CSE welcomes new faculty to campus for the 23/24 academic year

Steven Bogaerts – Summer 2023

Lecturer III
PhD, Computer Science
Indiana University, Bloomington, 2007

Steven Bogaerts finds his professional joy in creating learning experiences of lasting impact for his students. He has taught a wide range of courses in the undergraduate curriculum, including most core courses and upper-level courses in artificial intelligence and data science. He has also created various introductory educational experiences set in the context of advanced topics, such as machine learning and parallelism. He joins CSE after spending several years as an associate professor of computer science at DePauw University.

Ang Chen – Fall 2023

Associate Professor
PhD, Computer Science
University of Pennsylvania, 2017

Ang Chen is interested in building better computer systems. His work has received an NSF CAREER award (2020), FAST best paper (2021), VMWare Early Faculty award (2022), USENIX Security Distinguished Paper award (2023).

Prior to joining Michigan, he was an assistant and then associate professor at Rice University.

David Jurgens – Fall 2023

Associate Professor
PhD, Computer Science
University of California, Los Angeles, 2014

David Jurgens researches human behavior through techniques from natural language processing and computational social science. His work has been published in top venues like ACL, EMNLP, WWW, ICWSM, and PNAS and received multiple best paper awards and nominations. He has been supported by the NSF, NIH, and IARPA, including by an NSF CAREER. Jurgens joined the faculty at the University of Michigan School of Information in 2017; he now has a 50% appointment with CSE.

Jeong Joon Park – Fall 2023

Assistant Professor
PhD, Computer Science and Engineering, 2021
University of Washington

Jeong Joon (JJ) Park’s research interests lie in the intersection of computer vision and graphics, where he studies realistic reconstruction and generation of 3D scenes using neural and physical representations. Generations of large-scale, dynamic, and interactive 3D scenes are his current primary targets. His group will explore 3D vision and graphics, and their applications to robotics, medical imaging, and scientific problems. He is the lead author of DeepSDF, which introduced neural implicit representation and made a profound impact on 3D computer vision. Before coming to Michigan, he was a postdoctoral researcher at Stanford University and a Ph.D. student at U of Washington, supported by Apple AI/ML Fellowship.

Gokul Ravi – Fall 2023

Assistant Professor
PhD, Computer Engineering
University of Wisconsin, Madison, 2020

Gokul Ravi’s research targets quantum computing architecture and systems, primarily on themes at the intersection of quantum and classical computing. He was awarded the 2020 Best ECE Dissertation Award from UW-Madison and named a 2019 Rising Star in Computer Architecture. His quantum and classical computing research have resulted in publications at top computer architecture, systems, and engineering venues, as well as two granted and three pending patents. His contributions have been recognized with the 2022 HPCA Best Paper Award, a 2023 IEEE Micro Top Picks Honorable Mention, and as an ICRC 2022 Highlight Paper. Prior to joining Michigan, he was an NSF CI Fellows postdoctoral scholar at the University of Chicago.

Alexander Rodríguez

Assistant Professor
PhD, Computer Science
Georgia Institute of Technology, 2023

Alexander Rodríguez’s research is at the intersection of machine learning, time series, and scientific modeling, and his main application domains are public health and community resilience. He has published at top venues such as AAAI, NeurIPS, ICLR, KDD, WWW, AAMAS, PNAS and has organized workshops and tutorials at AAAI and KDD. His work won the best paper award at ICML AI4ABM 2022 and was awarded the 1st place in the Facebook/CMU COVID-19 Challenge and the 2nd place in the C3.ai COVID-19 Grand Challenge. He was also invited to the Heidelberg Laureate Forum in 2022, and named a Rising Star in Data Science by the University of Chicago Data Science Institute in 2021 and a Rising Star in ML & AI by the University of Southern California in 2022.

Nishil Talati

Assistant Research Scientist
PhD, Computer Science and Engineering
University of Michigan, 2022

Nishil Talati’s research focuses on improving the efficiency of computing systems for emerging data-intensive workloads (e.g., graph analytics and recommendation models) using computer architecture and systems software. His research is published at several top-tier venues including ISCA, MICRO, HPCA, ASPLOS, and others. Nishil’s work has been recognized as the 2021 HPCA best paper award, 2023 DATE best paper honorable mention, and 2023 IISWC best paper nominee.

Nicole Wein

Assistant Professor
PhD, Computer Science
Massachusetts Institute of Technology, 2021

Nicole Wein is very excited to join U-M as an assistant professor starting January 2024. She is a theoretical computer scientist and her research interests include algorithms, data structures, graph theory, and fine-grained complexity. Nicole was recently a postdoc at DIMACS at Rutgers University. During Fall 2023, she is spending a semester at the Simons Institute as a Research Fellow.

Elizabeth Bondi-Kelly

Assistant Professor
PhD, Computer Science, 2022
Harvard University

Elizabeth Bondi-Kelly is focusing her research on artificial intelligence for social impact, particularly spanning the fields of multi-agent systems and data science. Her work, which has been published in venues such as AAAI, AAMAS, AIES, and IJCAI, has applications in conservation and public health, and has been deployed to support conservation efforts. She also founded and currently leads Try AI, a 501(c)(3) nonprofit committed to increasing diversity, equity, inclusion, and belonging in the field of AI through community-building educational programs, largely focused on AI for social impact. Prior to Michigan, Bondi-Kelly was a postdoctoral fellow at MIT through the CSAIL METEOR Fellowship.

Lin Ma

Assistant Professor
PhD, Computer Science, 2021
Carnegie Mellon University

Lin Ma’s research focuses on the intersection of database management systems and machine learning, especially using machine learning techniques to automate database administration and remove human obstacles. Ma is a lead developer of the NoisePage self-driving database system, and he has given talks on database management and machine learning at Google, Microsoft Research, IBM Research, and Facebook. Most recently, Ma was addressing challenges in the Lakehouse data platform at Databrinks, Inc.