AI News and Accomplishments

Professor Tianyi Chen: Press Release
June 22, 2021   

NSF CAREER grant supports the dual design of resource-friendly machine learning algorithms and learning-driven wireless networks

TROY, N.Y. — Artificial intelligence and machine learning are revolutionizing the ways in which we live, work, and spend our free time, from the smart devices in our homes to the tasks our phones can carry out. This transformation is being made possible by a surge in data and computing power that can help machine learning algorithms not only perform device-specific tasks, but also help them gain intelligence or knowledge over time.

 In the not-so-distant future, artificial intelligence and machine learning tasks will be carried out among connected devices through wireless networks, dramatically enhancing the capabilities of future smartphones, tablets, and sensors, and achieving what’s known as distributed intelligence. As technology stands right now, however, machine learning algorithms are not efficient enough to be run over wireless networks and wireless networks are not yet ready to transmit this type of intelligence.

 With the support of a National Science Foundation Faculty Early Career Development Program (CAREER) grant, Tianyi Chen, an assistant professor of electrical, computer, and systems engineering at Rensselaer Polytechnic Institute and member of the Rensselaer-IBM Artificial Intelligence Research Collaboration (AIRC), is exploring how to make such knowledge-sharing tools a reality.

 “I think in the future, the main terminal of intelligence will be our phones. Our phones will be able to control our computers, our cars, our meeting rooms, our apartments,” Chen said. “This will be powered by resource-efficient machine learning algorithms and also the support of future wireless networks.”

 Through his collaboration with the Lighting Enabled Systems and Applications (LESA) Center at Rensselaer, Chen will validate the algorithms he develops using the center’s smart conference room. The conference room is equipped with devices that are capable of sensing the environment, processing that information, and efficiently sharing it with other devices on the network — the same framework the algorithms are being designed to function within. 

“We need to redesign our wireless networks to support not only traditional traffic, like video and voice, but to support new traffic such as transmittable intelligence,” Chen said. “We need to design more efficient learning algorithms that are suitable for running on the wireless network.”

Chen also stressed the importance of ensuring that knowledge-sharing algorithms only extract anonymized information in order to maintain data privacy as our devices — and daily lives — become increasingly networked. While the goals of this research are foundational in nature, Chen said the potential for future applications is wide-ranging — from power grids to urban transportation systems. 


WSAI community's top 50 Innovators in 2020

Professor Francesca Rossi, IBM Fellow & AI Ethics Global Leader,  IBM Research, ranked #7 in the World Summit AI community's top 50 Innovators in 2020

ECSE Prof. Tianyi Chen is an inaugural recipient of IEEE Signal Processing Society (SPS) Best PhD dissertation award

ECSE Prof. Tianyi Chen is an inaugural recipient of IEEE Signal Processing Society (SPS) Best PhD dissertation award

Congratulations to Prof. Chen on being an inaugural recipient of IEEE Signal Processing Society (SPS) Best PhD dissertation award! 


Rensselaer News Feed

Four experts in diverse aspects of artificial intelligence have joined Rensselaer Polytechnic Institute as part of the Artificial Intelligence Research Collaboration (AIRC), a recently formed joint initiative of Rensselaer and IBM Research.

TROY, N.Y. —Machine learning has the potential to vastly advance medical imaging, particularly computerized tomography (CT) scanning, by reducing radiation exposure and improving image quality.

Those new research findings were just published in Nature Machine Intelligence by engineers at Rensselaer Polytechnic Institute and radiologists at Massachusetts General Hospital and Harvard Medical School.

TROY, N.Y. — A wide-eyed, soft-spoken robot named Pepper motors around the Intelligent Systems Lab at Rensselaer Polytechnic Institute. One of the researchers tests Pepper, making various gestures as the robot accurately describes what he’s doing. When he crosses his arms, the robot identifies from his body language that something is off. 

“Hey, be friendly to me,” Pepper says.

TROY, N.Y. — Generating comprehensive molecular images of organs and tumors in living organisms can be performed at ultra-fast speed using a new deep learning approach to image reconstruction developed by researchers at Rensselaer Polytechnic Institute.

The research team’s new technique has the potential to vastly improve the quality and speed of imaging in live subjects and was the focus of an article recently published in Light: Science and Applications, a Nature journal.

TROY, N.Y. — Researchers at Rensselaer Polytechnic Institute who developed a blood test to help diagnose autism spectrum disorder have now successfully applied their distinctive big data-based approach to evaluating possible treatments.

Past AIHN Seminars Playlist