Dr. Chamanzar has developed noninvasive, automated, and reliable diagnostic and monitoring methods for worsening brain injury and stroke. In the process, he has, developed novel AI techniques tailored to these problems, and performed rigorous statistical analyses on non-invasive neural sensing technologies such as electroencephalography (EEG). These works have appeared in journals such as Nature Communications Medicine and Nature Communications Biology, enabled by multidisciplinary collaborations with clinicians, scientists and engineers that he initiated and has led.
He is the PI of the NeuroVision Lab. The focus of NeuroVision Lab is to: (i) advance our understanding of how the brain processes visual information by combining cutting-edge neuroscience with computational modeling and behavioral and electrophysiological experiments, (ii) uncover the neural basis of visual disorders and develop innovative noninvasive diagnostic and monitoring solutions for patients with brain injuries.
- Postdoc - Carnegie Mellon University and Massachusetts General Hospital, 2024
- PhD - Carnegie Mellon University, 2022
- MS - Carnegie Mellon University, 2020
- MS - Sharif University of Technology, 2016
- BS - Sharif University of Technology, 2014
Education & Training
Alireza Chamanzar, Erez Freud, Pulkit Grover, and Marlene Behrmann. “Lesion-network mapping in task-dependent frequencies uncovers remote consequences of focal damage”. In: Imaging Neuroscience (2025).
Alireza Chamanzar, Jonathan Elmer, Lori Shutter, Jed Hartings, and Pulkit Grover. “Noninvasive and reliable automated detection of spreading depolarization in severe traumatic brain injury using scalp EEG”. In: Nature Communications Medicine 3.1 (2023), p. 113.
Morteza Zabihi, Alireza Chamanzar, Pulkit Grover, and Eric Rosenthal. “HyperEnsemble Learning from Multimodal Biosignals to Robustly Predict Functional Outcome after Cardiac Arrest”. In: Computing in cardiology (2023).
Han Yi Wang, Xujin Liu, Pulkit Grover, and Alireza Chamanzar. “A Spatial-Temporal Graph Attention Network for Automated Detection and Width Estimation of Cortical Spreading Depression Using Scalp EEG”. In: 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE. 2023, pp. 1–4.
Alireza Chamanzar, Marlene Behrmann, and Pulkit Grover. “Neural silences can be localized rapidly using non-invasive scalp EEG”. In: Nature Communications Biology 4.1 (2021), p. 429.
Alireza Chamanzar, Sarah M Haigh, Pulkit Grover, and Marlene Behrmann. “Abnormalities in cortical pattern of coherence in migraine detected using ultra high-density EEG”. In: Brain Communications 3.2 (2021), fcab061.
Alireza Chamanzar and Yao Nie. “Weakly supervised multi-task learning for cell detection and segmentation”. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI). IEEE. 2020, pp. 513–516.
Sarah M Haigh, Alireza Chamanzar, Pulkit Grover, and Marlene Behrmann. “Cortical hyper-excitability in migraine in response to chromatic patterns”. In: Headache: The Journal of Head and Face Pain 59.10 (2019), pp. 1773–1787.
Neural Engineering and Technology, Computational Neuroscience, Translational Neuroscience, Diseases & Disorders, Sensation & Perception, Computational, Mathematical & Statistical Methods, Non-Invasive Brain Monitoring