Data-Inspired Discovery * Algorithmic Fairness Lab
The DID*AF Lab at the University of St. Thomas in Houston, Texas is dedicated to:
- developing methods and models that harness data for inspiring discovery
- systematically studying algorithmic bias
- creating a multi-disciplinary and multi-institutional approach for algorithmic fairness
- fostering careers in computing (especially, but not limited to, learners from traditionally underrepresented groups)
Carlos Monroy, Ph.D.
Dr. Monroy is an Assistant Professor at UST and Director of the DID*AF Lab. His research interests include: socio-technical systems, data and text mining, algorithmic fairness, data science, learning analytics, and computer science pedagogy.
Kaily is majoring in Computational Biology. She is interested in data analytics as well in solving and understanding human biological issues and systems with computation. Currently doing research with endangered indigenous plants of Houston.
Originally from Mexico, Sergio is a Computer Science major, interested in machine learning and data science. His background in sales has led him to become interested in applying data science methods for: a) increasing revenue, b) estimating risks, and c) making everyday tasks more automated.
Sarita is majoring in Computational Biology. Presently conducting genetic research in C elegans in the lab of Dr. Alexandra Simmons. While working in that field, she developed an interest in the use of computing technologies for exploring and analyzing Biological data sets.
Jose Luis Zuñiga
A Music Performance and Computer Science double-major, Jose Luis is interested in applying computing concepts to music theory. Current research includes development of computer-generated Schenkerian analyses and figured-bass realizations.
Majoring in Computer Science and a Colombian native, Andres' interest in machine learning stems from ideas derived from a family business, which has made him realized the need for systems and process automation. Presently working on a research project to find a data-driven solution for a better-regulated food distribution in Texas.