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!
Our project on characterizing mobility patterns during COVID-19 has been featured in ACM Tech News!!! Check it out here!!!January 2021
Our project on characterizing mobility patterns during COVID-19 using cellular network data is in the news!!! Check out the article here!!!December 2020
Our paper on predicting non-emergency response time has been accepted for publication in Journal of Pervasive and Mobile Computing!December 2020
Our paper on characterizing mobility patterns during COVID-19 using cellular network data is now on Arxiv!!October 2020
Our paper on analyzing societal impact during the early days of the COVID-19 pandemic got accepted to SocialCom 2020!!October 2020
Our paper on wireless channel quality prediction using sparse Gaussian conditional random fields got accepted to Consumer Communications and Networking Conference (CCNC)!September 2020
Gissella Bejarano passes her PhD prospectus!! Congratulations, Gissella!!September 2020
Our paper on predicting emergency resolution time using deep learning has been accepted for publication at IEEE CPSCom!