School of Computing Welcomes 11 New Faculty Members

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 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 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 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 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 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 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’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 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 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 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.