Our research is motivated by real-world problems in interdisciplinary studies such as social science, health informatics, and recommender systems. We endeavor to design transparent machine learning and data analytical techniques to better discover dynamic and interpretable patterns from large-scale data.

I am proud of each one of my students and mentees. I enjoy working with all of them!

If you are interested in joining our lab: Please check out openings and requirements.

Current PhD students

Chang Lu, Computer Science
Area of study: Graph Neural Networks for Healthcare
2019 - present

Xiaoxue Han, Computer Science
Area of study: Dynamic Graph Learning
2021 - present

Jing Chen, Data Science (co-advise with Dr. German Creamer)
Area of study: Multi-source Epidemic Forecasting
2021 - present

Adam Sadej, Data Science (part-time)
Area of study: Fairness and Explainability in AI
2022 - present

Alumni

Songgaojun Deng, CS PhD at Stevens, 2018 - 2022
Dissertation: Modeling and Understanding Societal Events via Graph Neural Networks
Recipient of the Excellence in Graduate Research Award (2022)

Remington Kim, Bergen County Academies
Summer intern 2021
Publication at Smoky Mountains Computational Sciences Data Challenge.

Lyna Bacha, High Tech High School
Summer intern 2021
Now: Duke University

Hima Kolavennu, South Brunswick High School
Summer intern 2020
Now: UC Berkeley

Weronika Zamlynny, CS undergraduate at Stevens
Visited in 2019
Now: Microsoft

Ameya Vaidya, Bridgewater-Raritan High School
Summer intern 2019
Publication at ICWSM20.
Now: Princeton University

Kun Wu, CS Master at Stevens
Visited in 2020
Now: PhD at Stevens