Name: Flatiron Internal Conference 2022: Flatiron-wide Algorithms and Mathematics
Start: 2022-10-27T08:15:00-04:00
End: 2022-10-28T21:00:00-04:00
Location: 162 5th Avenue

Flatiron Internal Conference 2022: Flatiron-wide Algorithms and Mathematics

from Thursday, October 27, 2022 (8:15 AM) to Friday, October 28, 2022 (9:00 PM)

Monday, October 24, 2022
Tuesday, October 25, 2022
Wednesday, October 26, 2022
Thursday, October 27, 2022

9:30 AM Welcome
Welcome
9:30 AM - 9:40 AM
Room: 2nd Floor: Ingrid Daubechies Auditorium

9:40 AM Interpolation and integration in low dimensions (tutorial/overview)
Interpolation and integration in low dimensions (tutorial/overview)
9:40 AM - 10:40 AM
Room: 2nd Floor: Ingrid Daubechies Auditorium

10:40 AM Maximum entropy closure for kinetic theories of complex fluids (case study)
Maximum entropy closure for kinetic theories of complex fluids (case study)
10:40 AM - 11:10 AM
Room: 2nd Floor: Ingrid Daubechies Auditorium

11:10 AM

11:10 AM - 11:30 AM
Room: 2nd Floor: Ingrid Daubechies Auditorium

11:30 AM Compressing functions with tensor networks: Applications to PDEs and DFTs (tutorial/overview)
Compressing functions with tensor networks: Applications to PDEs and DFTs (tutorial/overview)
11:30 AM - 12:30 PM
Room: 2nd Floor: Ingrid Daubechies Auditorium

12:30 PM Lunch
Lunch
12:30 PM - 1:50 PM
Room: 2nd Floor: Ingrid Daubechies Auditorium

1:50 PM Tensor network compression for high dimensional integration and its application to Feynman diagrams (case study)
Tensor network compression for high dimensional integration and its application to Feynman diagrams (case study)
1:50 PM - 2:20 PM
Room: 2nd Floor: Ingrid Daubechies Auditorium

2:20 PM Numerical solution of ODEs: a practical guide (tutorial/overview)
Numerical solution of ODEs: a practical guide (tutorial/overview)
2:20 PM - 3:20 PM
Room: 2nd Floor: Ingrid Daubechies Auditorium

3:20 PM Coffee break
Coffee break
3:20 PM - 3:40 PM
Room: 2nd Floor: Ingrid Daubechies Auditorium

3:40 PM Physical considerations for when time integration of gas dynamics fails (case study)
Physical considerations for when time integration of gas dynamics fails (case study)
3:40 PM - 4:10 PM
Room: 2nd Floor: Ingrid Daubechies Auditorium

4:10 PM Solving ODEs in a Bayesian model (case study)
Solving ODEs in a Bayesian model (case study)
4:10 PM - 4:40 PM
Room: 2nd Floor: Ingrid Daubechies Auditorium
Friday, October 28, 2022

9:30 AM What every programmer should know about HPC (tutorial/overview)
What every programmer should know about HPC (tutorial/overview)
9:30 AM - 10:30 AM
Room: 2nd Floor: Ingrid Daubechies Auditorium

10:30 AM How not to write a high-performance N-body code (case study)
How not to write a high-performance N-body code (case study)
10:30 AM - 11:00 AM
Room: 2nd Floor: Ingrid Daubechies Auditorium

11:00 AM Coffee break
Coffee break
11:00 AM - 11:20 AM
Room: 2nd Floor: Ingrid Daubechies Auditorium

11:20 AM GPUs: the good, the bad, and the ugly (tutorial/overview)
GPUs: the good, the bad, and the ugly (tutorial/overview)
11:20 AM - 12:20 PM
Room: 2nd Floor: Ingrid Daubechies Auditorium

12:20 PM Lunch
Lunch
12:20 PM - 1:40 PM
Room: 2nd Floor: Ingrid Daubechies Auditorium

1:40 PM Intro to machine learning by building Scikit-Learn estimators from scratch (tutorial/overview)
Intro to machine learning by building Scikit-Learn estimators from scratch (tutorial/overview)
1:40 PM - 2:40 PM
Room: 2nd Floor: Ingrid Daubechies Auditorium

2:40 PM Building a connectome of the mini-wasp brain using deep learning (case study)
Building a connectome of the mini-wasp brain using deep learning (case study)
2:40 PM - 3:10 PM
Room: 2nd Floor: Ingrid Daubechies Auditorium

3:10 PM Coffee break
Coffee break
3:10 PM - 3:30 PM
Room: 2nd Floor: Ingrid Daubechies Auditorium

3:30 PM Stochastic gradient descent, tricks, learning rates, and momentum (tutorial/overview)
Stochastic gradient descent, tricks, learning rates, and momentum (tutorial/overview)
3:30 PM - 4:30 PM
Room: 2nd Floor: Ingrid Daubechies Auditorium

4:30 PM Improving exoplanet detection with discriminative linear regression (case study)
Improving exoplanet detection with discriminative linear regression (case study)
4:30 PM - 5:00 PM
Room: 2nd Floor: Ingrid Daubechies Auditorium

5:00 PM Reception
Reception
5:00 PM - 7:00 PM
Room: 2nd Floor: Ingrid Daubechies Auditorium

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