Dr. Swapnita Srivastava is a highly accomplished researcher and educator in the field of computer science and engineering. With a Ph.D. in Cache Memory from Madan Mohan Malaviya University of Technology, Gorakhpur, she has made significant contributions to optimizing cache replacement policies using machine learning techniques. Her expertise extends to teaching and she has served as an Assistant Professor at prestigious institutions such as G L Bajaj Institute of Technology and Management and Galgotias University. Her research prowess is evidenced by her extensive publication record, including articles in renowned journals and presentations at international conferences. She is also a patent holder and has actively participated in faculty development programs and workshops. With a strong foundation in technical skills and a passion for research, she continues to make valuable contributions to the field of computer science.

Hi, my name is Peiqi Wang, a fourth-year Ph.D. candidate in computer science and technology from Tsinghua University, Beijing. I also have a one-year visiting experience in UCSB. My current research interests include accelerator architecture, computing with emerging devices, and deep learning optimization.

I am an Assistant Professor in Computer Science and Engineering at Arizona State University (ASU). I am also the Associate Director of the NSF I/UCRC Center for Embedded Systems (CES). Before joining ASU, I held a number of industrial internship positions with Intel, IBM, and Google. I am a senior member of both ACM and IEEE.

My research area lies in Computer and System Architectures. In particular, my research interests include high-performance and energy-efficient computer architectures through

• hardware heterogeneity,
• energy harvesting techniques for emerging computing devices,
• temperature and energy management for portable electronics,
• performance characterization, analysis and prediction, and
• memory subsystem designs.

I am the recipient of the 2017 NSF CAREER Award, the 2017 IEEE Young Engineer of the Year Award, the 2014 IEEEE Best of Computer Architecture Letter Award, the 2013 Science Foundation of Arizona Bisgrove Early Career Award, and the 2011-12 Intel Ph.D. Fellowship Award. My research has been supported by both industry sources and the National Science Foundation to a level over $1.8 million.

I serve on the Executive Committee of the IEEE Technical Committee on Computer Architecture from 2017-19 and am the Program Chair for the IEEE International Symposium on Workload Characterization, 2018. I completed my M.A. and Ph.D. degrees in Electrical Engineering from Princeton University in 2008 and 2012, respectively, and received a B.Sc. degree in Electrical and Computer Engineering from Cornell University.

I am a research scholar in the Electrical Engineering and Computer Science department at University of California, Irvine advised by Prof. Nader Bagherzadeh. I am interested lie in Accelerator based architecture, memory systems, multicore and parallel computing, and heterogeneous architectures. Currently, I am working on mitigating memory bottleneck in deep neural networks (DNN) accelerator based architectures.