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 paper on sparse Gaussian CRFs for non-emergency response prediction got accepted to Mobiquitous conference! Congrats Dave!July 2018
Our paper on fine-grained analysis of cyberbullying using weakly supervised topic models got accepted to DSAA conference! Congrats Yue!April 2018
Gissella Bejarano passes RPE - the first PhD milestone! Congrats Gissella!April 2018
David Defazio's poster wins the best undergraduate poster and Yue Zhang's poster wins an honorable mention in the data science poster competition at Binghamton University! Congrats Dave and Yue!April 2018
Our paper on designing a predictive analytics framework for smart water management got accepted to Smartcomp smart industries workshop!March 2018
Our paper on employing renewables to effectively cut load in electric grids accepted to Smartcomp 2018.February 2018
Our paper on cyberbullying got accepted to the WWW Cybersafety workshop. Will post preprint and the poster soon.January 2018
Arpita Chakraborty is one of 200 attendees selected to attend CRA-W conference. She will be presenting a poster on her work on cyberbullying.December 2017
Our paper - Structured approach to Understanding Recovery and Relapse in AA got accepted for publication in WWW, 2018! Will post preprint soon.