About Me

Currently, I am a Postdoctoral Research Scientist at Columbia University in the Causal Artificial Intelligence Lab, directed by Dr. Elias Bareinboim. I am an NSF-funded Computing Innovation Research Fellow. I did my PhD in biomedical engineering, specializing in computational neuroscience and machine learning at Johns Hopkins University. I worked with Dr. Sridevi V. Sarma in the Neuromedical Control Systems group. I also jointly obtained a MS in Applied Mathematics and Statistics with a focus in statistical learning theory, optimization and matrix analysis. I was fortunate to be a NSF-GRFP fellow, Whitaker International Fellow, Chateaubriand Fellow and ARCS Chapter Scholar during my time at JHU.

My research interests are broadly in the intersection areas of neuroscience, statistical machine learning, causal inference, control theory and dynamical systems. I am also extremely passionate about open-source everything.

For a list of my publications, see my Google Scholar. If there are any publications not publicly accessible, please shoot me a message and I'm happy to share with you.

Research Work

My PhD thesis work focused on developing algorithms for seizure localization in drug-resistant epilepsy patients. I worked with multivariate time-series, such as intracranial EEG and scalp EEG data. I also did 2D and 3D image analysis with T1 MRI and CT scans of patients to understand the spatial relationships of epileptic networks. Specifically, I analyzed EEG data using dynamical systems, control theory, machine learning and statistics. I combined biological understanding of the brain with mathematical models of dynamical systems. I also utilized machine learning and statistical models to answer relevant hypothesis-driven questions, with a focus on learning manifold structure from a low number of samples. Moreover, I developed a suite of open-source software tools contributing to packages such as the Brain Imaging Data Structure (BIDS), MNE, The Virtual Brain (TVB), scikit-learn and other scientific packages.

My interest in engineering and medicine started at UCSD, where I graduated in 2015 with a double major in Bioengineering and Mathematics. It was there under the guidance of many great faculty, such as Dr. Todd Coleman, that I became interested in applied mathematics, data analytics and machine learning in healthcare. It led me to pursue a PhD, with the ultimate goal of bringing together technology expertise with biomedical domain knowledge to solve challenging medical problems.

Personal Background

I'm originally from Los Angeles, CA and consider myself a true CA native even though I wasn't born there. I have a range of hobbies, including, but not limited to: running, gymming (weight lifting), reading, hacking, traveling and photography.

Non-Research Work

For a summary of my involvement in nonprofit and charity work, see here

Technical/Soft Skills

As an engineer, I consider myself an expert in Matlab and Python, being able to work with Numpy, Pandas, Scipy, Keras/Tensorflow, Pytorch and more. I am skilled with Cython for optimizing speed in Python. I am familiar with Bash, Javascript, HTML, CSS, MongoDB and SQL. I have experience with Django, Pelican web frameworks.

I like building stuff with Arduino, and Raspberry Pi; I've made a self-driving toy car using an ultrasound sensor and setup a hacked-version of Alexa using Raspberry Pi. As a data analyst, I have experience with PBS/SLURM scheduling systems and GNU parallel.

My domain experience includes neuroscience, linear systems, data wrangling, machine learning and algorithm development.

Everything on this site reflects my personal views only. It'll generally range from research/science thoughts, to photo blogs (in progress in the backend), to travel blogs (for my own benefit of keeping track of where I went :p).