BIDS Machine Learning and Science Forum
Date: Monday, February 7, 2022
Time: 12:00 - 1:00 PM Pacific Time [ Note alternate time, for this session only ]
Location: Participate remotely using this Zoom link
The changing landscape of AI-driven system optimization
Speaker: Somdeb Majumdar, Machine Learning Research Lead, Intel AI Lab
Abstract: With the unprecedented success of modern machine learning in areas like computer vision and natural language processing, a natural question is where can it have maximum impact in real life. At Intel Labs, we are actively investing in research that leverages the robustness and generalizability of deep learning to solve system optimization problems. Examples of such systems include individual hardware modules like memory schedulers and power management units on a chip, automated compiler and software design tools as well as broader problems like chip design. In this talk, I will address some of the open challenges in systems optimization and how Intel and others in the research community are harnessing the power of modern reinforcement learning to address those challenges.
The BIDS Machine Learning and Science Forum meets biweekly to discuss current applications across a wide variety of research domains in the physical sciences and beyond. Hosted by BIDS Affiliates Uroš Seljak (professor of Physics at UC Berkeley) and Ben Nachman (physicist at Lawrence Berkeley National Laboratory), these active sessions bring together domain scientists, statisticians, and computer scientists who are either developing state-of-the-art methods or are interested in applying these methods in their research. To receive email notifications about upcoming meetings, or to request more information, please contact the organizers at berkeleymlforum@gmail.com. All interested members of the UC Berkeley and Berkeley Lab communities are welcome and encouraged to attend.
Speaker(s)
Somdeb Majumdar
Dr. Somdeb Majumdar is the Director of AI Lab – a research organization within Intel Labs. He received his PhD from University of California, Los Angeles and spent several years developing ultra-low-power communication systems, wearable medical devices and deep learning systems. He has published at top tier journals and conferences and holds 25 US patents. At Intel Labs, he leads a multi-disciplinary team investigating several areas including reinforcement learning, robotics, computer vision, natural language processing and general representational properties of machine learning systems.