Biography
Pegah Khosravi is an associate professor in the Institute for Artificial Intelligence (IAI) at the University of Central Florida, with a joint appointment in the Department of Computer Science and the Department of Clinical Sciences. Her research lies at the intersection of artificial intelligence and biomedicine, with a focus on ensemble learning, multimodal models, and explainable AI. She develops advanced AI frameworks for the analysis of complex biomedical data, including pathology, embryology, and radiology images, with the goal of improving disease detection, diagnosis, and treatment planning.
She completed postdoctoral training at the Institute for Research in Fundamental Sciences (IPM) in Tehran and at Weill Cornell Medicine in New York, followed by a research appointment at Memorial Sloan Kettering Cancer Center, where she advanced deep learning methods for multimodal analysis and computer vision applications in medical imaging. Prior to joining UCF, Khosravi was an assistant professor at the City University of New York (CUNY). She currently serves as a senior deputy editor for the Journal of Magnetic Resonance Imaging (JMRI) and contributes to the academic community through editorial leadership and participation in international program committees.
RESEARCH INTERESTS
• Artificial Intelligence and Machine Learning in Healthcare
• Deep Learning: Ensemble Methods, Multimodal Architectures, and Explainable AI
• Foundation Models and Multimodal Large Language Models for Medical Data
• Generative AI for Image Synthesis, Augmentation, and Representation Learning
• Computer Vision for Biomedical Imaging including Pathology, Embryology, and Radiology
• AI for Robotic and Real-Time Multimodal Imaging Systems
• Biomedical Data Fusion and Predictive Modeling for Precision Oncology
• Medical Image Analysis for Computer-Aided Diagnostics and Treatment Planning
• Responsible and Ethical AI in Clinical Applications
Education & Specialties
Educations and Experiences
Assistant Professor – City University of New York (CUNY), NY, USA
Senior Deputy Editor – Journal of Magnetic Resonance Imaging (JMRI), USA
Senior II Computational Biologist – Memorial Sloan Kettering Cancer Center, NY, USA
Postdoctoral Associate – Weill Cornell Medical College, NY, USA
Postdoctoral Research Fellow – Institute for Research in Fundamental Sciences (IPM), Tehran, Iran
Ph.D. in Bioinformatics – University of Tehran, Tehran, Iran
REPRESENTATIVE PUBLICATIONS
– Khosravi P., Fuchs, T., Joon Ho, D. Artificial Intelligence-Driven Cancer Diagnostics: Enhancing Radiology and Pathology through Reproducibility, Explainability, and Multi-Modality. Cancer Research (2025).
– Khosravi P., Saikali, S., Alipour, A., Mohammadi, S., Boger, M., Diallo, D.M., Smith, C., Moschovas, M.C., Hajirasouliha, I., Hung, A.J., Venkataraman, S.S., Patel, V., AutoRadAI: A Versatile Artificial Intelligence Framework Validated for Detecting Extracapsular Extension in Prostate Cancer. Biology Methods and Protocols (2025).
– Papaioannou, G., Mitrogiannis, C., Schweitzer, M., Michailidis, N., Pappa, M., Khosravi, P., Karantanas, A., Starling, S., Ruberg, C. Towards the Performance Characterization of a Robotic Multimodal Diagnostic Imaging System. Journal of Imaging, 11(5), 147 (2025).
– Khosravi P., Mohammadi, S., Zahiri, F., Khodarahmi, M., Zahiri, J. AI-Enhanced Detection of Clinically Relevant Structural and Functional Anomalies in MRI: Traversing the Landscape of Conventional to Explainable Approaches. Journal of Magnetic Resonance Imaging (2024).
– Khosravi P., and Schweitzer M. Artificial intelligence in neuroradiology: a scoping review of some ethical challenges. Frontiers in Radiobiology (2023).
– Barnes J., Brendel M., Gao V. R., Rajendran S., Kim J., Li Q., Malmsten J. E., Sierra J. T., Zisimopoulos P., Sigaras A., Khosravi P., Meseguer M., Zhan Q., Rosenwaks Z., Elemento O., Zaninovic N., Hajirasouliha I., A non-invasive artificial intelligence approach for the prediction of human blastocyst ploidy: a retrospective model development and validation study, The Lancet Digital Health, 5 (2023).
– Brendela M., Getseva V., Al Assaad M., Sigouros M., Sigaras A., Kane T., Khosravi P., Mosquer J. M., Elemento O., Hajirasouliha I., Weakly-supervised tumor purity prediction from frozen H&E stained slides, EBioMedicine, 80 (2022).
– Boehm K. M., Khosravi P., Vanguri R., Gao J., Shah P. S., Harnessing multimodal data integration to advance precision oncology, Nature Reviews Cancer (2021), 22: 114-126.
– Xu Z., Verma A., Naveed U., Bakhoum S.F., Khosravi P., Elemento O. Deep Learning Predicts Chromosomal Instability from Histopathology Images. iScience, 24(5), 102513 (2021).
– Khosravi P., Lysandrou M., Eljalby M., Brendel M., Li Q., Kazemi E., Zisimopoulos P., Sigaras A., Barnes J., Ricketts C., Meleshko D., Yat A., McClure T. D., Robinson B. D., Sboner A., Elemento O., Chughtai B., Hajirasouliha I., A Deep Learning Approach to Diagnostic Classification of Prostate Cancer Using Pathology-Radiology Fusion, Journal of Magnetic Resonance Imaging, 54 (2021).
– Khosravi P., Kazemi, Zhan Q., Toschi M., Malmsten J., Cooper L., Hickman C., Meseguer M., Rosenwaks Z., Elemento O., Hajirasouliha I., Deep Learning Enables Robust Assessment and Selection of Human Blastocysts after In-vitro Fertilization, npj Digital Medicine-Nature (2019), 4;2:21.
– Khosravi P., Kazemi E., Imielinski M., Elemento O., Hajirasouliha I., Deep Convolutional Neural Networks Enable Discrimination of Heterogeneous Digital Pathology Images, EBioMedicine (2018), 27: 317-328.
https://sites.google.com/view/aimindlab