Data Science / AI / Robotics

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.

The AI4CE Lab works to advance fundamental automation and intelligence technologies, to enable their use in civil and mechanical engineering applications.

The Algorithms and Foundations Group at NYU's Tandon School of Engineering is composed of researchers interested in applying mathematical and theoretical tools to a variety of disciplines in computer science. We study problems in machine learning, geometry, computational biology, computational mathematics, and beyond.

We aim to establish mathematical models, quantitative criteria, and algorithmic/computational foundations toward their implementations in robotics (for design and control), biomechanical systems (for prediction and analysis), and their intersections such as lower-body wearable robots. 

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.

The Building Informatics and Visualization Lab (biLAB) is part of the Department of Civil and Urban Engineering at the NYU School of Engineering. It focuses on understanding the operational challenges associated with construction and operation of facilities and infrastructure systems in urban settings.

Our goal is to make responsible AI synonymous with AI.

The Center for Urban Science and Progress (CUSP) is an interdisciplinary research center dedicated to the application of science, technology, engineering, and mathematics in the service of urban communities across the globe. Using New York City as our laboratory and classroom, we strive to develop novel data- and technology-driven solutions for complex urban problems.

The overarching goal of our research is to develop the principles needed to incorporate unstructured, Internet and mobile data into a better understanding of population-level health. We primarily develop computational methods across data mining, natural language processing, and machine learning to generate features for spatio-temporal population-level public health models.

Professor Rose Faghih group develops biomedical signal processing and control algorithms for human-technology interactions and monitoring. These state-of-the-art tools are employed for prognosis, diagnosis, and treatment of pathological conditions related to neuro-endocrine and neuro-psychiatric disorders.

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.

CCRL conducts research projects on unmanned vehicles, autonomy and navigation, control systems, cyber-security, and machine learning.

Cybersecurity for Democracy is a research-based, nonpartisan, and independent effort to expose online threats to our social fabric — and recommend how to counter them. We are part of the Center for Cybersecurity at the NYU Tandon School of Engineering.

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.

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. 

The Hartman Research Laboratory investigates the kinetics of chemical reactions and the design of the reactors in which they take place. Catalysis and reaction engineering is at the heart of virtually every process or system in which a chemical transformation occurs.

Professor Ruggles’ laboratory focuses on understanding human health and biology using data science, data visualization, and predictive modeling. A primary goal of this research is to analyze and integrate diverse data modalities, including bulk and single-cell sequencing, phospho- and global- proteomics, metagenomics, flow cytometry, imaging, and clinical data. These multi-omic methods are used to better understand cancer, heart diseases, and other disorders.

The ML4G Lab is focused on development of novel machine learning methods for addressing critical urban problems and improving public health, safety, and security.

We try to understand the fundamental principles for robot locomotion and manipulation that will endow robots with the robustness and adaptability necessary to efficiently and autonomously act in an unknown and changing environment.

The lab provides undergraduate and graduate students a real-world, hands-on experience in modern DSP- and PC- based data acquisition and real-time control.

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.

The aim of Dr. Chklovskii’s research is to understand how the brain analyzes large and complex datasets streamed by sensory organs. Informed by anatomical and physiological neuroscience data, his group develops algorithms that model brain computation and solve machine learning tasks. The overarching goal it to build artificial neural systems and treating mental illness.

We develop computational models and signal processing tools to decode brain connectivity and function using genomics and imaging. We are particularly interested in constructing a bridge between genetics and behavior through interpretable models that operate on multi-modal neural data at molecular, circuit and whole-brain resolutions.

The NYU Tandon Future Labs are the first public-private partnership with New York City tasked with creating a sustainable incubation program focused on increasing the success rate of new ventures and generating positive economic impact.

Social behaviors such as fighting, defense, and parenting, are innate and ubiquitous across the animal kingdoms. The research in Dr. Lin’s laboratory centers on understanding the neural circuits underlying these behaviors. Various genetic engineering, tracing, functional manipulation, in vivo electrophysiological recording and computational tools are combined to dissect the neural circuits in a great detail.

SONYC researchers have launched a citizen science initiative to train artificial intelligence (AI) technology to understand exactly which sounds are contributing to unhealthy levels of noise in New York City. A first-of-its-kind project addressing urban noise pollution, SONYC is based at NYU Tandon’s Center for Urban Science and Progress (CUSP).

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.

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. 

ViDA consists of computer scientists who work closely with domain experts to apply the latest advances in computing to problems of critical societal importance, and simultaneously generate hypotheses and methods that new data demands.