I’m a PhD student in the Speech and Language Technologies CDT at University of Sheffield. My work is focused on the intersection of NLP, Speech, and Vision. Previously, I was a student at University of Sheffield where I graduated with Distinction of the Advanced Computer Science Master’s degree; and I completed my undergraduate’s degree (BSc. Computer Engineering) at Instituto Tecnológico Autónomo de México. I’ve gained industrial experience working as data scientist at deep_dive, a startup based in Mexico City dedicated to empower companies with data science.
My research interests are natural language processing, machine learning, and data science, and my field of study is multimodal computational social science.
I believe technology is a powerful tool for building a better world.
We present a study on predicting the POI type a social media message was posted from. We develop a large-scale data set with tweets mapped to their POI category, and conduct an analysis to uncover features specific to place type. Also we train predictive models to infer the POI category using only tweet text and posting timeTech At Bloomberg - Point-of-Interest Type Inference from Social Media Text - December 4, 2020
We present a first study of parody using methods from computational linguistics and machine learning. We introduce a freely available large-scale data set containing a total of 131,666 English tweets from 184 real and corresponding parody accounts, and evaluate a range of neural models achieving high predictive accuracy.