Hitesh Sapkota
Applied Scientist at Amazon
Sunnyvale, California, United States
Summary
👤
Senior🎓
Top SchoolHitesh Sapkota is an Applied Scientist based in Sunnyvale with eight years of experience bridging cutting-edge machine learning research and production systems. A PhD researcher and EB-1 Outstanding Researcher awardee, he has published at NeurIPS, ICLR, ICML, CVPR and KDD on topics including vision-language models, calibrated neural networks, anomaly detection, and DNN sparsification. He has delivered applied ML solutions at Amazon (including AWS) and led research projects at Rochester Institute of Technology, demonstrating an ability to transition ideas from theory to scalable products. Regularly serving as a reviewer for top-tier conferences, he combines deep technical rigor with practical deployment experience. Notably, his work emphasizes trustworthy VLMs and multiple-instance learning—areas that reduce real-world risk in model outputs beyond typical benchmark gains.
8 years of coding experience
6 years of employment as a software developer
Doctor of Philosophy - PhD, Multiple Instance Learning, Trustworthy VLM, Generative AI, DNN Sparsification, Doctor of Philosophy - PhD, Multiple Instance Learning, Trustworthy VLM, Generative AI, DNN Sparsification at Rochester Institute of TechnologyBachelor's degree, Electrical, Electronics and Communications Engineering, 80.88, Bachelor's degree, Electrical, Electronics and Communications Engineering, 80.88 at Tribhuvan University, IOE, Pulchowk Campus
English, Nepali
Github Skills (13)
classification6
adaboost5
trust4
nlp4
sentiment-analysis4
sentiment4
decision-tree4
text-classification4
python3
machine-learning3
paper3
natural-language-processing2
boosting2
Programming languages (2)
Github contributions (5)
hiteshsapkota/OSS-Trust
Jun 2019 - Oct 2019
Code for the paper "Estimating Trust between OSS Developers"
Contributions:5 releases, 49 commits, 2 PRs in 4 months
estimatingtrustoss
hiteshsapkota/English-Dutch-Language-Classifier
Dec 2018 - Jul 2019
Text classification using Decision Tree and Adaptive Boosting
Contributions:1 release, 27 commits, 2 PRs in 7 months
decision-treenlppythondecisionadaptive
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