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
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.
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.
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.