Welcome! As a Research Scientist at MIT's CSAIL, I am passionate about harnessing the power of neurosymbolic programming techniques to explore and deepen our understanding of the world. My interdisciplinary research spans across machine learning, programming languages, neuroscience and mental health. By leading various initiatives, including the NSF Expeditions in Computing project as project manager, and AI efforts in Latin America, I am dedicated to fostering scientific discovery and driving global positive impact. Dive in to explore my background, research projects, publications, and interdisciplinary endeavors. I am always open to collaborations, speaking opportunities, and connecting with fellow researchers to exchange ideas and insights.

To learn more about my research interest, here is a perspective paper my team and I wrote, and here is a talk I gave recently summarizing it.

I am fortunate to be the project manager of the multidisciplinary NSF expeditions in computing project:  Understanding the world through code led by Prof. Armando Solar-Lezama. Our goal is to advance artificial intelligence and programming languages research for scientific discovery.

I also lead an initiative for artificial intelligence in Latin America.

Check out the neurosymbolic programming for science seminars I have been hosting as part of our NSF expeditions in computing initiative.

Highlighted Research:

Computer Science

  1. Neurosymbolic Programming for Science (Neurips 2022)
  2. Lemma – Bootstrapping High-Level Mathematical Reasoning with Learned Symbolic Abstractions (Neurips 2022)

Neuroscience

  1. Pattern Recognition of Deep and Superficial Layers of the Macaque Brain Using Large-Scale Local Field Potentials (MIT News) (Cognitive Computational Neuroscience)
  2. Automatic Methods for Cortex-Wide Layer Identification of Electrophysiological Signals Reveals a Cortical Motif for the Expression of Neuronal Rhythms (Cognitive Computational Neuroscience)

Mental Health

  1. Counterfactual Explanations of Clinical Alerts in Schizophrenia using Digital Phenotyping (working paper)
  2. Developing Clinical Insights into Schizophrenia at an Individual Level (n=1) using a Combination of Digital Phenotyping and Network Analysis Techniques Focusing on Variations in the Number of Recorded Daily Locations Visited (in press)

Engineering and Computer Science

  1. Gait Analysis Applications to Healthcare and Security with Machine Learning (PhD Thesis)

Featured Projects:

Fairness in algorithmic decision-making

Mental Health

Digital healthcare

Latin America

Education

Academic Journey: