Chandan Reddy

About

Chandan Reddy is an Associate Professor in the Department of Gender, Women & Sexuality Studies at the University of Washington. He holds a B.A. in Literature from the University of California, an M.A. and Ph.D. in English/Comparative Literature from Columbia University. His fields of interest include Asian American studies, Critical Race Theory, Global Studies, Globalization Studies, Queer Studies, and Sexuality. Reddy's research encompasses topics such as queer kinships, Asian resident alien experiences, and subaltern politics, with recent works exploring militant motherhood in Kurdish resistance and queer migrations among P’urhépecha communities. He has taught courses on trans/gender queries, feminist social theory, and women’s studies, and has been actively involved in scholarly discussions and editorial roles, including serving as co-editor of GLQ. His contributions extend to advising research projects and engaging in public discussions on issues related to race, sexuality, and social justice.

Research topics

• Computer Science
• Artificial Intelligence
• Natural Language Processing
• Information Retrieval
• Programming language
• Linguistics

Selected publications

• H-STAR: LLM-driven Hybrid SQL-Text Adaptive Reasoning on Tables

  2025-01-01 · 2 citations

  articleOpen accessSenior author

  Nikhil Abhyankar, Vivek Gupta, Dan Roth, Chandan K. Reddy. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

  PublisherOA PDFDOI

• Multivariate Probabilistic Assessment of Speech Quality

  2025-08-17 · 1 citations

  article
  PublisherDOI

• SATA-BENCH: Select All That Apply Benchmark for Multiple Choice Questions

  ArXiv.org · 2025-05-31

  preprintOpen accessSenior author

  Large language models (LLMs) are increasingly evaluated on single-answer multiple-choice tasks, yet many real-world problems require identifying all correct answers from a set of options. This capability remains underexplored. We introduce SATA-BENCH, the first dedicated benchmark for evaluating LLMs on Select All That Apply (SATA) questions across diverse domains, including reading comprehension, law, and biomedicine. Our evaluation of 27 open-source and proprietary models reveals a significant…

  PublisherOA PDFDOI

• DriveMind: A Dual Visual Language Model-based Reinforcement Learning Framework for Autonomous Driving

  arXiv (Cornell University) · 2025-06-01

  preprintOpen access

  End-to-end autonomous driving systems map sensor data directly to control commands, but remain opaque, lack interpretability, and offer no formal safety guarantees. While recent vision-language-guided reinforcement learning (RL) methods introduce semantic feedback, they often rely on static prompts and fixed objectives, limiting adaptability to dynamic driving scenes. We present DriveMind, a unified semantic reward framework that integrates: (i) a contrastive Vision-Language Model (VLM) encoder…

  PublisherOA PDFDOI

• FalseReject: A Resource for Improving Contextual Safety and Mitigating Over-Refusals in LLMs via Structured Reasoning

  ArXiv.org · 2025-05-12

  preprintOpen accessSenior author

  Safety alignment approaches in large language models (LLMs) often lead to the over-refusal of benign queries, significantly diminishing their utility in sensitive scenarios. To address this challenge, we introduce FalseReject, a comprehensive resource containing 16k seemingly toxic queries accompanied by structured responses across 44 safety-related categories. We propose a graph-informed adversarial multi-agent interaction framework to generate diverse and complex prompts, while structuring res…

  PublisherOA PDFDOI

Recent grants

• III: Small: New Machine Learning Approaches for Modeling Time-to-Event Data

  NSF · $203k · 2016–2019

• Differential Network Interrogations of Epithelial to Mesenchymal Transition

  NIH · $160k · 2013–2015

• EAGER: Efficient Methods for Characterizing Large-Scale Network Dynamics

  NSF · $100k · 2012–2015

Frequent coauthors

• Ross Cutler

  Microsoft (United States)

  33 shared

• Issa Panahi

  The University of Texas at Dallas

  29 shared

• Andrea Tagarelli

  26 shared

• Ulrik Brandes

  ETH Zurich

  26 shared

• Moon Ho

  Columbia University

  25 shared

• Bill Fletcher

  New York University

  25 shared

• Martha Biondi

  Wayne State University

  25 shared

• Margaret Levi

  Stanford University

  25 shared

Labs

• Chandan Reddy LabPI

Education

• PhD, Computer Engineering

  Cornell University

  2007

Awards & honors

• 2023 Freedom Scholar by Marguerite Casey Foundation
• UW Simpson Center Awards (2022)

Similar researchers at Virginia Tech

• Priti Ramamurthyh-13
• Shirley J. Yeeh-13
• Amanda Lock Swarrh-11