Software
| Author(s) | Title | Description | Language | Documentation | Code | Papers |
|---|---|---|---|---|---|---|
| Cécile Ané | PhyloNetworks | Inference and manipulation of phylogenetic networks, and their use for trait evolution | Julia | https://crsl4.github.io/PhyloNetworks.jl/dev/ | https://github.com/crsl4/PhyloNetworks.jl | https://doi.org/10.1093/molbev/msx235 |
| Cécile Ané | PhyloPlots | phylogenetic network visualization | https://cecileane.github.io/PhyloPlots.jl/dev | |||
| Cécile Ané | QuartetNetworkGoodnessFit | phylogenetic networks analyses using four-taxon subsets | https://cecileane.github.io/QuartetNetworkGoodnessFit.jl/dev/ | |||
| Cécile Ané | PhyloCoalSimulations | simulate phylogenies under the coalescent | https://cecileane.github.io/PhyloCoalSimulations.jl/dev/ | |||
| Vivak Patel, Daniel Adrian Maldonado | RLinearAlgebra.jl | Deploy randomized linear solvers | Julia | https://numlinalg.github.io/RLinearAlgebra.jl/dev/ | https://github.com/numlinalg/RLinearAlgebra.jl | |
| Sameer Deshpande | flexBART | A faster and more flexible implementation of Bayesian Additive Regression Trees | R, C++ | https://github.com/skdeshpande91/flexBART | https://arxiv.org/abs/2211.04459 | |
| Owen Melia, Eric Jonas, Rebecca Willett | Rotation Invariant Random Features | Code for "Rotation-Invariant Random Features Provide a Strong Baseline for Machine Learning on 3D Point Clouds" | Python | https://github.com/meliao/rotation-invariant-random-features | ||
| Suzanna Parkinson, Greg Ongie, Rebecca Willett | Linear layers in neural networks | Code for https://arxiv.org/abs/2305.15598 | Python | https://github.com/suzannastep/linearlayers | https://arxiv.org/abs/2305.15598 | |
| Elena Orlova, Aleksei Ustimenko, Ruoxi Jiang, Peter Y. Lu, Rebecca Willett | Deep Stochastic Mechanics | Code for https://arxiv.org/abs/2305.19685 | Python | https://github.com/elena-orlova/deep-stochastic-mechanics | https://arxiv.org/abs/2305.19685 | |
| Yuming Chen, Daniel Sanz-Alonso, Rebecca Willett | Reduced-Order Autodifferentiable Ensemble Kalman Filters (ROAD-EnKF) | Code for https://arxiv.org/abs/2301.11961 | Python | https://github.com/ymchen0/ROAD-EnKF | https://arxiv.org/abs/2301.11961 | |
| Yuming Chen, Daniel Sanz-Alonso, Rebecca Willett | Auto-differentiable Ensemble Kalman Filters (AD-EnKF) | Code for https://epubs.siam.org/doi/abs/10.1137/21M1434477 | Python | https://github.com/ymchen0/torchEnKF | https://epubs.siam.org/doi/abs/10.1137/21M1434477 | |
| Elena Orlova, Haokun Liu, Raphael Rossellini, Benjamin Cash, Rebecca Willett | Beyond Ensemble Averages: Leveraging Climate Model Ensembles for Subseasonal Forecasting | Code for https://arxiv.org/abs/2211.15856 | Python | https://github.com/elena-orlova/SSF-project | https://arxiv.org/abs/2211.15856 | |
| Ruoxi Jiang, Rebecca Willett | Embed and Emulate: Learning to estimate parameters of dynamical systems with uncertainty quantification | Code for https://arxiv.org/abs/2211.01554 | Python | https://github.com/roxie62/Embed-and-Emulate | https://arxiv.org/abs/2211.01554 | |
| Joseph Shenouda, Rahul Parhi, Kangwook Lee, Robert D. Nowak | Vector-Valued Variation Spaces and Width Bounds for DNNs: Insights on Weight Decay Regularization | Code for https://arxiv.org/abs/2305.16534 | Python | https://github.com/joeshenouda/vv-spaces-nn-width | https://arxiv.org/abs/2305.16534 | |
| Karan Srivastava | Generating large isosceles-free lattice subsets with reinforcement learning | Code for training models and repository maintained for research | Python | https://github.com/ksrivastava1/isosceles_triangles_rl | ||
| Karl Rohe | longpca | This package introduces a novel formula syntax for PCA. In modern applications (where data is often in "long format"), the formula syntax helps to fluidly imagine PCA without thinking about matrices. In other words, it provides a layer of abstraction above matrices. | https://karlrohe.github.io/longpca/ | |||
| Karl Rohe | gdim | This package estimates graph dimension using cross-validated eigenvalues, via the graph-splitting technique. Theoretically, the method works by computing a special type of cross-validated eigenvalue which follows a simple central limit theorem. This allows users to perform hypothesis tests on the rank of the graph. | https://rohelab.github.io/gdim/ | https://arxiv.org/abs/2108.03336 | ||
| Nicholas Henderson/Michael Newton | rvalues | A collection of functions for computing "r-values" from various kinds of user input such as MCMC output or a list of effect size estimates and associated standard errors | https://cran.r-project.org/web/packages/rvalues/index.html | |||
| Zihao Zheng/Michael A. Newton | MixTwice | Implements large-scale hypothesis testing by variance mixing. It takes two statistics per testing unit, an estimated effect and its associated squared standard error, and fits a nonparametric, shape-constrained mixture separately on two latent parameters | https://cran.r-project.org/web/packages/MixTwice/index.html |
Books
Ellenberg, Jordan, Shape: The Hidden Geometry of Information, Biology, Strategy, Democracy, and Everything Else, Penguin Books, 2022. https://www.penguinrandomhouse.com/books/612131/shape-by-jordan-ellenberg/
Wright, Stephen J., and Benjamin Recht, Optimization for Data Analysis, Cambridge University Press, 2022. https://doi.org/10.1017/9781009004282
Diakonikolas, Ilias, and Daniel M. Kane, Algorithmic High-Dimensional Robust Statistics, Cambridge University Press, 2023. https://doi.org/10.1017/9781108943161
Nan Chen, Stochastic Methods for Modeling and Predicting Complex Dynamical Systems, Springer Cham, 2024. https://doi.org/10.1007/978-3-031-22249-8
Roch, Sebastien, Modern Discrete Probability: An Essential Toolkit, Cambridge University Press, 2024. https://doi.org/10.1017/9781009305129
Educational Materials & Tools
Online textbook on “Mathematical Methods in Data Science (with Python)” by Sebastien Roch. https://mmids-textbook.github.io/
Online tutorial on “Comparative methods on reticulate phylogenies”. https://cecileane.github.io/networkPCM-workshop/
Online textbook on “Causal Inference” by Amy Cochran: https://amy-cochran.gitbook.io/causal-inference
Lecture notes for a year-long course on the “Mathematics of Data Science” by Dmitriy Drusvyatskiy: https://sites.math.washington.edu/~ddrusv/crs/Math_581_2023/MATH581.html
Lecture notes and video lectures for a course on the “Mathematical Foundations of Machine Learning” by Rebecca Willett: https://willett.psd.uchicago.edu/teaching/mathematical-foundations-of-machine-learning-fall-2021/
Autograder: a server to automatically grading coding assignments. https://github.com/edulinq/autograder-server
Quiz Generator: allows a general format for quiz banks, that then can be uploaded in a variety of forms (gradescope, Canvas, pdf, html, qti, etc.); support for latex, wide variety of question types; and allows support for collaboration by using standard tools like git for managing questions. https://github.com/edulinq/quizgen
Canvas Tools: a suite of tools and Python interface for Instructure’s Canvas LMS. https://github.com/edulinq/py-canvas