Ashish Papanai
Research Associate - HiWi
Tübingen, Baden-Württemberg, Germany
Summary
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RockstarAshish Papanai is a machine learning researcher and engineer with seven years of experience building and optimizing computer vision systems, currently working as a Research Associate at the Tübingen AI Center while pursuing an MSc in Machine Learning at University of Tübingen. He has contributed to impactful agri-ML projects at Wadhwani AI—delivering pest detection, low-resolution disease classification, and on-device model optimizations to support smallholder farmers. His work spans research and engineering: from weakly supervised object detection and explainable AI for medical imaging at IIT Delhi to practical model compression and OpenVINO deployment for edge devices. An active open-source contributor, he has added visualizations and model examples to the well-known probml/pyprobml repository, helping translate probabilistic ML concepts into reproducible code and figures. Notably, Ashish combines hands-on deployment skills with research rigor—often shrinking inference times by an order of magnitude while maintaining model fidelity. He brings a practical, impact-driven approach to multimodal and vision problems, especially where resource constraints and real-world deployment matter.
6 years of coding experience
1 year of employment as a software developer
Hindi, English
Stackoverflow
88reputation
16kreached
7answers
3questions
Github Skills (20)
data-visualizations10
python10
scikit10
machine-learning10
data-visualisation10
scikit-learn10
jupyter-notebook10
data-visualization10
matplotlib9
probabilistic-programming9
numpy9
google-colaboratory7
google-colab7
pycharm6
pygame6
Programming languages (7)
Github contributions (5)
probml/pyprobml
Mar 2021 - Mar 2021
Python code for "Probabilistic Machine learning" book by Kevin Murphy
Role in this project:
Data Scientist
Data ScientistContributions:49 commits, 20 PRs, 70 comments in 12 days
Contributions summary:Ashish primarily contributed to the development and visualization of machine learning models within the repository. They added and refined plotting functions for kernel visualizations and implemented a Naive Bayes model using scikit-learn. The user also generated figures, including class conditional probabilities and log-likelihood surfaces, to demonstrate model behavior and performance. Code was also added to generate latex tables.
pythonpmlflaxcolabpymc3
ashishpapanai/stockDL
Feb 2021 - Nov 2021
A financial deep learning library for stocks price prediction and comparison with traditional investment strategies. The Library is based on LSTM-Neural Networks and Conv1D + LSTM Neural Networks. Investments are subject to market risks, The AUTHOR HOLDS NO RESPONSIBILITY for any financial loss.
Contributions:18 releases, 112 commits, 31 PRs in 9 months
pythonpipinvestment-strategiesfinancial-analysisloss
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