2019 Flatiron Wide Algorithms and Mathematics
FWAM (“Flatiron-wide Autumn Meeting”, previously “Flatiron-wide Algorithms and Mathematics”) is a two-day internal conference with the goal of introducing and reviewing scientific and computational tools of broad and significant usefulness to Flatiron researchers across all centers of the institute.
Agenda
October 30, 2019
OP: Optimization, FADE: Function Approximation and Differential Equations, SA: Sampling, DL: Deep Learning, DRF: Dimension Reduction and Factorization
| Time | Topic | Speaker |
| 8:15AM-9:00AM | Breakfast | |
| 9:00AM-9:10AM | Welcome | |
| 9:10AM-10:10AM | (OP) Optimization landscapes - a gentle multi-disciplinary introduction to optimization | Christian L. Mueller |
| 10:10AM-10:20AM | Break | |
| 10:20AM-11:20AM | (OP) Optimization for Machine Learning | Elad Hazan |
| 11:20AM-11:40AM | Break | |
| 11:40AM-12:05PM | (OP) A practical introduction to adjoint methods | Leslie Greengard |
| 12:05PM-12:30PM | (OP) Research Applications of Optimization: Data-Driven Spectroscopy | (OP) Research Applications of Optimization: Data-Driven Spectroscopy |
| 12:30PM-2:00PM | Lunch | |
| 2:00PM-3:00PM | (FADE) Introduction to interpolation, integration and spectral methods | Alex Barnett |
| 3:00PM-3:10PM | Break | |
| 3:10PM-3:40PM | (FADE) Overview of various methods to solve differential equations | Keaton Burns |
| 3-40PM-4:00PM | Break | |
| 4:00PM-4:25PM | (FADE) PDEs: The long and the short. | Michael Shelley |
| 4:25PM-4:50PM | (FADE) Introduction to Integral Equation Methods | Jun Wang |
| 4:50PM-5:15PM | (FADE) Wavelets | Joakim Andén |
| 5:15PM-6:15PM | Reception |
October 31, 2019
OP: Optimization, FADE: Function Approximation and Differential Equations, SA: Sampling, DL: Deep Learning, DRF: Dimension Reduction and Factorization
| Time | Topic | Speaker |
| 8:15AM-9:00AM | Breakfast | |
| 9:00AM-9:45AM | (SA) Introduction to Markov chain Monte Carlo | Dan Foreman-Mackey |
| 9:45AM-9:55AM | Break | |
| 9:55AM-10:40AM | (SA) Scalable Bayesian Inference | Mariano Gabitto |
| 10:40AM-11:00AM | Break | |
| 11:00AM-11:50AM | (SA) The quantum-ness in quantum Monte Carlo: mathematical and algorithmic implications | Shiwei Zhang |
| 11:50AM-12:10PM | (SA) Hierarchical Modeling and Stellar Velocities | Emily Cunningham |
| 12:00PM-2:00PM | Lunch | |
| 2:00PM-2:45PM | (DL) Introduction to Deep Learning | Gabriella Contardo |
| 2:45PM-2:55PM | Break | |
| 2:55PM-3:40PM | (DL) Introduction to Deep Learning | Shirley Ho |
| 3:40PM-4:00PM | Break | |
| 4:00PM-4:25PM | (DL) Uncertainty Estimation with Neural Networks | |
| 4:25PM-4:50PM | (DL) Deep Generative Modeling for Statistical and Quantum Physics | Laurence Levasseur |
| 4:50PM-5:15PM | (DL) Biological neural network algorithms | Giuseppe Carleo |
| 5:15PM-6:15PM | Reception | Mitya Chklovskii |
November 1, 2019
OP: Optimization, FADE: Function Approximation and Differential Equations, SA: Sampling, DL: Deep Learning, DRF: Dimension Reduction and Factorization
| Time | Topic | Speaker |
| 8:30AM-9:15AM | Breakfast | |
| 9:15AM-9:40AM | (DRF) Randomized linear algebra and matrix approximation | Eftychios Pnevmatikakis |
| 9:40AM-10:05AM | (DRF) Spectral Clustering and Dimensionality Reduction | Marina Spivak |
| 10:05AM-10:30AM | (DRF) Clustering in low dimensions | Jeremy Magland |
| 10:30AM-10:50AM | Break | |
| 10:50AM-11:35AM | (DRF) Fast algorithms for hierarchically structured matrices | Manas Rachh |
| 11:35AM-12:00PM | (DRF) The why and how of nonnegative matrix factorization | Johannes Friedrich |
| 12:00PM-12:25PM | (DRF) Introduction to Tensor Network Methods | Katharine Hyatt |