Gagandeep Singh
Assistant Professor
Studies trustworthy and efficient AI, including formal verification of neural networks and systems for machine learning.
Gagandeep Singh's lab at UIUC works on trustworthy & safe ML, efficient training & inference and compilers & programming languages. 63 papers since 2023, 31 at top venues, mostly ICLR and TMLR. 9 PhD students and 2 master's students. 14 alumni, most often to Google and Google DeepMind. Applications close .
At a glance
- papers, 2023–2026
- 16/ year
- at T1 venues
- 49%
- PhD students
- 9
- MS students
- 2
- alumni listed
- 14
Papers by yearfrom the lab homepage, 2023–2026
- 21 papers2026 · T1 9 · collecting
- 17 papers2025 · T1 8
- 18 papers2024 · T1 10
- 7 papers2023 · T1 4
T1 venuesOtherVenues: ICLR, TMLR, NeurIPS, PLDI, AISTATS
Research focuspaper titles that use each topic's words
- Efficient training & inference10 papers
- Trustworthy & safe ML2 papers
- Compilers & programming languages0 papers
- formal verification0 papers
- studies trustworthy0 papers
Paper list
From the lab homepage.
202621 papers
- ISSTAARQ: A Mixed-Precision Quantization Framework for Accurate and Certifiably Robust DNNs
- VerifAI@ICLRBEAVER: An Efficient Deterministic LLM Verifier
- AISTATS (T1)Certifying Reading Comprehension in Large Language Models
- AISTATS (T1)Corruption Robust Thompson Sampling for Gaussian Bandits
- TMLR (T1)Data Shifts Hurt CoT: A Theoretical Study
- ESOPEfficient Ranking Function-Based Termination Analysis via Bidirectional Decompositional Search
- VerifAI@ICLREnforcing Temporal Constraints for LLM Agents
- PLDI (T1)Evolving Abstract Transformers for Gradient-Guided, Adaptable Abstract Interpretation
- ICLR (T1)How Catastrophic is Your LLM? Certifying Risk in Conversation
- COLM , also at DL4C@NeurIPSImproving Assembly Code Performance with Large Language Models via Reinforcement Learning
- AI4GOOD@ICMLMatching Ranks Over Probability Yields Truly Deep Safety Alignment
- Mech Interp@ICMLMechanistic Interpretability of Adversarial Suffixes Reveals Non-Robust Shortcuts of Safety Monitors
202517 papers
- IEEE ETHICSA Conceptual Framework for Guiding the Workforce on Using Responsible AI
- OOPSLAAutomated Verification of Soundness of DNN Certifiers
- Sasa Misailovic COLM , also at TokShop@ICMLByte-level Tokenizers Unavoidably Enable LLMs to Generate Ill-formed UTF-8
- ICLR (T1)Certifying Counterfactual Bias in LLMs
- ICML also at VerifAI@ICLRCRANE: Expressive Grammar-Constrained LLM Generation
- NeurIPS (T1)DINGO: Constrained Inference for Diffusion LLMs
- ICLR (T1)IterGen: Iterative Semantic-aware Structured LLM Generation with Backtracking
- ARLET@NeurIPSLearning a Pessimistic Reward Model in RLHF
- VerifAI@ICLRNeural Abstract Interpretation
- TAIG@ICMLPosition: Formal Methods are the Principled Foundation of Safe AI
- Foundations and Trends in Programming LanguagesSafety and Trust in Artificial Intelligence with Abstract Interpretation
- ML4Wireless@ICML , ICSE PostersSpecification Generation for Neural Networks in Systems
202418 papers
- LAFI@POPLAbstract Interpretation for Automatic Differentiation
- Tiny Papers@ICLRBypassing the Safety Training of Open-Source LLMs with Priming Attacks
- MLSys (T1)COMET: Neural Cost Model Explanation Framework
- SASConstraintFlow: A DSL for Specification and Verification of Neural Network Analyses
- ECCV (T1)Cross-Input Certified Training for Universal Perturbations
- Tiny Papers@ICLRExploiting Time Channel Vulnerability of Learned Bloom Filters
- ICLR (T1)FedCompass: Efficient Cross-Silo Federated Learning on Heterogeneous Client Devices Using a Computing Power-Aware Scheduler
- ICLR (T1)Incremental Randomized Smoothing Certification
- PLDI (T1)Input Relational Verification of Deep Neural Networks
- ICLR (T1)Interpreting Robustness Proofs of Deep Neural Networks
- Tiny Papers@ICLRIs Watermarking LLM-Generated Code Robust?
- NeurIPS Datasets and Benchmarks TrackNN4SysBench: Characterizing Neural Network Verification for Computer Systems
20237 papers
- SAS (Invited Abstract)Building Trust and Safety in Artificial Intelligence with Abstract Interpretation
- TMLR (T1)Efficient Reward Poisoning Attacks on Online Deep Reinforcement Learning
- NSDI (T1)Exploring Practical Vulnerabilities of Machine Learning-based Wireless Systems
- PLDI (T1)Incremental Verification of Neural Networks
- ICLR (T1)Provable Defense Against Geometric Transformations
- OOPSLASynthesizing Precise Static Analyzers for Automatic Differentiation
- WFVML@ICMLToward Continuous Verification of DNNs
Where alumni went14 listed, 36% to industry
Industry 4Academia 7
Most often to
- Google DeepMind
- Meta
Also to
- MIT
- Stanford University
- UIUC
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
- CNZhejiang
Congfu Xu
- Recommendation, search & web
- GBUCL
Shi Zhou
- Graph mining & networks
- CNSJTU
Xinbing Wang
- Graph mining & networks
- USNYU
Foster Provost
- Recommendation, search & web
- CNSJTU
Guihai Chen
- Data management & big data
- SGNTU
Wee Peng Tay
- Signal processing & information theory
Your notes
Save this lab to keep notes.
Data
Checked against the lab homepage.
- Collected
- Verified
- Papers
- The lab homepage
- Members
- Lab homepage
- Alumni
- Lab homepage
Tiers are venue classifications, not lab rankings.
UIUCcloses