Dr. Alex Fedorov is an Assistant Research Professor at the Center for Data Science within the Nell Hodgson Woodruff School of Nursing at Emory University. He earned his Ph.D. in Electrical and Computer Engineering from the Georgia Institute of Technology in December 2023, specializing in Digital Signal Processing and Bioengineering. Dr. Fedorov holds a Master of Science in Electrical and Computer Engineering from the University of New Mexico and a Master’s in Applied Mathematics from the National Research University of Electronic Technology. Throughout his academic pursuits, Dr. Fedorov has interned at leading technology companies such as Meta, Descript, Microsoft Research, and the academic lab Mila - Quebec AI Institute.

Fedorov, Alex, Eloy Geenjaar, Lei Wu, Tristan Sylvain, Thomas P. DeRamus, Margaux Luck, Maria Misiura et al. "Self-supervised multimodal learning for group inferences from MRI data: Discovering disorder-relevant brain regions and multimodal links." NeuroImage 285 (2024): 120485.

Hjelm, R. Devon, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, and Yoshua Bengio. "Learning deep representations by mutual information estimation and maximization." In International Conference on Learning Representations (ICLR), 2019.

Fedorov, Alex, Jeremy Johnson, Eswar Damaraju, Alexei Ozerin, Vince Calhoun, and Sergey Plis. "End-to-end learning of brain tissue segmentation from imperfect labeling." In 2017 International Joint Conference on Neural Networks (IJCNN), pp. 3785-3792. IEEE, 2017.

Dr. Fedorov teaches Foundations of Computation for Nursing Research course as part of the PhD in Nursing and Artificial Intelligence (AI) track. His teaching focuses on equipping students with computational skills critical for nursing research, with an emphasis on leveraging AI technologies. Dr. Fedorov’s goal is to prepare future nurse scientists to integrate advanced computational methods and AI into healthcare research, empowering them to contribute to the technological evolution of the field.

Dr. Alex Fedorov’s research focuses on developing trustworthy medical AI systems using diverse healthcare data. He designs semi-supervised and self-supervised multimodal deep learning algorithms that adapt to new datasets, enabling their application across various clinical settings. Dr. Fedorov builds models that provide deeper insights into patient health and treatment outcomes by integrating data from Electronic Health Records, medical imaging, and physiological time series. His work aims to transform healthcare delivery through innovative, data-driven approaches and interdisciplinary collaboration.

Outstanding Reviewer at ICML 2022 (Top 10% of reviewers)

IEEE ICHI 2021: Best Student Paper Award in Analytics Track

NeurIPS ML4H Workshop 2019: Travel Grant Award

IEEE IJCNN 2017: Intel Travel Grant