About

Amir Yazdan Bakhsh is a highly skilled and experienced Research Scientist with a strong background in designing efficient specialized hardware for machine learning applications. He is passionate about exploring the mutual interplay between machine learning techniques and efficient computing system design. Amir has published his research in multiple well-recognized peer-reviewed conferences and journals and has been awarded multiple prestigious fellowships, including the Qualcomm Innovation Fellowship and the Microsoft Graduate Research Fellowship. Currently, Amir is working as a Research Scientist at Google, where he is responsible for developing and implementing machine learning algorithms and models. He previously worked as a Google AI Resident, where he gained extensive experience in developing and optimizing deep learning models for various applications. Amir also worked as a Deep Learning Research Intern at NVIDIA, where he contributed to the development of efficient deep learning algorithms and models for various applications. He has over 7 years of relevant experience in the field of machine learning, AI, and deep learning. Amir holds a Doctor of Philosophy (PhD) in Computer Science from the Georgia Institute of Technology, where he conducted research on designing specialized hardware for machine learning applications. He also holds a Master of Science (M.Sc.) in Computer Engineering from the University of Wisconsin-Madison and a Master of Science (M.Sc.) in Computer Engineering and Computer Architecture from the University of Tehran. He earned his Bachelor of Science in Computer Engineering from Shiraz University. Amir's technical expertise includes Research Scientist, ML, AI, and Deep Learning. He is a highly motivated and results-driven individual with a proven track record of delivering high-quality results in a fast-paced and dynamic environment.