Past Awardees

Dr. Goldfarb is an Assistant Professor in the Departments of Psychiatry and Psychology and member of the Wu Tsai Institute. She completed her PhD in Psychology: Cognition & Perception with Dr. Elizabeth Phelps at New York University and a postdoctoral fellowship in the Departments of Diagnostic Radiology and Psychiatry with Dr. Rajita Sinha at Yale. Her research investigates different forms of learning and memory, how stress changes which parts of our experiences we remember, and the impact of memory on later behavior.

Karim Ibrahim is an Assistant Professor at the Yale Child Study Center. His translational neuroscience research focuses on brain networks associated with emotion regulation impairments in childhood-onset psychiatric disorders. Dr. Karim Ibrahim’s research is interdisciplinary and integrates multimodal imaging methods including functional and structural MRI, machine learning, and network neuroscience/connectomics approaches to identify biomarkers relevant to child psychopathology. His recent interests lie in using and developing tools predictive modeling/machine learning approaches that leverage large-scale neuroimaging datasets, including data from the Adolescent Brain Cognitive Development (ABCD) study, for identifying robust brain-based biomarkers. Among other things in this area, his research also investigates dynamics of the human functional connectome and large-scale networks, how brain connectivity is altered in mental health disorders (such as a disruptive behavior disorders, anxiety disorders, and autism spectrum disorder) and the neural response to treatment in youths. As a licensed clinical child psychologist, he also has extensive experience in developmental psychopathology, including assessments and cognitive-behavioral interventions for autism spectrum disorder, mood, anxiety, and disruptive behavior. Karim completed a postdoctoral fellowship at the Yale Child Study Center T32 research program in Translational Developmental Neuroscience and through an award from the Yale Center for Clinical Investigation multidisciplinary research training program.

My focus is on the engineering aspects of data science. Just as a civil engineer brings together physics, geology, and materials science to build a bridge connecting two sides of a river my interest is how do we bring together, statistics, software development, and computing systems to address real world data analysis challenges. My work has four main focuses, health disparities epidemiology, applied machine learning, engineering reproducible software, and designing data storage architectures amenable to machine learning scale analyses. Previous work has identified disparities in health outcomes based on a vast array of socioeconomic and demographic factors. The Veterans health administration is a unique setting to investigate these disparities because of the large number of patients it covers, the comprehensiveness of the care it provides, and how accessible the care is. My work has used generalized linear mixed models to explore the effects of national origin, whether a Veteran was born domestically, in a U.S. Territory or abroad, on hypertension and the effects of race and ethnicity on prescription of continuous glucose monitors. While generalized linear models are a powerful tool for exploring relationships between risk factors and outcomes, their rigid structure limits their predictive power. The more complex decision surfaces explored by machine learning methods offer the potential to close this gap. Recently I have worked on imputing gulf war illness status from surveys done as part of the Million Veteran Project (MVP). During the gulf war Veterans were exposed to a vast array of toxins that are believed to be related to an increase in a set of physical and cognitive symptoms. To better characterize gulf war illness a survey was constructed and administered to a subset of MVP enrollees. To improve the sample size for downstream analyses we are working to use other MVP data to impute gulf war illness status. Data analysis projects present a unique software engineering challenge because of their highly nonlinear and exploratory nature. Often a project will begin with an idea which will then be tested and the result used to generate the next hypothesis. Since the next hypothesis, however, may be radically different from the original hypothesis, the programming abstractions that made sense originally will no longer be workable. Through my work on applied projects, I am interested in how to create flexible abstractions that withstand this highly dynamic development environment. Like the demands data analysis places on software design, it also places unique demands on data storage and organization. Data architecture design often occurs separately from data analysis and has separate concerns. Data storage designs typically focus a minimizing space usage and access times. This results in designs where data must be re-coded in the process of analysis. This re-coding is feasible for targeted analyses that involve only a few well-known variables but becomes prohibitive as analyses scale to machine learning scope for untargeted analyses. A core question I have focused on is how we store the data so that is still efficient in terms of space and access time but is more directly computable.

Soumya Yandamuri, MSE, PhD is a biomedical engineer and researcher of autoimmune neurologic diseases. Her research focuses on the role of antibodies, their effector functions, and natural killer cells, and their utility as biomarkers for demyelinating diseases of the central nervous system, including neuromyelitis optica spectrum disorder (NMOSD), myelin oligodendrocyte glycoprotein antibody disease (MOGAD), and multiple sclerosis (MS). Dr. Yandamuri conducted her postdoctoral training as a Yale Center for Clinical Investiation and National Multiple Sclerosis Society fellow under the guidance of Dr. Kevin O'Connor. Prior to joining the Yale School of Medicine, Dr. Yandamuri completed her Bachelor’s and Master’s degrees in Biomedical Engineering at the University of Michigan, Ann Arbor, and subsequently conducted her doctoral studies as a NIH Ruth L. Kirschstein Predoctoral NRSA Fellow in the Laboratory of Dr. Thomas Lane, then at the University of Utah, where she studied protective neural stem cells and microglia in a coronavirus-based model of MS. Dr. Yandamuri has trained in immunology, neurobiology, microbiology and virology, and advanced imaging techniques, and has previously worked in industry as a researcher and consultant.

Dr. Courtney Choy is a postdoctoral fellow in Chronic Disease Epidemiology at the Yale School of Public Health. In Samoa, Courtney Choy serves as a Fogarty Global Health Scholar at the Ministry of Health, where she is co-leading the fourth wave of data collection for the Ola Tuputupua’e study and supporting research efforts of the Obesity Lifestyle and Genetic Adaptation (OLaGA) group. She will also be involved in research on the influence of school environments on modifiable risk factors, body size, and cardiometabolic disease markers among children in Samoa. This work is an extension of her six years of experience in Samoa, starting from her Yale MPH summer research, 2016-2017 Fulbright Research Fellowship, and to 2019-2020 National Institute of Health Predoctoral fellowships (F31 Ruth Kirschstein National Research Service Award and Fogarty Global Health Scholar Program). Dr. Choy's current research focuses broadly on child growth and development, cardiometabolic disease, Asian-Pacific Islander population health, the application of longitudinal and multilevel data analyses, and life course epidemiology. Her mentors are Dr. Nicola Hawley in the US and Leausa S.T. Dr. Take Naseri in Samoa.