Yasaman Mohammadpour
About Me
I am a Ph.D. student in Data Science, Analytics, and Engineering at Arizona State University. My research interests include healthcare AI, clinical risk prediction, multimodal machine learning, deep learning, Natural Language Processing (NLP), and large language models (LLMs). I am particularly interested in developing reliable data-driven methods for analyzing complex biomedical, behavioral, physiological, and multimodal data.
Before beginning my Ph.D., I earned an M.Sc. in Psychology at Arizona State University with an interdisciplinary focus on data science and statistics. During my master's program, I conducted research on stress detection using wearable-device data, physiological signals, feature engineering, and deep neural networks under the supervision of Dr. Nicholas Duran and Dr. Nicole Roberts. I also worked as a research assistant in the SHaDE Lab under the supervision of Dr. Ariane Middel, where I contributed to research involving thermal comfort, urban heat, environmental sensing, physics-based modeling, and machine learning.
I hold a bachelor's degree in Statistics from the University of Tehran. During my undergraduate studies, my research focused on applications of artificial intelligence in ophthalmology, including medical image analysis, disease diagnosis, and ethical considerations surrounding AI-based healthcare systems.
If you have questions about my research, projects, or work in data science, machine learning, healthcare AI, or programming, please feel free to reach out.
Education
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Research
- Healthcare AI and clinical risk prediction
- Multimodal machine learning for biomedical and clinical data
- Natural language processing and large language models for healthcare
- Deep learning for physiological signals, medical imaging, and complex data
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Research
- Wearable-based stress detection using physiological and behavioral data
- Signal preprocessing, interpolation, noise reduction, and feature engineering
- Machine learning and deep neural networks for real-world stress prediction
- Natural language processing and LLM-based analysis of human communication
- Regression
- Biostatistics
- Artificial Intelligence
- Computational Statistics
- Multivariate Methods
- Healthcare Applications
Projects
Advisor: Dr. Malena Español | Summer 2026 – Present
Developing a full-array and hemiarray Electrical Impedance Tomography pipeline for biomedical conductivity imaging using finite element modeling and deep learning. Implemented and validated EIT data-generation and forward-modeling modules, including mesh and electrode setup, conductivity simulation, current injection patterns, and boundary voltage extraction.
Supervisor: Dr. Ariane Middel | Spring 2025 – Present
Conducting real-time thermal comfort research by integrating MaRTiny sensor data with airport weather data to estimate Mean Radiant Temperature using physics-based models. Applying machine learning interpolation to improve temperature-mapping accuracy and spatial coverage.
Supervisors: Dr. Bjoern Hagen and Dr. Ariane Middel | Spring 2025
Assessed the heat mitigation potential of various urban tree species on the Arizona State University Tempe campus. Conducted statistical analysis in R to evaluate how tree traits and ground-cover characteristics affect Mean Radiant Temperature reduction.
Advisor: Dr. Nicholas Duran | Co-Advisor: Dr. Nicole Roberts
Summer 2024 – Spring 2025
Designed and implemented a stress-detection system using wearable-device data and deep learning. The project focused on real-world data preprocessing and wrangling, interpolation, noise reduction, feature engineering, and the development of robust machine learning models.
Advisor: Dr. Nicholas Duran | Spring 2024
Assessed and updated ALIGN, a Python library for analyzing linguistic interactions, using large language models and transformer-based models, including GPT, RoBERTa, and LLaMA.
Advisor: Dr. Hedieh Sajedi | Fall 2021 – Spring 2022
Examined ethical considerations surrounding artificial intelligence in medicine and ophthalmology. Developed artificial intelligence systems for diagnosing anterior segment diseases.
Publications
Social, Cultural, and Behavioral Modeling (SBP-BRiMS 2025), Lecture Notes in Computer Science, Vol. 16127, pp. 56–65, Springer Nature Switzerland, 2025.
Springer Scholar
arXiv preprint, arXiv:2510.09945, 2025.
arXiv Scholar