Who’s Fan Chen
July 1st, 2022
Indiana University Bloomington
Beyond-CMOS Computing, Quantum Machine Learning, Accelerator Architecture for Emerging Applications, Emerging Nonvolatile Memory
Fan Chen is an assistant professor in the Department of Intelligent Systems Engineering at the Indiana University Bloomington. Dr. Chen received her Ph.D. from the department of Electrical and Computer Engineering at Duke University. Dr. Chen is a recipient of the 2022 NSF Faculty Early Career Development Program (CAREER) Award, the 2021 Service Recognition Award of Great Lakes Symposium on VLSI (GLSVLSI), the 2019 Cadence Women in Technology Scholarship, the Best Paper Award and the Ph.D. forum Best Poster Award at 2018 Asia and South Pacific Design Automation Conference (ASP-DAC). Dr. Chen serves as the publication chair of ISLPED 2022/2021, chair of SIGDA University Booth at DAC 2022/2021, web and registration chair of GLSVLSI 2022, proceedings chair of ASAP 2021, arrangement chair of GLSVLSI 2021. Dr. Chen also serves on the editorial board of IEEE Circuits and Systems Magazine (CAS-M). She is a technical reviewer for over 30+ international conferences/journals, such as IEEE TC, IEEE TCAS-I, IEEE TNNLS, IEEE D&T, IEEE IoT-J, ACM TACO, ACM TODAES, ACM JETC, etc.
Prof Chen’ research interests are focused on beyond-CMOS computing, quantum machine learning, accelerator architecture for emerging applications. Her latest work on quantum machine learning investigates fundamentally novel quantum equivalent of deep learning frameworks derived from the working principles of quantum computers, paving the way for general-purpose quantum algorithms on noisy intermediate-scale quantum devices. Another notable contribution of Prof. Chen’s work is accelerator architecture designs for emerging applications including deep learning and bioinformatics. The memory and computing requirements of such big-data applications pose significant technical challenges for their adoption in a broader range of services. Prof. Chen investigates how system/architecture/algorithm co-designed domain-specific accelerators can help on performance and energy efficiency. Prof. Chen’s works have been recognized by the academic community and appeared in top conferences, such as HPCA, DAC, ICCAD, DATE, ISLPED, ASP-DAC, and ESWEEK. Her research on “System Support for Scalable, Fast, and Power-Efficient Genome Sequencing” has been honored with the National Science Foundation Faculty Early Career Development CAREER Award.