Summary
Priyank Pathak is an applied scientist with 9 years of experience at the intersection of computer vision and machine learning, currently pursuing PhD research at UCF while interning at Amazon. His work spans person re-identification, distribution shift, test-time training, and medical synthetic datasets with publications at ICCV, BMVC and ICLR, and a patent filing tied to real-world deployment challenges. He has industrial research experience at Clarifai and Amobee where he built large-scale ML pipelines and a Scala-based sandbox for bid-recommendation on 50M daily records. A versatile educator and researcher, he has graded and assisted courses under leading faculty (including Yann LeCun and Rob Fergus) and contributed across NLP, robotics, and audio VAE projects. Trained at IIT Kanpur and NYU, and affiliated with NUS and Stony Brook, Priyank combines strong academic rigor with hands-on production engineering and a habit of distilling complex vision problems into practical systems. An under-the-radar strength is his cross-domain fluency—from cryptographic eSign proofs to high-performance GPU workflows—which helps him bridge research and product needs.
9 years of coding experience
2 years of employment as a software developer
Indian Institute of Technology Kanpur
Master of Science - MS, Computer Science (Machine Learning), 3.94/4, Master of Science - MS, Computer Science (Machine Learning), 3.94/4 at New York University
Doctor of Philosophy - PhD, Computer Science, 4.00, Doctor of Philosophy - PhD, Computer Science, 4.00 at Stony Brook University