Research
Areas I am actively exploring and hope to contribute to as a graduate student and future researcher.
Research Statement
My research focuses on the reliability and robustness of AI systems in clinical decision-making. My M.Sc. thesis asks whether tool-using clinical LLMs correctly change their decisions when a small but clinically decisive variable crosses a meaningful decision boundary — studied through a counterfactual benchmark in clinical toxicology. More broadly, I work at the intersection of Clinical AI, reliable and robust language models, AI-enabled software systems, and the distributed and MLOps infrastructure that production AI depends on.
Current Research
Decision-Boundary Robustness in Tool-Using Clinical LLMs: A Counterfactual Benchmark in Clinical Toxicology
Islamic Azad University
Developing a counterfactual benchmark to evaluate whether tool-using clinical LLM agents recognize meaningful decision boundaries and appropriately change decisions when clinically important variables change.
The work develops a structured counterfactual benchmark based on minimal case pairs in clinical toxicology. Each pair keeps the clinical context fixed while changing a single decision-relevant variable, allowing controlled evaluation of boundary recognition, unsafe use of invalid outputs, triage behavior, and correct next-step routing.
The study also evaluates runtime guards and structured decision reporting to better understand when LLM-based agents fail safely, fail silently, or correctly redirect the clinical workflow.
Research highlights
- Clinical LLM evaluation
- Counterfactual benchmarks
- Tool-using AI agents
- LLM robustness
- Runtime safety guards
- Structured evaluation
Supervision
- SupervisorDr. Yahya ForghaniFaculty Member · Department of Computer Engineering, Islamic Azad University, Mashhad
AdvisorDr. Omid Mehrpour, MD, FACMTMedical Toxicologist · Founder, Medical Toxicology LLC (Arizona, USA)
Conference Contributions
Clinical systems I engineered that were presented at scientific conferences, with my exact role and the official sources.
- NACCT 2026 · Conference Poster · Engineering ContributionA Novel Web-Based Decision Support System for Acetaminophen Toxicity ManagementOctober 23, 2026 · My role: Lead Software Engineer & System Architect (ApapTox)
- NACCT 2026 · Oral Presentation · Featured TechnologyMedSpeech: An AI-Enabled Voice-to-Text Platform for Poison-Center DocumentationOctober 25, 2026 · My role: Lead Software Engineer & System Architect (ToxLumen)
Research Interests
Clinical AI & Decision Support
AI systems that support or evaluate clinical decision-making, with an emphasis on safety, validity, and clinically meaningful behavior.
Reliable & Robust Large Language Models
Evaluation of LLM reliability, robustness, decision-boundary behavior, counterfactual sensitivity, and safe use of tool-augmented language models.
AI-Enabled Software Systems
Design and engineering of production software systems that integrate machine learning and generative AI into real-world workflows.
Distributed Systems, MLOps & Cloud Infrastructure
Reliable backend, distributed, and deployment infrastructure for AI applications, including reproducibility, asynchronous processing, containerization, and production ML systems.
Background & Context
Industry Experience
My research is grounded in production work: I lead backend and AI systems development for a medical toxicology team and own ToxLumen, an AI-enabled clinical toxicology platform used by clinical toxicologists — which is where the thesis question comes from. Distributed-system failures and LLM API orchestration in production shape the systems side.
Academic Preparation
Undergraduate coursework in AI fundamentals (19.25/20), research methods (19/20), applied linear algebra (18.85/20), and software engineering, followed by graduate courses in Machine Learning and Deep Learning (17/20), provides the foundation for AI and systems research.
Current Trajectory
Pursuing an M.Sc. in Artificial Intelligence at Islamic Azad University with a thesis on decision-boundary robustness in tool-using clinical LLMs, while serving as Teaching Assistant for the Machine Learning course.
Long-Term Goal
Seeking a Ph.D. position in a group working on trustworthy AI agents, clinical AI evaluation, or reliable intelligent systems at scale.