2026
Under review at NeurIPS
Corrupted Plans, Clean Traces: Studying CoT Monitoring via Plan Injection
Keertana Chidambaram, Andrew Ilyas, Vasilis Syrgkanis
About
I am a fifth-year PhD student in Operations Research at the Department of Management Science and Engineering at Stanford University. I am broadly interested in improving the personalization, safety and alignment of generative AI systems. I am part of the Stanford Causal AI Lab, advised by Vasilis Syrgkanis. I also collaborate with Andrew Ilyas. My research is generously supported by the William R. and Sara Hart Kimball Stanford Graduate Fellowship.
I interned at Netflix Research in Summer 2026 and 2025, where I worked with Adith Swaminathan, Nathan Kallus, Myungha Jung and Qiuling Xu on topics in personalizing generative AI systems. Previously I received my master’s degree in Computational Social Science (Economics track) from the University of Chicago and my bachelor’s degree in Mechanical Engineering from the Indian Institute of Technology Madras.
News
- “Pigeonholing” accepted to EMNLP Findings 2026.
- Two papers on chain-of-thought monitoring submitted to NeurIPS, with Andrew Ilyas.
- New preprint from my Netflix internship on post-training generative recommenders is out.
- DPO under unobserved preference heterogeneity accepted to AISTATS 2026.
- Best Paper Runner-up & Oral at the LM4UC workshop, NAACL.
Research
* denotes equal contribution
2026
Under review at NeurIPS
Implementation Matters in Measuring Chain-of-Thought Monitorability
Matan Shtepel, Keertana Chidambaram, Vasilis Syrgkanis, Andrew Ilyas
2026
Under review at AAAI
Exponential Reward Weighting for Fine-Tuning Generative Recommenders under Sparse and Noisy Feedback
Keertana Chidambaram, Sanath Kumar Krishnamurthy, Qiuling Xu, Ko-Jen Hsiao, Moumita Bhattacharya
2026
EMNLP Findings
Pigeonholing: how bad prompts hurt models, causing collapse and mistakes
HyunJi Nam, Keertana Chidambaram, Dorottya Demszky, Natasha Jaques
2026
AISTATS
🏆 Best Paper Runner-up & Oral @ LM4UC Workshop, NAACL
Direct Preference Optimization With Unobserved Preference Heterogeneity
Keertana Chidambaram, Karthik Vinay Seetharaman, Vasilis Syrgkanis
2024
Workshop on Adaptive Foundation Models, NeurIPS
Personalized Adaptation via In-Context Preference Learning
Allison Lau, Younwoo (Ethan) Choi*, Vahid Balazadeh*, Keertana Chidambaram*, Rahul G Krishnan, Vasilis Syrgkanis
2024
NeurIPS
Workshop on AutoRL, ICML
Sequential Decision Making with Expert Demonstrations under Unobserved Heterogeneity
Vahid Balazadeh, Keertana Chidambaram, Viet Nguyen, Rahul G Krishnan, Vasilis Syrgkanis