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Title: TheraMind 2.0: A Multi-LLM Ensemble System for Accelerating Drug Repurposing in Non-Small Cell Lung Cancer

Abstract: Published clinical case reports, review articles, and clinical trials represent valuable yet underutilized sources of evidence for drug repurposing in cancer therapy. In this talk, I will present TheraMind 2.0, an AI-powered system I developed that leverages multiple large language models to automate the identification and analysis of relevant literature supporting potential drug repurposing for non-small cell lung cancer (NSCLC). My enhanced pipeline integrates three parallel data streams, research articles, clinical trials, and case reports, scraped from PubMed and ClinicalTrials.gov. Using coordinated data extraction and standardized prompts, I deployed an ensemble of GPT-4o-mini, Gemini-2.0-Flash, and Mixtral models in three complementary classification strategies: rule-based decision trees, individual model validators, and majority-vote consensus. TheraMind 2.0 also produces structured outputs including patient demographics, therapeutic responses, and concise summaries with complete audit trails. This scalable, transparent framework bridges the gap between computational drug discovery and clinical adoption by transforming unstructured biomedical literature into actionable, clinician-ready evidence for therapeutic decision-making.

Bio: Vrushket is a graduate student in the School of Computing at °®¶¹´«Ã½'s Watson College of Engineering & Applied Science. His research focuses on applying artificial intelligence and large language models to biomedical text mining and drug repurposing, particularly for cancer therapy. Working under Dr. Nancy L. Guo, they have developed TheraMind, an innovative multi-LLM ensemble system for mining clinical evidence from published case reports, review articles, and clinical trials. Vrushket’s work aims to accelerate the translation of computational drug discovery findings into clinical practice by providing oncologists with structured, evidence-based therapeutic options for patients with limited treatment alternatives.

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Title: Autonomous Culvert Inspection Using Legged Robots

Abstract: We are working on culvert inspection in the Erie canal using legged robots. As seen in this , such inspection presents several challenges including stable navigation of legged robots, inspection in low-light conditions, accurate fault detection andÌý detailed visual reconstruction for future inspections. In this talk, I’ll describe our approach for each of these problems along with implications for general robot autonomy. CaRT is a context-aware adaptation filter that uses temporal sequence sampling to improve stability in legged locomotion. NightHawk jointly optimizes external light and camera parameters for optimal image capture in low-light conditions. VISION is a system that uses vision-language models (VLMs) with best-view planning to perform fault detection in culverts in a zero shot manner. And EXPLORE is our adaptation of Gaussian Splatting for active 3D reconstruction of the culvert for both visual and structural reasoning.Ìý I’ll also briefly highlight our other project on .

Bio: I am an Associate Professor in Computer Science and Engineering at University at Buffalo, State University of New York (UB). I received my Ph.D. in Computer Science from University of Southern California in 2010 and was a Postdoctoral Fellow in the EECS Department at Harvard University from 2010-13. My research spans the areas of mobile systems and robotics. Most recently, my group works on field robotics in the areas of infrastructure inspection and autonomous excavation. My research is supported by several grants from National Science Foundation, DARPA, AFOSR, AFRL, ONR and others including the NSF Faculty Early Career Award which I received in 2019. For my research, I have received the IEEE Region 1 award for technological innovation (academic) in 2022, elevated to IEEE Senior Member in 2023. I am the founding director of the Center for Embodied Autonomy and Robotics, a university-wide Center that brings together research, entrepreneurship, and outreach in robotics. I also co-direct the Master’s program in Robotics.

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