2 citations · 3 across the 18 of their papers we have counts for
18 papers
Watermarks Without Verification: AI Text Watermarking After the EU AI Act
Alexander Nemecek, Vipin Chaudhary, Erman Ayday
On August 2, 2026, the obligations of Article 50 of the EU AI Act took effect, requiring generative AI providers to mark the content their systems produce and ensure it can be dete…
StochBench: A Domain-Specific Benchmark for Stochastic Processes in Lean
Idan Davidovich, Debargha Ganguly, Vikash Singh +1
Leading benchmarks for formal theorem proving with large language models are small collections drawn from competition math, such as the IMO and Putnam, that poorly represent field-…
Auditing Cross-Lingual Fairness in Language Model Watermarking
Alexander Nemecek, Osama Zafar, Debargha Ganguly +3
Watermarking schemes for large language model output are evaluated almost exclusively on English text using each scheme's detection threshold and a narrow set of quality measuremen…
Privacy Policy Enforcement Guardrails for Data-Sensitive Retrieval-Augmented Generation
Osama Zafar, Alexander Nemecek, Yiqian Zhang +5
Standard PII filters often miss contextual data leakage in RAG systems, such as non-regulated attribute clusters that collectively identify individuals. We introduce a Privacy Poli…
CausalGuard: Conformal Inference under Graph Uncertainty
Vikash Singh, Weicong Chen, Debargha Ganguly +12
Estimating treatment effects from observational data requires choosing an adjustment set, but valid adjustment depends on an unknown causal graph. Graph misspecification can cause…
InterQ: Communication-Aware Scheduling Across Modular QPUs with Classical and Quantum Links
Vinooth Kulkarni, Jaehyun Lee, Lauren Li +5
As quantum computing scales toward practical workloads, future systems are expected to move beyond single monolithic processors toward modular architectures that connect multiple Q…
Toward Guarantees for Clinical Reasoning in Vision Language Models via Formal Verification
Vikash Singh, Debargha Ganguly, Haotian Yu +5
Vision-language models (VLMs) show promise in drafting radiology reports, yet they frequently suffer from logical inconsistencies, generating diagnostic impressions unsupported by…
Robust Ultra-High-Dimensional Variable Selection With Correlated Structure Using Group Testing
Wanru Guo, Juan Xie, Binbin Wang +4
Background: High-dimensional genomic data exhibit strong group correlation structures that challenge conventional feature selection methods, which often assume feature independence…
Trust The Typical
Debargha Ganguly, Sreehari Sankar, Biyao Zhang +8
Current approaches to LLM safety fundamentally rely on a brittle cat-and-mouse game of identifying and blocking known threats via guardrails. We argue for a fresh approach: robust…
Context Determines Optimal Architecture in Materials Segmentation
Mingjian Lu, Pawan K. Tripathi, Mark Shteyn +4
Segmentation architectures are typically benchmarked on single imaging modalities, obscuring deployment-relevant performance variations: an architecture optimal for one modality ma…
Exploring Membership Inference Vulnerabilities in Clinical Large Language Models
Alexander Nemecek, Zebin Yun, Zahra Rahmani +4
As large language models (LLMs) become progressively more embedded in clinical decision-support, documentation, and patient-information systems, ensuring their privacy and trustwor…
Efficient Fine-Grained GPU Performance Modeling for Distributed Deep Learning of LLM
Biyao Zhang, Mingkai Zheng, Debargha Ganguly +4
Training Large Language Models(LLMs) is one of the most compute-intensive tasks in high-performance computing. Predicting end-to-end training time for multi-billion parameter model…
LABELING COPILOT: A Deep Research Agent for Automated Data Curation in Computer Vision
Debargha Ganguly, Sumit Kumar, Ishwar Balappanawar +6
Curating high-quality, domain-specific datasets is a major bottleneck for deploying robust vision systems, requiring complex trade-offs between data quality, diversity, and cost wh…
: Online Log Anomaly Detection Via Unsupervised Typicality Learning
Weicong Chen, Vikash Singh, Zahra Rahmani +3
Existing Log Anomaly Detection (LogAD) methods are often slow, dependent on error-prone parsing, and use unrealistic evaluation protocols. We introduce , an unsupervised and p…
Proof of Thought : Neurosymbolic Program Synthesis allows Robust and Interpretable Reasoning
Debargha Ganguly, Srinivasan Iyengar, Vipin Chaudhary +1
Large Language Models (LLMs) have revolutionized natural language processing, yet they struggle with inconsistent reasoning, particularly in novel domains and complex logical seque…
Forte : Finding Outliers with Representation Typicality Estimation
Debargha Ganguly, Warren Morningstar, Andrew Yu +1
Generative models can now produce photorealistic synthetic data which is virtually indistinguishable from the real data used to train it. This is a significant evolution over previ…
Visual Concept Networks: A Graph-Based Approach to Detecting Anomalous Data in Deep Neural Networks
Debargha Ganguly, Debayan Gupta, Vipin Chaudhary
Deep neural networks (DNNs), while increasingly deployed in many applications, struggle with robustness against anomalous and out-of-distribution (OOD) data. Current OOD benchmarks…
Privacy-Preserving Collaborative Genomic Research: A Real-Life Deployment and Vision
Zahra Rahmani, Nahal Shahini, Nadav Gat +7
The data revolution holds significant promise for the health sector. Vast amounts of data collected from individuals will be transformed into knowledge, AI models, predictive syste…