2026 Nitesh V. Chawla: Computer Science Researcher – H-Index, Publications & Awards

Discipline name	D-Index	World Ranking	Current World Ranking	National Ranking	Current National Ranking	Publications	Citations
Computer Science	84	818	789	444	426	448	79064

Overview

Nitesh V. Chawla is affiliated with the University of Notre Dame in the United States. Their research primarily lies within the domain of Computer Science, with a notable focus on Artificial Intelligence, along with contributions in Statistical and Nonlinear Physics, Computer Vision and Pattern Recognition, Molecular Biology, and Information Systems.

Their main topics of work include:

Advanced Graph Neural Networks
Topic Modeling
Complex Network Analysis Techniques
Imbalanced Data Classification Techniques
Text and Document Classification Technologies
Recommender Systems and Techniques
Natural Language Processing Techniques

Recent papers authored or co-authored by Chawla reflect a range of interests within computational and machine learning fields. Selected works include:

"DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data" (2022), published in IEEE Transactions on Neural Networks and Learning Systems
"Few-Shot Knowledge Graph Completion" (2020), presented at the Proceedings of the AAAI Conference on Artificial Intelligence
"Graph Few-Shot Learning via Knowledge Transfer" (2020), presented at the Proceedings of the AAAI Conference on Artificial Intelligence
"On the use of real-world datasets for reaction yield prediction" (2023), published in Chemical Science
"Graph Barlow Twins: A self-supervised representation learning framework for graphs" (2022), published in Knowledge-Based Systems

Their frequent collaborators include researchers such as Chuxu Zhang, Nuno Moniz, Zhichun Guo, Xiangliang Zhang, and Meng Jiang.

Chawla's works have been published in venues with varying scopes and impact, notably:

arXiv (Cornell University)
Proceedings of the AAAI Conference on Artificial Intelligence
Machine Learning
Frontiers in Big Data
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence

Best Publications

SMOTE: synthetic minority over-sampling technique

Nitesh V. Chawla;Kevin W. Bowyer;Lawrence O. Hall;W. Philip Kegelmeyer

33152
Citations
SMOTE: Synthetic Minority Over-sampling Technique

N. V. Chawla;K. W. Bowyer;L. O. Hall;W. P. Kegelmeyer

9175
Citations
Editorial: special issue on learning from imbalanced data sets

Nitesh V. Chawla;Nathalie Japkowicz;Aleksander Kotcz

2785
Citations
SPECIAL ISSUE ON LEARNING FROM IMBALANCED DATA SETS

N Chawla;N Japkowicz;A Kolcz

2449
Citations
metapath2vec: Scalable Representation Learning for Heterogeneous Networks

Yuxiao Dong;Nitesh V. Chawla;Ananthram Swami

2253
Citations
SMOTEBoost: Improving Prediction of the Minority Class in Boosting

Nitesh V. Chawla;Aleksandar Lazarevic;Lawrence O. Hall;Kevin W. Bowyer

2075
Citations
SMOTE for learning from imbalanced data: progress and challenges, marking the 15-year anniversary

Alberto Fernández;Salvador García;Francisco Herrera;Nitesh V. Chawla

2018
Citations
Data Mining for Imbalanced Datasets: An Overview

Nitesh V. Chawla

1815
Citations
Heterogeneous Graph Neural Network

Chuxu Zhang;Dongjin Song;Chao Huang;Ananthram Swami

1341
Citations
SVMs Modeling for Highly Imbalanced Classification

Yuchun Tang;Yan-Qing Zhang;N.V. Chawla;S. Krasser

1189
Citations
A unifying view on dataset shift in classification

Jose G. Moreno-Torres;Troy Raeder;RocíO Alaiz-RodríGuez;Nitesh V. Chawla

1049
Citations
New perspectives and methods in link prediction

Ryan N. Lichtenwalter;Jake T. Lussier;Nitesh V. Chawla

876
Citations
A Deep Neural Network for Unsupervised Anomaly Detection and Diagnosis in Multivariate Time Series Data

Chuxu Zhang;Dongjin Song;Yuncong Chen;Xinyang Feng

827
Citations
Bringing Big Data to Personalized Healthcare: A Patient-Centered Framework

Nitesh V. Chawla;Darcy A. Davis

576
Citations
DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Data.

Damien Dablain;Bartosz Krawczyk;Nitesh V. Chawla

466
Citations
Inferring social status and rich club effects in enterprise communication networks.

Yuxiao Dong;Jie Tang;Nitesh V. Chawla;Tiancheng Lou

439
Citations
Learning from streaming data with concept drift and imbalance: an overview

T. Ryan Hoens;Robi Polikar;Nitesh V. Chawla

365
Citations
Learning Decision Trees for Unbalanced Data

David A. Cieslak;Nitesh V. Chawla

354
Citations
Combating imbalance in network intrusion datasets

D.A. Cieslak;N.V. Chawla;A. Striegel

353
Citations
Link Prediction and Recommendation across Heterogeneous Social Networks

Yuxiao Dong;Jie Tang;Sen Wu;Jilei Tian

330
Citations
Big Data Opportunities and Challenges: Discussions from Data Analytics Perspectives [Discussion Forum]

Zhi-Hua Zhou;Nitesh V. Chawla;Yaochu Jin;Graham J. Williams

315
Citations
Proceedings of the 2017 SIAM International Conference on Data Mining

Nitesh Chawla;Wei Wang

211
Citations
Discovering Knowledge in Data: An Introduction to Data Mining

Nitesh Chawla

37
Citations

Frequent Co-Authors

Yuxiao Dong Tsinghua University

Kevin W. Bowyer University of Notre Dame

Lawrence O. Hall University of South Florida

Omar Lizardo University of California, Los Angeles

Jie Tang Tsinghua University

Meng Jiang University of Notre Dame

Dong Wang Peking University

Chaoli Wang University of Notre Dame

Douglas Thain University of Notre Dame

Ananthram Swami United States Army Research Laboratory

External Links

Personal website of Nitesh V. Chawla Google Scholar page

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