Symposium 2019

The 2019 MINDS Symposium on the Foundations of Data Science will be held November 20, 2019 in Shriver Hall.

Leading data science researchers will discuss the importance of data science in their fields and the continued need for advancement.

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Confirmed Speakers

Raman Arora
Raman Arora
Johns Hopkins University
Sanjeev Arora
Sanjeev Arora
Princeton University
Joan Bruna
Joan Bruna
New York University
Arnaud Doucet
Arnaud Doucet
University of Oxford / DeepMind
Anna Gilbert
Anna Gilbert
University of Michigan
Charles Meneveau
Charles Meneveau
Johns Hopkins University
Amit Singer
Amit Singer
Princeton University
Jeremias Sulam
Jeremias Sulam
Johns Hopkins University
Archana Venkataraman
Archana Venkataraman
Johns Hopkins University
Martin Wainwright
Martin Wainwright
University of California at Berkeley
Rachel Ward
Rachel Ward
UT Austin

Agenda

TimeSpeakerAffiliationTitle of Presentation

Opening Ceremony

9:00Ed SchlesingerBenjamin T. Rome Dean, Whiting School of Engineering, Johns Hopkins UniversityIntroductory Remarks
9:10René VidalHerschel Seder Professor of Biomedical Engineering, Director of the Mathematical Institute for Data Science, Johns Hopkins UniversityAnnoucement of TRIPODS & Awards Ceremony

Session 1

9:30Sanjeev AroraCharles C. Fitzmorris Professor of Computer Science, Princeton UniversityIs optimization the right language to understand deep learning?
10:00Raman AroraAssistant Professor of Computer Science, Johns Hopkins UniversityOnline Learning in Reactive Environments
10:15Lin YangAssistant Professor of Electrical & Computer Engineering, UCLATaming big data by streaming
10:30Coffee Break

Session 2

11:00Martin WainwrightChancellor's Professor of Statistics, Electrical Engineering and Computer Sciences, UC BerkeleyFrom Optimization to Statistical Learning: A View from the Interface
11:30Arnaud DoucetProfessor of Statistics, University of Oxford & Research Scientist, DeepMindExact Simulation for State-Space Models
12:00Rachel WardAssociate Professor of Mathematics, University of Texas at AustinConcentration inequalities for products of independent matrices
12:30Lunch Break2nd Floor of Shriver Hall

Session 3

14:00Joan BrunaAssistant Professor of Computer Science, Courant Institute and Center for Data Science, NYUMathematics of Deep Learning: myths, truths and enigmas
14:30Anna GilbertHerman H. Goldstine Collegiate Professor of Mathematics, University of MichiganLearning Metric Representations: Theory and Applications
15:00Jeremias SulamAssistant Professor of Biomedical Engineering, Johns Hopkins UniversitySparse Priors in Neural Architectures
15:15Chong YouPostdoctoral Researcher, University of California, BerkeleySparse Methods for Learning Multiple Subspaces from Large-scale, Corrupted and Imbalanced Data
15:30Coffee Break

Session 4

16:00Amit SingerProfessor Department of Mathematics, Princeton UniversityMathematics of Cryo-Electron Microscopy
16:30Charles MeneveauLouis M. Sardella Professor in Mechanical Engineering, Associate Director of IDIES, Johns Hopkins UniversityFluid Turbulence and Learning from Big Numerical Data Sets
17:00Archana VenkataramanJohn C. Malone Assistant Professor of Electrical Engineering, Johns Hopkins UniversityGenerative-Deep Hybrids for Decoding the Brain
17:15Closing