Samuel Mehr (2017/2019 Fellow)
Samuel Mehr is a Research Associate in the Department of Psychology at Harvard University, where he directs the Music Lab. Sam studies music: how the design of the human mind leads us to perceive, create, and engage with music, and how this psychology of music may be leveraged to improve health outcomes in infancy and adulthood. These questions are multidisciplinary, drawing insights from the cognitive sciences, evolutionary biology, anthropology, ethnomusicology and music theory, linguistics, and computer science. Originally a musician, Sam earned a B.M. in Music Education from the Eastman School of Music before diving into science at Harvard, where he earned an Ed.D. in Human Development and Education under the mentorship of Elizabeth Spelke, Howard Gardner, and Steven Pinker. To learn more about Sam's research and to participate in music research online, please visit http://themusiclab.org.
Michelle Ntampaka (2017/2019 Fellow)
Michelle Ntampaka's research focuses on constraining cosmological models with galaxy clusters. She uses machine learning and statistical tools to tease out complicated patterns in the data that are inaccessible through more traditional means. She received her Ph.D. from Carnegie Mellon University.
Michelle is a former high school teacher and is actively involved in education and public outreach. She has traveled to Africa to train Rwandan high school science teachers on delivering memorable lecture demonstrations, and more recently, has been developing lesson plans to teach astronomy to kindergarteners through stories and play.
Melanie F. Pradier (2017/2019 Fellow)
Melanie F. Pradier is currently a DSI Postdoctoral Fellow working on probabilistic models for data exploration. She received her Ph.D. at Universidad Carlos III in Madrid, funded by a Marie Curie ITN Fellowship from the European Union. In 2014-2015, Melanie was a researcher at the Memorial Sloan Kettering Cancer Center in New York. Melanie studied Telecommunication Engineering at the Technical University of Madrid (UPM), and obtained her MSc in Information Technology at the University of Stuttgart in 2011. Just before her PhD, she spent two years working at the industry in the European Sony Research Center in Stuttgart, and Sony Corporation R&D in Tokyo. Her current research interests include interpretable machine learning, probabilistic graphical models, approximate inference techniques (MCMC and variational techniques), dependent random measures, clustering and topic modeling, biomedical applications, and information theory. A detailed CV and list of publications or patents can be found at: http://www.melaniefpradier.work/
Xu Shi (2017/2019 Fellow)
Xu Shi is currently a DSI Postdoctoral Fellow in the Department of Biostatistics at the Harvard TH Chan School of Public Health. She is interested in developing statistical methods to generate real world evidence from large-scale electronic health records (EHR) data. The growing availability of routinely collected EHR data opens new opportunities in biomedical and health policy research to evaluate factors associated with both individual benefit and potential harm. However, EHR data is not collected for research purposes and comes with unique challenges, which motivates Xu’s methodological research. In particular, her research focuses on developing causal inference methods tailored to EHR data, automatic translation and linkage of data across healthcare systems, post-marketing drug safety surveillance, and comparison of healthcare utilization.
Xu Shi received her Ph.D. in Biostatistics from University of Washington, Seattle. Prior to this, she obtained a B.S. in Mathematics and Applied Mathematics and a minor in English language and literature at the Chu Kochen Honors College from Zhejiang University, China.
Minsuk Shin (2017/2019 Fellow)
Minsuk Shin’s research focuses on high-dimensional Bayesian model selection and sparsity inference. His research is mainly about investigating theoretical properties related to the choice of priors under high-dimensional settings, and he is also interested in scalable computations for Bayesian inference. He received his PhD in statistics from Texas A&M University in 2017. Currently he is mainly working with Jun Liu and Natesh Pillai in the Department of Statistics, Harvard.
Kun-Hsing Yu (2017/2019 Fellow)
Kun-Hsing Yu received his PhD in Biomedical Informatics and PhD Minor in Computer Science from Stanford University under the supervision of Professors Michael Snyder and Russ B. Altman. His doctoral research integrated lung cancer patients' omics (genomics, epigenomics, transcriptomics, and proteomics) profiles with quantitative histopathology patterns to predict their clinical phenotypes. He developed a fully-automated algorithm to extract thousands of features from whole-slide histopathology images, discovered the molecular mechanisms underpinning the microscopic phenotypes of tumor cells, and successfully identified previously unknown cellular morphologies associated with patient prognosis. Prior to joining Stanford, he obtained his MD from National Taiwan University and designed a bioinformatics framework to identify protein markers of colorectal cancer in Academia Sinica. Yu's current research interests include translational bioinformatics, machine learning, integrative studies on omics and medical images, biomarker discovery for complex diseases, and precision medicine