Yogesh Rawat is an Associate Professor at the University of Central Florida specializing in computer vision, deep learning, and multimedia computing, with nine years of post-PhD experience bridging rigorous research and competitive systems-building. He earned his PhD from the National University of Singapore and a BTech from IIT-BHU, and has a track record of leading teams to top placements in international challenges (ActEV, TRECVID, ASAPS) and organizing flagship tutorials and workshops at CVPR and ACM-MM. His work blends foundational research with practical evaluation—winning activity recognition challenges and contributing to trustworthy AI and tiny action recognition benchmarks—while serving as an area chair and associate editor for major venues. Notably, he began his career in industry developing RTL compiler features for Mentor Graphics’ Veloce emulator, giving him rare experience across hardware-aware tools and cutting-edge vision research.
9 years of coding experience
6 years of employment as a software developer
Bachelor of Technology (B.Tech.), Computer Science and Engineering, Bachelor of Technology (B.Tech.), Computer Science and Engineering at Indian Institute of Technology (Banaras Hindu University), Varanasi
Doctor of Philosophy (Ph.D.), Computer Science, Doctor of Philosophy (Ph.D.), Computer Science at National University of Singapore
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