Nitesh Chawla

Nitesh Chawla

Founding Director, Lucy Family Institute for Data & Society and Frank M. Freimann Professor of Computer Science and Engineering


384 Nieuwland Science Hall
574.631.1090 |

Primary Interest Areas:

Data Science, Network Science, Healthcare Analytics, Information Networks, Business Analytics, National Security, Climate Sciences


Nitesh Chawla joined the University of Notre Dame faculty in 2007. He serves as the Founding Director of the Lucy Family Institute for Data & Society. In addition to this role, he is the Frank M. Freimann Professor of Computer Science and Engineering and the Director of the Data Inference Analysis and Learning Lab. Chawla is an expert in artificial intelligence, data science, and network science, and is motivated by the question of how technology can advance the common good through interdisciplinary research. As such, his research is not only at the frontier of fundamental methods and algorithms but is also making interdisciplinary and translational advances for societal impact.

Recognitions & Awards

Chawla is the recipient of multiple awards for research and teaching innovation including Outstanding Teacher Awards at Notre Dame, a National Academy of Engineers New Faculty Fellowship, and a number of best paper awards and nominations. He also is the recipient of the 2015 IEEE CIS Outstanding Early Career Award; the IBM Watson Faculty Award; the IBM Big Data and Analytics Faculty Award; and the 1st Source Bank Technology Commercialization Award.

In recognition of the societal and community driven impact of his research, Chawla was recognized with the Rodney F. Ganey Award and Michiana 40 under 40 honor.

Professional Affiliations & Partnerships

Featured Projects & Publications

Large Language Models for Graphs: Progresses and Directions (2024)

Can we soft prompt LLMs for graph learning tasks? (2024)

Are we making much progress? Revisiting chemical reaction yield prediction from an imbalanced regression perspective (2024)

Understanding imbalanced data: XAI & interpretable ML framework (2024)

A Property-Guided Diffusion Model For Generating Molecular Graphs (2024)

Towards a holistic view of bias in machine learning: bridging algorithmic fairness and imbalanced learning (2024)

Corrigendum: A community focused approach toward making healthy and affordable daily diet recommendations (2024)

Representing Outcome-driven Higher-order Dependencies in Graphs of Disease Trajectories (2023)

What can Large Language Models do in chemistry? A comprehensive benchmark on eight tasks (2023)

Environment and shipping drive environmental DNA beta‐diversity among commercial ports (2023)