I am currently a postdoctoral fellow at the Miller lab. Our lab is affiliated to the Picower Institute for Learning and Memory and the Brain + Cognitive Sciences department at the Massachusetts Institute of Technology (MIT).

My research interest lies in developing machine learning models from large-scale neural recordings to gain insight into the underling mechanisms of cognition and memory. I aim to develop new approaches to study neural dynamics with state-of-the-art machine learning methodologies such as deep learning and unsupervised learning. 

You can find my CV here (updated August 2019).

NEW! I will be attending the Society of Neuroscience conference 2019 and giving a talk at the mini-symposium entitled "Artificial Intelligence and Neuroscience: From Neural Dynamics to Artificial Agents"  on  October 21, 2019, 8:30 AM - 11:00 AM -  Room S406A. Hope to see you there!

NEW! Check out our new paper on "Pattern recognition of deep and superficial layers of the macaque brain using large-scale local field potentials" published at the 2019 Conference on Cognitive Computational Neuroscience: https://ccneuro.org/2019/Papers/ViewPapers.asp?PaperNum=1159 

I am also excited to be leading the organization of the first Latin American artificial intelligence summit to be held at MIT in January 2020. The summit will bring together key players in industry, academia, and government across the region to discuss the importance and impact of artificial intelligence is set to have in the region.

Before joining MIT, I have worked on machine learning research in the context of securityhealthcare, indoor positioning, and cancer research.

I have also worked at the Alan Turing Institute (National institute of data science in the UK) -  Data Study groups that bringing together some of the UK's top talent from data science, artificial intelligence, and wider fields, to analyze real-world data science challenges. Here is a report of a project I worked on and a subsequent machine learning tool developed from the work developed at the study group.

My research currently holds the world record in footstep recognition by developing a machine learning technique that allowed obtaining an equal error rate of 0.7 in the largest footstep recognition database.

I obtained my Ph.D. at the University of Manchester where I focused my doctoral thesis in machine learning for gait analysis understanding in the context of security and healthcare.

For my master's degree thesis, I worked on indoor positioning, simultaneous localization and mapping in Robotics (SLAM) and cancer research.

My full list of publications is in google scholar.

If you are interested in any of my work don't hesitate to contact me at [c]@mit.edu for questions or resources.

You can also find me on twitter.

I love experimental, creative music that blends the old (Jazz, classical) with the new (electronic, ambient) in interesting and pleasant ways. Some of my favorites artists are Nils FrahmApparatSkinshapeJon HopkinsHelios, and Alaskan Tapes. I also like to tell stories with music.

I also love running and always looking to run my next marathon. I will be competing in the New York City marathon 2019.

I also competed in the London triathlon of 2016.

[c] = costilla