National Science Foundation funding supports Rutgers professor’s collaboration to understand dark matter

Coupling technological advances in data science and artificial intelligence/machine learning with established scientific theories is the basis for a study in the detection of dark matter by Rutgers School of Engineering professor Waheed Bajwa and other university colleagues. 

Waheed Bajwa, associate professor in the Department of Electrical and Computer Engineering at Rutgers, and a team of scientists have received a $1 million National Science Foundation (NSF) grant to expedite and support the discovery of new subatomic particles through advances in data science and artificial intelligence. The grant is part of ten research and process “big ideas” the NSF has designated as long-term research areas that will push forward new discoveries in science and engineering.

According to Bajwa, the overarching goal of this project is to lay the groundwork for incorporating scientific domain knowledge into data science and artificial intelligence methods, using the tens to hundreds of terabytes of data that are being produced through the physical sciences of biology, astrophysics, earth and materials sciences, oceanography, and others.

“An abundance of data in physical sciences is changing how we advance science,” says Bajwa who has expertise in signal processing, statistics, and machine learning. “Through data science and artificial intelligence, new computational techniques are being developed that can take advantage of the data and accelerate exciting new scientific discoveries.”

Bajwa’s co-investigators are Rice University astrophysicist and lead investigator Christopher Tunnell and Hagit Shatkay, a professor of computer and information sciences at the University of Delaware. The team formed at an Ideas Lab run by the NSF and Knowinnovation that brought together scientists and engineers to facilitate novel data science ideas that did not fit any disciplinary mold.

While dark matter comprises 85 percent of our universe—essentially binding it together—scientists have no lab-based experimental knowledge of its properties. In searching for dark matter, the researchers will use data science and machine learning algorithms to measure astroparticle interactions and measure faint dark matter signals. Probabilistic graphical models and inverse problem formulations will be employed within the XENON1T, a sophisticated dark matter detector located under a mountain in Italy.

“Scientists have traditionally been reluctant to adopt artificial intelligence due to its supposed black-box nature, but this trend is slowly changing,” says Bajwa. “Finding ways to marry physical models with data driven models offers the synergy to focus in on extremely important technological challenges, opening doors to exciting discovery.”

The grant also includes funds for educational outreach and engaging the broader scientific community in the use of domain-specific artificial intelligence techniques for scientific discovery.

September 18, 2019

The ECE Department is happy to announce that Sumati Sehajpal will be joining our department on September 1, 2019 as Assistant Teaching Professor.

Sumati Sehajpal received her PhD in Electrical and Computer Engineering from Rutgers University in the Spring of 2017. During the past two years, she has taught courses for Rutgers University as a Part Time Lecturer for both the Mathematics and Electrical and Computer Engineering departments. Prior to moving to the United States in 2008 she taught various electrical and electronic engineering courses from 2005 to 2007 working as a full time Lecturer at the Lovely Institute of Technology located in Jalandhar, Punjab, India.

She received her Master's in Electronics Product Design and Technology at the Punjab Engineering College located in Chandigarh, India in 2005 and her Bachelor's in Electrical Engineering from the Adesh Institute of Engineering and Technology in 2003.

Her research interests include electrical circuit theory and analysis, the class E and class G RF power amplifiers, and the modern state-­space based approach used to both model and analyze electronic systems.

Welcome Professor Sumati Sehajpal !

Yingying Chen will serve as the inaugural Associate Undergraduate Director of the ECE department. Yingying has been an outstanding contributor to the department's mission through her world class research program, student mentoring and leadership roles at WINLAB. The ECE Department is grateful that she has taken on this newly created role in ECE and work with Undergraduate Director Wade Trappe, to meet the various challenges of our increasingly successful undergraduate program.

Please join us in welcoming Yingying as the Associate Undergraduate Director.

ECE Associate Professor Waheed Bajwa is the recipient of a new NSF award for the project entitled “Distributed Machine Learning in the Age of Fast Data Streams.” This is a three-year $450,000 project.

In this project, Waheed will develop and analyze an algorithmic framework for real-time, in-network machine learning that acknowledges and accounts for the mismatch between the communications rate and the rate of distributed data streams in many emerging applications, such as Internet-of-Things (IoT) systems, multiagent systems, high-performance computing clusters, and federated computing systems, where continuous data gathering is cheap and communications is over infrastructure-free device-to-device and/or machine-to-machine links. The research will formalize this setting as a distributed stochastic approximation problem, in which the optimum machine learning model is iteratively trained using the random data streaming into individual devices and machines. The research will then focus on the design and analysis of collaborative strategies that operate in the regime of (extremely) fast streaming rates.

The project details can be found here: https://nsf.gov/awardsearch/showAward?AWD_ID=1907658

Congratulations Waheed!