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Leaders in Pharmaceutical Business Intelligence Group, LLC, Doing Business As LPBI Group, Newton, MA

Healthcare analytics, AI solutions for biological big data, providing an AI platform for the biotech, life sciences, medical and pharmaceutical industries, as well as for related technological approaches, i.e., curation and text analysis with machine learning and other activities related to AI applications to these industries.

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Yash Choudhary, Research Assistant 3

Roles @ LPBI

Implement Wolfram NLP on 13 articles in Cardiovascular disease the Role of Calcium in Cardiac Function. Completion of Proof-of-Concept

Areas of Interest and Selection of Research Categories

1. Artificial Intelligence Applications in Health Care

2. Artificial Intelligence in Medicine – Application for Diagnosis

3. Deep Learning in Pathology

4. Machine Learning

5. Machine learning in predicting type 2 diabetes

6. Drug Discovery Chemistry

7. Artificial intelligence applications for cardiology

8. Scientific Publishing

9. big data analytics

10. data science

11. Natural Language Processing (NLP)

12. vision

Yash Choudhary


+91 7354490145 | yashch664422@gmail.com | linkedin.com/in/yash | github.com/yash


Education

Indian Institute of  Technology Kanpur                                                                         Kanpur (U.P.), India

Bachelor of Technology in Material Science and Engineering,    

Aug. 2019 – May 2023 (expected)

Sardana public school.                                                                                                        Dewas (M.P.), India

Higher Secondary                                                                                                              

May. 2016 – March 2018


Projects

Neuromatch Academy Summer School 2020 | Human Connectome Project dataset.                             May 2020

  • Worked in a team of 3, Hypothesize that brain activity during different language task (Story comprehension and arithmetic calculation) has distinct differentiating characteristics.
  • Verified our hypothesis by using popular machine learning techniques to classify fMRI data into two categories .
  • Used t-SNE and PCA for dimensionality reduction.
  • Compared accuracy for 3 different models:- Linear regression , Linear discrimination analysis and Linear support vector classifier and achieved accuracy of 90%.
  • Analyzed the weights of successful models to understand these characteristic differences.


Relevant courses

MTH101 (Real Analysis and multivariable calculus).              MTH 102 (Linear algebra and Differential equation).

ESC101 (Fundamentals of C programming).                                              MSO203  (Partial differential equations).

ESC101 (Fundamentals of C programming).                                              MSO203  (Partial differential equations).

PHY102 (Mechanics).                                                                                               PHY103 (Electrodynamics).

ESC101 (Fundamentals of C programming).                                             MSO203  (Partial differential equations).


Technical Skills

Machine learning (basics).Deep learning (basics).PythonC language
C++HTMLCSSAutoCAD
NumpyPandasScikit-learn 


Interests

1) Deep learning in NLP and CV.

2) Bayesian Methods for probabilistic inference.

3) Competitive programming.

4) Neuroscience.

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