26 citations · 36 across the 9 of their papers we have counts for
13 papers
Adversarially Robust Geometric Safety Certificates for Nonholonomic Robots Against Maneuvering Obstacles
Chandan Kumar Sah, Bazeela Banday, Jishnu Keshavan
Safe navigation against obstacles that can actively maneuver within bounded capabilities remains challenging: robust control barrier function methods typically treat obstacle actio…
Exact Feasibility Certification and Optimal Responsibility Allocation for Multi-Robot CBF Safety Filters
Chandan Kumar Sah, Jishnu Keshavan
Multi-robot Control Barrier Function (CBF) safety filters can become infeasible, but a failed quadratic program (QP) does not indicate why the conflict occurred or how to resolve i…
Beyond Self-Knowledge: Propagating Uncertainty Across Reasoning and Retrieval in LLMs
Chandan Kumar Sah, Li Zhang, Xiaoli Lian
Retrieval-augmented generation improves knowledge-intensive question answering, but indiscriminate retrieval can introduce irrelevant evidence and unnecessary computation. We inves…
Beyond "What to Retrieve": Uncertainty in Retrieval-Augmented Code Generation
Chandan Kumar Sah, Li Zhang, Xiaoli Lian
Repository-level code generation relies on heterogeneous evidence whose relevance, compatibility, and completeness are inherently uncertain. Similar-code examples, repository conte…
SACK : Safe Active Continual Koopman Learning for Uncertain Systems with Contractive Guarantees
Chandan Kumar Sah, Rajpal Singh, Jishnu Keshavan
Koopman operator theory provides a powerful framework for representing nonlinear dynamics through a linear operator acting on lifted observables, enabling the use of linear control…
Uncertainty and Fairness Awareness in LLM-Based Recommendation Systems
Chandan Kumar Sah, Xiaoli Lian, Li Zhang +2
Large language models (LLMs) enable powerful zero-shot recommendations by leveraging broad contextual knowledge, yet predictive uncertainty and embedded biases threaten reliability…
PerFairX: Is There a Balance Between Fairness and Personality in Large Language Model Recommendations?
Chandan Kumar Sah
The integration of Large Language Models (LLMs) into recommender systems has enabled zero-shot, personality-based personalization through prompt-based interactions, offering a new…
CleanMAP: Distilling Multimodal LLMs for Confidence-Driven Crowdsourced HD Map Updates
Ankit Kumar Shaw, Kun Jiang, Tuopu Wen +5
The rapid growth of intelligent connected vehicles (ICVs) and integrated vehicle-road-cloud systems has increased the demand for accurate, real-time HD map updates. However, ensuri…
FairEval: Evaluating Fairness in LLM-Based Recommendations with Personality Awareness
Chandan Kumar Sah, Xiaoli Lian, Tony Xu +1
Recent advances in Large Language Models (LLMs) have enabled their application to recommender systems (RecLLMs), yet concerns remain regarding fairness across demographic and psych…
Advancing Autonomous Vehicle Intelligence: Deep Learning and Multimodal LLM for Traffic Sign Recognition and Robust Lane Detection
Chandan Kumar Sah, Ankit Kumar Shaw, Xiaoli Lian +5
Autonomous vehicles (AVs) require reliable traffic sign recognition and robust lane detection capabilities to ensure safe navigation in complex and dynamic environments. This paper…
RUL forecasting for wind turbine predictive maintenance based on deep learning
Syed Shazaib Shah, Tan Daoliang, Sah Chandan Kumar
Predictive maintenance (PdM) is increasingly pursued to reduce wind farm operation and maintenance costs by accurately predicting the remaining useful life (RUL) and strategically…
Adaptive Koopman Embedding for Robust Control of Complex Nonlinear Dynamical Systems
Rajpal Singh, Chandan Kumar Sah, Jishnu Keshavan
The discovery of linear embedding is the key to the synthesis of linear control techniques for nonlinear systems. In recent years, while Koopman operator theory has become a promin…
Unveiling Bias in Fairness Evaluations of Large Language Models: A Critical Literature Review of Music and Movie Recommendation Systems
Chandan Kumar Sah, Lian Xiaoli, Muhammad Mirajul Islam
The rise of generative artificial intelligence, particularly Large Language Models (LLMs), has intensified the imperative to scrutinize fairness alongside accuracy. Recent studies…