Nitesh V. Chawla is a computer scientist and data scientist currently serving as the Frank M. Freimann Professor of Computer Science and Engineering at the University of Notre Dame. He is the Founding Director of the Lucy Family Institute for Data & Society. Chawla's research expertise lies in machine learning, data science, and network science. He is also the co-founder of Aunalytics, a data science software and cloud computing company. Chawla is a fellow of the Association for Computing Machinery (ACM) and a Fellow of the Institute of Electrical and Electronics Engineers (IEEE). He has received multiple awards, including the 1st Source Bank Commercialization Award in 2017 and the IBM Big Data Award in 2013. One of Chawla's most recognized publications, with a citation count of over 24,000, is the research paper titled "SMOTE: Synthetic Minority Over-sampling Technique." Chawla's research has garnered a citation count of over 56,000 and an H-index of 78.

Background 
Nitesh V. Chawla completed his Bachelor of Engineering in Computer Science from the University of Pune, India, in 1997. In 1999, Chawla earned a Master of Science degree in Computer Science and Engineering from the University of South Florida. He went on to complete his Ph.D. in Computer Science and Engineering at the University of South Florida in 2002. He worked at the Canadian Imperial Bank of Commerce as a Senior Risk Modeling Manager from 2002 - 2004. He joined the University of Notre Dame as a Research Assistant Professor in 2004. He began his tenure-track career also at Notre Dame in 2007.

Academic Career

University of Notre Dame 
Chawla started as a tenure-track assistant professor in Computer Science and Engineering in 2007.  He specializes in data science, network science, artificial intelligence, and machine learning, holding the position of Frank M. Freimann Professor of Computer Science and Engineering since 2015. Chawla is also a concurrent professor of Information Technology Analytics and Operations in the College of Business, as well as Applied and Computational Mathematics and Statistics in the College of Science at University of Notre Dame. He also served as the director of the Center for Network and Data Science from 2011 to 2020. He is also the Founding Director of the Lucy Family Institute for Data and Society. Additionally, he holds Fellowships in multiple institutes at Notre Dame, including the Pulte Institute for Global Development (since 2019), the Kellogg Institute for International Studies (since 2017), the Kroc Institute for Peace Studies (since 2015), the Liu Institute for Asia and Asian Studies (since 2014), and the Reilly Center for Science, Technology and Values (since 2012).

Lucy Family Institute of Data & Society 
Guided by Notre Dame’s Mission, the Lucy Family Institute adventurously collaborates on advancing data-driven convergence research, translational solutions, and education to ethically address social problems.  The Institute is the innovative nexus of academia, industry, and the public, fostering data science access to strengthen diverse and inclusive capacity building within communities. Nitesh Chawla serves as the Founding Director of the Lucy Family Institute for Data & Society.

Fellowships 

 Association for Computing Machinery (ACM) Fellow
 Institute of Electrical and Electronics Engineers (IEEE) Fellow
 Asia-Pacific Artificial Intelligence Association (AAIA) Fellow

Entrepreneurship 
In 2012, he co-founded Aunalytics Inc., a company specializing in data science software and solutions. Additionally, in 2020, he co-founded Intrepid Phoenix, an organization that utilizes artificial intelligence to offer personalized recovery paths for individuals dealing with substance use.

Research and publications 
Chawla's research has garnered a citation count of over 54,800 and an H-index of 78. His primary areas of focus include artificial intelligence, machine learning, data and network sciences. He has received several awards for his research work, including being a Best Paper Candidate at ACM WWW in 2019, winning the Best Application Paper at IEEE Data Science and Advanced Applications in 2017, and securing the IEEE Computational Intelligence Magazine Outstanding Paper Award.

Honors and Distinctions 

 Association for Computing Machinery (ACM) Fellow
 Institute of Electrical and Electronics Engineers (IEEE) Fellow
 Asia-Pacific Artificial Intelligence Association (AAIA) Fellow
 1st Source Bank Commercialization Award, 2017
 IEEE CIS Outstanding Early Career Award, 2015
 IBM Big Data Award, 2013
 IBM Watson Faculty Award, 2012
 Rodney F. Ganey Community Based Research Award, 2014
 Michiana 40 under 40 Honor, 2013

Selected works 

 Chawla, Nitesh V., et al. "SMOTE: synthetic minority over-sampling technique." Journal of artificial intelligence research 16 (2002): 321–357. https://doi.org/10.1613/jair.953
 Fernández, Alberto, et al. "SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary." Journal of artificial intelligence research 61 (2018): 863–905. https://doi.org/10.1613/jair.1.11192
 Chawla, Nitesh V., Nathalie Japkowicz, and Aleksander Kotcz. "Special issue on learning from imbalanced data sets." ACM SIGKDD explorations newsletter 6.1 (2004): 1–6. https://dl.acm.org/doi/10.1145/1007730.1007733
 Chawla, Nitesh V., et al. "SMOTEBoost: Improving prediction of the minority class in boosting." Knowledge Discovery in Databases: PKDD 2003: 7th European Conference on Principles and Practice of Knowledge Discovery in Databases, Cavtat-Dubrovnik, Croatia, September 22–26, 2003. Proceedings 7. Springer Berlin Heidelberg, 2003. https://doi.org/10.1007/978-3-540-39804-2_12
 Chawla, Nitesh V. "Data mining for imbalanced datasets: An overview." Data mining and knowledge discovery handbook (2010): 875–886. DOI: 10.1007/978-0-387-09823-4_45
 Lichtenwalter, Ryan N., Jake T. Lussier, and Nitesh V. Chawla. "New perspectives and methods in link prediction." Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining. 2010. https://doi.org/10.1145/1835804.1835837
 Dong, Y., Chawla, N. V., & Swami, A. (2017, August). metapath2vec: Scalable representation learning for heterogeneous networks. In Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 135-144).
 Zhang, C., Song, D., Huang, C., Swami, A., & Chawla, N. V. (2019, July). Heterogeneous graph neural network. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining (pp. 793-803).
 Chawla, N. V., & Davis, D. A. (2013). Bringing big data to personalized healthcare: a patient-centered framework. Journal of general internal medicine, 28, 660-665.
 Zhang, C., Song, D., Chen, Y., Feng, X., Lumezanu, C., Cheng, W., ... & Chawla, N. V. (2019, July). A deep neural network for unsupervised anomaly detection and diagnosis in multivariate time series data. In Proceedings of the AAAI conference on artificial intelligence (Vol. 33, No. 01, pp. 1409-1416).
 Yang, Yang, Nitesh V. Chawla, and Brian Uzzi. "A network’s gender composition and communication pattern predict women’s leadership success." Proceedings of the National Academy of Sciences 116.6 (2019): 2033–2038. https://doi.org/10.1073/pnas.1721438116

References 

Living people
Year of birth missing (living people)