🚀 I'll be joining NVIDIA Research to work on AI Security and Privacy.
After almost 2 years at JP Morgan’s Explainable AI CoE, I’ve decided to take up a new role at NVIDIA Research, where I’ll be focusing on AI Security and Privacy.
About Me
I am a Sr. Research Scientist at NVIDIA. My current research focuses on building secure and efficient agentic systems.
I obtained my PhD from Georgia Tech in 2022, where I was advised by Prof. Moinuddin K. Qureshi. In addition to my work on AI security and privacy, I have published in the areas of computer architecture, hardware security and AI accelerators.
I’ve also been fortunate to intern at several industrial research labs including FAIR and IBM Research during my PhD.
Please check my Google Scholar for an up-to-date list of publications.
Interests
- AI Security & Privacy
- Trustworthy AI
Education
-
PhD
Georgia Tech
-
MS
Georgia Tech
-
BE
Sri Jayachamarajendra College of Engineering
News
🎉 Our work "Progressive Inference- Explaining Decoder-Only Sequence Classification Models Using Intermediate Predictions" was accepted at ICML 2024!
A new XAI framework to explain decoder-only sequence classification models.
🎉 Our work "Information Flow Control in Machine Learning through Modular Model Architecture" was accepted at Usenix Security 2024!
Enabling secure machine learning for access controlled data.
🎉 Our work "SHAP@k- Efficient and Probably Approximately Correct (PAC) Identification of Top-K Features" was accepted for an oral presentation at AAAI 2024!
Efficiently identifying important features with PAC guarantees.
Recent Publications
(2026).
ReasoningBomb: A Stealthy Denial-of-Service Attack by Inducing Pathologically Long Reasoning in Large Reasoning Models.
ACM CCS 2026.
(2025).
Interpretable LLM-based Table Question Answering.
Transactions on Machine Learning Research (TMLR).
(2024).
Information flow control in machine learning through modular model architecture.
33rd USENIX Security Symposium (USENIX Security 24).
(2024).
Progressive Inference: Explaining Decoder-Only Sequence Classification Models Using Intermediate Predictions.
International Conference on Machine Learning.
(2024).
SHAP@ k: Efficient and Probably Approximately Correct (PAC) Identification of Top-k Features.
Proceedings of the AAAI Conference on Artificial Intelligence.
Experience
-
Sr. Research Scientist
NVIDIAAI Privacy and Security -
Sr. AI Research Associate
JP Morgan Chase, XAI CoEExplainable AI -
AI Research Intern
Meta, FAIRFederated Learning -
AI Research Intern
Meta AIUncertainty quantification, Conformal prediction -
AI Research Intern
Meta AISemi-supervised learning to improve conversion prediction -
AI Research Intern
IBM ResearchNoise resilient deep learning models for analog AI accelerators
Education
-
PhD
Georgia TechRead ThesisGPA: 4.0/4.0
Thesis: Understanding and Mitigating Privacy Vulnerabilities in Deep Learning. Supervised by Prof. Moinuddin Qureshi.
-
MS
Georgia TechGPA: 4.0/4.0 -
BE
Sri Jayachamarajendra College of EngineeringGPA: 3.78/4.0