skip to main content

Hari Sundaram

Hari Sundaram
Hari Sundaram
Associate Professor
(217) 300-4878
2126 Siebel Center for Comp Sci

For more information

Education

  • Ph.D., Electrical Engineering, Columbia University, New York, 2002.

Biography

Sundaram received his Ph.D. in Electrical Engineering at Columbia University (2002) under Shih-Fu Chang's supervision. After his Ph.D., he was recruited by Arizona State University to help co-found (along with Thanassis Rikakis) the School of Arts, Media and Engineering (AME). AME is a transdisciplinary school that emphasizes scholarship and pedagogy with roots in the arts, design, and engineering. He served as Associate Director for AME (2009-12). He joined the University of Illinois in 2014 as an Associate Professor with a joint appointment between the departments of Computer Science and the Charles H. Sandage Department of Advertising. He received the Eliahu Jury award for best dissertation (2002), IBM faculty awards (2007, 2008), and several best-paper awards and best-paper runner-up honors from IEEE and ACM conferences. He was elected as ACM distinguished scientist in 2019 and IEEE senior member in 2019.

Sundaram's research spans applied machine learning, network science, and human-computer interaction. He develops algorithms and builds systems that help individuals to understand and to act. Constraining system and algorithm design to account for human cognition limits is a central thread in his research. Among his major contributions in Multimedia Computing include a feedback-control algorithm for a ground-breaking, real-time mixed-reality system for stroke patient rehabilitation, which aimed to help stroke survivors re-learn how to reach and grasp objects. Other major contributions within Network Science include algorithms for identifying homogenous communities within large heterogeneous networks, detecting collective behavior, and efficient sampling of large graphs.

Sundaram’s current focus is motivated by the need to solve large-scale collective-action problems (e.g., public health; climate change). Collective-action problems require us to confront a fundamental tension: How do we reconcile an individual’s desire for agency and choice in what they do with the greater common good? Computer Science can facilitate a scaling effect on theories of collective action. He develops algorithms for robust inference of behavior, including systemic bias, designs mechanisms to improve social-welfare, designs algorithms for synthesis of messages, and develops computational infrastructure to enable large-scale field experiments.

Courses Taught

  • ADV 490 - Computational Advertising
  • CS 412 - Introduction to Data Mining
  • CS 498 - Computaional Advertising
  • CS 498 - Computational Advertising
  • CS 498 - Social & Information Networks
  • CS 598 - Adv Social&Information Network
  • CS 598 - Advanced Social & Information
  • CS 598 - Special Topics