Jing (Jane) Li is a Dugald C. Jackson Assistant Professor in the department of Electrical and Computer Engineering and Computer Science (affiliated) the University of Wisconsin – Madison. She is part of Computer Architecture @ UW-Madison and Machine Learning @ UW-Madison. She is one of the PIs in SRC JUMP center – Center for Research on Intelligent Storage and Processing-In-Memory (CRISP). She spent her early career at IBM T. J. Watson Research Center as a Research Staff Member after obtaining her PhD degree from Purdue University in 2009. She is broadly interested in the big problems in computer system across the full stack. She is also a passionate computer experimentalist and enjoy building real computer systems (both hardware and software). In particular, she has made contributions to the following “memory-centric” areas: 1) domain-specific accelerator and its interaction with emerging memories (HMC/HBM/NVM), 2) programmable in-memory computing architecture enabled by emerging nonvolatile memories (PCM/RRAM/STT RAM), 3) system support (e.g., virtualization) for accelerators (e.g., FPGA), and 4) FPGA-based full system simulation infrastructure (MEG) for memory system research. A summary of her research can be found at https://wicil.ece.wisc.edu/research/. A research vision on the importance of interaction between machine learning and computer system can be found at our white paper (co-authored with a number of great researchers in both fields).

One key differentiator of her research is that besides modeling and simulations, she put strong emphasis on real hardware demonstration through architecting, designing and testing new prototypes, both at chip level and system level. The systems that she built with her students have achieved several key milestones, including a scalable graph analytics system ranked No. 1 on the GreenGraph500 list, a deep learning system that set the world-record in performance and energy efficiency for accelerating dense convolutional neural network using FPGA. She and her research team has also taped out several new computer chips with complete system support (programming model, runtime, virtualization, API, etc.) that are built with post-CMOS nonvolatile memory technology (e.g., RRAM) integrated with silicon CMOS through monolithic 3D integration, including Liquid Silicon which won DARPA Young Faculty Award (one out of 2 in computer area and one out of 26 across all areas nationwide, the first awardee in computer engineering and computer science at UW-Madison), and Two-Dimensional Associative Processor which won NSF CAREER Award, in addition to the first fabricated in-memory processing chip for search and a variable-bit storage chip that I built at IBM which won IBM's highest technical achievement award, the IBM's CEO Milestone.

Mitali is a PhD student at Advanced Multicore Systems (AMS) lab, IIIT Delhi. She holds a gold medal in both Bachelor’s and Master's degrees. Her research interests include heterogeneous systems architectures and hardware security. She also works on designing efficient communication platforms for heterogeneous multi-core systems. She is currently focusing on design space optimization and security analysis of accelerator-rich heterogeneous system-on-chips (ArSoCs). Her recent works on energy-optimization of ArSoCs and SoC security were published in premier journals like TODAES'20 and TETC'21. She has published 1 book chapter and contributed to research works published in conferences including ASAP'18, ISCAS'18, ISVLSI'18, IGSC'18, ISCAS'20. She has experience of almost a year in academia as a lecturer and a research associate.

My research interests are centered around Computer Systems. I am currently focusing on storage and persistent memory technologies, with an emphasis on their impact on the way we manage large-scale data for emerging workloads in Data Science and the Internet of Things.

I completed my PhD in Computer Science at EPFL/the University of Sydney in 2020, advised by Prof. Willy Zwaenepoel. My dissertation research was on the design and implementation of efficient key-value stores for future hardware and performance requirements.

Hai “Helen” Li is currently Clare Boothe Luce Associate Professor of Electrical and Computer Engineering Department at Duke University, USA. She received the B.S. and M.S. degrees from Tsinghua University, China, and the Ph.D. degree from the Department of Electrical and Computer Engineering, Purdue University, USA. Her current research interests include memory design and architecture, neuromorphic architecture for brain-inspired computing systems, and architecture/circuit/device cross-layer optimization for low power and high performance. Dr. Li is a distinguished member of ACM, a distinguished speaker of ACM (2017-2020), and a distinguished lecture of IEEE CAS society (2018-2019).