85

6.6.2 Market size and forecast by country 86

CHAPTER 7: CAR T-CELL THERAPY MARKET, BY APPLICATION

7.1 OVERVIEW 87

7.1.1       Market size and forecast 88

7.2 ACUTE LYMPHOBLASTIC LEUKEMIA (ALL) 89

7.2.1       Market size and forecast 90

7.2.2       Market size and forecast by country 91

7.3 DIFFUSE LARGE B-CELL LYMPHOMA (DLBCL) 92

7.3.1       Market size and forecast 93

7.3.2       Market size and forecast by country 94

7.4 MULTIPLE MYELOMA (MM) 95

7.4.1       Market size and forecast 96

7.4.2       Market size and forecast by country 97

7.5 ACUTE MYELOID LEUKEMIA (AML) 98

7.5.1       Market size and forecast 99

7.5.2       Market size and forecast by country 100

7.6 OTHERS 101

7.6.1       Market size and forecast 102

7.6.2       Market size and forecast by country 103

CHAPTER 8: CAR T-CELL THERAPY MARKET, BY REGION

8.1 OVERVIEW 104

8.1.1       Market size and forecast 104

8.2 NORTH AMERICA 105

8.2.1       Key market trends 105

8.2.2       Key growth factors and opportunities 105

8.2.3       Market size and forecast, by country 106

8.2.4       Market size and forecast, by approach type 106

8.2.5       Market size and forecast, by antigen type 107

8.2.6 Market size and forecast, by application 107

8.2.6.1 U.S. market size and forecast, by approach type 108

8.2.6.2 U.S. market size and forecast, by antigen type 108

8.2.6.3 U.S. market size and forecast, by application 109

8.2.6.4 Canada market size and forecast, by approach type 110

8.2.6.5 Canada market size and forecast, by antigen type 110

8.2.6.6 Canada market size and forecast, by application 111

8.2.6.7 Mexico market size and forecast, by approach type 112

8.2.6.8 Mexico market size and forecast, by antigen type 112

8.2.6.9 Mexico market size and forecast, by application 113

8.3 EUROPE 114

8.4.1 Key market trends 114

8.4.2 Key growth factors and opportunities 114

8.4.3 Market size and forecast, by country 115

8.4.4 Market size and forecast, by approach type 115

8.4.5 Market size and forecast, by antigen type 116

8.4.6 Market size and forecast, by application 116

8.3.6.1 UK market size and forecast, by approach type 117

8.3.6.2 UK market size and forecast, by antigen type 117

8.3.6.3 UK market size and forecast, by application 118

8.3.6.4 Germany market size and forecast, by approach type 119

8.3.6.5 Germany market size and forecast, by antigen type 119

8.3.6.6 Germany market size and forecast, by application 120

8.3.6.7 France market size and forecast, by approach type 121

8.3.6.8 France market size and forecast, by antigen type 121

8.3.6.9 France market size and forecast, by application 122

8.3.6.10 Spain market size and forecast, by approach type 123

8.3.6.11 Spain market size and forecast, by antigen type 123

8.3.6.12 Spain market size and forecast, by application 124

8.3.6.13 Italy market size and forecast, by approach type 125

8.3.6.14 Italy market size and forecast, by antigen type 125

8.3.6.15 Italy market size and forecast, by application 126

8.3.6.16 Rest of Europe market size and forecast, by approach type 127

8.3.6.17 Rest of Europe market size and forecast, by antigen type 127

8.3.6.18 Rest of Europe market size and forecast, by application 128

8.4 ASIA-PACIFIC 129

8.4.1 Key market trends 129

8.4.2 Key growth factors and opportunities 129

8.4.3 Market size and forecast, by country 130

8.4.4 Market size and forecast, by approach type 130

8.4.5       Market size and forecast, by antigen type 131

8.4.6 Market size and forecast, by application 131

8.4.6.1 China market size and forecast, by approach type 132

8.4.6.2 China market size and forecast, by antigen type 132

8.4.6.3 China market size and forecast, by application 133

8.4.6.4 Japan market size and forecast, by approach type 134

8.4.6.5 Japan market size and forecast by antigen type 134

8.4.6.6 Japan market size and forecast, by application 135

8.4.6.7 India market size and forecast, by approach type 136

8.4.6.8 India market size and forecast, by antigen type 136

8.4.6.9 India market size and forecast, by application 137

8.4.6.10 South Korea market size and forecast, by approach type 138

8.4.6.11 South Korea market size and forecast, by antigen type 138

8.4.6.12 South Korea market size and forecast, by application 139

8.4.6.13 Rest of Asia-Pacific market size and forecast, by approach type 140

8.4.6.14 Rest of Asia-Pacific market size and forecast, by antigen type 140

8.4.6.15 Rest of Asia-Pacific market size and forecast, by application 141

8.5 LAMEA 142

8.5.1 Key market trends 142

8.5.2 Key growth factors and opportunities 142

8.5.3 Market size and forecast, by country 143

8.5.4 Market size and forecast, by approach type 143

8.5.5 Market size and forecast, by antigen type 144

8.5.6 Market size and forecast, by application 144

8.5.6.1 Brazil market size and forecast by approach type 145

8.5.6.2 Brazil market size and forecast, by antigen type 145

8.5.6.3 Brazil market size and forecast, by application 146

8.5.6.4 South Africa market size and forecast, by approach type 147

8.5.6.5 South Africa market size and forecast, by antigen type 147

8.5.6.6 South Africa market size and forecast, by application 148

8.5.6.7 Rest of LAMEA market size and forecast by approach type 149

8.5.6.8 Rest of LAMEA market size and forecast, by antigen type 149

8.5.6.9 Rest of LAMEA market size and forecast, by application 150

CHAPTER 9: CLINICAL TRIALS ANALYSIS & PRODUCT PROFILES

9.1 OVERVIEW 151

9.1.1      No. of Clinical Trials from 1997 to 2019 151

9.1.2      Clinical Trials from 1997 to 2019: Based on Approach Type 152

9.1.3      Clinical Trials from 1997 to 2019: Based on Antigen Type 153

9.1.4      Clinical Trials from 1997 to 2019: Based on Application 154

9.1.5      Clinical Trials from 1997 to 2019: Based on Region 155

9.2 EXPECTED APPROVALS 156

9.3 APPROVED PRODUCTS PROFILES 157

9.3.1      KYMRIAH® 157

9.3.2      YESCARTA® 159

9.3.3      TECARTUS™ 161

CHAPTER 10: COMPANY PROFILES

10.1       Abbvie Inc. 162

10.2       Adaptimmune Therapeutics Plc 164

10.3 Allogene Therapeutics, Inc. 166

10.4 Amgen, Inc 168

10.5 Anixa Biosciences, Inc. 170

10.6 Arcellx, Inc. 172

10.7 Atara Biotherapeutics, Inc. 173

10.8 Autolus Therapeutics Plc. 175

10.9 Beam Therapeutics, Inc. 177

10.10 Bellicum Pharmaceuticals, Inc. 179

10.11 BioNtech SE 181

10.12 Bluebird Bio, Inc. 183

10.13 Carsgen Therapeutics, Ltd 185

10.14 Cartesian Therapeutics, Inc. 187

10.15 Cartherics Pty Ltd. 188

10.16 Celgene Corporation 189

10.17 Cellectis SA 191

10.18 Cellular Biomedicine Group, Inc. 193

10.19 Celularity, Inc. 195

10.20 Celyad SA 196

10.21 CRISPR Therapeutics AG 198

10.22 Eureka Therapeutics, Inc. 200

10.23 Fate Therapeutics, Inc. 201

10.24 Fortress Biotech, Inc 203

10.25 Gilead Sciences, Inc. 205

10.26 Gracell Biotechnology Ltd 207

10.27 icell Gene Therapeutics 208

10.28 Johnson & Johnson 209

10.29 Juventas Cell Therapy Ltd. 211

10.30 Kuur Therapeutics 212

10.31 Legend Biotech Corp. 213

10.32 Leucid Bio Ltd. 214

10.33 Minerva Biotechnologies Corp. 215

10.34     Molecular Medicine SPA (Molmed) 216

10.35     Nanjing Bioheng Biotech Co., Ltd. 218

10.36     Noile-Immune Biotech Inc. 219

10.37     Novartis AG 220

10.38     Oxford Biomedica PLC 222

10.39     Persongen Biotherapeutics (Suzhou) Co., Ltd. 224

10.40     Poseida Therapeutics, Inc. 226

10.41     Precigen, Inc. 227

10.42     Precision Biosciences, Inc. 229

10.43     Sorrento Therapeutics, Inc. 231

10.44     Takara Bio Inc. 233

10.45     Takeda Pharmaceutical Company Ltd. 235

10.46     TC Biopharm Ltd. 237

10.47     Tessa Therapeutics Pte Ltd. 238

10.48     Tmunity Therapeutics, Inc. 239

10.49     Unum Therapeutics Inc. 240

10.50     Xyphos Inc. 242

10.51     Ziopharm Oncology, Inc. 243

CHAPTER 11: CONCLUSION & STRATEGIC RECOMMENTATIONS

11.1     STRATEGIC RECOMMENDATIONS 245

11.2     CONCLUSION 247

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MIKE WOOD

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