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 Machine Learning and Natural Language Processing with applications to Healthcare, Software, Transportation, and E-commerce. His research has been funded by NSF, NIH, DOE, DOT, and various industries. He has published over 175 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, ACM TIST, 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, Software, Transportation, and E-Commerce.

Selected Recent Publications [ Complete List ]  [ Google Scholar ]

• Complex Logical Reasoning over Knowledge Graphs using Large Language Models, Arxiv 2023
• Transformer-based Planning for Symbolic Regression, Arxiv 2023
• Execution-based Code Generation using Deep Reinforcement Learning, Arxiv 2023
• CodeAttack: Code-based Adversarial Attacks for Pre-Trained Programming Language Models, AAAI 2023
• Multilingual Code Snippets Training for Program Translation, AAAI 2022

• SeeGULL: A Stereotype Benchmark with Broad Geo-Cultural Coverage Leveraging Generative Models, Arxiv 2023
• Supervised Contrastive Learning for Interpretable Long-form Document Matching, TKDD 2023
• Self-supervised Short Text Modeling through Auxiliary Context Generation, TIST 2022
• 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
• Neural Abstractive Text Summarization with Sequence-to-Sequence Models, TDS 2021
• Question Answering with Long Multiple-Span Answers, EMNLP 2020
• LATTE: Latent Type Modeling for Biomedical Entity Linking, AAAI 2020
• Deep Reinforcement Learning for Sequence-to-Sequence Models, TNNLS 2020

• Graph-based Multi-ODE Neural Networks for Spatio-Temporal Traffic Forecasting, TMLR 2023
• A Unification Framework for Euclidean and Hyperbolic Graph Neural Networks, IJCAI 2023
• Graph-based Multilingual Language Model: Leveraging Product Relations for Search Relevance, KDD 2022
• Probabilistic Entity Representation Model for Reasoning over Knowledge Graphs, NeurIPS 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

• Self-Supervised Transformer for Sparse and Irregularly Sampled Multivariate Clinical Time-Series, TKDD 2022
• Differentially Private Synthetic Medical Data Generation using Convolutional GANs, Information Sciences 2022
• Collaborative Graph Learning with Auxiliary Text for Temporal Event Prediction in Healthcare, IJCAI 2021
• Text-to-SQL Generation for Question Answering on Electronic Medical Records, WWW 2020