Nitesh Chawla

Nitesh Chawla

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

Contact:

384 Nieuwland Science Hall
574.631.1090 | nchawla@nd.edu

Primary Interest Areas:

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

Biography

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

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)

HetGPT: Harnessing the Power of Prompt Tuning in Pre-Trained Heterogeneous Graph Neural Networks (2023)

Fairness-Aware Mixture of Experts with Interpretability Budgets (2023)

Modeling non-uniform uncertainty in Reaction Prediction via Boosting and Dropout (2023)

Graph Neural Prompting with Large Language Models (2023)

Pure Message Passing Can Estimate Common Neighbor for Link Prediction (2023)

KDD Workshop on Machine Learning in Finance (2023)

Foundations and Applications in Large-scale AI Models: Pre-training, Fine-tuning, and Prompt-based Learning (2023)