Dr. Alireza Chamanzar joined the Department of Ophthalmology after previously working at the University of Pittsburgh’s Computational Pathology and AI Center of Excellence (CPACE). He has worked in various technology positions in private industry.
Dr. Chamanzar analyzes the data gathered by the EEGs using data modeling techniques, refining his problem and solution statements as he gains more information. Dr. Chamanzar first began working with brain EEGs while studying brain computer interfaces in his Masters program at Carnegie Mellon University. In this program, he developed hardware that helped people in wheelchairs navigate more smoothly. He worked on both the algorithms and the hardware for these projects.
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.
Currently, his research focuses on using EEGs to monitor brain injuries in order to detect problems before they progress. The EEGs have the capability to monitor the entirety of the brain and track how injuries change over time. If damage is appearing in a new part of the brain, the data from the EEGs enables doctors to adapt their treatments accordingly.
Dr. Chamanzar piloted this approach in human patients with severe traumatic brain injury. He is now gathering data from three more sites to further test this approach’s impact and reliability. His test sites currently work with patients with traumatic brain injury, hemorrhage, and stroke. In the future, he also hopes to test this approach with patients with more mild injuries and concussions. He is also collaborating with faculty from the Pitt Department of Ophthalmology on treatments for stroke patients, working to identify the location of their injury.
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, Electrical and Computer Engineering, Carnegie Mellon University, 2022
- MS, Electrical and Computer Engineering, Carnegie Mellon University, 2020
- MS, Electrical Engineering, Sharif University of Technology, 2016
- BS, Electrical Engineering, 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.
- Neuroscience, brain waves
- Artificial intelligence’s applications in medicine
- Migraines
- Depression
- Silence localization
- EEG systems