Flatiron Internal Conference 2022: Flatiron-wide Algorithms and Mathematics

Contribution List

28. Building a connectome of the mini-wasp brain using deep learning

Jingpeng Wu (CCN)

19. Compressing functions with tensor networks: Applications to PDEs and DFTs

Matt Fishman, Miles Stoudenmire (Flatiron CCQ)

26. GPUs: the good, the bad, and the ugly

Geraud Krawezik (SCC), Robert Blackwell (SCC)

25. How not to write a high-performance N-body code

Lehman Garrison (SCC)

30. Improving exoplanet detection with discriminative linear regression

Lily Zhao (CCA)

18. Interpolation and integration in low dimensions

Manas Rachh (Flatiron)

27. Intro to machine learning by building Scikit-Learn estimators from scratch

Michael Eickenberg (CCM)

31. Maximum entropy closure for kinetic theories of complex fluids

Scott Weady (CCB)

21. Numerical solution of ODEs: a practical guide

Fruzsina Agocs (CCM)

22. Physical considerations for when time integration of gas dynamics fails

Chris White (CCQ)

23. Solving ODEs in a Bayesian model

Charles Margossian (CCM)

29. Stochastic gradient descent, tricks, learning rates, and momentum

Robert Gower (CCM)

20. Tensor network compression for high dimensional integration and its application to Feynman diagrams

Olivier Parcollet (CCQ)

24. What every programmer should know about HPC

Dhairya Malhotra