I'm a Research Scientist at Meta Responsible AI interested in explainable NLP/ML for social good. As of Summer 2022, I'm a PhD graduate in Electrical & Computer Engineering (ECE) at UT Austin where I worked with my advisor Dr. Joydeep Ghosh and committe including Alex Dimakis, Harris Vikalo, Atlas Wang and Byron Wallace.

My research interests broadly involve explaining Machine Learning model decisions on natural language and multi-modal data, and learning representations of entities for downstream tasks. My PhD defense is titled "In-process Diagnostic methods for Entity Representation Learning on Sequential data at Scale" and focuses on methods that allow neural networks to be more transparent, explainable, and diagnosable during the process of learning and inference as opposed to in a post-hoc analysis fashion.

My research has dealt with learning sparse interpretable biomedical text representations (ACL 21), dense entity retrieval using dual encoders (CoNLL 19), prototypical learning of time series data (ICML 19(short) IJCAI 19 (long)), efficient entity-based knowledge injection for KBVQA (WWW22) amongst other things.

During my PhD I did 2 research internships with Google AI ( Mountain View '18) and Google Cloud AI ( Seattle '19) and one with IBM Research ( NY remote '20). In the spring of 22 I was accepted as a Fulbright Specialist scholar to lead a workshop and develop a tool on gender bias in judicial proceedings using NLP in Azul, Argentina. Prior to my PhD I was a Data Science for Social Good fellow at the University of Chicago ( Summer '16). I have a masters in Data Science from la Universitat Politecnica de Catalunya in Barcelona and Bachelors degrees in Computer Science and Political Science from UT Austin.