I'm a PhD researcher interested in explainable AI for social good (public policy/healthcare/inequality/creativity)
Expected Graduation Summer 2022 and actively looking for job opportunities in NLP/ML research starting in Fall 2022
I'm currently a 5th year PhD candidate in Electrical & Computer Engineering (ECE) at UT Austin
working with Dr. Joydeep Ghosh's IDEAL lab and Dr. Paydayfar's group at Dell Medical School.
My research interests broadly involve explaining Machine Learning model decisions on Natural Language and sequential data, and learning representations of entities and political networks for downstream tasks. My PhD proposal accepted in June 2021 by professors Joydeep Ghosh, Alex Dimakis, Harris Vikalo, Atlas Wang & Byron Wallace 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) IJAI 19 (long)), amongst other things. I'm currently looking at knowledge injection for the Knowledge Aware Visual Question Answering task where external world knowledge about entities is required for obtaining a correct response.
key words: interpretable entity representations, dense retrieval, in-network prototype learning, dual encoders, feature importance methods, counterfactual explanations
recent news
Summer 2021, my PhD Proposal on "In-process Diagnostic methods for Entity Representation Learning on Sequential Data" has been accepted for candidacy.
Our paper "Biomedical Interpretable Entity Representations" has been accepted at ACL-ICJNLP 2021. Arxiv Preprint
For the Summer 2020, I have been selected to be an IBM Research PhD fellow for Social Good and will work on a Drug Repurposing for Cancer project using NLP.
I recently gave a high level talk to Cognitive Scale on Explainable AI for NLP (slides link).
In Summer 2019, I interned at Google Cloud AI in Seattle with Besim Avci working on explaining seq2seq models using feature attribution methods on Transformer and LSTM based architectures with attention for machine translation.
In Spring 2019: I was a TA for Responsible AI Graduate Seminar ( lots of papers and presentations on Explainability, Fairness, etc!)
In Summer 2018, I interned at Google Research in Mountain View with Jason Baldridge working on entity linking.
In Fall 2017: I started at UT and was a TA for an Advanced Data Mining Masters course.
In Summer 2016, I was selected as an Eric & Wendy Schmidt Data Science for Social Good 2016 fellow at the Unviersity of Chicago and worked with SEDESOL of Mexico on improving their distribution of social services.
I also do alot of Data Visualizations. Here is a lecture I have given on modern data visualization and particulary the d3 javascript library for masters level CS/ML students.
I obtained a Masters of Data Science at the UPC in Barcelona in July 2015 (masters thesis on automated construction of political networks).
I have a bachelors in Computer Science, Political Science and Hispanic Studies with a Business minor from UT Austin.
A selection of publications and projects, academic, professional and personal.
IJCAI 19 Knowledge and Health Discovery Long paper)
Identify Blue Counties in America that are surrounded by Red ones, and Predict if a county is a blue island based on just socio-economic and public health data.
List of Spotify's Clarify Data Stories series articles written by Rob Mitchum for which I contributed data mining and data visualization, 2016.
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How Music Crosses American Borders
Debt Anxiety in Today’s Pop Music
Visualizations related to my masters thesis project in Barcelona about Texas Politics. Click here for Thesis Presentation Slides
