BROOKLYN, New York, Wednesday, March 4, 2020 - Robots hold promise for a large number of people with neurological movement disorders severely affecting the quality of their lives. Now researchers have tapped artificial intelligence techniques to build an algorithmic model that will make the robots more accurate, faster, and safer when battling hand tremors. Their model, which is ready for others to deploy, appears this month in Scientific Reports, an online journal of Nature. The international team reports the most robust techniques to date to characterize pathological hand tremors symptomatic of the common and debilitating motor problems affecting a large number of aging adults. One million people throughout the world have been diagnosed with Parkinson's disease, just one of the neurodegenerative diseases that can cause hand tremors. While technology such as sophisticated wearable exoskeleton suits and neurorehabilitative robots could help people offset some involuntary movements, these robotic assistants need to precisely predict involuntary movements in real-time - a lag of merely 10 or 20 milliseconds can thwart effective compensation by the machine and in some cases may even jeopardize safety.

BROOKLYN, New York, Tuesday, October 22, 2019 – The ubiquity and sheer volume of data generated today give experts in virtually every domain ample information to track everything from financial trends, disaster evacuation routes, and street traffic, to animal migrations, weather patterns, and disease vectors. But using this data to build visualizations of complex predictive models using machine learning is a challenge to experts who lack the requisite computer science skills. A team at the NYU Tandon School of Engineering's Visualization and Data Analytics (VIDA) lab, led by Claudio Silva, professor in the department of computer science and engineering, developed a framework called VisFlow, by which those who may not be experts in machine learning can create highly flexible data visualizations from almost any data. Furthermore, the team made it easier and more intuitive to edit these models by developing an extension of VisFlow called FlowSense, which allows users to synthesize data exploration pipelines through a natural language interface. The research, "FlowSense: A Natural Language Interface for Visual Data Exploration with a Dataflow System" won the best-paper award at this year's IEEE Conference on Visual Analytics Science and Technology (VAST).

BROOKLYN, New York, Tuesday, September 3, 2019 - As artificial intelligence and data science enable computer tools to make predictions previously made by skilled humans, a different knowledge gap looms: These black-box tools often offer highly trained medical personnel little understanding of their inner workings. Equally little understood: how deploying these tools affects experts' work practices, perceptions of the value of work, and the expert-patient relationship. Researchers from New York University and Georgia Tech are conducting foundational research to understand and improve expert work in an age of data-intensive enhanced cognition, especially in healthcare, where new technologies are rapidly being deployed. The National Science Foundation recently awarded the team $2 million for the four-year project, which is expected to transform the future of expert work through a combined redesign of technology, workflow, and interactions. "Better understanding of how new technologies impact healthcare expert work will lead to more effective use of healthcare technologies, a healthier and better-informed population, and the more efficient use of human capabilities in restructured healthcare occupations," said NYU Tandon School of Engineering Professor of Technology Management and Innovation Oded Nov, the principal investigator.

The first event will feature Yann LeCun, Facebook's director of AI research and a member of the New York University faculty. LeCun will inaugurate the series on Tuesday, February 20, 2018, from 10 to 11 am in Downtown Brooklyn, at NYU Tandon's Pfizer Auditorium, 5 MetroTech Center. Registration is free and open to all. His address, "Obstacles to Deep Learning and AI," will explore a new frontier: predictive models that capture the "common sense" exhibited by humans and animals, who often learn by observation and occasional action. LeCun's pioneering work in the application of neural networks to computer vision and other AI areas led to products and services deployed across most technology companies.

Self-driving cars could account for 21 million new vehicles sold every year by 2035. Over the next decade alone such vehicles -- and vehicles with assisted-driving technology -- could deliver $1 trillion in societal and consumer benefits due to their improved safety. For autonomous vehicles to make good on that promise they will need onboard artificial intelligence (AI) technology able to link them to highly detailed maps that reflect every change in the status of lanes, hazards, obstacles, and speed-limits in real time. Researchers at the NYU Tandon School of Engineering are making this critical machine-to-machine handshake possible. Yi Fang, a research assistant professor in the Department of Electrical and Computer Engineering and a faculty member at NYU Abu Dhabi, and Edward K. Wong, an associate professor in the NYU Tandon Department of Computer Science and Engineering, are developing a deep learning system that will allow self-driving cars to navigate, maneuver, and respond to changing road conditions by mating data from onboard sensors to information on HERE HD Live Map, a cloud-based service for automated driving.