I am an assistant professor and faculty fellow at the Department of Computer Science, Courant Institute of New York University and a member of the CILVR Lab. My research interests include studying generalization in artificial intelligence models, particularly sequence modeling architectures, through self-supervised and transfer/few-shot learning methods. I also actively work on developing resources and methodology for low-resource natural language processing.
The main goal of my research is extending the applicability of language technologies in more languages and tasks. Language technology is a highly promising tool with the potential of transforming how we use and benefit from education and the media, although state-of-the-art approaches are still not competitive enough to be deployed in a large portion of the world languages. This problem is related to the nature of assumptions made in formulating the statistical language models that usually fail to generalize to languages with various syntactic typology, in particular the ones with relatively high data sparsity. I find this highly constrained design problem quite intriguing due to its potential in benefiting a tremendous amount of future applications; at the same time, in line with my background in engineering, where I had specialised on developing optimized software solutions for real-time computation-intensive multimedia technology. Previous to joining the New York University, I was a post-doctoral researcher and lecturer at the University of Zürich, and an applied research scientist intern at the Amazon Alexa research, where I worked on developing novel methods for improving generative models in low-resource and morphologically-rich languages.
Ph.D. in Information Engineering and Computer Science, 2019
University of Trento
Ph.D. in Informatics (Visiting Post-graduate Student), 2018
University of Edinburgh
M.Sc. in Embedded Systems and Multimedia Technology, 2015
University of Leuven
B.Sc. in Electrical and Electronics Engineering, 2013
Middle East Technical University
Congratulations to undergraduate Computer Science students Grace Wang (NYU, Dean’s Undergraduate Research Scholar) and Tia Chen (Tufts University, research scholar at NYU Pathways to AI Program) for getting their paper accepted at the Eval4NLP Workshop at IJCNLP-AACL 2023. Their research project investigates how linguistic typology affects the applicability of evaluation metrics for generative models in different languages.
I was selected as one of the principal investigator awardees of the Microsoft Accelerating Foundation Models Research Program. The project will be conducted in collaboration with ACL SIGTURK and will investigate novel methods for improving accessibility to foundation models in low-resourced languages.