About

I am an Assistant Professor of Information Systems in the DOIT department at The University of Maryland Robert H. Smith School of Business. My research advances the design and governance of algorithmic systems to support equitable, effective decision-making in data-driven platforms. I focus on machine learning (ML) methodology, algorithmic personalization, and content moderation, with particular attention to fairness, organizational impact, and the social consequences of digital platforms. I use diverse methodologies including ML, simulation, econometric analysis, and both field and lab experiments.

My work has been published in leading Information Systems and Machine Learning venues such as MIS Quarterly, Information Systems Research, and Data Mining and Knowledge Discovery. It has also been presented at top conferences such as ICIS, CIST, WITS, and WISE. In 2023, my work on “Who Are You and What Are You Selling? The Effects of Creator-Based and Product- Based Racial Cues on Crowdfunding Success” was awarded “Social Justice Best Paper” by INFORMS ISS. In 2019, my work on “Crowdfunding Community Formation: Fundraiser Race and Gender Homophily” was awarded Best paper at the Winter Conference in Business Analytics. In 2017, my work on “Mining Massive Fine-Grained Behavior Data to Improve Predictive Analytics” was awarded European Research Paper of the Year by AIS and CIONET. I have also received the Allen J. Krowe Award for Teaching Excellence and the Smith School Distinguished Teaching Award three times.

I completed my PhD in Information Systems at NYU Stern in 2017.  My advisor was Foster Provost. Previously, I worked for four years at an online advertising agency, doing predictive modeling and data mining.  I have a BS in Mathematical Sciences from Worcester Polytechnic Institute.