About

I am a third year PhD student in Computer Science at the University of Illinois Chicago, advised by Professor Elena Zheleva.

My research interests lie at the intersection of data science, causal inference, machine learning, and econometrics. In particular, the vision of my research is to address various biases (e.g., position bias, exposure bias, popularity bias) in different types of recommender systems—including learning-to-rank models, collaborative filtering methods, social recommender systems, and generative AI-based recommender systems—to facilitate unbiased and fair recommendations. My current work leverages methods from econometrics, causal inference, machine unlearning, and large language models to mitigate these biases, particularly in graph-based and learning-to-rank-based recommendation frameworks. While my primary focus is on debiasing recommender systems, I am also interested in other compelling applications within recommender systems, e-commerce, social networks, and broader data science.