4 citations · 5 across the 10 of their papers we have counts for
11 papers
When Measurement Constraints Favor Quantum Computational Sensing for Stealthy Power-Grid Attack Detection
Saisubramaniam Gopalakrishnan, Supreeth B S, Pranav Sarda +2
Power-grid defenses that rely on digital telemetry remain vulnerable to stealthy attacks that preserve plausible reported states while altering the underlying physical system. We s…
Stop Thinking, Start Looking: Efficient Post-Training for Multimodal Document Question Answering via Reasoning-Free Alignment
Harikrishnan P M, Goutham Vignesh, Ganesh Parab +4
Efficient multimodal document question answering with explicit visual grounding, locating the precise document region that supports each answer remains an open challenge. Current a…
Surrogate-Guided Quantum Discovery in Black-Box Landscapes with Latent-Quadratic Interaction Embedding Transformers
Saisubramaniam Gopalakrishnan, Dagnachew Birru
Discovering configurations that are both high-utility and structurally diverse under expensive black-box evaluation and strict query budgets remains a central challenge in data-dri…
Search-Based Risk Feature Discovery in Document Structure Spaces under a Constrained Budget
Saisubramaniam Gopalakrishnan, Harikrishnan P M, Dagnachew Birru
Enterprise-grade Intelligent Document Processing (IDP) systems support high-stakes workflows across finance, insurance, and healthcare. Early-phase system validation under limited…
Intelligent Human-Machine Partnership for Manufacturing: Enhancing Warehouse Planning through Simulation-Driven Knowledge Graphs and LLM Collaboration
Himabindu Thogaru, Saisubramaniam Gopalakrishnan, Zishan Ahmad +1
Manufacturing planners face complex operational challenges that require seamless collaboration between human expertise and intelligent systems to achieve optimal performance in mod…
Thinking in Many Modes: How Composite Reasoning Elevates Large Language Model Performance with Limited Data
Zishan Ahmad, Saisubramaniam Gopalakrishnan
Large Language Models (LLMs), despite their remarkable capabilities, rely on singular, pre-dominant reasoning paradigms, hindering their performance on intricate problems that dema…
Leveraging Knowledge Graphs and LLM Reasoning to Identify Operational Bottlenecks for Warehouse Planning Assistance
Rishi Parekh, Saisubramaniam Gopalakrishnan, Zishan Ahmad +1
Analyzing large, complex output datasets from Discrete Event Simulations (DES) of warehouse operations to identify bottlenecks and inefficiencies is a critical yet challenging task…
Leveraging Latent Evolutionary Optimization for Targeted Molecule Generation
Siddartha Reddy N, Sai Prakash MV, Varun V +2
Lead optimization is a pivotal task in the drug design phase within the drug discovery lifecycle. The primary objective is to refine the lead compound to meet specific molecular pr…
Minimizing Factual Inconsistency and Hallucination in Large Language Models
Muneeswaran I, Shreya Saxena, Siva Prasad +5
Large Language Models (LLMs) are widely used in critical fields such as healthcare, education, and finance due to their remarkable proficiency in various language-related tasks. Ho…
Synergistic Fusion of Graph and Transformer Features for Enhanced Molecular Property Prediction
M V Sai Prakash, Siddartha Reddy N, Ganesh Parab +3
Molecular property prediction is a critical task in computational drug discovery. While recent advances in Graph Neural Networks (GNNs) and Transformers have shown to be effective…
Knowledge Capture and Replay for Continual Learning
Saisubramaniam Gopalakrishnan, Pranshu Ranjan Singh, Haytham Fayek +2
Deep neural networks have shown promise in several domains, and the learned data (task) specific information is implicitly stored in the network parameters. Extraction and utilizat…