Nokia announced the winners of the 2020 Bell Labs Prize, a competition to recognize innovations that will define the next industrial revolution.
Nokia received proposals from 208 academics in 26 countries.
Firooz Aflatouni, associate professor in the Department of Electrical and Systems Engineering at University of Pennsylvania, received $100,000 for winning the competition with his proposal for Integrated Photonic-mmWave Deep Networks.
Nokia launched the 2020 Bell Labs Prize competition in March 2020 and announced finalists in June 2020. Each finalist was assigned both a leader from the Nokia Bell Labs research team and a research mentor, and they worked together to advance their innovations throughout the year.
Finalists presented proposals in front of a panel of experts and leaders who judged the proposals based on a credible proof-of-concept or set of results that validate the innovation’s full potential. The judges selected three winners based on the demonstrated disruptive potential of their work:
The first-place prize of $100k was awarded to Firooz Aflatouni, associate professor in the Department of Electrical and Systems Engineering at the University of Pennsylvania, for his proposal “Integrated Photonic-mmWave Deep Networks”.
Using deep neural network photonic chips as a platform for artificial intelligence, his system has demonstrated that image and video recognition in the optical domain brings a wealth of possibilities for the future.
Photonic platforms interpret and recognize images at the speed of light, some six billion times a second, making them significantly faster than today’s digital computational platforms. The system is small, all-optical, energy-efficient and low-cost, making it easy to incorporate into a myriad of other solutions such as embedded AI in camera systems.
The second-place prize of $50k was awarded to Sanjeev Arora, professor in Computer Science, Princeton University, and his teammates, Yangsibo Huang (Ph.D. student at Princeton University), Kai Li (Professor of Department of Computer Science at Princeton University) and Zhao Song (Postdoc Researcher at Princeton University).
Their proposal “How to allow deep learning on your data without revealing your data” solves a significant problem with the lack of privacy in machine learning tools. Their InstaHide solution is a universal method for encrypting training images that is efficient to apply with only minor impact on model accuracy. This innovative approach will allow the sharing of data to fully leverage the power of machine learning models without sacrificing privacy or compromising security.
The third-place prize of $25k was awarded to Cheng Qi, Ph.D. student at Georgia Institute of Technology, and his teammates, Francesco Amato (Post-Doctoral Researcher at Tor Vergata University in Rome, Italy) and Gregory Durgin (Professor at Georgia Institute of Technology, GA), for their proposal “Hyper RFID: A Revolution for The Future of RFID”.
Their innovative tags are based on a new type of quantum tunneling radio positioning (QTRP) system that provides highly accurate wireless positioning with an extended RFID coverage range of meters today to more than a kilometer in the future, with low-cost tags with a battery life of up to ten years. These revolutionary capabilities for RFID tags should greatly facilitate the tracking of people and assets or the navigation of autonomous drones and vehicles using drop-and-forget waypoints.
“We are on the verge of a new value paradigm of ‘Remote X’ enabled by 5G as the critical networking infrastructure that will allow the remote access to, interaction with, and intelligent control of everything, from anywhere,” Marcus Weldon, president of Nokia Bell Labs and CTO of Nokia, said.
