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 2024-2025 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, Instructors, and Visitors
Current Students
Noah Amsel (Ph.D.)
Deep Learning Theory, Numerical Linear Algebra, Continuous Optimization
Aritra Bhowmick (Ph.D.)
Streaming and Sketching Algorithms, Deep Learning, Graphs
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
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
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
Atsushi Shimizu (M.S.)
Machine Learning, Numerical Linear Algebra, Algorithms
Danrong Li (M.S.)
Algorithmic Machine Learning and Data Science
Pruthuvi Maheshakya Wijewardena
Approximation Algorithms, Distributed and Federated Learning
Ameya Joshi (Ph.D.)
Robust ML, Deep Generative Models, Physics Informed Learning