Abstract: Combinatorial optimization methods are routinely used in many scientific fields to identify optimal solutions among a large but finite set of possible solutions for problems of interests. Given the recent success of machine learning techniques in classification of natural signals (e.g., voice, image, text), it is natural to ask how machine learning methods can be used to improve the quality of solution or the runtime of combinatorial optimization algorithms? In this talk I will provide a general taxonomy and research directions for the use of machine learning techniques in combinatorial optimization. I will illustrate these directions using a number of case studies from my group’s research, which include (1) improving the quality of results of integer linear programming (ILP) solver using deep metric learning, and (2) using reinforcement learning techniques to optimize the size of graphs arising in digital circuit design.
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Abstract: The representation of non-negative functions as sums of squares has become an important tool in many modeling and optimization tasks. Traditionally applied to polynomial functions, it requires rich tools from algebraic geometry that led to many developments in the last twenty years. In this talk, I will look at this problem from a functional analysis point of view, leading to new applications and new results on the performance of sum-of-squares optimization.
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Abstract: The recent artificial intelligence (AI) boom has been primarily driven by three confluence forces: algorithms, big-data, and computing power enabled by modern integrated circuits (ICs), including specialized AI accelerators. This talk will present a closed-loop perspective for synergistic AI and agile IC design with two main themes, AI for IC and IC for AI. As semiconductor technology enters the era of extreme scaling and heterogeneous integration, IC design and manufacturing complexities become extremely high. More intelligent and agile IC design technologies are needed than ever to optimize performance, power, manufacturability, design cost, etc., and deliver equivalent scaling to Moore’s Law. This talk will present some recent results leveraging modern AI and machine learning advancement with domain-specific customizations for agile IC design and manufacturing, including open-sourced DREAMPlace (DAC’19 and TCAD’21 Best Paper Awards), DARPA-funded MAGICAL project for analog IC design automation, and LithoGAN for design-technology co-optimization. Meanwhile on the IC for AI frontier, customized ICs, including those with beyond-CMOS technologies, can drastically improve AI performance and energy efficiency by orders of magnitude. I will present our recent results on hardware and software co-design for optical neural networks and photonic ICs (which won the 2021 ACM Student Research Competition Grand Finals 1st Place). Closing the virtuous cycle between AI and IC holds great potential to significantly advance the state-of-the-art of each other.
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Arya Mazumdar, Associate Professor, UC San Diego Abstract: The problems of heterogeneity pose major challenges in extracting meaningful information from data as well as in the subsequent decision making or prediction tasks. Heterogeneity brings forward some very fundamental theoretical questions of machine learning. For unsupervised learning, a standard technique is the use of mixture models for […]
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CODE OF ETHICS TILOS Code of Ethics All TILOS members and participants in TILOS activities are encouraged and expected to pursue the highest level of ethical practice. We agree to abide by the following code of ethics: We work with the utmost scientific integrity accountable for our professional actions and impact of our work; forthcoming […]
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< BACK TO FACULTY ROSTER Publications by TILOS Faculty Nikolay Atanasov C. Nguyen, A. Altawaitan, T. Duong, N. Atanasov and Q. Nguyen, “Variable-Frequency Model Learning and Predictive Control for Jumping Maneuvers on Legged Robots,” IEEE Robotics and Automation Letters, 2025. [Link] X. Liu, J. Lei, A. Prabhu, Y. Tao, I. Spasojevic, P. Chaudhari, N. Atanasov […]
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CONSIDER AN INDUSTRIAL PARTNERSHIP Toward its long-term growth, impact, and sustainability, TILOS seeks additional support for its research, educational, broadening impacts, and translation activities. Partners can provide such support while deepening collaborations via the TILOS Industrial Partner Program. For additional information, please contact TILOS director Yusu Wang.
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Publications acknowledging TILOS support (NSF CCF-2112665)
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TILOS Members and Research in the News No post found
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COLLABORATIVE & CONTEXTUAL ROBOTICS
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