Avishek Kumar
Senior Data Scientist II
Chicago, Illinois, United States
Summary
🤩
Rockstar🎓
Top SchoolAvishek Kumar is a Senior Data Scientist with 12 years of experience applying machine learning and high-performance computing to personalized medicine, public policy, and social-good projects. He has built and deployed production ML systems for healthcare retention, lead-hazard detection, water infrastructure risk, and recidivism, partnering with city and state agencies and earning recognition like the Academy Health Local/State Innovation Prize. Comfortable across R, Python, C/C++, SQL, and parallel programming, he scaled solutions on large clusters and even administered a 1408-node supercomputer during his research career. An active educator and mentor, he teaches executive data-science courses for government leaders and has a track record of classroom and graduate mentoring at ASU. His open-source contributions include a hands-on NLP tutorial for the widely used "Hitchhiker's Guide to Data Science for Social Good," highlighting both practical tooling and clear technical communication.
12 years of coding experience
13 years of employment as a software developer
Doctor of Philosophy (Ph.D.) Physics, Doctor of Philosophy (Ph.D.) Physics at Arizona State UniversityBachelor of Science (B.S.) Physics, Bachelor of Science (B.S.) Physics at Carnegie Mellon UniversityHigh School Diploma, High School Diploma at Blair Academy
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (8)
scikit-learn10
nltk10
machine-learning10
nlp10
python10
text-analysis10
tf-idf10
scikit10
Programming languages (7)
Github contributions (5)
dssg/hitchhikers-guide
Oct 2016 - Oct 2019
The Hitchhiker's Guide to Data Science for Social Good
Role in this project:
Data Scientist
Data ScientistContributions:96 commits, 104 PRs, 73 pushes in 3 years
Contributions summary:Avishek contributed a text analysis tutorial, demonstrating expertise in natural language processing and machine learning. They implemented data loading, preprocessing steps (like tokenization, stop-word removal, and stemming), and built a model using techniques like TF-IDF and LDA (Latent Dirichlet Allocation). This included writing the tutorial, explaining the steps of text analysis, and using Python libraries like NLTK and scikit-learn to perform the tasks, and providing relevant context.
pythonsciencedata-sciencemachine-learningdssg
avishekrk/DFI
Oct 2014 - Oct 2019
Dynamic Flexibility Index -- Python Library for studying structural dynamics of proteins
Contributions:1 release, 402 commits, 23 PRs in 5 years
for-studyingpython-libraryproteinspythonstudying
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