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).
Monika Mellem, PhD, is currently a Senior Data Scientist using her neuroscience and engineering training to investigate biomarkers related to psychiatric disorders and their treatment. Since 2016, she has researched the development of multimodal biomarkers for transdiagnostic symptoms as inspired by the NIMH RDoC initiative and investigated personalized medicine approaches using machine learning-based prediction of treatment response while at BlackThorn Therapeutics.
Her training as a NIMH intramural postdoctoral fellow (2013-2016) in the Section on Cognitive Neuropsychology under Dr. Alex Martin included investigations of language and social-emotional interactions in the brain and the intrinsic organization of oscillatory activity across the human cortex. She additionally had the pleasure of serving on the NIMH Fellows Committee supporting several training initiatives. Monika obtained her PhD in neuroscience from Georgetown University in 2013 during which she was awarded a F31 grant. She also holds a MS in electrical engineering from the University of Illinois, Urbana-Champaign (2003). She hopes to continue using her combined skill set to improve healthcare through data science approaches and also eventually to teach data science.
Mellem, M. S., Kollada, M., Tiller, J., Lauritzen, T. (under review). Explainable AI enables clinical trial patient selection to retrospectively improve treatment effects in schizophrenia. MedRxiv 2021.01.11.20248788, doi: https://doi.org/10.1101/2021.01.11.20248788.
Mellem, M. S., Liu, Y., Gonzalez, H., Kollada, M., Martin, W. J., Ahammad, P. (2019). Machine learning models identify multimodal measurements highly predictive of transdiagnostic symptom severity for mood, anhedonia, and anxiety. Biol Psychiatry Cogn Neurosci Neuroimaging, 5(1), 56-67.
Mellem, M. S., Wohltjen, S., Ghuman, A., Gotts, S., Martin, A. (2017). Intrinsic frequency profiles and biases across human cortex. J. Neurophysiology, 118(5), 2853-2864.
Mellem, M. S., Jasmin, K., Peng., C., Martin, A. (2016). Sentence processing in the anterior superior temporal lobe shows a social-emotional bias. Neurospychologia, 89, 217-224.
Mellem, M. S., Friedman, R. B., & Medvedev, A. V. (2013). Gamma- and theta-band synchronization during semantic priming reflect local and long-range lexical-semantic networks. Brain and Language, 127(3), 440-451.