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
Battery lifetime prediction considering domain-variate error
Journal Article Integration · September 1, 2025 —With the rapid development of rechargeable battery technology, battery lifespan prediction has become a hot topic in current research. Data-driven models, due to their superior performance, have been widely applied in the field of battery lifespan predict ... Full text CiteTransforming waste to value: Enhancing battery lifetime prediction using incomplete data samples
Journal Article Journal of Energy Chemistry · July 1, 2025 The widespread usage of rechargeable batteries in portable devices, electric vehicles, and energy storage systems has underscored the importance for accurately predicting their lifetimes. However, data scarcity often limits the accuracy of prediction model ... Full text CiteLeveraging multi-view imputation strategy for robust battery lifetime prediction under missing-data scenarios
Journal Article Energy Storage Materials · June 1, 2025 While lifetime prediction of rechargeable batteries is crucial for ensuring the reliability and sustainability of electric devices, the accuracy and robustness of prediction models are often impacted by practical non-idealities in operational scenarios. In ... 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