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 150 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, Transportation, and E-Commerce.

News and Events @ KDD2022

• Co-chairing Deep Learning Day
• Organizing KDDCup Challenge on Improving Product Search
• Invited Keynote Talk at the Time-series Forecasting Workshop
• Paper Presentation on Graph-based Multilingual Language Model
• Tutorial Presentation on Hyperbolic Networks

Preprints on Arxiv.org

• StructCoder: Structure-Aware Transformer for Code Generation
• CodeAttack: Code-based Adversarial Attacks for Pre-Trained Programming Language Models
• XLCoST: A Benchmark Dataset for Cross-lingual Code Intelligence
• Towards Scalable Hyperbolic Neural Networks using Taylor Series Approximations
• Pseudo-Poincaré: A Unification Framework for Euclidean and Hyperbolic Graph Neural Networks
• Shopping Queries Dataset: A Large-Scale ESCI Benchmark for Improving Product Search
• Multi-Label Clinical Time-Series Generation via Conditional GAN
• Text Enriched Sparse Hyperbolic Graph Convolutional Networks

Selelcted Recent Publications [ Complete List ]  [ Google Scholar ]

• Charu Aggarwal and Chandan K Reddy, Data clustering: algorithms and applications, Chapman & Hall/CRC, 2014
• Graph-based Multilingual Language Model: Leveraging Product Relations for Search Relevance, KDD 2022
• Multilingual Code Snippets Training for Program Translation, AAAI 2022
• GraphZoo: A Development Toolkit for Graph Neural Networks with Hyperbolic Geometries, WWW 2022 (Demo)
• ANTHEM: Attentive Hyperbolic Entity Model for Product Search, WSDM 2022
• Attention-based Aspect Reasoning for Knowledge Base Question Answering on Clinical Notes, BCB 2022
• Self-supervised Short Text Modeling through Auxiliary Context Generation, TIST 2022
• Self-Supervised Transformer for Sparse and Irregularly Sampled Multivariate Clinical Time-Series, TKDD 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
• A Simple and Effective Self-Supervised Contrastive Learning Framework for Aspect Detection, AAAI 2021
• Self-Supervised Graph Learning with Hyperbolic Embedding for Temporal Health Event Prediction, IEEE Cybernetics 2021
• Corpus-level and Concept-based Explanations for Interpretable Document Classification, TKDD 2021
• Neural Abstractive Text Summarization with Sequence-to-Sequence Models, TDS 2021
• Differentially Private Synthetic Medical Data Generation using Convolutional GANs, Information Sciences 2021
• Question Answering with Long Multiple-Span Answers, EMNLP (findings) 2020
• Text-to-SQL Generation for Question Answering on Electronic Medical Records, WWW 2020
• LATTE: Latent Type Modeling for Biomedical Entity Linking, AAAI 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