Varun Chandrasekaran
Assistant Professor
Studies security and privacy of machine learning, including what private information can be inferred from models and the reliability of large language models.
Varun Chandrasekaran's lab at UIUC works on trustworthy & safe ML and large language models. 19 papers since 2023, 11 at top venues, mostly ICLR and ACL. Applications close .
At a glance
- papers, 2023–2026
- 5/ year
- at T1 venues
- 58%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom DBLP, 2023–2026
- 3 papers2026 · T1 1 · collecting
- 8 papers2025 · T1 5
- 7 papers2024 · T1 5
- 1 papers2023 · T1 0
T1 venuesOtherVenues: ICLR, ACL, Findings of ACL, ICML
Research focuspaper titles that use each topic's words
- Large language models9 papers
- Trustworthy & safe ML0 papers
- studies security0 papers
- reliability0 papers
- private0 papers
Paper list
From DBLP.
20263 papers
- Trans. Mach. Learn. Res.Efficiently Attacking Memorization Scores (opens in a new tab)
- ACL (T1)SACTOR: LLM-Driven Correct and Idiomatic C to Rust Translation with Static Analysis and FFI-Based Verification (opens in a new tab)
- Proc. Priv. Enhancing Technol.Understanding How University Guidelines Address Privacy and Security Issues of Generative AI in Academic Settings (opens in a new tab)
20258 papers
- ACL (T1)Attention Speaks Volumes: Localizing and Mitigating Bias in Language Models (opens in a new tab)
- ICLR (T1)Generative Monoculture in Large Language Models (opens in a new tab)
- J. Data-centric Mach. Learn. Res.MM-GEN: Principled and Generalizable Data Curation for Enhancing Task Performance in VLMs (opens in a new tab)
- ICML (T1)Not All Wrong is Bad: Using Adversarial Examples for Unlearning (opens in a new tab)
- HotNetsThe Privacy Quagmire: Where Computer Scientists and Lawyers May Disagree (opens in a new tab)
- ICLR (T1)Training Robust Ensembles Requires Rethinking Lipschitz Continuity (opens in a new tab)
- ICLR (T1)Unearthing Skill-level Insights for Understanding Trade-offs of Foundation Models (opens in a new tab)
- SaTMLVerifiable and Provably Secure Machine Unlearning (opens in a new tab)
20247 papers
- ICLR (T1)Attention Satisfies: A Constraint-Satisfaction Lens on Factual Errors of Language Models (opens in a new tab)
- ACL (T1)Bypassing LLM Watermarks with Color-Aware Substitutions (opens in a new tab)
- Findings of ACLDesigning Informative Metrics for Few-Shot Example Selection (opens in a new tab)
- IEEE Big DataHierarchical Federated Learning with Privacy (opens in a new tab)
- ICLR (T1)KITAB: Evaluating LLMs on Constraint Satisfaction for Information Retrieval (opens in a new tab)
- ICLR (T1)Privately Aligning Language Models with Reinforcement Learning (opens in a new tab)
- ICLR (T1)Teaching Language Models to Hallucinate Less with Synthetic Tasks (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
Apply to UIUCFall 2027
UIUC Siebel Computer Science PhD
- Round
- Single round
- Opens
- —
- GRE
- optional
- English
- TOEFL iBT / IELTS
- Fee
- USD 90
Not yet confirmed for this intake.
Similar labsnearest by research summary
- HKCUHK
Wing Cheong Lau
- Learning theory & optimization
- CNTsinghua
Hang Su
- Trustworthy & safe ML
- SGNTU
Yang Liu
- Trustworthy & safe ML
- USUW
Sewoong Oh
- Learning theory & optimization
- USCMU
Ruslan Salakhutdinov
- Reinforcement learning
- SGNTU
Tat-Jen Cham
- Generative & foundation models
Your notes
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Data
Some numbers could not be verified:
- The member list could not be found on the lab site.
- The alumni list could not be found on the lab site.
- Collected
- Verified
- Papers
- DBLP
- Members
- Not collected
- Alumni
- Not collected
Tiers are venue classifications, not lab rankings.
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