Maryam Rahimimovassagh

I am a Ph.D. student at the University of Central Florida (UCF), advised by Dr. Niloofar Yousefi and Dr. Waldemar Karwowski. My research focuses on advancing bioinformatics through single-cell analysis, multi-omics integration, biomedical imaging, and transcriptomics. I am currently developing AI-driven computational frameworks that combine machine learning, graph-based modeling, and biological data fusion to uncover molecular mechanisms and improve data-driven biomedical discovery. My work aims to enable interpretable, biologically grounded, and robust computational approaches capable of linking cellular-level patterns with system-level behavior. I am also expanding my research into imaging transcriptomics and single-cell multimodal modeling, exploring how gene-expression signatures relate to neural activity, disease progression, and cognitive function. By integrating diverse biological datasets, I aim to create holistic computational models that bridge molecular, cellular, and systems-level understanding in biomedicine.

Recent News

Abstract Accepted — IISE Annual Conference & Expo , Arlington, Texas, 2026.
Conference Participation — Attended the NeurIPS 2025 Conference, San Diego, California, USA (2025)

Education

University of Central Florida (UCF), United States

Doctor of Philosophy (Ph.D.) in Industrial Engineering and Management Systems

Advisor: Dr. Niloofar Yousefi

My doctoral research focuses on developing foundation models, large language models (LLMs), and graph neural networks (GNNs) for analyzing the complexity of the human brain and neurological disorders. My work integrates single-cell analysis, multi-omics fusion, neuroimaging, and transcriptomics to uncover causal patterns underlying disorders such as ALS, epilepsy, and Alzheimer’s disease. I aim to build interpretable, data-efficient, and clinically aligned AI frameworks that advance precision neuroscience and biomedical discovery.

Aug 2025 – Present

Virginia Tech, United States

Master of Engineering (M.Eng.) in Industrial and Systems Engineering

Graduate coursework and research focused on artificial intelligence, human–machine systems, and computational modeling, contributing to research groups working on neurotechnology and intelligent systems engineering.

Aug 2021 – Feb 2024

Sharif University of Technology, Iran

Master of Science (M.S.) in Industrial and Systems Engineering

Thesis: “A Comparative Approach Between Deep Learning and MLE for Monitoring Multivariate Processes with Chaotic Trends”

Conducted research on statistical learning, deep neural architectures, and nonlinear process monitoring.

Sep 2015 – Jun 2018

University of Tehran, Iran

Bachelor of Engineering (B.Eng.) in Biosystems Engineering

Undergraduate concentration in biological systems, engineering analytics, and early exposure to signal processing and computational modeling.

Sep 2007 – Jun 2013

Research & Insights

I occasionally write about my ongoing research, methodologies, and experiments at the intersection of Large Language Models (LLMs), Computational Biology, and Biomedical AI. This section highlights key ideas and progress from my recent work.

Understanding the Brain Through Single-Cell and Multi-Omics AI

A deep dive into how interpretable AI models can integrate single-cell data, multi-omics profiles, and neuroimaging to uncover early biomarkers and mechanistic insights into neurological disorders.

Imaging Transcriptomics: Linking Genes to Brain Function

Exploring how imaging transcriptomics integrates gene-expression patterns with neuroimaging to reveal molecular drivers of cognition, disease progression, and early biomarkers in neurological disorders.

Werner, A., Islam, M. S., Nachiappan, A., Bafna, T., Movassagh, M., & Jeon, M. “See you on the other side: A crosswalk navigation system with multimodal alert system for distracted and visually impaired crosswalk users,” Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 67(1), 1894–1899, 2023. [PDF]
Rahimi Movassagh, M., Roofigari-Esfahan, N., Won Lee, S., Evia, C., Hicks, D., & Jeon, M. “Human factors considerations for teaming between construction workers and voice-based intelligent virtual agent (VIVA),” Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 65(1), 72–76, 2021. [PDF]
Keramat-Jahromi, M., Mohtasebi, S. S., Mousazadeh, H., Ghasemi-Varnamkhasti, M., & Rahimi-Movassagh, M. “Real-time moisture ratio study of drying date fruit chips based on on-line image attributes using kNN and random forest regression methods,” Measurement, vol. 172, 108899, 2021. [PDF]

For a complete list of my publications, visit my Google Scholar profile.

Conferences

During Ph.D. at University of Central Florida (2025–Present)

My doctoral journey at the University of Central Florida (UCF) began with attending the 7th Molecular Machine Learning (MoML) Conference held at the Massachusetts Institute of Technology (MIT), Cambridge, USA (2025). This prestigious event brought together global leaders in molecular AI and computational biology, where I learned about Large Language Models (LLMs) for antimicrobial resistance (AMR) prediction and drug discovery frameworks. It marked a defining milestone in my Ph.D. research on trustworthy, data-efficient biomedical AI systems.

Presenting my poster at the Virginia Tech's Symposium

Labmates and advisors at MoML 2025, MIT

With Professors and Lab Mates at NeurIPS

Contact

I’m always open to discussing research collaborations, conference invitations, or ideas in AI, Bioinformatics, and Computational Biology.

📧 maryam DOT rahimimovassagh AT ucf DOT edu

University of Central Florida (UCF) · Orlando, FL, USA