Machine Programming Research (MPR) Team
At Intel Labs, I formed and lead the Machine Programming Research (MPR) group. Below is an abbreviated list of some of the members of MPR. However, we collaborate with many wonderful people inside and outside of Intel.
Dr. Todd Anderson
Dr. Niranjan Hasbnis
Celine Lee (intern, PhD student @ Cornell)
Dr. Ryan Marcus
Dr. Tim Mattson
Roshni Iyer (intern, PhD student @ UCLA)
Dr. Nesime Tatbul
Dr. Jesmin Tithi
Dr. Joseph Tarango
Dr. Javier Turek
Dr. Anand Venkat
Dr. Abdul ("Wasay") Wasay
Fangke Ye (intern, PhD student @ Georgia Tech)
Shengtian ("Stanley") Zhou
Machine Programming Research @ Intel Labs
Dr. Todd Anderson (MPR collaborator)
Todd A. Anderson is a senior research scientist at Intel Labs where he focuses on data analytics in high productivity programming environments. He received a Ph.D. in Computer Science from the University of Kentucky. His research interests primarily include programmer productivity, automatic parallelization and distribution, and program synthesis.
Dr. Niranjan Hasabnis
Niranjan Hasabnis is a Research Scientist in the Machine Programming Research (MPR) group where he applies techniques from machine learning, compilers, and programming languages to develop tools to assist software development. He received his PhD in Computer Science from Stony Brook University. His research interests primarily include machine programming, code generation, and program analysis.
Celine Lee is a research scientist intern in the Machine Programming Research group at Intel Labs. Her research in machine programming focuses on natural language and programming language semantics understanding. She graduated from the University of Pennsylvania, where she completed a bachelors-masters degree program. In May 2020, she completed her Bachelor's degree in Electrical Engineering and Computer Science; in December 2020, she completed her Master's degree in Embedded Systems. She will be starting her PhD at Cornell University in the Fall of 2021. Her short bio video can be found here.
Dr. Ryan Marcus
Ryan Marcus's research focuses on the development and application of new machine programming techniques, primarily focusing on systems such as runtime garbage collection, query optimization, data layout, and cloud management. In addition to his work at Intel's MPR, he is jointly affiliated with MIT working with Professor Tim Kraska on learned index structures and data management systems.
Dr. Tim Mattson (MPR collaborator)
Tim Mattson earned a PhD. in Chemistry for his work on quantum molecular scattering. This was followed by a Post-doc at Caltech where he ported his molecular scattering software to the Caltech/JPL hypercubes. Since then, he has held a number of commercial and academic positions with computational science on high performance computers as the common thread. Dr. Mattson joined Intel in 1993 to work on a variety of parallel computing problems. This included benchmarking, system performance modeling, and applications research. He was a senior scientists on Intel’s ASCI teraFLOPS project: a project that resulted in the first computer to run MPLINPACK in excess of one teraFLOPS. Currently, he is working in Intel’s Parallel Computing Laboratory. His goal is to develop technologies that will make parallel computing more accessible to the general programmer. This includes OpenMP, OpenCL, and runtime systems for Exascale computers.
Dr. Nesime Tatbul (MPR collaborator)
Nesime Tatbul is a senior research scientist at Intel Labs and a visiting scientist at MIT’s Computer Science and Artificial Intelligence Lab. Since 2013, she has been Intel's research lead for the Intel Science and Technology Center for Big Data (ISTC-BigData), followed by the Intel-Google-Microsoft Data Systems and AI Lab (DSAIL) based at MIT. Previously, she served on the computer science faculty of ETH Zurich, after receiving a Ph.D. degree from Brown University. Her research interests are in large-scale data management systems and modern data-intensive applications. She is most known for her contributions to stream processing, which include the Aurora/Borealis systems (now TIBCO StreamBase) and the S-Store system (the first streaming OLTP system). Nesime is the recipient of an IBM Faculty Award (2008), two ACM SIGMOD Best Demonstration Awards (2005 and 2019), and ACM DEBS Grand Challenge and Best Poster Awards (2011). She has served on the organization and program committees for various conferences and workshops, including SIGMOD (group leader in 2011, industrial co-chair in 2014), VLDB (demo co-chair in 2019, workshops co-chair in 2020, "Scalable Data Science" category co-chair in 2021, associate editor for PVLDB 2011-12 and 2020-21), ICDE (area chair in 2013), SIGMOD/aiDM (PC co-chair in 2020), NeurIPS/WiML (area chair and mentor in 2019), and IJCAI (2020), and on the editorial boards of the SIGMOD Record (2012-2017) and the VLDB Journal (2019-present). She is an elected member of the VLDB Endowment Board of Trustees (2020-2025).
Dr. Jesmin Jahan Tithi (Honorary member)
Jesmin Jahan Tithi is a research scientist and her main research focus is high-performance computing and software-hardware codesign of next-generation processors targeting large-scale machine learning and graph applications. She received her Ph.D. from Stony Brook University, New York (SUNYSB), and worked as an intern in Google, Intel, and PNNL during her Ph.D. After finishing her B.Sc. in Computer Science and Engineering from the top Engineering School of Bangladesh (Bangladesh University of Engineering and Technology ), she also worked as a Lecturer in the same prestigious department. Jesmin has been actively involved in Women in HPC and STEM workshops and taught girls about HPC in the WISC courses at SUNYSB. Jesmin is also associated with the University of Goethe, Germany, where she is working on the Z-inspection, a practical auditing process for Trustworthy Ethical AI, a member of the ACM Future of Computing Academy, ACM-Selects and ACM Code of Professional Ethics Board.
Dr. Joseph Tarango (MPR collaborator)
Joe (or Joey) is a machine learning engineer focused on automation and intelligent ecosystems. After achievements in his internship, Intel sponsored his Ph.D. education in Computer Science & Engineering from University of California, Riverside. His interests research includes: code acceleration, compilers, generation of static/re-configurable arithmetic pipelines, machine learning, numerical analysis, Instruction Set Architecture (ISA) specialization, and similarity search. As an adjunct Professor in Electrical, Computer & Energy Engineering at University of Colorado Boulder; he shares his industry experience and knowledge with the next generation.
Dr. Javier Turek (MPR collaborator)
Javier Turek is a research scientist in the Brain-Inspired Computing Lab at Intel Labs, where he serves as a technical advisor and collaborator for many MPR research activities. He focuses on new representation learning methods that can bridge the existing gap between the capabilities of the human brain and the limitations of machine learning models, with particular interest in Natural Language Processing (NLP). Javier received his doctorate, master’s, and a bachelor’s in computer science from the Israel Institute of Technology, Technion, in Israel. His research interests include core Machine Learning and its applications to Computer Vision, Natural Language, and Neuroscience.
Dr. Anand Venkat
Anand Venkat is a Research Scientist in the Machine Programming Research (MPR) group where he focuses on compiler optimizations for High Performance Computing (HPC) and Machine Learning (ML) applications. He received his PhD in computer science from the University of Utah. His research interests primarily include polyhedral compilation, compilers for deep neural networks, machine programming, and sparse matrix computations.
Dr. Abdul Wasay
Wasay is a research scientist in the Machine Programming (MPR) Group at Intel. His research is at the intersection of systems and machine learning. Wasay identifies co-design opportunities between the two fields to develop techniques that accelerate data science and deep learning pipelines by removing computation and data movement bottlenecks. He received his Ph.D. in Computer Science from Harvard University.
Fangke Ye is a research intern in the Machine Programming Research group and a PhD student at Georgia Institute of Technology advised by Professor Vivek Sarkar. He received his bachelor’s degree in Computer Science and Technology from Tsinghua University. His research primarily focuses on machine learning-based program analysis and code semantics similarity.
Shengtian (“Stanley") Zhou is a Research Scientist in the Machine Programming Research (MPR) group, where he focuses on applying machine learning techniques on code similarity related problems. Stanley received his master’s degree in Embedded Systems from the University of Pennsylvania and bachelor’s degree in Computer Engineering from the University of California Irvine. Prior to joining Intel, Stanley worked as a deep learning intern at Siemens Healthineers. Stanley’s primary research interests include semantic code similarity, automated defect detection, and program understanding.
MS advisor (University of Pennsylvania): Brad MacDonald -> Tesla
MS advisor (University of Pennsylvania): Celine Lee -> Intel Labs, then PhD student @ Cornell
PhD committee member (Lehigh University): PanteA Zardoshti -> Microsoft Research
PhD committee member (University of Washington): Maaz Ahmad -> Adobe Research