Our AI experts and roboticists are working in concert with our data scientists, who are discovering new ways to analyze, visualize, and use the 2.5 quintillion bytes of data the world generates each and every day in the form of GPS signals, shopping transactions, taxi rides, social media posts, online videos, and digital photos, among other sources. It’s a collaborative ecosystem with a single goal: to harness the collective power of data, machine learning techniques, and autonomous systems to address the issues facing the world. More on Robotics
Research Labs and Groups
Agile Robotics and Perception Lab
ARPL performs fundamental and applied research in robot autonomy. The lab develops agile autonomous drones that can navigate on their own using only onboard sensors without relying on maps, GPS or motion capture systems.
Brooklyn Application, Architecture, and Hardware Lab (BAAHL)
Led by Prof. Brandon Reagen, our research group specializes in computer hardware design, with a primary goal of making privacy-preserving computation practical.
We also focus on optimizing machine learning systems for private computation. With a strong emphasis on energy-efficiency and security, our work aims to accelerate secure computation and enable privacy-preserving machine learning.
Control and Network (CAN) Lab
The CAN Lab, led by Professor Zhong-Ping Jiang, develops fundamental principles and tools for the stability analysis and control of nonlinear dynamical networks, with applications to information, mechanical, and biological systems.
Control/Robotics Research Laboratory (CRRL)
CCRL conducts research projects on unmanned vehicles, autonomy and navigation, control systems, cyber-security, and machine learning.
DICE (Data, Intelligence, and Computation in Engineering) Lab
The Data, Intelligence and Computation in Engineering (DICE) Lab is led by Assistant Professor Chinmay Hegde and focuses on theoretical and applied aspects deep learning and machine learning.
Dynamical Systems Laboratory (DSL)
Professor Maurizio Porfiri’s group conducts multidisciplinary research in the theory and application of dynamical systems, motivated by the objectives of advancing engineering science and improving society. Their theoretical expertise is in controls, networks, nonlinear dynamics, and time-series, while our application domain is in modeling and analysis of physical, social, and technical systems.
Machine Learning for Good Laboratory (ML4G Lab)
The ML4G Lab is focused on development of novel machine learning methods for addressing critical urban problems and improving public health, safety, and security.
Medical Robotics and Interactive Intelligent Technologies (MERIIT)
Led by S. Farokh Atashzar, the MERIIT Lab develops and implements artificial intelligence algorithms, smart wearable hardware, advanced control systems, and signal processing modules systems to augment human capabilities using multimodal robotic technologies.
The NanoBioX initiative at NYU stimulates fundamental research and technological innovation at the intersection of nanotechnology, biomedical research, and data science.
Urban Complexity Lab (UCOMP)
Urban Complexity Lab is unfolding complexity of urban systems for research, innovation and applications. We leverage big urban data and cutting-edge machine learning and network analysis techniques to make our cities more smart, efficient, sustainable, and resilient – better places to live in.
Urban Modeling Group
Our mission is to change the way urban engineering is done by bridging the gap between Civil Engineering and Computer Science. We focus on developing tools to better understand the urban built environment through pioneering new means to optimize and synthesize multi-modal data collection, storage, and processing.