Stern Family Professor Mathematics and Computer Science
Secondary Appointment, Mechanical Engineering
Abani Patra is inaugural director of DISC, Stern Family Professor of Computer Science and Professor of Mathematics. Prior to joining Tufts, Dr. Patra was the founding director of the Institute for Computational and Data Sciences at University at Buffalo and has directed programs at the National Science Foundation and the U.S. Department of Energy. Dr. Patra received his PhD in Computational and Applied Mathematics from University of Texas, Austin and M.S. in Mechanical Engineering from University of Missouri-Rolla.
Dr. Patra is a computational and data scientist with interests in computational mathematics, large-scale HPC based simulation and modeling of physical systems, uncertainty quantification, analysis of numerical and modeling error, adaptive finite elements, modeling and inference based on large data sets. Dr Patra’s contributions have had wide-ranging impacts in the field of geophysical modeling. His work has led to development of tools, now used by thousands of researchers around the world to characterize, predict, and simulate physical events such as volcanic avalanches and landslides.
DISC Data Scientists
Senior Data Scientist
Anna Haensch is a senior data scientist at Tufts University. She earned her Ph.D. in mathematics in 2013 from Wesleyan University. Before joining Tufts, Anna was an associate professor of mathematics at Duquesne University and visiting researcher at the Max Plank Institute for Mathematics. The roots of her research are in computational number theory, specifically in using modern computational tools and capabilities to answer longstanding, previously intractable, open problems. More recently, her interest in algorithmic development led her to work at a start-up called Tagup where she developed machine learning methods for fault and anomaly detection in heavy industrial equipment. Anna's current research involves applying methods of Bayesian inference to understanding complicated multi-tiered timeseries data. In addition to technical applications of data science, Anna is also interested in the ways that data and numerical literacy more generally shape the way we produce and consume media. In 2013 Anna was awarded the AAAS Mass Media Fellowship and worked as a reporter on the National Public Radio Science Desk.
Senior Data Scientist
Karin Knudson is a Data Scientist at Tufts University. Her research has involved the development and application of methods from machine learning, Bayesian statistics, and compressive sensing, particularly to neural data. She is interested in using approaches from data science to support scholarship across and between a range of disciplines, and is also interested in education for data science. Before joining Tufts, she was a Research Fellow in the Department of Neurology at Massachusetts General Hospital and Harvard Medical School, and was previously the Chair of the Department of Mathematics, Statistics, and Computer Science at Phillips Academy. She completed her Ph.D. in Mathematics at The University of Texas at Austin.
Data Scientist II
Michael Wojnowicz is a Data Scientist at Tufts University. He earned his Ph.D. from Cornell University in 2012, where his work in Cognitive Science led to the Dallenbach Fellowship for Research Excellence, the Cognitive Science Dissertation Proposal Award, and the Cognitive Science Graduate Research Award. Mike also has master's degrees in Mathematics (University of Washington) and Statistics (University of California at Irvine). Before joining Tufts University, Mike was the Distinguished Data Scientist at Cylance. At Cylance, Mike developed statistical machine learning models for detecting malicious computer files and anomalous user activity, leading to 10 patents (5 granted, 5 pending). Mike’s current research interests include time series modeling, variational inference, and nonparametric Bayesian modeling.
Data Scientist II
Eric Reed is a Data Scientist at Tufts University. He earned an M.S. in Biostatistics from the University of Massachusetts Amherst in 2015 and a Ph.D. in Bioinformatics from Boston University in 2020. Eric's research is focused on working with biomedical researchers to implement cutting-edge high-throughput profiling techniques and develop analytical approaches to better interrogate the biological questions at hand. His dissertation work encompassed advancement of large-scale transcriptomic profiling for toxicogenomic screening. This included the benchmarking scalable library preparation techniques and development of machine learning methods and software. Through numerous collaborative projects, Eric's work has led to contributions to various biomedical fields including environmental health, metabolic diseases, oral cancer, breast cancer, Huntington's disease, and addiction.
Data Scientist II
Georgios Georgalis is a Data Scientist at Tufts University, primarily focusing on introducing uncertainty quantification to machine learning and building surrogate models for large scale simulations. Georgios earned his PhD from the School of Aeronautics and Astronautics at Purdue University. He has a Master's degree (Purdue University) and an undergraduate degree in Mechanical Engineering from the National Technical University of Athens (NTUA). His doctoral research included the development of a crowd-based prototype to predict future project failures using machine learning and human analytics, he also investigated whether targeted feedback helps in preventing such failures. He has previously worked with jet engines, rocket propulsion systems, and on an experiment that flew on Blue Origin's New Shepard rocket to space.
Assistant Professor, Computer Science
Bert Huang is an assistant professor in the Department of Computer Science and the Data Intensive Studies Center at Tufts University. He earned his Ph.D. from Columbia University in 2011, was a postdoctoral research associate at the University of Maryland, and previously was an assistant professor at Virginia Tech.
His research addresses topics surrounding machine learning, including structured prediction, weakly supervised learning, and algorithmic fairness. His papers have been published at conferences including NeurIPS, ICML, UAI, and AISTATS, and he is an action editor for the Journal of Machine Learning Research.
Project Director, Center for Quantitative Methods and Data Sciences at the Institute for Clinical Research and Health Policy Studies
Director of Tufts CTSI Biostatistics, Epidemiology, and Research Design (BERD) Center
Paola Sebastiani, PhD, received training in Mathematics and Statistics in Italy and the United Kingdom. Dr. Sebastiani has held faculty positions in Italy, UK, and Boston University before joining Tufts Medical Center in 2020, where she is director of BERD, and of the Center on Quantitative Methods and Data Science in the Institute for Clinical Research and Health Policy Studies at Tufts Medical Center. Dr. Sebastiani is a multidisciplinary biostatistician, with a long track record of developing new methodologies in Bayesian statistics, decision theory, machine learning, artificial intelligence, and statistical experimental design, in addition to teaching, mentoring, and leading interdisciplinary research projects. Paola has introduced innovative Bayesian techniques for the analysis of genomic and genetic data and was a pioneer in using networks to model the genetic and phenotypic basis of the complications of sickle cell anemia.
She is also a renowned biostatistician in the fields of the biology and epidemiology of human aging and longevity: she introduced original methods to design observational studies of human longevity, and to discover genetic and non-genetic risk factors that contribute to healthy aging. She is Co-PI of the NIH funded Longevity Consortium, and of the Long Life Family Study. She is also multiple PI of a project to characterize the molecular targets of the protective APOE2 allele, and of a project that will generate multi-omics profiles of humans and other species to discover targets for healthy aging. Her current research focuses on the genetics and epidemiology of extreme human longevity, analysis of rare genetic variants, and integrative analysis of multi-omics data using statistical and machine learning methods.
To engage Tufts faculty in the development and growth of DISC and to provide them an opportunity to grow their research interests in this domain DISC will appoint a number of faculty fellows. Fellows will anchor new DISC initiatives and thrust areas and will work closely with other DISC programs as appropriate.
Professor, Department of Mathematics
Adjunct Professor, Computer Science
Adjunct Professor, Department of Physics
Bruce Boghosian is a Professor of Mathematics at Tufts University, with secondary appointments in the Departments of Computer Science and Physics. His research interests include mathematical fluid dynamics and kinetic theory, and, more recently, the application of these disciplines to problems of inequality and wealth distribution. He has been a fellow of the American Physical Society since 2000, and a recipient of Tufts University’s Distinguished Scholar Award in 2010. He has held numerous visiting academic positions throughout the world, and is a member of the editorial boards of three scientific journals.
Prior to coming to Tufts University, Boghosian was a Research Associate Professor at the Center for Computational Science at Boston University (1994-2000), a Senior Scientist at Thinking Machines Corporation (1986-1994), and a staff scientist at the Lawrence Livermore National Laboratory (1978-1986). He holds B.S. and M.S. degrees from MIT, and a Ph.D. from the University of California, Davis.
Research Assistant Professor, Department of Immunology
Albert Tai is a Research Assistant Professor of Immunology at Tufts University. His research work focuses on providing current research technology to basic research community within and outside of the University, including next generation sequencing (NGS), high throughout screen (HTS), high content screen (HGS), robotics automation and flow cytometry. These technologies, especially NGS and HCS, generates significant amount of data and require specialized analytical approaches.
A part of his research centers on creating or optimizing these analytical approaches, via utilizing existing software/pipeline and/or developing new ones. Furthermore, research projects that utilize multiple technologies, or multi-omics, are becoming more popular, a mean to allow association and visualization of multi-omics data is also of interest.
Senior Bioinformatics Scientist, Research Technology
Rebecca Batorsky is senior bioinformatics scientist in Research Technology, part of Tufts Technology Services. She earned her PhD in Physics in 2012 from Tufts University, where she focused on Mathematical and computational modeling of virus evolution. Before becoming Tufts staff, she worked as a bioinformatic software developer at a clinical genomics start-up company. Rebecca works to enable researchers to answer biological questions with data-driven methods, such as analysis of high-throughput DNA and RNA sequencing data. She is especially interested in developing methods to use multiple 'omics technologies simultaneously to give insight into biological pathways and processes.
William Walker Professor of Mathematics at Tufts University
Adjunct Professor of Computer Science at Tufts University
Deputy Director of ICERM at Brown University
Misha Elena Kilmer is the William Walker Professor of Mathematics and Adjunct Professor of Computer Science at Tufts University. Beginning July 2021, she will serve as Deputy Director of ICERM at Brown University. She has been a Tufts DISC Faculty Fellow since January 2021. In 2019, Prof. Kilmer was named a SIAM Fellow "for her fundamental contributions to numerical linear algebra and scientific computing, including ill-posed problems, tensor decompositions, and iterative methods." She served as Chair of the Tufts Departments of Mathematics from 2013-2019. She is a 2001 recipient of the Tufts Undergraduate Initiative in Teaching Award and was promoted directly from Assistant to Full Professor in 2005.
Prof. Kilmer currently serves as a Section Editor for SIAM Review Research Spotlights and as an associate editor for La Matematica, AWM's new flagship journal. She spent 12 years as Associate Editor for SIAM Journal on Scientific Computing and is a former associate editor for both SIAM Journal on Matrix Analysis and Applications and SIAM Undergraduate Research Online. She is the author of numerous refereed articles and conference proceedings papers appearing in a wide range of computational math and engineering publications. Her work has been or is being funded by the NSF, NIH, IARPA, DARPA, and IBM. She was co-Chair of the SIAM Computational Science and Engineering Meeting (CSE21) and co-Chair of SIAM Applied Linear Algebra Conference (LA21). She sits on the Advisory Committee for the International Linear Algebra Society (ILAS) and has been a member of several international award selection committees.
Valencia Joyner Koomson
Associate Professor in the Department of Electrical and Computer Engineering
Secondary appointment in the Tisch College of Civic Life at Tufts University
Dr. Martin Luther King Jr. visiting Professor at MIT
Prof. Valencia Joyner Koomson is an Associate Professor in the Department of Electrical and Computer Engineering, with a secondary appointment in the Tisch College of Civic Life at Tufts University. She is currently serving as a Dr. Martin Luther King Jr. Visiting Professor (2021) at MIT in the Department of Electrical Engineering and Computer Science. Her research lies at the intersection of biology, medicine, and electrical engineering. Her interests are in micro/nanoscale system design, mobile health devices. and health informatics. She is conducting research on mobile health interventions and data analytics to improve chronic disease outcomes. Prof. Koomson is the author of numerous refereed publications, book chapters, and patents. She is a member of several professional societies, technical program committees, and editorial boards for high impact journals. Prof. Koomson's research funding sponsors include NSF, DARPA, Catalyst Foundation, and W.M. Keck Foundation. She is a George C. Marshall Scholar, Intel Foundation Scholar, National Science Foundation Graduate Research Fellow, and recipient of the NSF Faculty Early Career Development (CAREER) Award. She has held visiting appointments at Rensselaer Polytechnic Institute and Boston University.
Prior to joining Tufts University, Prof. Koomson was an adjunct Professor at Howard University in the Department of Electrical Engineering and Computer Science and VLSI Research Scientist at the University of Southern California's Information Sciences Institute (USC/ISI). She holds B.S. and M.ENG. degrees from MIT and a Ph.D. from the University of Cambridge, where she studied as a George C. Marshall Scholar.
Research interests: Data reduction, Hierarchical learning, Kernel Methods, Statistical modeling
Prashant received his masters in Mechanical Engineering from University at Buffalo in 2016 and Ph.D. in Computational and Data Enabled Sciences from University at Buffalo in 2019. His current research focusses on learning hierarchical sparse representations for large datasets aimed at data reduction and efficient learning. Prashant works with modeling datasets from cryosphere, remote sensing, numerical models and other related domains making joint inferences based on data and physics of the systems.