Welcome to the machine learning research group at Binghamton! Our group works broadly on designing machine learning models for complex, relational, unstructured, and heterogeneous data. Our group focuses both on designing novel algorithms for such complex interconnected data and applications of these algorithms on real-world data.
Specifically, our group works on statistical relational learning, deep learning, probabilistic graphical, and latent variable models. We demonstrate the utility of these models on applications including smart energy, computational social science, education, social media, and urban computing data. For a complete list of ongoing research projects, see our research page.
We are looking for passionate PhD, Masters and Undergraduate students to join the team!
My PhD student Yunlong Xu passes RPE. Congrats Yunlong!January 2020
Two papers accepted in ECAI 2020! Our papers on learning fairness-aware relational structures and mixed-membership stochastic blockmodels with interpretable structured priors got accepted for publication in ECAI 2020. Congrats Yue!December 2019
Our paper on adversarial model extraction of GNNs got accepted to AAAI Deep Learning for Graphs workshop! Congrats Dave!October 2019
Our DeepChannel paper accepted to IEEE Transactions on Vehicular Technology 2019.August 2019
Our paper on designing a deep learning based fitness center equipment usage prediction got accepted for publication in Mobiquitous 2019!July 2019
Our paper on smart water consumption prediction got accepted for publication in BuildSys 2019!May 2019
Introducing A3SL: Asynchronous Advantage Actor-critic for Structure Learning in HL-MRFs, that got accepted for publication in IJCAI. Congrats, Yue!!May 2019
My PhD student Shawn Bailey passes RPE! Congrats Shawn!April 2019
My PhD student David Defazio passes RPE! Congrats Dave!