# Machine Learning at the Flatiron Institute

- Center for Computational Mathematics
- Center for Computational Neuroscience
- Center for Computational Astrophysics
- Center for Computational Biology
- Center for Computational Quantum Physics

In recent years machine learning has emerged as an indispensable tool for computational science. It is also an active and growing area of study throughout the Flatiron Institute. Researchers at Flatiron are especially interested in the core areas of deep learning, probabilistic modeling, optimization, learning theory and high dimensional data analysis. They are also applying machine learning to problems in cosmological modeling, quantum many-body systems, computational neuroscience and bioinformatics. Below is a list of researchers who work in these areas; prospective visitors should feel free to contact them for more information.

### Researchers in CCM

### Alberto Bietti

Research Scientist, CCM**Areas of Interest:**Learning theory, optimization, deep learning, kernel methods

__David Blei__

Visiting Scholar, CCM**Areas of Interest:**Topic models, probabilistic modeling, approximate Bayesian inference

__Joan Bruna__

Visiting Scholar, CCM**Areas of Interest:**Learning theory, deep learning, machine learning for science, high dimensional statistics, algorithms

__Andreas Buja__

Senior Research Scientist, CCM**Areas of Interest:**Statistical methodology, model misspecification, replicability, causality, applications in the genetics of autism

__Bob Carpenter__

Senior Research Scientist, CCM**Areas of Interest:**Probabilistic programming, Markov chain Monte Carlo methods, variational inference

__Michael Eickenberg__

Research Scientist, CCM**Areas of Interest:**Machine learning for science, applied statistics and signal processing, deep learning, neuroimaging and computational cognitive neuroscience

__Anna Gilbert__

Visiting Scholar, CCM**Areas of Interest:**Theory and algorithms for high dimensional data analysis, metric representations, non-Euclidean embeddings

__Robert Gower__

Research Scientist, CCM**Areas of Interest:**Stochastic optimization, interpolation, adaptive methods for deep learning, convergence of algorithms and second order methods

__Jiequn Han__

Flatiron Research Fellow, CCM**Areas of Interest:**Multiscale modeling, numerical methods for partial differential equations, machine learning for science

__Stephane Mallat__

Distinguished Research Scientist, CCM**Areas of Interest:**Signal processing, harmonic analysis, deep learning

__Charles Margossian__

Flatiron Research Fellow, CCM**Areas of Interest:**Probabilistic programming, Markov chain Monte Carlo methods, variational inference

__Ruben Ohana__

Flatiron Research Fellow, CCM**Areas of Interest:**Deep learning, randomized algorithms, high dimensional statistics, differential privacy

__Loucas Pillaud-Viven__

Flatiron Research Fellow, CCM**Areas of Interest:**Learning theory, optimization, deep learning

__Bruno Régaldo-Saint Blancard__

Flatiron Research Fellow, CCM**Areas of Interest:**Machine learning for astrophysics, applied signal processing, generative modeling

__Lawrence Saul__

Group Leader, Machine Learning, CCM**Areas of Interest:**High dimensional data analysis, latent variable models, deep learning, variational inference, kernel methods

__Neha Wadia__

Flatiron Research Fellow, CCM**Areas of Interest:**Learning theory, continuous-time optimization, high dimensional statistics

__Yuling Yao__

Flatiron Research Fellow, CCM**Areas of Interest:**Scalable Bayesian workflows, meta-learning, causal inference

__Wenda Zhou__

Flatiron Research Fellow, CCM**Areas of Interest:**Deep learning for structured data (e.g., molecular graphs, CAD models, scientific imaging)

### Researchers in CCN

__Mitya Chklovskii__

Group Leader, Neural Circuits and Algorithms, CCN**Areas of Interest:**Theoretical neuroscience, connectomics, biologically inspired AI, dynamics and control

__SueYeon Chung__

Project Leader, Geometric Data Analysis, CCN**Areas of Interest:**Theoretical neuroscience, statistical physics of learning, high dimensional geometry and statistics

### Jenelle Feather

Flatiron Research Fellow, CCN**Areas of Interest:**Theoretical neuroscience, analysis of high dimensional auditory and visual representations

__Siavash Golkar__

Associate Research Scientist, Neural Circuits and Algorithms, CCN**Areas of Interest:**Biological learning, deep learning, machine learning for science

### Sarah Harvey

Flatiron Research Fellow, CCN**Area of Interest:**Theoretical neuroscience, statistical physics, ML methods for neural data analysis

__Brett Larsen__

Flatiron Research Fellow, CCN/CCM**Areas of Interest:**Deep learning, optimization, loss-landscape analysis, sparsity, high-dimensional statistics

__David Lipshutz__

Associate Research Scientist, CCN**Areas of Interest:**Theoretical neuroscience, neuro-inspired ML, stochastic analysis, dynamical systems

__Amin Nejatbakhsh__

Flatiron Research Fellow, CCN**Areas of Interest:**Computational neuroscience, machine learning, statistics, dynamical systems, computer vision

__Eero Simoncelli__

Director, CCN**Areas of Interest:**Analysis and representation of visual information in biological and artificial networks. Coding and inference

__Tiberiu Tesileanu__

Associate Research Scientist, Neural Circuits and Algorithms, CCN**Areas of Interest:**Biological learning, deep learning

__Alex Williams__

Associate Research Scientist, Statistical Analysis of Neural Data, CCN**Areas of Interest:**Unsupervised learning, uncertainty quantification in deep learning, topological data analysis, covariance estimation

### Researchers in CCQ

__Anna Dawid__

Flatiron Research Fellow, CCQ**Areas of Interest:**Machine learning for (quantum) science, interpretability, deep learning theory

__Domenico Di Sante__

Affiliate Research Fellow, CCQ**Areas of Interest:**Theoretical neuroscience, statistical physics of learning, high dimensional geometry and statistics

### Matija Medvidović

Graduate Student, CCQ**Areas of Interest:**Machine learning for many-body quantum physics, sampling, optimization

__Andrew Millis__

Co-Director, CCQ**Areas of Interest:**Theoretical condensed matter physics, high-temperature superconductivity, numerical methods for the many-electron problem

### Javier Robledo Moreno

Graduate Student, CCQ**Areas of Interest:**Machine learning for many-body quantum physics, neural network representation of quantum states, quantum computing

__Anirvan Sengupta__

Visiting Scholar, CCQ**Areas of Interest:**Representation learning, dynamics and control, applications to quantum systems, systems neuroscience

__Jiawei Zang__

Graduate Student, CCQ**Areas of Interest:**Machine learning for many-body quantum physics, dimensionality reduction

### Researchers in CCB

__Xi Chen__

Research Scientist, CCB**Areas of Interest:**Distribution learning, Markov chain Monte Carlo, semi-supervised learning

__Adam Lamson__

Flatiron Research Fellow, CCB**Areas of Interest:**Interpretable neural networks, biophysical and genomics modeling, reservoir computing

__Suryanarayana Maddu__

Flatiron Research Fellow, CCB**Areas of Interest:**Physics-informed machine learning, statistical learning theory, high dimensional statistics

__Zhicheng Pan__

Flatiron Research Fellow, CCB**Areas of Interest:**Deep learning for genomics, graphical neural networks

__Christopher Park__

Research Scientist, CCB**Areas of Interest:**Probabilistic modeling, deep learning and statistical genetics

__Natalie Sauerwald__

Flatiron Research Fellow, CCB__Natalie Sauerwald__

**Areas of Interest:**Machine learning for genomics and genetics, optimization, interpretable models

__Rachel Sealfon__

Research Scientist, CCB**Areas of Interest:**Machine learning for genomics, analysis of functional genomic data

__Mao Weiguang__

Flatiron Research Fellow, CCB**Areas of Interest:**Deep learning, graphical models, dimensionality reduction

### Researchers in CCA

__Miles Cranmer__

Flatiron Research Fellow, CCA**Areas of Interest:**Open-source tooling, model interpretability, learning to simulate, learned coarsening, physics-based inductive biases, graph neural networks, sparsity, symbolic regression/program synthesis, model distillation

__Daniel Foreman-Mackey__

Research Scientist, CCA**Areas of Interest:**Probabilistic programming, Markov chain Monte Carlo, Gaussian Processes

__Shirley Ho__

Group Leader, Cosmology X Data Science, CCA**Areas of Interest:**Machine learning for science, deep learning for simulation, neuro-symbolic models, high dimensional inference

__David W. Hogg__

Group Leader, Astronomical Data, CCA**Areas of Interest:**Causal models, enforcing physical symmetries, adversarial attacks, models of cameras and spectrographs

__Chirag Modi__

Flatiron Research Fellow, Cosmology X Data Science, CCA joint with CCM**Areas of Interest:**Machine learning for science, differentiable simulations, Markov chain Monte Carlo methods, approximate Bayesian inference

__Francisco Villaescusa-Navarro__

Research Scientist, CCA**Areas of Interest:**Neuro-simulations, graph neural netwoks, likelihood-free inference, manifold learning, generative models, symmetries for deep learning.

__Kaze Wong__

Flatiron Research Fellow, Gravitational Wave Astronomy, CCA**Areas of Interest:**Deep learning for data analysis and simulation in astrophysics