About UsWe are passionate about improving runtime languages and environments in the cloud, and are building a community of like-minded professionals and academics.
Kingsum has been a lead scientist at Alibaba Cloud Intelligence System Software Hardware Co-Optimization driving performance optimization at the scale of data centers since 2016. Since receiving Ph.D. in Computer Science and Engineering from the University of Washington in 1996, he has been working on performance, modeling and analysis of software applications. He has been issued more than 20 patents. He has presented more than 100 technical papers. He appeared four times in JavaOne keynotes. In his spare time, he volunteers to coach multiple robotics teams to bring the joy of learning Science, Technology, Engineering and Mathematics to the K-12 students in USA and China.
Andrea is a Senior Principal Engineer at Arm. Since 2016 he has been leading the efforts the performance and workloads team for servers and networking. Andrea obtained a PhD from the University of Michigan, Ann Arbor.
Suresh Srinivas is a Principal Engineer at Intel focused on Runtimes. He has a PhD in Computer Science and 25 years of Industry Experience developing JITs, HW/SW codesign, and Runtimes. He is also a Yoga and Meditation teacher. Outside of work he volunteers in the community and enjoys hiking the Pacific Northwest with his dog Luna.
Mei-Chin Tsai is a Group Principal Engineer Manager at Microsoft, responsible for .NET Languages and .NET Runtimes. She currently leads the charge to continuously evolve the .NET Runtime to handle modern workloads. Mei-Chin graduated from University of Illinois at Urbana-Champaign with a Ph.D. degree in Computer Science. She joined Microsoft in 1994 and was one of the original developers on .NET. She is passionate about scalability and performance.
Chris is currently a DL (Deep Learning) performance architect at Nvidia focused on modelling and maximizing DL performance on Nvidia GPUs. Prior to that, Chris was at Intel for 22 years working both in the labs and in the product focused CPU architecture team. Chris has worked on a broad range of topics including low-voltage processors, circuits, microarchitecture and design, low-power design, branch prediction, processor cache design and management, processor reliability and resiliency. Chris has authored or co-authored over 40 articles in top-tier refereed conferences and journals and has received over 50 patents.
University of Utah
Rajeev Balasubramonian is a Professor at the School of Computing, University of Utah. He received his Ph.D. in Computer Science at the University of Rochester in 2003. His current research focuses on memory hierarchies, memory security, and neuromorphic architectures. He is a recipient of an NSF CAREER award, multiple teaching awards/commendations from the University of Utah, and multiple best paper awards at conferences. He has collaborated with several industry groups, including groups at Intel, IBM, Samsung, HP, NVIDIA, and AMD. Prof. Balasubramonian’s research group has contributed multiple tools to the architecture research community, including CACTI 6, USIMM, and CACTI 7. He has served as Program Chair of ISPASS 2011 and General Chair of ISPASS 2012 and ASPLOS 2014.
University of Utah
Karl is a 5th year Ph.D. student at the University of Utah, advised by Rajeev Balasubramonian. His research focuses on system performance optimization. In the past, he has explored post silicon hardware tuning in collaboration with Intel Labs, where he completed two internships. This work has led to an ACM student research competition as well as an ISPASS publication. Currently, Karl has shifted his focus towards warehouse scale performance optimization. Karl recently completed an internship at Facebook with the Warehouse Java Efficiency team, where he plans to join full-time in 2020.
Yingying Wen is a 4th year Ph.D. student at the Zhejiang University. Her research interest focuses on machine learning, probabilistic models and data science. She is currently an intern at Alibaba working on analytics for system performance at scale.