I am currently a postdoctoral researcher at the Miller lab, affiliated to the MIT's Picower Institute for Learning and Memory and the Brain + Cognitive Sciences department at the Massachusetts Institute of Technology (MIT).
My research interest lies in investigating the role of machine learning models to build data-driven models from large-scale neural recordings to gain insight into cognition and memory. I aim to develop new approaches to study neural dynamics with state-of-the-art machine learning methodologies.
I have also worked at the Alan Turing Insitute (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 analyse 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 did research on 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 in cancer research.
My full list of publications is in google scholar. If you are interested in my work don't hesitate to contact me at [c]@mit.edu.
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 Frahm, Apparat, Skinshape, Jon Hopkins, Helios and Alaskan Tapes. I also like to tell stories with music.
[c] = costilla