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Suparna Bhattacharya

4 accepted papers

2026

InfrastructureSentinel: Policy Enforced Guardrails for Secure MCP-driven Infrastructure Agents

Tarun Kumar, Aalap Tripathy, Gayathri Saranathan, Martin Foltin, Suparna Bhattacharya, Scott Hinchley +4
AAAI 2026technical

The proliferation of Model Context Protocol (MCP) servers in enterprise infrastructure management has revolutionized AI-driven automation while introducing critical multi-layered security vulnerabilities that traditional cybersecurity frameworks cannot adequately address. This paper presents a compr

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2026

Position: It is Time to Virtualize Foundation Models with a Self-evolving Operating System Layer

Suparna Bhattacharya, Tarun Kumar, Cong Xu, Satish Mopur, Jiahao Li, Ashish Mishra +4
ICML 2026poster

AI applications have shifted from single, mono-lithic foundation models (FM) to compound agentic systems. Yet today’s stacks remain fragmented: even as protocols (e.g., MCP, A2A) ease tool/agent connectivity, each framework embeds an implicit runtime for state, memory, budgets, and guardrails, makin…

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2025

SubLIME: Subset Selection via Rank Correlation Prediction for Data-Efficient LLM Evaluation

Gayathri Saranathan, Cong Xu, Mahammad Parwez Alam, Tarun Kumar, Martin Foltin, Soon Yee Wong +1
ACL 2025long

The rapid expansion of Large Language Models (LLMs) and natural language processing datasets has made exhaustive benchmark evaluations computationally prohibitive. Inspired by high-stakes competitions like the International Mathematical Olympiad-where a few well-chosen problems suffice to differenti…

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2021

TMCOSS: Thresholded Multi-Criteria Online Subset Selection for Data-Efficient Autonomous Driving

Soumi Das, Harikrishna Patibandla, Suparna Bhattacharya, Kshounis Bera, Niloy Ganguly, Sourangshu Bhattacharya
ICCV 2021poster

Training vision-based Autonomous driving models is a challenging problem with enormous practical implications. One of the main challenges is the requirement of storage and processing of vast volumes of (possibly redundant) driving video data. In this paper, we study the problem of data-efficient tra…

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