Overview
Prof. Xin Li received the Ph.D. degree in Electrical and Computer Engineering from Carnegie Mellon University, Pittsburgh, Pennsylvania, in 2005, and the M.S. and B.S. degrees in Electronics Engineering from Fudan University, Shanghai, China, in 2001 and 1998, respectively.
In 2005, he co-founded Xigmix Inc. to commercialize his PhD research, and served as the Chief Technical Officer until the company was acquired by Extreme DA in 2007. In 2011, Extreme DA was further acquired by Synopsis (Nasdaq: SNPS). From 2009 to 2012, he was the Assistant Director for FCRP Focus Research Center for Circuit & System Solutions (C2S2), a national consortium of 13 research universities (CMU, MIT, Stanford, Berkeley, UIUC, UMich, Columbia, UCLA, among others) chartered by the U.S. semiconductor industry and U.S. Department of Defense to work on next-generation integrated circuit design challenges. From 2014 to 2015, he was the Assistant Director for the Center for Silicon System Implementation (CSSI), a CMU research center with 20 faculty members working on integrated circuits and systems. His research interests include integrated circuit, signal processing and data analytics.
He was an Associate Editor of IEEE Trans. on Biomedical Engineering (TBME), IEEE Trans. on Computer-Aided Design of Integrated Circuits and Systems (TCAD), ACM Trans. on Design Automation of Electronic Systems (TODAES), IEEE Design & Test (D&T), and Journal of Low Power Electronics (JOLPE). He was the Guest Editor for IEEE TCAD, IEEE TNANO, IEEE TBD, IEEE D&T, IEEE JETCAS, ACM TCPS, ACM JETC and VLSI Integration. He served on the Executive Committee of ACM Special Interest Group on Design Automation (SIGDA), IEEE Systems, Man, and Cybernetics Society Technical Committee on Cybernetics for Cyber-Physical Systems (TCCCPS), and IEEE Computer Society Technical Committee on VLSI (TCVLSI). He was the General Chair of ISVLSI, iNIS and FAC, and the Technical Program Chair of CAD/Graphics. He also served on the ACM/SIGDA Outstanding PhD Dissertation Award Selection Committee, the IEEE TTTC E. J. McCluskey Best Doctoral Thesis Selection Committee, the IEEE Outstanding Young Author Award Selection Committee, the Executive Committee of ISVLSI, GLSVLSI and iNIS, and the Technical Program Committee of DAC, ICCAD, ITC, ISVLSI, FAC, CAD/Graphics, ASICON and VLSI. He received the NSF Faculty Early Career Development Award (CAREER) in 2012, two IEEE Donald O. Pederson Best Paper Awards in 2013 and 2016, the Best Paper Award from Design Automation Conference (DAC) in 2010, two IEEE/ACM William J. McCalla ICCAD Best Paper Awards in 2004 and 2011, and the Best Paper Award from International Symposium on Integrated Circuits (ISIC) in 2014. In addition to these awards, he also received six Best Paper Nominations from Design Automation Conference (DAC), International Conference on Computer-Aided Design (ICCAD) and Custom Integrated Circuits Conference (CICC).
Current Appointments & Affiliations
Recent Publications
Robust analog/RF circuit design via Cycle-Consistent Generative Adversarial Networks
Journal Article Integration · November 1, 2025 In this paper, we propose a novel method based on Cycle-Consistent Generative Adversarial Networks (Cycle-GAN) to efficiently synthesize robust analog/RF circuits. The key idea is to learn a mathematical mapping between nominal and robust designs by using ... Full text CiteFine-Grained Sentiment Analysis through Aesthetic Caption Fusion and Semantic Filtering
Conference Mcge 2025 Proceedings of the 3rd International Workshop on Multimedia Content Generation and Evaluation New Methods and Practice Co Located with mm 2025 · October 26, 2025 With the rapid development of social networks,people are increasingly expressing their emotions through tweets on these platforms. This has led to the emergence of Multimodal Aspect-Based Sentiment Classification (MASC), which aims to classify the polarity ... Full text CiteDecomposition and Foresight: Comparing Human and Simulated Teacher in Preference-Based Reinforcement Learning
Conference Mcge 2025 Proceedings of the 3rd International Workshop on Multimedia Content Generation and Evaluation New Methods and Practice Co Located with mm 2025 · October 26, 2025 Preference-based reinforcement learning (PBRL) algorithms train intelligent agents efficiently by learning reward functions from human preferences, bypassing the need for costly pre-existing reward functions. However, prior PBRL research has predominantly ... Full text CiteRecent Grants
IUCRC Proposal Phase 1 Duke: Center for Alternative Sustainable and Intelligent Computing (ASIC)
ResearchParticipating Faculty Member · Awarded by National Science Foundation · 2018 - 2025SHF: Small: Fast Sign-Off of Machine Learning Systems: From Circuit-Level Modeling to Statistical System Validation
ResearchPrincipal Investigator · Awarded by National Science Foundation · 2018 - 2022SHF: Small: Re-thinking Polynomial Programming: Efficient Design and Optimization of Resilient Analog/RF Integrated Systems by Convexification
ResearchPrincipal Investigator · Awarded by National Science Foundation · 2017 - 2019View All Grants