Karm Patel
Data Scientist - II
Bengaluru, Karnataka, India
Summary
🤩
Rockstar🎓
Top SchoolKarm Patel is a data scientist with 5 years of experience, currently working as Data Scientist - II at Meesho and pursuing a master’s at IISc Bangalore in Computer Science and Automation. He specializes in machine learning and Bayesian methods, contributing notable Jupyter notebooks and implementations (HMC, SGLD, variational information bottleneck, Bayesian neural nets) to the well-regarded probml/pyprobml repository and was a GSoC'22 contributor to TensorFlow. Proficient in Python and familiar with C/C++/Java/PHP, he blends production-oriented data science with research instincts developed through internships and academic projects. Comfortable shipping ML models in industry settings, he also emphasizes model diagnostics and visualization to make probabilistic models interpretable. An interesting detail: his open-source work shows a rare focus on both advanced samplers and practical plotting, signaling strengths in inference mechanics and communication of uncertainty.
5 years of coding experience
1 year of employment as a software developer
Computer Science and Automation, Computer Science and Automation at Indian Institute of Science (IISc)Bachelor of Engineering - BE Computer Engineering, Bachelor of Engineering - BE Computer Engineering at Vishwakarma Government Engineering CollegeHigher Secondary Science (PCM), Higher Secondary Science (PCM) at S.S. Divine
Stackoverflow
244reputation
38kreached
2answers
3questions
Github Skills (18)
variational-inference10
ml10
mle10
mcmc10
bayesian-methods10
jax10
flax9
machine-learning-models8
deep-learning7
firebase6
ruby-on-rails6
android6
package6
firebase-realtime-database6
ngrok6
Programming languages (6)
Github contributions (5)
probml/pyprobml
Mar 2022 - Oct 2022
Python code for "Probabilistic Machine learning" book by Kevin Murphy
Role in this project:
ML Engineer
ML EngineerContributions:14 reviews, 2868 commits, 85 PRs in 7 months
Contributions summary:Karm implemented and added several Jupyter notebooks related to Bayesian machine learning models, including the variational information bottleneck and Bayesian neural networks for classification. They also worked on visualization, including generating contour plots and plotting samples from the MCMC traces, indicating a focus on both model development and model diagnostics. Their contributions extend to implementing HMC and SGLD methods.
pythonpmlflaxcolabpymc3
pyprobml-review/pyprobml-review
Apr 2022 - Jul 2022
Contributions:66 reviews, 47 PRs, 42 pushes in 3 months
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