We are the Algorithms and Foundations Group in the Computer Science and Engineering Department at NYU's Tandon School of Engineering. Our group is composed of researchers interested in applying mathematical and theoretical tools to a variety of disciplines in computer science, from machine learning, to systems, to geometry, to computational biology, and beyond. You can visit our individual webpages to learn more. If you would like to join our mailing list (for news, relevant talks, etc.) please request to be added here.

We are seeking independent and mathematically strong Ph.D. students to join our group for the 2023-2024 academic year. You should be interested in algorithms, theoretical computer science (TCS), theoretical machine learning, applied math, or related areas. All applications should be made to NYU Tandon's Ph.D. program in CSE.

We co-host the NYU Theory Seminar with the NYU Courant Theoretical Computer Science Group, held weekly on Thursdays in Manhattan. Visit the page for more information if you would like to attend or give a talk!

## Faculty

##### Boris Aronov

Computational and Combinatorial Geometry, Algorithms

##### Yi-Jen Chiang

Data Visualization, Motion Planning, Computational Geometry, Algorithms

##### Chinmay Hegde

Machine Learning, Algorithms, Signal and Image Processing

##### Lisa Hellerstein

Computational Learning Theory, Machine Learning, Algorithms, Complexity

##### Christopher Musco

Algorithms, Scalable Machine Learning, Numerical Linear Algebra

## Postdocs and Instructors

## Current Students

##### Noah Amsel (Ph.D.)

Deep Learning Theory, Numerical Linear Algebra, Continuous Optimization

##### Minsu (Daniel) Cho (Ph.D.)

Automated ML, Model Compression, Generative Models, Signal Processing

##### Majid Daliri (Ph.D.)

Statistics, Information Theory, Optimization, ML theory, Graphs and Networks.

##### Haya Diwan (Ph.D.)

Algorithms, Machine Learning and AI, Theory of Computation, Discrete Math

##### Feyza Duman Keles (Ph.D.)

Machine Learning, Deep Learning Theory, Approximation Algos, Randomized Algos

##### Aarshvi Gajjar (Ph.D.)

Sampling + Sketching, Approximation Theory, High Dimensional Geometry

##### Ameya Joshi (Ph.D.)

Robust ML, Deep Generative Models, Physics Informed Learning

##### Danrong Li (M.S.)

Algorithmic Machine Learning and Data Science

##### Kelly Marshall (Ph.D.)

Machine learning, Deep Reinforcement Learning, Generative Models

##### Raphael Meyer (Ph.D.)

Statistical Learning Theory, Randomized Algorithms, Optimization

##### Minh Pham (Ph.D.)

Machine Learning

##### Apoorv V. Singh (Ph.D.)

Algorithmic Machine Learning, Robust Statistics, Randomized Algorithms

##### R. Teal Witter (Ph.D.)

Algorithms, Graph Theory, Boolean Functions, ML, Quantum Computing

##### Indu Ramesh (Ph.D.)

Algorithms, Graph Theory, Computational Geometry

##### Atsushi Shimizu (M.S.)

Machine Learning, Numerical Linear Algebra, Algorithms

## Affiliates

## Past Members

##### Prathamesh Dharangutte (M.S.)

Machine Learning, Spectral Graph Theory, and Optimization

##### Gauri Jagatap (Ph.D.)

Machine Learning, Signal Processing, Generative Models, Model Compression

##### Xinyu Luo (M.S.)

Machine Learning, Approximation Algorithms, High-dimensional Geometry, Random Matrix Theory

##### Mengxi Wu (M.S.)

Algorithms, Algorithmic Machine Learning and Data Science, Data Visualization