Exploring and Exploiting High-dimensional Phenomena in Statistical Learning and Inference
The massive datasets being compiled by our society present new challenges and opportunities to the field of statistical learning and inference. The increasing dimensionality of modern datasets lead to unique geometric and probabilistic phenomena, including scaling limits, phase transitions, and universality. A deeper understanding and clever exploitation of such fascinating (and sometimes counter-intuitive) high-dimensional phenomena can translate to both theoretical breakthroughs and novel algorithms.
In this talk, I will present several lines of recent work on signal estimation and multilayer neural networks where such high-dimensional phenomena are explored and exploited.
Date and Time
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- Date: 14 Sep 2022
- Time: 12:00 PM to 01:00 PM
- All times are (GMT-05:00) US/Eastern
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- 1000 River Road
- Teaneck , New Jersey
- United States 07666
- Building: Muscarelle Center, M105,
- Room Number: M105
- Contact Event Hosts
- Co-sponsored by Fairleigh Dickinson University
- Starts 12 July 2022 12:00 PM
- Ends 14 September 2022 01:00 PM
- All times are (GMT-05:00) US/Eastern
- No Admission Charge
Speakers
Dr. Yue M. Lu of John A. Paulson School of Engineering and Applied Sciences, Harvard University
Exploring and Exploiting High-dimensional Phenomena in Statistical Learning and Inference
The massive datasets being compiled by our society present new challenges and opportunities to the field of statistical learning and inference. The increasing dimensionality of modern datasets lead to unique geometric and probabilistic phenomena, including scaling limits, phase transitions, and universality. A deeper understanding and clever exploitation of such fascinating (and sometimes counter-intuitive) high-dimensional phenomena can translate to both theoretical breakthroughs and novel algorithms.
In this talk, I will present several lines of recent work on signal estimation and multilayer neural networks where such high-dimensional phenomena are explored and exploited.
Biography:
Dr. Yue M. Lu attended the University of Illinois at Urbana-Champaign, where he received the M.Sc. degree in mathematics and the Ph.D. degree in electrical engineering, both in 2007. After his postdoctoral training at the Audiovisual Communications Laboratory at Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland, he joined Harvard University, where he is currently Gordon McKay Professor of Electrical Engineering and of Applied Mathematics at the John A. Paulson School of Engineering and Applied Sciences. He is also fortunate to have held visiting appointments at Duke University in 2016 and at the École Normale Supérieure (ENS) in 2019. His research interests include the mathematical foundations of statistical signal processing and machine learning in high dimensions.
Address:United States
Agenda
The massive datasets being compiled by our society present new challenges and opportunities to the field of statistical learning and inference. The increasing dimensionality of modern datasets lead to unique geometric and probabilistic phenomena, including scaling limits, phase transitions, and universality. A deeper understanding and clever exploitation of such fascinating (and sometimes counter-intuitive) high-dimensional phenomena can translate to both theoretical breakthroughs and novel algorithms.
In this talk, I will present several lines of recent work on signal estimation and multilayer neural networks where such high-dimensional phenomena are explored and exploited.