COMPUTATIONAL RESEARCH in BOSTON and BEYOND (CRIBB)
Welcome! This is an archive page for a previous or upcoming year of the Computational Research in Boston and Beyond Seminar (CRiBB). To see current seminar information visit the Home Page.
To subscribe to a low-traffic mailing list for announcements related to the forum, please visit the CRiB-list web page.
For more information, e-mail Professor Alan Edelman (edelman AT math.mit.edu) and/or Professor Jeremy Kepner (kepner AT ll.mit.edu).
Organizers : 2022
| Dr. Patrick Dreher | (MIT - Laboratory for Nuclear Science) |
| Professor Alan Edelman | (MIT - Math & CSAIL) |
| Dr. Chris Hill | (MIT - Earth and Atmospheric Science) |
| Professor Steven G. Johnson | (MIT - Math & RLE) |
| Dr. Jeremy Kepner | (MIT - Lincoln Laboratory) |
| Dr. Albert Reuther | (MIT - Lincoln Laboratory) |
Meetings : 2022
Meetings will be held virtually via ZOOM on the first Friday of the month from 12:00 PM - 1:00 PM. Upcoming talks are listed below:
https://mit.zoom.us/j/96155042770 | Meeting ID: 961 5504 2770
| Jan 7 |
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| Feb 4 |
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| Mar 4 |
Justin Finkel (University of Chicago) "Short weather forecasts inform long-term climatology of sudden stratospheric warming" |
| Apr 1 |
Paul Zhang (Massachusetts Institute of Technology) "Local Decomposition of Hexahedral Singular Nodes into Singular Curves" |
| May 6 |
Madelyn Cain (Harvard University) "Quantum Optimization of Maximum Independent Set using Rydberg Atom Arrays" |
| Jun 3 |
Dipti Jasrasaria (University of California,Berkeley) "Interplay of Surface and Interior Modes in Exciton-Phonon Coupling at the Nanoscale" |
| Jul 1 |
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| Aug 5 |
Baolin Li (Northeastern University) "Leveraging Heterogeneous Hardware Resources for Efficient Machine Learning Inference Service" |
| Sep 2 |
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| Oct 7 |
Christiane Adcock (Stanford) "Hybrid Modeling for Energy System Simulation and Control" |
| Nov 4 |
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| Dec 2 |
Kyle Ravi Lennon (Massachusetts Institute of Technology) "Math, Methods, and Models for Data-Driven Rheology" |
| Dec 16 |
Cristina Martin-Linares (The Johns Hopkins University) Physics-assisted Machine-learning Models in Fluid Mechanics and Agent-based Systems |
Archives
Acknowledgements
We thank the generous support of MIT IS&T, CSAIL, and the Department of Mathematics for their support of this series.