For general information about the program, information for the current year, and documentation of results from past years, see the tabs and pull-down menus on this webpage. For specific questions, do not hesitate to send e-mail to our mailbox at firstname.lastname@example.org.
Jianwu Wang, Ph.D. (jianwu _at_ umbc.edu) [Director and Faculty Mentor, Associate Professor of Data Science in the Department of Information Systems]
Dr. Jianwu Wang is an Associate Professor of Data Science in the Department of Information Systems, and leads the Big Data Analytics Lab at UMBC. He is also an Affiliated Faculty at the Joint Center for Earth Systems Technology (JCET), UMBC. His research interests include Big Data Analytics, Scientific Workflow, Distributed Computing, Service Oriented Computing. He has published 100+ papers with more than 2000 citations (h-index: 23). He is/was associate editor or editorial board member of four international journals, conference organization committee member of eight conferences and co-chair of eight related workshops. He is also program committee member for over 40 conferences/workshops, and reviewer of over 15 journals or books. Since joining UMBC in 2015, he has received multiple external grants as PI funded by NSF, NASA, DOE, State of Maryland, and Industry. He is also an NSF CAREER awardee. He was PI of the NSF initiative CyberTraining: Big Data + HPC + Atmospheric Physics at UMBC. His current research interests include Big Data Analytics, Distributed Computing and Scientific Workflow with application focuses on climate and manufacturing.
Matthias K. Gobbert, Ph.D. (gobbert _at_ umbc.edu) [Co-Director and Faculty Mentor, Professor of Mathematics in the Department of Mathematics and Statistics]
Matthias K. Gobbert is Professor of Mathematics in the Department of Mathematics and Statistics at UMBC and an affiliate Professor in the Department of Computer Science and Electrical Engineering at UMBC. Dr. Gobbert’s research interests include scientific and parallel computing, the numerical solution of partial differential equations, industrial mathematics, and most recently data science, typically in collaboration with application scientists. Dr. Gobbert has extensive experience in initiatives. Among others, he initiated the UMBC High Performance Computing Facility in 2008 with funding from the NSF MRI program, directed the REU Site: Interdisciplinary Program in High Performance Computing from 2010 to 2017 with funding from NSF, NSA, and DOD, was co-PI of the NSF initiative CyberTraining: Big Data + HPC + Atmospheric Physics at UMBC. Dr. Gobbert has been involved with over 200 publications, including over 40 in peer-reviewed journals, 40 in refereed proceedings, and 40 student publications and theses. Dr. Gobbert has to date graduated six Ph.D. students, five M.S. students, and has supervised eleven undergraduate theses for graduating with departmental honors. Dr. Gobbert has accumulated extensive experience in teaching with state-of-the-art technology. Since 2019, his classes use online comprehension quizzes on the lectures and fully online submission of all assignments, complete with online grading. Since starting online teaching full-time in 2020, the synchronous class meetings are used additionally for student presentations to maximize active student engagement. Dr. Gobbert received the University System of Maryland Board of Regents’ Faculty Award for Excellence in Mentoring in 2010.