Myeongjae “MJ” Jeon (Ph.D. ’14) is an Assistant Professor in the Department of Computer Science and Engineering at UNIST, South Korea. Prior to joining UNIST, he spent several years in industry with the Systems Research Group in Microsoft Research Redmond and the R&D group at ARM Semiconductors.
MJ’s research interests span distributed systems, data analytics engines, computer architecture, and applied machine learning. His research goal is to advance the state of the art in emerging large-scale computing platforms by making them more efficient, responsive, intelligent and programmable. Currently at UNIST, he attempts to realize such goal in the context of distributed processing of deep learning workloads, real-time stream data analytics at cloud-IoT scale, and blockchain architectures.
MJ also values real-world impacts systems research can bring out. Thus far, his prior research work has been deployed in production systems in Microsoft, including Bing search engine, Open Platform for AI (OpenPAI), and Azure telemetry monitoring system.
Mary Hall (B.A. ’85, Ph.D. ’91) is a Professor in the School of Computing at the University of Utah. Her research focuses on compiler-based approaches to obtaining high performance on state-of-the-art and experimental architectures, including multi-cores, GPUs and petascale platforms.
Her research team is developing auto-tuning compiler technology to systematically map application code to make efficient use of these diverse architectures. An auto-tuning compiler generates a set of alternative implementations of a computation, and uses empirical measurement to select the best-performing solution. The team’s compiler can work automatically or collaboratively with application programmers to accelerate their performance tuning and in some cases, produce results far better than is possible with manual tuning. Her group has access to DOE Leadership Class computing facilities, the University of Utah Center for High Performance Computing systems, and an Nvidia Tesla system with over 30,000 cores.
She is also an advocate for improving cultural and gender balance in CS academic programs and industry roles.
“There are times when you doubt yourself,” she said. “We all have. Just remind yourself that you can do it and go find someone who will encourage you. I still need that. Everywhere I’ve worked, I built a network [of people like me] and we help each other.”
Hall talked about reaching into other groups or departments to find and build her network. “Sometimes you have to look a little farther. You look around and you are just surrounded by all these guys – or if you are minority – all these Caucasians and Asians –and you think, ‘no one understands me or what I’m going through’ and it is nice to find those people who you can talk to about that. You help each other.”
Read her Rice CS Alumni Profile: https://www.cs.rice.edu/maryhall
Kathryn McKinley (B.A. ’85, M.S. ’90, Ph.D. ’92) is a Senior Staff Research Scientist at Google, but she launched her career as a Computer Science professor at the University of Massachusets at Amherst and the University of Texas at Austin.
She is interested in creating systems (programming languages, compilers, runtimes, and architectures) that make programming easy and the resulting programs correct and efficient. She and her collaborators have produced several widely used tools: the DaCapo Java Benchmarks (30,000+ downloads), the TRIPS Compiler, Hoard memory manager, MMTk memory management toolkit, and the Immix garbage collector.
Her awards include the ACM SIGPLAN Programming Languages Software Award; ACM SIGPLAN Distinguished Service Award; and Best and Test-of-Time paper awards from ASPLOS, OOPSLA, ICS, SIGMETRICS, IEEE Micro Top Picks, SIGPLAN Research Highlights, and CACM Research Highlights. She served as program chair for ASPLOS, PACT, PLDI, ISMM, and CGO. She is currently a CRA and CRA-W Board member. She is an IEEE Fellow and an ACM Fellow and has graduated 22 Ph.D. students.
She is also an advocate for gender parity across technology-related programs and careers in academia and industry. Her February 2018 SIGARCH blog post, “What Happens to Us Does Not Happen to Most of You,” helped start deeper conversations and subsequent action in the CS academic community.
Read her Rice CS Alumni Profile: https://www.cs.rice.edu/mckinley.
Felix Xiaozhu Lin (Ph.D. ’14) is an Assistant Professor of Electrical and Computer Engineering at Purdue University.
His research expertise is in computer systems software, as exemplified by operating systems and runtime. He investigates the question of how to build energy-efficient, heterogeneous, and low-latency systems. One of his ongoing efforts is re-examining the principle of operating systems design for Internet-of-Things (IoT). As IoT becomes a key part of the everyday infrastructure, the traditional methods of managing resources, handling inputs, and detecting failures are not long applicable. One of his group’s recent research projects overcomes obstacles in handling colossal IoT data.
His research has drawn wide attention of various domains. His research is being supported by NSF (including a CAREER award) and industrial colleagues such as Google.
Dan Grove (B.A. ’92) is the Director of Engineering for Dart at Google, where he is helping to build a highly productive, high performance language for developing client applications across web and mobile devices. He has worked in programming languages for most of his career and has led teams at both start-ups (like Napster!) and global companies.
Read his Rice CS Alumni profile: https://www.cs.rice.edu/dangrove.
Bob Hearn (BA ’87) currently spends most of his time running very long distances. He has been involved in a number of startups, most successfully when he co-wrote ClarisWorks with Scott Holdaway (BA ’87), and sold it to Claris. Then he headed to MIT for a PhD in Computer Science, studying artificial intelligence and computational complexity. His Nondeterministic Constraint Model of Computation, developed with Erik Demaine, helped launch the popular sub-field of theoretical CS known as Combinatorial Reconfiguration. This work is further expanded in their book “Games, Puzzles, and Computation”.