I am a faculty at Penn State Behrend where I teach Computer Architecture and Programming with C++/MATLAB. I am interested in optimizing current architecture for emerging technologies such as artificial intelligence and deep learning.

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.

My main research focus is in hardware accelerators, especially FPGAs. I have done research in floating point implementations, unsupervised learning, medical imaging, and privacy preserving data processing. At Northeastern I am head of the Reconfigurable Computing Laboratory and a member of the Computer Engineering group. Throughout my career, I have been funded by both government agencies and companies, including AMD, DARPA, NSF, Google, and MathWorks. I am the recipient of a Fulbright Scholar Award and a Charter Member of the IEEE Computer Society Distinguished Contributor Recognition Program. My current research focus is on FPGAs in the data center and FPGAs directly attached to the network.

Qiuyun Llull joined VMware in 2017, working on the performance of next-generation Software-defined Data Center. She is broadly interested in system and architecture for emerging applications, such as data analytics, machine learning, IoT, and autonomous driving. She received her Ph.D. in Computer Engineering from Duke University in 2017, working with Prof. Benjamin Lee. Her doctoral research applies microeconomic models to manage resources in large-scale data centers; her dissertation received Outstanding Dissertation Award from the Duke ECE department. Her recent publication (Amdahl's law in the Datacenter Era: A Market for Fair Processor Allocation) won the Best Paper Award at HPCA 2018. Before coming to the United States, she obtained her M.S. degree from University Paris Sud, France and B.Eng. degree from Huazhong University of Science and Technology, China