Chandan Reddy

Short Bio

Chandan Reddy is a Professor in the Department of Computer Science at Virginia Tech. He received his Ph.D. from Cornell University and M.S. from Michigan State University. His primary research interests are Data Mining and Machine Learning with applications to Healthcare Analytics and Social Network Analysis. His research has been funded by NSF, NIH, DOE, DOT, and various industries. He has published over 130 peer-reviewed articles in leading conferences and journals. He received several awards for his research work including the Best Application Paper Award at ACM SIGKDD conference in 2010, Best Poster Award at IEEE VAST conference in 2014, Best Student Paper Award at IEEE ICDM conference in 2016, and was a finalist of the INFORMS Franz Edelman Award Competition in 2011. He is serving on the editorial boards of ACM TKDD and IEEE Big Data journals. He is a senior member of the IEEE and distinguished member of the ACM.

Research Interests

• Algorithms: Deep Learning, Data Analytics, Natural Language Processing, and Web Search.
• Applications: Healthcare, Cybersecurity, Transportation, and E-Commerce.

News and Events

• You can now follow our research work in a new Youtube channel.
• Workshop on Data-Efficient Machine Learning to be held (virtually) at the KDD Conference.
• One of our graduate students, Ping Wang, accepted a tenure-track assistant professor position in the CS dept. at Stevens Institute of Technology.
• Workshop on Data-Efficient Machine Learning for Web Applications to be held (virtually) at The Web Conference.

Selelcted Recent Publications [ Complete List ]  [ Google Scholar ]

• Collaborative Graph Learning with Auxiliary Text for Temporal Event Prediction in Healthcare, IJCAI 2021
• Interpretable Graph Similarity Computation via Differentiable Optimal Alignment of Node Embeddings, SIGIR 2021
• Self-Supervised Learning of Contextual Embeddings for Link Prediction in Heterogeneous Networks, WWW 2021
• Self-Supervised Hyperboloid Representations from Logical Queries over Knowledge Graphs, WWW 2021
• A Simple and Effective Self-Supervised Contrastive Learning Framework for Aspect Detection, AAAI 2021
• T-Miner: A Generative Approach to Defend Against Trojan Attacks on Deep Text Models, USENIX Security 2021
• Tensor-based Temporal Multi-Task Survival Analysis, TKDE 2021
• Neural Abstractive Text Summarization with Sequence-to-Sequence Models, TDS 2021
• Question Answering with Long Multiple-Span Answers, EMNLP (findings) 2020
• Text-to-SQL Generation for Question Answering on Electronic Medical Records, WWW 2020
• Efficient Implicit Unsupervised Text Hashing using Adversarial Autoencoder, WWW 2020
• Language-Agnostic Representation Learning for Product Search on E-Commerce Platforms, WSDM 2020
• LATTE: Latent Type Modeling for Biomedical Entity Linking, AAAI 2020
• Jekyll: Attacking Medical Image Diagnostics using Deep Generative Models, Euro S&P 2020
• Novel Probabilistic Topic Modeling for Comparative Analysis of Document Collections, TKDD 2020
• Deep Reinforcement Learning for Sequence-to-Sequence Models, TNNLS 2020
• Semi-Supervised Deep Learning Approach for Transportation Mode Identification using GPS Trajectory Data, TKDE 2020