Institute Announces 2017 Junior Faculty Fellows

The Hariri Institute for Computing at Boston University is pleased to announce its seventh cohort of Junior Faculty Fellows. They are:

JFF.2017

The Hariri Institute Junior Faculty Fellows program was established in 2011 both to recognize outstanding junior faculty at Boston University working in diverse areas of the computational sciences, as well as to provide focal points for supporting broader collaborative research in these areas at BU and beyond. Junior Fellows are selected by the Hariri Institute Steering Committee based on nominations received each spring, and are appointed for a three-year term.

The Institute’s Junior Faculty Fellows demonstrate the benefits achieved in making the leap from quantitative, statistically-driven research to computational, algorithmically-driven research, and the program’s success is a tell-tale sign of the increasing importance of the Institute’s mission of bringing the computational lens to bear on our data-driven world. Commenting on this seventh cohort of Hariri Junior Fellows, Professor Azer Bestavros, Founding Director of the Institute, notes that “the profile of this particular cohort of fellows speaks to BU’s steady growth in data science and the amazing quality of junior faculty members we are able to recruit, covering the methodological aspects of data science that are rooted in statistics and algorithmics as well as the application of data science across the landscape of academic disciplines, from political science and economics to public health.”

Over the next academic year, each of the Junior Faculty Fellows will be giving a lecture at the Institute. For more information and to receive notices about this and other Institute activities, please join our mailing list by becoming an affiliate member or by subscribing to the Institute’s mailing list for general announcements.  For more information, please visit our web site or connect via Facebook and Twitter.

About the Fellows

8/11/16 - Boston, Massachusetts Studio portraits of faculty member Alina Ene for CAS Postcard Photo by Dan Watkins for Boston University Photography

Alina Ene is an assistant professor in the Computer Science Department at Boston University. Her research interests include the design and analysis of algorithms, the mathematical aspects of combinatorial optimization topics such as submodularity and graphs, and their applications to machine learning. Prior to joining BU, she was an Assistant Professor at the University of Warwick, a Faculty Fellow at the Alan Turing Institute for Data Science, and a postdoc in the Center for Computational Intractability at Princeton University. Alina obtained her PhD in computer science from the University of Illinois at Urbana-Champaign in 2013. She graduated with a BSE degree in computer science from Princeton University in 2008, with high honors in computer science.

Professor Mark Crovella, Chair of the Computer Science Department, states that “Alina’s formal training is in theoretical computer science and combinatorial optimization. Building from this base her research takes an inherently outward-looking and interdisciplinary approach with the goal to pursue fundamental questions that lie at the intersection of theoretical computer science and machine learning, with an open eye to multiple application domains. This work has the potential to have a major impact in theory and practice by building the theoretical foundations of submodularity and applying the resulting ideas and insights to applications. Since submodular functions play a fundamental role in many domains in Computer Science, Mathematics, and Economics, her work brings new mathematical insights and computational tools to these diverse domains.”

JenkinsHelen Jenkins is an assistant professor of biostatistics at the School of Public Health. She holds a MSc in Biostatistics from the London School of Hygiene and Tropical Medicine and a Phd in Infectious Disease Epidemiology from Imperial College, London. Helen is interested in novel ways to analyze data that can have a public health impact in the field of infectious diseases. In recent years, she’s focused on tuberculosis, including developing new estimates of pediatric TB incidence and mortality, and spatial methods to understand geographic heterogeneity of TB.

Professor Josée Dupuis, Chair of the Department of Biostatistics, states that “Helen’s work is at the interface of statistics and infectious disease epidemiology, using data-driven methods and statistical models to further our understanding of epidemics. She has an extensive publication record for such an early stage in her career, with 32 publications, more than half of which are first-author publications, many in very high impact journals. She brings substantial experience of using data-driven approaches to draw inferences that have real policy implications and have influenced decision-making, for example, with the UK government for bovine TB and at the World Health Organization for polio and pediatric TB.”

Maxwell Palmermaxwell_palmer is an Assistant Professor of Political Science. He joined the department and Boston University in 2014, after receiving his PhD in political science at Harvard University. His research focuses on American political institutions, including Congress, the judiciary, and local government. His current projects examine the returns to office for former politicians, the effects of local political institutions on housing development, and new methods for analyzing redistricting plans and gerrymandering.

Associate Professor Dino P. Christenson, Department of Political Science, notes that “Since Max joined us in 2014 I have been consistently impressed by him and his work. In only two and a half years he has chalked up a number of topnotch peer reviewed articles, not to mention a law review that has received national media coverage. Max is not only working at record speed but also producing high quality products that are both substantively interesting and methodolog- ically sophisticated—making use of big data and recent advances in computational social science, including scraping public records as well as auto-content analyzing terabytes worth of textual data His work holds promise for a  variety of applications in political science, legal studies, and other fields where we want to understand the effects of rules on a sequence of decisions.”

sussmanDaniel Sussman is an assistant professor in the Mathematics and Statistics Department. Prior to joining BU, Daniel served as a postdoctoral fellow at Harvard University for two years. He received his PhD in applied math and statistics from Johns Hopkins University in 2014. Daniel develops theory and methods for studying network data. He has studied spectral methods and other tools to embed the nodes of a network in Euclidean space. He is also interested in tools to study multiple networks simultaneously, especially when the networks arise from disparate sources. Finally, he researches how to perform and analyze experiments on networked groups of individuals.

Professor Tasso Kaper, Chair of the Department of Mathematics & Statistics, notes that “Daniel is an exciting young statistician at the forefront of developing foundational methods and solving important scientific and engineering problems, which all involve data and computational questions at their core. Daniel is a quick study and fast learner who has considerabl interest in – and exceptional skills at –  collaborating with biomedical engineers, neuroscientists, social scientists, and other colleagues. In the three years since completing his PhD, Daniel has published 11 original research articles, one book chapter, seven referenced conference preceedings, with three more articles under review.”

Stephen_TerryStephen Terry is an assistant professor of economics and a macroeconomist with a PhD in economics from Stanford University in 2015. Stephen studies the role of individual firms and business managers in driving outcomes for the economy as a whole. In one set of work, Stephen studies short-termism. By building factories, inventing new products, hiring new workers, and entering new markets, firms continually make choices that build long-term value for society. Using a mixture of empirical and simulation-based analysis, Stephen’s work in this area models the economy as made up of individual managers facing incentives to sometimes sacrifice such long-term gains, and macroeconomic growth, for the short-term profits of their firm. Stephen’s also studies models of the economy in which very disaggregated considerations, like the time and resources required to plan for the construction of factories or hire new workers, can collectively influence the severity of recessions and the size of booms for the economy as a whole. Stephen’s research typically employs large datasets of firm behavior alongside models of the economy built on individual firm-level   simulation.

Professor Barton L. Lipman, Chair of the Department of Economics, notes that “Stephen is an extremely promising young researcher who makes good use – and would like to learn to make better use – of advanced computational techniques. In his research on short-termism, the pressure that firm managers face to produce short-term results even at the cost of significant long-term sacrifices, he uses quantitative theoretical models of firm behavior, requiring the efficient numerical solution of dynamic optimization problems as well as an anlysis of large micro-level datasets of firm, manager, and household behavior. His work is critical to understanding how the compensation and reputational incentives of managers can impact the performance of a firm and the economy as a whole.”