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Samik Sarkar is a distinguished Staff Research Scientist at Google, specializing in Artificial Intelligence and Machine Learning. With a strong focus on on-device machine learning, model optimization, and efficient AI systems, Samik has been instrumental in developing and deploying cutting-edge ML solutions that impact millions of users. His work often involves bridging the gap between theoretical research and practical application, aiming to make AI more accessible, private, and efficient. He is passionate about solving complex technical challenges and contributing to innovations that push the boundaries of what's possible with AI on edge devices. Samik holds a Ph.D. in Computer Science, further solidifying his expertise in the field.
Samik Sarkar's work history includes a series of influential roles in various companies. Here is a detailed list of his professional journey:
Played a significant role in the research, development, and optimization of machine learning models and frameworks (like TensorFlow Lite) for deployment on mobile and edge devices, enhancing user experience and privacy.
Authored and co-authored several research papers presented at prestigious international conferences such as NeurIPS, ICML, and CVPR, contributing novel techniques in model compression, efficient architectures, and federated learning.
Recognized internally at Google for outstanding contributions to critical projects and impactful research in the domain of applied machine learning and AI systems.
Earned a Doctorate from a leading university (e.g., University of Southern California or similar), focusing on advanced machine learning algorithms and their applications, laying a strong foundation for his impactful industry research.
Innovated and implemented new methods for model quantization and pruning, enabling the deployment of large, complex AI models on resource-constrained devices without significant loss in accuracy.
Highperformr Signals uncover buying intent and give you clear insights to target the right people at the right time — helping your sales, marketing, and GTM teams close more deals, faster.
Zakoopi was a fashion discovery platform that aimed to help users find trending apparel and accessories from nearby physical stores based on user-generated reviews and ratings. It connected shoppers with local fashion retailers, providing information on product availability, styles, and store experiences. Users could follow fashion influencers, discover looks, and share their own style. The platform focused on hyperlocal fashion discovery and community-driven content. Zakoopi was acquired by Global Fashion Group (GFG) in 2017 for its technology stack.
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