Welcome to MLRG @ Binghamton

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!

News

April 2021

Our paper, RelEx, a model-agnostic relational model explainer has been accepted for publication to AAAI/ACM Conference on AI, Ethics, and Society (AIES), 2021!!!

March 2021

Congratulations to Gissella Bejarano for successfully defending her thesis titled Machine Learning Models for Designing Smart Cities and Communities!! She will be joining as a professor at a renowned university, UPCH in Peru!!!

March 2021

Our project on predicting COVID-19 spread using cell-phone date has been covered by Bing News!!! Check it out here!!!

January 2021

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 DeepER project is in the news!!! It was covered by Bing News, WBNG, Voice of America, and Government Computer News!!!

October 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!!

... see all News