Research Groups Overview

The Computational Intelligence, Learning, Vision, and Robotics (CILVR) Lab, founded in 2009 by Yann LeCun and Rob Fergus, focuses on advancing AI and machine learning. Shared with the Courant Institute of Mathematical Sciences, key research areas include deep learning, self-supervised learning, generative models, and probabilistic inference. CILVR applies its research to computer vision, healthcare, robotics, and natural language processing. The lab aims to develop more natural and human-like AI systems capable of continuous learning, adaptation, and reasoning in complex environments.

The Math and Data (MaD) group, also shared with the Courant Institute, advances the mathematical and statistical foundations of data science. Focus areas include signal processing and inverse problems, machine learning and deep learning, and high-dimensional statistics and probability. Through its seminar series and research activities, MaD bridges theoretical mathematics with practical data science applications. The group explores novel mathematical approaches to statistical problems and investigates the interplay between statistical challenges and computational complexity.

This initiative explores the intersection of neuroscience, cognitive science, and AI. It aims to enhance our understanding of natural intelligence through machine learning advances and develop AI technologies informed by human cognition. The initiative investigates key ingredients of intelligence that current AI systems may be missing and explores the interpretability and alignment of AI systems, particularly large language models. Activities include interdisciplinary research, seminars, conferences, and student engagement opportunities such as the annual Glushko Prize for outstanding undergraduate theses.

Minds, Brains, and Machines benefits from collaborations with the broader CILVR AI lab and the computational cognitive science community at NYU, as well as with external partners.

ML² is at the forefront of Natural Language Processing (NLP) research in the era of large language models. The group develops state-of-the-art machine learning methods for NLP, explores the implications of language models, and fosters interdisciplinary collaborations.

Key research areas include attention mechanisms in neural machine translation, language model evaluation benchmarks (e.g., GLUE and SuperGLUE), conversational AI with a focus on healthcare applications, and investigating the impact of language models on writing and content diversity. ML² hosts seminars, supports the NYU AI School initiative, and facilitates collaborations across disciplines. ML² is affiliated with the larger CILVR lab.

The Statistics: Tools, Algorithms, and Theory (STAT) group advances statistical methodologies and their applications in data science and machine learning. Focus areas include high-dimensional statistics, Bayesian inference, nonparametric methods, statistical learning theory, causal inference, time series analysis, and spatial statistics. STAT aims to contribute to theoretical foundations while developing practical tools for data analysis. The group brings together researchers from CDS and the Courant Institute, fostering a collaborative environment that encourages cross-pollination of ideas between statistics, mathematics, and computer science.

These research groups form the cornerstone of CDS’s research initiatives, driving innovation and preparing the next generation of data scientists. Through their diverse expertise and collaborative approach, they ensure NYU remains at the forefront of data science research and education.