14 citations · 60 across the 41 of their papers we have counts for
45 papers
Can LLMs Reason Over Long Horizons? An Empirical Evaluation of Context Strategies for Longitudinal Clinical Reasoning
Taye Akinrele, Noorbakhsh Amiri Golilarz, Subash Neupane +2
Longitudinal clinical reasoning requires large language models (LLMs) to identify and integrate relevant evidence distributed across extended patient histories. Although long-conte…
Staying on the Attractor: Supervising Neural Surrogates of 3D Turbulence Where They Leave It
Yilong Dai, Shaswata Mitra, Raj Patel +7
Neural surrogates are trained to predict 3D turbulent flows in place of direct numerical simulation (DNS). For chaotic flows, the goal is short-term pointwise accuracy followed by…
Evaluating the NIST Bugs Framework Against CWE as a Successor for Automated Vulnerability Classification
Md Nazmul Hoque, Shaswata Mitra, Subash Neupane +2
Vulnerability classification based on root cause weaknesses is essential for numerous cybersecurity activities, where the Common Weakness Enumeration (CWE) serves as a public repos…
Gated Activation Steering for Reducing Sycophancy & Hallucination in Medical Question Answering
Himanshu Tripathi, Subash Neupane, Shaswata Mitra +3
Sycophancy and hallucination are persistent failure modes of Large Language Models (LLMs) across domains. However, it becomes particularly consequential in clinical question answer…
How Compliant is Sepsis Treatment? An Expert-Guided Neuro-symbolic Pipeline for Generating Clinical Compliance Insights
Himanshu Tripathi, Kaushik Roy, Subash Neupane +1
Verifying whether clinical care follows evidence-based protocols is a natural neuro-symbolic problem, yet the safety-critical setting defeats either paradigm alone. We present an e…
Find Before You Fine-Tune: A Diagnostic Study of Small LLMs for Cybersecurity QA
Shaswata Mitra, Subash Neupane, Trisha Chakraborty +4
Large Language Models (LLMs) are increasingly fine-tuned for critical-domain Question-Answering (QA), yet choosing which small model to adapt, before paying the cost of adaptation,…
The Value of Spike Timing: A Leakage-Resistant Benchmark of SNN Design Choices for Network Intrusion Detection
Raj Patel, Shaswata Mitra, David Amebley +4
Spiking neural networks (SNNs) are increasingly studied for network intrusion detection, but comparative evidence on how neuron models and spike encodings affect performance remain…
IRSDA: An Agent-Orchestrated Framework for Enterprise Intrusion Response
Damodar Panigrahi, Raj Patel, Shaswata Mitra +2
Modern enterprise systems face escalating cyber threats that are increasingly dynamic, distributed, and multi-stage in nature. Traditional intrusion detection and response systems…
LLMZ+: Contextual Prompt Whitelist Principles for Agentic LLMs
Tom Pawelek, Raj Patel, Charlotte Crowell +4
Compared to traditional models, agentic AI represents a highly valuable target for potential attackers as they possess privileged access to data sources and API tools, which are tr…
Estimating Reliability of Electric Vehicle Charging Ecosystem using the Principle of Maximum Entropy
Himanshu Tripathi, Subash Neupane, Shahram Rahimi +3
This paper addresses the critical challenge of estimating the reliability of an Electric Vehicle (EV) charging systems when facing risks such as overheating, unpredictable, weather…
FALCON: Transforming Cyber Threat Intelligence into Deployable IDS Rules with Self-Reflection
Shaswata Mitra, Subash Neupane, Martin Duclos +5
Signature-based Intrusion Detection Systems (IDS) detect malicious activity by matching network or host events against predefined rules. Security analysts manually develop these ru…
Patient Journey Ontology: Representing Medical Encounters for Enhanced Patient-Centric Applications
Hassan S. Al Khatib, Subash Neupane, Sudip Mittal +3
The healthcare industry is moving towards a patient-centric paradigm that requires advanced methods for managing and representing patient data. This paper presents a Patient Journe…
Towards Secure MLOps: Surveying Attacks, Mitigation Strategies, and Research Challenges
Raj Patel, Himanshu Tripathi, Jasper Stone +4
The rapid adoption of machine learning (ML) technologies has driven organizations across diverse sectors to seek efficient and reliable methods to accelerate model development-to-d…
Towards a HIPAA Compliant Agentic AI System in Healthcare
Subash Neupane, Sudip Mittal, Shahram Rahimi
Agentic AI systems powered by Large Language Models (LLMs) as their foundational reasoning engine, are transforming clinical workflows such as medical report generation and clinica…
Navigating MLOps: Insights into Maturity, Lifecycle, Tools, and Careers
Jasper Stone, Raj Patel, Farbod Ghiasi +2
The adoption of Machine Learning Operations (MLOps) enables automation and reliable model deployments across industries. However, differing MLOps lifecycle frameworks and maturity…
From Patient Consultations to Graphs: Leveraging LLMs for Patient Journey Knowledge Graph Construction
Hassan S. Al Khatib, Sudip Mittal, Shahram Rahimi +2
The transition towards patient-centric healthcare necessitates a comprehensive understanding of patient journeys, which encompass all healthcare experiences and interactions across…
RAAD-LLM: Adaptive Anomaly Detection Using LLMs and RAG Integration
Alicia Russell-Gilbert, Sudip Mittal, Shahram Rahimi +4
Anomaly detection in complex industrial environments poses unique challenges, particularly in contexts characterized by data sparsity and evolving operational conditions. Predictiv…
The AI Pentad, the CHARMED Model, and an Assessment of Current-State AI Regulation
Di Kevin Gao, Sudip Mittal, Jiming Wu +3
Artificial Intelligence (AI) has made remarkable progress in the past few years with AI-enabled applications beginning to permeate every aspect of our society. Despite the widespre…
CLINICSUM: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations
Subash Neupane, Himanshu Tripathi, Shaswata Mitra +4
This paper presents ClinicSum, a novel framework designed to automatically generate clinical summaries from patient-doctor conversations. It utilizes a two-module architecture: a r…
Multivariate Data Augmentation for Predictive Maintenance using Diffusion
Andrew Thompson, Alexander Sommers, Alicia Russell-Gilbert +7
Predictive maintenance has been used to optimize system repairs in the industrial, medical, and financial domains. This technique relies on the consistent ability to detect and pre…
Poison Attacks and Adversarial Prompts Against an Informed University Virtual Assistant
Ivan A. Fernandez, Subash Neupane, Sudip Mittal +1
Recent research has shown that large language models (LLMs) are particularly vulnerable to adversarial attacks. Since the release of ChatGPT, various industries are adopting LLM-ba…
AAD-LLM: Adaptive Anomaly Detection Using Large Language Models
Alicia Russell-Gilbert, Alexander Sommers, Andrew Thompson +7
For data-constrained, complex and dynamic industrial environments, there is a critical need for transferable and multimodal methodologies to enhance anomaly detection and therefore…
R-GAT: Cancer Document Classification Leveraging Graph-Based Residual Network for Scenarios with Limited Data
Elias Hossain, Tasfia Nuzhat, Shamsul Masum +2
Accurate classification of cancer-related biomedical abstracts is critical for advancing cancer informatics and supporting decision-making in healthcare research. Yet progress in t…
Transfer Learning Applied to Computer Vision Problems: Survey on Current Progress, Limitations, and Opportunities
Aaryan Panda, Damodar Panigrahi, Shaswata Mitra +2
The field of Computer Vision (CV) has faced challenges. Initially, it relied on handcrafted features and rule-based algorithms, resulting in limited accuracy. The introduction of m…