Ameya Prabhu
Postdoctoral Researcher at University of Tuebingen
Tübingen, Baden-Württemberg, Germany
Summary
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Senior🎓
Top SchoolAmeya Prabhu is a postdoctoral researcher based in Tübingen with a decade of experience at the intersection of machine learning research and applied AI. He earned a PhD in Artificial Intelligence from Oxford and has built production-aware ML systems during internships and industry residencies at Intel Labs, IBM, and Verisk, with publications spanning EMNLP and NeurIPS workshops. His recent work focuses on developing frontier benchmarks for foundation models, while earlier contributions tackled uncertainty estimation, calibration, and rapid adaptation in large-scale continual learning. Notably, his active learning research revealed surprising simplicity in deep-model sampling and practical dataset compression gains that translated to production. He combines rigorous academic scholarship (see publications and website) with hands-on engineering that bridges research ideas into scalable systems.
10 years of coding experience
1 year of employment as a software developer
Doctor of Philosophy - PhD, Artificial Intelligence, Doctor of Philosophy - PhD, Artificial Intelligence at University of OxfordBachelor and Masters by Research in Computer Science and Technology, Computer Engineering, 8.94 / 10, Bachelor and Masters by Research in Computer Science and Technology, Computer Engineering, 8.94 / 10 at International Institute of Information Technology
English, Hindi, Marathi, Konkani
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (66)
transfer-learning10
information-theory10
meta-learning10
pytorch-geometric10
in-progress9
theory9
expander9
gun9
philosophy9
incremental-learning9
builtin9
google-images9
deep-learning8
scrapy8
image-processing8
Programming languages (7)
Github contributions (5)
drimpossible/Linux-Installation-Scripts
Jul 2017 - Aug 2021
Contributions:18 commits, 34 pushes, 1 branch in 4 years 1 month
drimpossible/Deep-Expander-Networks
Aug 2018 - Jan 2020
Implementation of our ECCV '18 paper " Deep Expander Networks: Efficient Deep Networks from Graph Theory".
Contributions:18 commits, 1 PR, 18 pushes in 1 year 4 months
deep-networksgraph-convolutional-networksdeep-learningeccvpytorch-geometric
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