Promoted to Full Professor
Dr. Adrian Dobra is a core faculty member in the Department of Statistics and the Center for Statistics and Social Sciences (CS&SS). He is also an affiliate faculty in the Center for Studies in Demography and Ecology (CSDE), the eScience Institute, and faculty in the Department of Biobehavorial Nursing and Health Systems (BNHS). Dobra is currently serving as the Director of the UW Master of Science in Data Science program, Associate Chair and Graduate Chair of CS&SS, and the Chair of the Graduate School’s Interdisciplinary Data Science Group.
Dobra’s research focuses on the development of high-dimensional multivariate spatiotemporal models, variable and model selection, graphical models, models for multidimensional, hyper-sparse contingency tables, Bayesian statistics, and statistical computing. His methodological research has been published in more than 30 journals, including the Journal of the American Statistical Association, Annals of Statistics, Bayesian Analysis and the Annals of Applied Statistics. Dobra's applied work has a broad range with key foci related to public health, demography, computational social science, modeling of complex dynamical phenomena using big data, and on pathway determination and disease risk assessment from network analysis of large scale genomics data. This work has been published in top applied journals such as the Proceedings of the National Academy of Sciences, AIDS and the International Journal of Epidemiology. His work has been supported by the National Science Foundation (NSF) and by the National Institutes of Health (NIH), among others.
One of Dobra’s recent projects focuses on the transmission of SARS-Cov-2. This project develops a new strategy, called Sampling-Testing-Quarantine (STQ), for identifying and isolating individuals with asymptomatic SARS-CoV-2 in order to mitigate the epidemic without the disruption of broad shutdowns. STQ uses probability sampling in the general population, regardless of symptoms, then isolates the individuals who test positive along with their household members. Agent-based model simulations of the Seattle population show that STQ can substantially slow and decrease the spread of COVID-19.
Promoted to Full Professor
Dr. Emily Fox is a core faculty member in the Department of Statistics and the Paul G. Allen School of Computer Science & Engineering, and is the Amazon Professor of Machine Learning, as well as an adjunct faculty member in Electrical & Computer Engineering. She has been awarded a Presidential Early Career Award for Scientists and Engineers (PECASE, 2017), Sloan Research Fellowship (2015), ONR Young Investigator award (2015), NSF CAREER award (2014), National Defense Science and Engineering Graduate (NDSEG) Fellowship, NSF Graduate Research Fellowship, NSF Mathematical Sciences Postdoctoral Research Fellowship, Leonard J. Savage Thesis Award in Applied Methodology (2009), and MIT EECS Jin-Au Kong Outstanding Doctoral Thesis Prize (2009). Her work has been published in top conferences and journals such as NeurIPS, ICML, AOAS, and JMLR, among others.
Fox’s primary research focuses on time series modeling, scalable Bayesian inference, structured and sparse neural networks and generative models, and applications to health and computational neuroscience.
In recent years, Fox has been a Distinguished Engineer at Apple, Inc. leading the Health AI team to devise algorithms that enable detecting, tracking, and managing our health and wellness using the suite of devices and wearables pervasive in our lives. One of her most recent works involves developing an epidemiological model to infer the impact of human mobility on SARS-CoV-2 transmission in the United States using the Apple Maps mobility data. This framework enables real-time tracking of the effective reproductive number and forecasting of transmission under different mobility policies, informing both intervention evaluations and surge planning for healthcare systems. A pre-print of Fox’s work can be found on MedRxiv.
Promoted to Full Professor
Dr. Sham Kakade is core faculty member in the Department of Statistics and the Paul G. Allen School of Computer Science & Engineering, and is a Co-Director for Algorithmic Foundations of Data Science Institute (ADSI), as well as an adjunct faculty member in Electrical & Computer Engineering. He is the recipient of the ICML Test of Time Award (2020), the IBM Goldberg best paper award (in 2007) for contributions to fast nearest neighbor search and the best paper, INFORMS Revenue Management and Pricing Section Prize (2014). He has been program chair for COLT 2011 and has served as a Washington Research Foundation Data Science Chair from 2015 to 2020. His work has been published in top conferences and journals such as Neurips, ICML, Foundations of Computational Mathematics (FOCM), Journal of Machine Learning Research (JMLR), and IEEE Transactions on Signal Processing.
Kakade’s research involves mathematical foundations of machine learning and artificial intelligence, with a focus on designing provably efficient and practical algorithms that are relevant for a broad range of paradigms. He seeks to use these advancements to help in making progress on core AI problems. Kakade is currently leading the CovidSafe app project. This app, which will be available on both the Android and IOS platforms, will help contact tracers slow the spread of COVID-19. You can listen to a recent interview that Sham gave with KUOW’s Bill Radke here.