Akshay Kulkarni is a PhD candidate in Computer Science at UC San Diego specializing in interpretable and trustworthy machine learning, with eight years of research experience spanning academia and industry. He has contributed to top-tier publications on domain adaptation and semantic segmentation during a productive research stint at the Video Analytics Lab and has interned at Sony R&D and Lawrence Livermore National Laboratory working on generative AI and trustworthy ML problems. Akshay has taught graduate courses in Trustworthy ML and Numerical Linear Algebra, blending rigorous theoretical work with practical teaching and mentorship. His background in electrical engineering and flawless MS GPA reflect strong foundations in signal processing and numerical methods that inform his ML research. Notably, he has collaborated with industry researchers (Google Research) and participated in applied projects like airport ground management analytics, demonstrating an ability to move ideas from theory to deployed systems.
8 years of coding experience
University of California, San Diego
Bachelor of Technology, Electrical and Electronics Engineering, CGPA - 8.21/10.0, Bachelor of Technology, Electrical and Electronics Engineering, CGPA - 8.21/10.0 at Visvesvaraya National Institute of Technology
Repository for Autonomous Delivery Robot project of IvLabs, VNIT
Contributions:19 commits, 17 pushes, 1 branch in 2 years 1 month
roboticsautonomousrobotrosdelivery-robot
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