Office of Fellowship Training (OFT)
The mission of the Office of Fellowship Training is:
- To support and promote a productive and fulfilling research training experience in the NIMH Intramural Research Program
- To encourage career planning and guide career management through trainee use of Individual Development Plans (IDPs)
- To provide programs and services to assist trainees in discovering and clarifying career choices
- To provide opportunities and to encourage trainees to build a professional skill set which enables them to become world leaders in academic and non-academic careers
Come visit our booth and speak with an OFT staff member about the fellowship and training opportunities we offer at the NIH/NIMH. We will be at the following scientific meetings: Annual Biomedical Research Conference for Minority Students (ABRCMS), The American Society for Pharmacology and Experimental Therapeutics (ASPET), Society for Advancement of Chicanos/Hispanics and Native Americans in Science (SACNAS), Society of Biological Psychiatry (SOBP) and Society for Neuroscience (SfN).
Trainee Successes: Past & Present
Maryam Vaziri-Pashkam, M.D., Ph.D. is a cognitive neuroscientist interested in the intersection of visual cognition and action. She has completed her medical training at Tehran University of Medical Sciences and her Ph.D. training in cognitive psychology at Harvard University. After finishing her Ph.D., she worked as a post-doctoral fellow at Harvard University and then joined Dr. Leslie Ungerleider’s lab at NIMH. Since the unfortunate passing of Dr. Ungerleider, she has continued as a research fellow in the lab of Dr. Chris Baker at the Laboratory of Brain and Cognition. During her stay at NIMH, with the help of her supervisors, she ran a relatively independent research program that has led to several publications, with her as the senior author. She has also received a FARE award for her research. She has accepted an offer to become an assistant professor at the Department of Psychological and Brain Sciences at the University of Delaware from August 2023.
Her research aims to advance our understanding of the computational and neural mechanisms that enable real-time interaction with objects and people. In the past, she has established the existence of robust representations for objects and actions in the human parietal cortex, a region that bridges visual and motor areas of the brain that enables interaction with objects. She has also demonstrated humans’ remarkable prediction ability during real-time social interactions. In the future, she will continue delineating the neural circuitry and mechanisms involved in extracting object shapes and human body movements during natural interactions. To do this, she combines multiple methodologies, including body movement tracking, collection, and analysis of large datasets of human behavior in naturalistic settings, neuroimaging, and computational methods such as machine learning and natural language processing to obtain a deeper understanding of visual processing that applies better to everyday settings.
Ph.D., Psychology, Harvard University
M.A., Psychology, Harvard University
M.D., Department of Medicine, Tehran University of Medical Sciences
- Roberts S, Ungerleider L, Vaziri-Pashkam M, (2023) Disentangling object category representations driven by dynamic and static visual input. Journal of Neuroscience. 43(4),
- Yargholi E, Hossein-Zadeh GA, Vaziri-Pashkam M, (2022) Two distinct networks containing position-tolerant representations of actions in the human brain. Cerebral Cortex, bhac149.
- Xu Y, Vaziri-Pashkam M, (2021) Limits to visual representational correspondence between convolutional neural networks and the human brain. Nature Communications. 12 (1), 1-16.
- McMahon E, Kim D, Mehr SA, Nakayama K, Spelke E, Vaziri-Pashkam M, (2021) The ability to predict actions of others from distributed cues is still developing in children. Journal of Vision, 21 (5), 14-14.
- McMahon EG, Zheng CY, Pereira F, Gonzalez R, Ungerleider LG, Vaziri-Pashkam M, (2019) Subtle predictive movements reveal actions regardless of social context. Journal of Vision, 19(7), 16-16.
- Vaziri-Pashkam M, Xu Y, (2019) An information-driven 2-pathway characterization of occipitotemporal and posterior parietal visual object representations. Cerebral Cortex, 29:2034-2050.