Soheil Saghafi
Postdoctoral Research Fellow · Department of Biomedical Informatics, Emory University
About Me
I am a Postdoctoral Research Fellow in the Department of Biomedical Informatics at Emory University, where I develop mathematical, machine learning, and artificial intelligence methods for analyzing complex biomedical data. My research spans physiological time series, medical imaging, video, wearable sensing, and electronic health records, with the goal of transforming real-world clinical data into reliable, actionable insights.
My work combines applied mathematics, signal processing, statistical modeling, and deep learning to build robust computational systems that operate effectively on noisy, heterogeneous healthcare data. I am particularly interested in designing end-to-end pipelines—from data preprocessing and feature engineering to predictive modeling and deployment—that can be integrated into clinical workflows and translated into practical healthcare solutions.
I work closely with clinicians, researchers, engineers, and industry collaborators to develop scalable technologies that bridge the gap between methodological research and real-world clinical practice.
I received my Ph.D. in Mathematical Sciences from the New Jersey Institute of Technology (NJIT) in 2023, specializing in Applied Mathematics with a research focus on computational biology and computational neuroscience. My doctoral dissertation, Inferring Parameters of Pyramidal Neuron Excitability in Mouse Models of Alzheimer's Disease Using Biophysical Modeling and Deep Learning, developed mathematical and deep learning approaches for estimating neuronal biophysical parameters from electrophysiological data. Prior to my Ph.D., I earned both my Bachelor's and Master's degrees in Applied Mathematics.
Through my research, I aim to develop computational methods that are not only scientifically rigorous but also clinically meaningful, enabling data-driven healthcare solutions that improve patient care and support evidence-based decision making.
Current Research Interests