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to Nov 15

Machine Learning and Statistical Physics

  • The Science Center (Rm 4102) at The Graduate Center, CUNY (map)
  • Google Calendar ICS

The recent surge of activity at the interface of statistical physics and machine learning has brought novel tools and ideas to both fields. Some examples include the information bottleneck appearing as a fundamental lens through which to view neural networks, the renormalization group as a conceptual basis for understanding deep learning, and the identification of phases of matter using methods of machine learning. This workshop brings together a number of researchers taking a statistical physics approach to machine learning with the intention of using insights from physics to understand learning systems.

For more info and to register, visit the event site here.
Download full schedule pdf here..

Tuesday, November 13th
9:00am - Coffee & Bagels
9:30am - A Universal Jeffreys Prior - Jordan Cotler
10:00am - Machine learning for many-body quantum physics - Guiseppe Carleo
10:30am - Break
11:00am - Layer-wise greedy optimization with an eye for RG - Zohar Ringel
11:30am - Neuroscience-based machine learning - Dmitri Chklovskii
12:00pm - Lunch
2:00pm - Density estimation using field theory - Justin Kinney
2:30pm - Discrete priors on simplified models optimize channel capacity from noisy experiments - Benjamin Machta
3:00pm - Break
3:30pm - Learning Quantum Emergence with AI - Eun-Ah Kim
4:00pm - Monte Carlo Study of Small Feedforward Neural Networks - Ariana Mann

Wednesday, November 14th
9:00am - Coffee & Bagels
9:30am - Manifold Tiling with an Unsupervised Neural Net - Anirvan Sengupta
10:00am - Reinforcement Learning to Prepare Quantum States Away from Equilibrium - Marin Bukov
10:30am - Break
11:00am - Quantum control landscapes and the limits of learning - Dries Sels
11:30am - Alex Alemi
12:00pm - Lunch
2:00pm - Entropy & mutual information in models of deep neural networks - Marylou Gabrié
2:30pm - Sloppy models, Differential geometry, and How Science Works - Jim Sethna
3:00pm - Break
3:30pm - Visualizing Probabilities: Intensive Principal Component Analysis - Katherine Quinn
4:00pm - Just do the best you can: statistical physics approaches to reinforcement learning Chris Wiggins
4:30pm - Break
5:00pm - Panel Discussion

Thursday, November 15th
9:00am - Coffee & Bagels
9:30am - Which ReLU Net Architectures Give Rise to Exploding and Vanishing Gradients? - Boris Hanin
10:00am - Neural networks as interacting particle systems - Grant Rotskoff
10:30am - Break
11:00am - SGD Implicitly Regularizes Generalization Error - Dan Roberts
11:30am - Expressiveness in Deep Learning via Tensor Networks and Quantum Entanglement - Nadav Cohen
12:00pm - Normalizing Flows and Canonical Transformations - Austen Lamacraft
12:30pm - Lunch
2:00pm - Discussion

Sponsored by Institute for Complex Adaptive Matter (ICAM) and the Initiative for the Theoretical Sciences.

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to Nov 13

Quantum and Classical Light-Matter Interactions

  • The Graduate Center, CUNY, Rm 9204 (map)
  • Google Calendar ICS

M Nov. 12 & T Nov. 13 (Rm 9204)

The interaction of light and matter has played a pivotal role since the early days of quantum mechanics. Experimental advances together with theoretical simulations in the past decade have led to the control of the dynamics of molecules, even at the attosecond time-scale, and in turn, using matter to generate light pulses with desired features. Strong light-matter coupling can be achieved by confining the system to a cavity, where even the vacuum field can modify molecular properties. The burgeoning field of “polaritonic chemistry” requires a full accounting of the correlated dynamics of electrons, nuclei, and photons. Experts will discuss state-of-the-art developments in fundamentals and applications of both quantum and classical light-matter interactions.

Register here.
Download full schedule pdf here.


9:00-9:30 Coffee and bagels

9:30-10:45 How photons change the properties of matter: QEDFT a first principles framework for modeling light-matter interactions
Angel Rubio, MPI-Hamburg and the Simons Institute, NY

11:00-12:15 Ab initio descriptions of non-perturbative light-matter interactions
Prineha Narang, Harvard University

12:15-1:30 Lunch

1:30-2:45 Strong light-matter interaction in low-dimensional systems
Vinod Menon, City College, CUNY

3:00-4:15 The emergent photochemistry & photophysics of molecular polaritons
Joel Yuen-Zhou, University of California, San Diego

4:30-5:45 Understanding light-matter interactions with quantum-classical intuition: Lessons from Nonadiabatic Dynamics
Joseph Subotnik, U. Penn


9:00-9:30 Coffee and bagels

9:30-10:45 Circularly polarized attosecond pulse generation and applications to ultrafast magnetism
Andre Bandrauk, U. Sherbrooke, Canada

11:00-12:15 Expanded Theory of Molecular J- and H-aggregates
Frank Spano, Temple University

12:15-1:30 Lunch

1:30-2:45 Time resolved spectroscopy of molecular dynamics: Comparing different approaches
Thomas Weinacht, SUNY Stonybrook University

3:00-4:15 Superradiant quantum materials in QED cavities
Antoine Georges, the Simons Institute, NY

4:30-5:00 Closing Remarks

Sponsored by the Initiative for the Theoretical Sciences, and by the CUNY doctoral programs in Chemistry and Physics. Please email with any questions.

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to Sep 14

Bits and Biology

  • The Science Center (Rm 4102), The Graduate Center, CUNY (map)
  • Google Calendar ICS

W Sept. 12 - F Sept. 14, in the Science Center (Rm 4102)

Much of the business of life is about the transmission and processing of information, but it is less clear what the full mathematical structure of information theory teaches us about the mechanisms at work in living systems. We use the 70th anniversary of Shannon’s foundational papers as an opportunity to address this question, across all scales from the folding of individual protein molecules to the dynamics of learning. We will explore the amount of information that is conveyed in these different processes, and the nature of its representation, using information theory as a tool for the characterization of biological systems. More deeply, we will explore examples where optimization of information transmission has been used as a principle from which aspects of biological function can be derived. Presentations will start with pedagogical background, and there will be ample opportunity for discussion.

Download full schedule here.

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