Computations in Science Seminars
Apr 2019
24
Wed 12:15
Risi Kondor, University of Chicago
e-mail:
Host: William Irvine ()
Organizer: Steven Strong ()
Covariant neural network architectures for learning physics

Deep neural networks have proved to be extremely effective in image recognition, machine translation, and a variety of other data centered engineering tasks. However, generalizing neural networks to learning physical systems requires a careful examination of how they reflect symmetries. In this talk we give an overview of recent developments in the field of covariant/equivariant neural networks. Specifically, we focus on three applications: learning properties of chemical compounds from their molecular structure, image recognition on the sphere, and learning force fields for molecular dynamics. The work presented in this talk was done in collaboration with Brandon Anderson, Zhen Lin, Truong Son Hy, Horace Pan, and Shubhendu Trivedi.

May 2019
1
Wed 12:15
Pankaj Mehta, Boston University
e-mail:
Host: Stefano Allesina ()
Organizer: Elizabeth Lee ()
Toward a Statistical Mechanics of Microbiomes

A major unresolved question in microbiome research is whether the complex ecological patterns observed in surveys of natural communities can be explained and predicted by fundamental, quantitative principles. Bridging theory and experiment is hampered by the multiplicity of ecological processes that simultaneously affect community assembly and a lack of theoretical tools for modeling diverse ecosystems. In the first part of the talk, I will present a simple ecological model of microbial communities that reproduces large-scale ecological patterns observed across multiple experimental settings including compositional gradients, clustering by environment, diversity/harshness correlations, and nestedness. Surprisingly, our model works despite having a “random metabolisms” and “random consumer preferences”. This raises the natural of question of why random ecosystems can describe real-world experimental data. In the second, more theoretical part of the talk, I will answer this question by showing that when a community becomes diverse enough, it will always self-organize into a stable state whose properties are well captured by a “typical random ecosystems”. If time permits, I will also highlight surprising connections between ecological dynamics, constrained optimization, and kernel-based machine learning methods such as Support Vector Machines.

Talk is based on: Advani et al J. Stat. Phys (2018); Golford et al Science (2018); Marsland et al. PLoS Comp Bio (2019); arXiv:1809.04221;arXiv:1901.09673; arXiv:1904.02610; unpublished

May 2019
2
Thu 4:00 PM
Phil Morrison, University of Texas, Austin
e-mail:
Host: Daniel Sanz-Alonso ()
Organizer: Peter Chung ()
Joint CAM Colloquium: 4 PM in Jones 226
May 2019
8
Wed 12:15
Thierry Emonet, Yale University
e-mail:
Host: Stephanie Palmer ()
Organizer: Zhiyue Lu ()
May 2019
15
Wed 12:15
David Lentik, Stanford
e-mail:
Host: William Irvine ()
Organizer: Peter Chung ()
May 2019
22
Wed 12:15
Joshua Shaevitz, Princeton University
e-mail:
Host: Arvind Murugan ()
May 2019
29
Wed 12:15
Xiaoming Mao, University of Michigan
e-mail:
Host: William Irvine () and Vincenzo Vitelli ()
Nov 2019
13
Wed 12:15
Orit Peleg, University of Colorado
e-mail:
Host: Arvind Murugan ()
Nov 2019
20
Wed 12:15
W. Benjamin Rogers, Brandeis University
e-mail:
Host: Arvind Murugan ()
Jan 2020
8
Wed 12:15
Bob Rosner, University of Chicago
e-mail:
Host: William Irvine ()