On January 13, 2023, the Utah Center for Data Science hosted at least 280 participants at Data Science Day, making this perhaps the largest data science event ever at the University of Utah. The event was a gathering of all those interested in data science around campus, including students, researchers, and faculty — as well as data scientists from local industry and prospective data scientists from around the Salt Lake City area. This year, Data Science Day was held in the Union Ballroom on the University of Utah campus.

An exciting aspect of the event was the chance for those interested in data science from around the university to gather and meet each other — especially after a few years of mostly online meetings during the COVID-19 pandemic. This was exemplified by the Research Expo part of the day, which saw dozens of research posters, demos, and information booths. The Research Expo provided a forum for researchers of all levels to show off their recent results and for institutes and centers to showcase the services they provide.

A highlight of the day was the research talks, especially the inspiring keynote by Jeff Leek, the chief data officer at the Fred Hutchinson Cancer Center in Seattle. His talk told the story of how he launched a massive online course in data science, and wherein the resulting curriculum has served more than 8 million students. He then described how he leveraged this towards designing and running a version of the curriculum, DataTrail, as an educational springboard for the underserved low-income communities of Baltimore and beyond. This work stimulated much discussion, including how it related to the maturing data science curriculum here at the University of Utah. Another engaging part of the day was an industry panel staffed by local data scientists that provided great insight into the trade-offs of in-person versus remote work, especially as it pertains to new data scientists.

The event also provided a great opportunity for many to interact with local industry. This included a career fair which included the day’s sponsors: Sorenson, Recursion, and bioMérieux. The organizers would also like to thank the Office of the Vice President of Research and the National Science Foundation for financial support and the One Utah Data Science Hub and Kahlert School of Computing for organizational support. The day was a great success and there is excitement to do it again.

New York, NY, October 19, 2022 – ACM, the Association for Computing Machinery, and the IEEE Computer Society announced today that Marcin Copik of ETH Zurich and Masado Alexander Ishii of the University of Utah are the recipients of the 2022 ACM-IEEE CS George Michael Memorial HPC Fellowships. Shelby Lockhart of the University of Illinois at Urbana-Champaign received an Honorable Mention. Copik is recognized for incorporating the Function-as-a-Service programming model into HPC applications and bringing high-performance into serverless to cut costs and increase efficiency of supercomputing. Ishii is recognized for developing lightweight, dimension-parameterized, parallel meshing algorithms with a focus on scalability and improving total time-to-solution for engineering applications. Lockhart is recognized for contributions in scalable iterative solvers using node-aware communication and low synchronization algorithms to reduce communication bottlenecks on supercomputers.

Marcin Copik

Copik’s research bridges the gap between high-performance programming and serverless computing. He is bringing the Function-as-a-Service (FaaS) programming model into the HPC domain by developing high-performance software and hardware solutions for the serverless stack. By solving the fundamental performance challenges of FaaS, he is building a fast, efficient programming model that brings innovative cloud techniques into HPC data centers, allowing users to benefit from pay-as-you-go billing and helping operators to decrease running costs and their environmental impact.

To that end, he has been working on tailored solutions for different levels of the FaaS computing stack, from computing and network devices up to high-level optimizations and efficient system designs. He has also proposed a new design for serverless platforms that applies HPC practices such as low-latency networking, data locality, and efficient communication.

Masado Alexander Ishii

Ishii is the main developer for the University of Utah’s Dendro-KT framework for four-dimensional adaptivity and parallel in time formulations. Given the ever-increasing levels of parallelism in the largest machines, parallelizing across space is not sufficient—and in many cases the inability to parallelize in time is the biggest bottleneck for several important problems. The Dendro-KT framework addresses this problem and also simplifies the development of high-order in time and spatially varying time increments, which are important to limit the computational work needed for a given accuracy.

Working with collaborators, Ishii has also been involved in developing methods and codes for large-scale fluid simulations around complex objects, including a case with multiple complex objects, to evaluate COVID-19 transmission risk in classrooms.

Shelby Lockhart

Lockhart has made contributions in parallel communication, core parallel numerical algorithms, and advancing capabilities of large-scale predictive simulation. Her focus has been on modeling performance in heterogeneous settings, with an eye on redesigning the message communication “under-the-hood” (aspects of the high-performance architecture that are not readily visible) as well as looking at fundamental algorithmic changes in order to significantly improve achievable performance.

Among her research highlights, she has provided detailed communication models to drive the selection of message routing, yielding impressive improvements across a range of problem types. She has also presented a strategy for achieving impressive reductions in communication costs in graphic processing unit (GPU) systems by communication through the host, accounting for different data volumes and GPU counts. Additionally, Lockhart’s work on fixed point solvers has made important contributions to the Suite of Nonlinear and Differential/Algebraic Equation Solvers (SUNDIALS) project.

The School of Computing is delighted to announce that eleven new faculty members have joined the School at the beginning of the 2022-23 academic year. The accomplished group of renowned scholars covers a breadth of research areas across the school. “The new faculty bring research expertise that strengthen existing and emerging areas in computing and will make it possible to enhance our undergraduate and graduate curriculum. Just this semester, we have introduced courses in artificial intelligence, cybersecurity, data science, graphics and theory,” says Director Mary Hall.

Daniel Brown

Assistant Professor
Robotics

Daniel Brown joined the Robotics Center and the School of Com-puting after completing a postdoc at UC Berkeley. He received his Ph.D. in Computer Science from UT Austin in 2020. His research focuses on helping robots to safely and efficiently interact with and learn from humans. In particular, he is interested imitation learning, preference learning, and human-in-the-loop reinforcement learning and has worked on applications in manipulation, autonomous driv-ing, multi-robot swarming, and assistive robotics.

Shireen Elhabian

Associate Professor
Image Analysis
Statistical Machine Learning

Shireen Elhabian has established her research program around biomedical problems that entail collaborating with scientists and domain experts of different disciplines and backgrounds to conduct interdisciplinary research projects at the intersection of image anal-ysis and statistical machine learning. Her long-term goal is to accel-erate the adoption and increase the clinical utility of machine-learn-ing-based image analysis systems that mitigate critical bottlenecks in attaining an expert-level understanding of the complexities of imaging data and have a broad impact in a range of clinical and biomedical research disciplines. Dr. Elhabian has been establishing foundational methods to solve inverse problems in image analy-sis and translating these methods to application domains through robust, flexible, and usable open-source software packages.

Nabil Makarem

Assistant Professor, Lecturer
Internet of Things

Nabil Makarem received his Master’s degree from the Lebanese American University in 2014 and his PhD degree in Computer Sci-ence from Sorbonne University in 2021. His current research area is the Internet of Things, with an emphasis on performance evalua-tion and improving congestion control mechanisms in IoT Networks. Nabil has worked in several universities and corporations, hold-ing different positions such as System and Network Engineer, IT Manager, and Lecturer. He has been teaching in the Electrical and Computer Engineering department at the American University of Beirut since 2019.

Anton Burtsev

Assistant Professor
Operating Systems

Anton Burtsev is a systems researcher whose work explores design and architecture of operating systems in the age of targeted security attacks, heterogeneous hardware, and datacenter-scale computing. Burtsev’s research spans topics of programming language safety and its impact on security and reliability, hardware support for isolation, and operating system support for disaggregat-ed heterogeneous datacenters. Burtsev received his PhD from the University of Utah, and spent six years as a faculty at the University of California, Irvine.

Kate Isaacs

Associate Professor
Data Visualization
High Performance Computing

Kate Isaacs is an Associate Professor in the School of Comput-ing and SCI Institute. Her research is at the intersection of data visualization and computing systems. She develops new methods of representing complex computing processes for exploration and analysis of their behavior, with applications to high performance computing, data science, and program analysis. She received a Department of Energy Early Career Research Program award in 2021 for research on visualizing program behavior in high perfor-mance computing contexts and a National Science Foundation CAREER award in 2019 for visualizing networks derived from computing systems. She received her Ph.D. in computer science from the University of California, Davis. Prior to joining the Univer-sity of Utah, she was an Assistant Professor in the Department of Computer Science at the University of Arizona.

Ana Marasovic

Assistant Professor
Natural Language Processing Artificial Intelligence

Ana Marasović received her Ph.D. from Heidelberg University. Be-fore joining University of Utah, she was a postdoctoral researcher at the Allen Institute for AI (AI2) and at the Paul G. Allen School of Computer Science & Engineering at the University of Washington. Her primary research interests are at the confluence of natural language processing (NLP), multimodality, and explainable artificial intelligence (XAI), with a focus on building trustworthy and intuitive language technology.

Stefan Nagy

Assistant Professor
Computer Security and Systems

Stefan Nagy joined the School of Computing as an Assistant Professor. He earned his Ph.D. in Computer Science from Virginia Tech in 2022, and his Bachelor’s from The University of Illinois at Urbana-Champaign in 2016. His research interests broadly span security, software, and systems. Some topics he actively works in are software testing, binary analysis, and vulnerability triage. He is especially interested in making efficient and effective quality assur-ance possible for today’s closed-source, complex, and otherwise challenging software and systems.

Prashant Pandey

Assistant Professor
Data Structures and Algorithms

Prashant Pandey’s goal as a researcher is to advance the theory and practice of resource-efficient data structures and employ them to democratize complex and large-scale data analyses. He designs and builds tools for large-scale data management problems across computational biology, stream processing, and storage. He is also the main contributor and maintainer of multiple open-source software tools that are used by hundreds of users across academia and industry. Before joining SoC at the University of Utah, Pandey was a Research Scientist at VMware Research. He did postdocs at University of California Berkeley and Carnegie Mellon University. He obtained his Ph.D. in Computer Science at Stony Brook Univer-sity in December 2018.

Paul Rosen

Associate Professor
Visualization Computational Geometry

Paul Rosen has joined the University of Utah’s School of Com-puting as an associate professor. Dr. Rosen joins the School of Computing from the University of South Florida Department of Computer Science and Engineering, where he was an associate professor. Dr. Rosen received his Ph.D. from the Computer Sci-ence Department of Purdue University in 2010. In his research, Dr. Rosen studies approaches to improving the efficacy of visualization tools by utilizing a mix of human-centered design and geometry- and topology-based methods to extract and emphasize important data features in the context of many data types, including scalar and vector fields, multidimensional data, and graphs.

Haitao Wang

Associate Professor
Computational Geometry Theoretical Computer Science

Haitao Wang joined the School of Computing as an associate professor in August 2022. Before that he taught at Utah State Uni-versity from 2012 to 2022. He received a Ph.D degree in Computer Science from University of Notre Dame in 2010 and stayed there for two more years as a research assistant professor. His research is mainly on algorithms, computational geometry, and theoretical computer science.

Yin Yang

Associate Professor
Computer Graphics

Yin Yang received his Ph.D. from the University of Texas at Dallas in 2013. He was a faculty member at Clemson University before joining the U. His research focus on Physical simulation and ap-plied computing in Graphics, Animation, Robotics, Vision, Machine Learning, Visualization, and Medical applications.