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Spatiotemporal Data Diagram

Our second research focus area concerns the study of large-scale problems where the underlying data have important and non-trivial spatial and/or temporal variations. Of specific concern are the following:

  1. Inverse-type problems where a physical sensing model relates the data available for processing to the spatio-temporal information of interest.
  2. Semi-supervised learning from time series comprised of data from multiple sources, sampled at irregular intervals, and where ground truth labels are exceedingly rare.
  3. Inferring causality from space-time data focusing on settings that match a potential-outcomes framework where the question is whether some binary treatment leads to improved outcomes of interest.
  4. Communications beyond the 5G paradigm where technologies such as massive distributed arrays and large intelligent surfaces bring about the possibility to communicate and coordinate a large amount of data in real time or under stringent latency constraints with high reliability using advanced machine learning ideas for solving problems in estimation, tracking, and optimization.

Spatiotemporal Data Diagram

 

Fall 2020 Programming:

Programming for this research focus will begin in the fall of 2020 with the T-TRIPODS Virtual Workshop on Spatio-Temporal Data looking at problems at the intersection of machine learning/data science and physics-based models and problems.   The event will take the form of a series of interactive seminars each comprising a 40-45 minute technical presentation followed by 20-30 minutes of discussion moderated by a member of the Tufts faculty and an associated graduate student.  The four speakers are:

  • Vipin Kumar, Regents Professor and William Norris Chair in Large Scale Computing, Department of Computer Science and Engineering, University of Minnesota

          Thursday, November 5, 2020, 3:00 – 4:00 PM Eastern Time

 

  • Andrew Stuart, Bren Professor of Computing and Mathematical Sciences, Caltech

          Friday, November 6, 2020, 10:30 – 11:45 AM Eastern Time

 

  • Evrim Acar Ataman, Head of Department, Chief Research Scientist, Simula Research Laboratory

          Friday, November 13, 2020, 10:30 – 11:45 AM Eastern Time

 

  • Rachel Ward, W. A. "Tex" Moncrief Distinguished Professor in Computational Engineering and Sciences—Data Science, Associate Professor of Mathematics, University of Texas at Austin

          Monday, November 30, 2020, 4:30 – 5:30 PM Eastern Time

     

    Spring 2021 Programming:

    Tripods Area II is presenting series of virtual colloquia on the topic of machine learning and data science in the field of wireless communications, with potential applications in 5G and beyond-5G systems, IoT networks, and personal wireless devices, organized by Mai Vu:

    • Theodore Rappaport, David Lee/Ernst Weber Professor of Electrical Engineering, Tandon School of Engineering; Professor of Computer Science, Courant Institute of Mathematical Sciences; Professor of Radiology Medicine, NYU Langone; Founding Director of NYU WIRELESS, New York University.

              Friday, March 12, 2021, 10:30 AM - 12:00 PM EST

     

    • Walid Saad, Professor of the Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University

              Friday, March 19, 2021, 10:30 AM - 12:00 PM EST

     

    • Xiaodong Wang, Professor of Electrical Engineering, Columbia University

              Friday, April 16, 2021, 10:30 AM - 12:00 PM EST

     

    • Athina Petropulu, Professor of Electrical and Computer Engineering, School of Engineering, Rutgers The State University of New Jersey

              Friday, April 30, 2021, 10:30 AM - 12:00 PM EST

              POSTPONEMENT Date: Tuesday, May 25, 2021, 3:00 PM - 4:30 PM EST

     

      For more information about this research area, please contacts any of the Tufts faculty working on this aspect of T-TRIPODS: