AI Researcher

Mohammad Mahdi
Azarbeik

Reinforcement Learning & Clinical Decision Support

Applying machine learning to critical care at Vienna University of Technology (TU Wien) and the Medical University of Vienna (MUW). My research bridges RL theory and clinical practice to improve patient outcomes in intensive care settings.

M. M. Azarbeik
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01 / About

Who I Am

I am a researcher working on machine learning with a focus on reinforcement learning, decision support in intensive care, and large language models. I hold an M.Sc. in Mechanical Engineering and currently pursue a Ph.D. in Computer Science (Informatics) at Vienna University of Technology (TU Wien).

My work applies RL-based methods to clinical data to help improve critical care outcomes, bridging the gap between algorithmic theory and real-world medical practice at the Medical University of Vienna (MUW).

Research Interests
  • 01
    Clinical AI & Decision Support Reinforcement Learning for Critical Care, Clinical Decision Support Systems, Off-Policy Evaluation.
  • 02
    Machine Learning Deep Reinforcement Learning, Generative AI, LLMs, Self-Supervised Learning.
  • 03
    Biomedical Data Physiological Time Series, Multi-Modal Clinical Data (EHR, Structured, Imaging), ICU Databases.
  • 04
    Robotics State Estimation, Data Fusion, Localization.
02 / Experience

Work & Teaching

Positions
Jul 2025 - Present
Researcher
Medical University of Vienna (MUW)
Feb 2025 - Apr 2026
Researcher
Ludwig Boltzmann Institute Digital Health and Patient Safety (LBI DHPS)
Oct 2023 - Jan 2025
University Assistant
Vienna University of Technology (TU Wien)
Teaching
  • →
    Reinforcement Learning
    TU Wien
  • →
    Generative AI
    TU Wien
  • →
    Data-oriented Programming Paradigms
    TU Wien
  • →
    Numerical Analysis
    K. N. Toosi University of Technology
03 / Publications

Research Output

05 2026
Learning and evaluating improved reinforcement learning-based policies for sepsis treatment on MIMIC-IV
M. M. Azarbeik et al.
Journal of Critical Care
DOI ↗ PDF
04 2025
Optimal timing for renal replacement therapy in critically ill patients using reinforcement learning algorithms
L. Kapral, M. M. Azarbeik et al.
Journal of Critical Care
DOI ↗ PDF
03 2024
TU Wien at SemEval-2024 Task 6: Unifying model-agnostic and model-aware techniques for hallucination detection
V. Arzt, M. M. Azarbeik et al.
SemEval-2024 Proceedings, ACL Anthology
DOI ↗ PDF
02 2023
Augmenting inertial motion capture with SLAM using EKF and SRUKF data fusion algorithms
M. M. Azarbeik et al.
Measurement, Elsevier
DOI ↗ PDF Visual
01 2022
An overview of the design experience and group analysis of a spinning ride from the perspective of engineering education
A. Meghdari et al.
Iranian Journal of Engineering Education
DOI ↗
04 / Education

Academic & Technical

Ph.D. Candidate
Computer Science
Vienna University of Technology (TU Wien)
Master of Science
Mechanical Engineering
Sharif University of Technology (SUT)
Bachelor of Science
Mechanical Engineering
Khaje Nasir Toosi University of Technology (KNTU)
Core Competencies
Programming
Python MATLAB SQL Linux (Ubuntu)
Clinical Data
MIMIC-IV ViennaAIdb ICU Time Series Preprocessing Physiological Signal Analysis EHR Feature Engineering
Deep Learning & ML
PyTorch scikit-learn
Robotics
Sensor & Data Fusion Kalman Filtering ROS Arduino IDE
Developer Tools & Misc
Git GCP PostgreSQL Academic Research Scientific Writing