I conduct research at the intersection of artificial intelligence (AI) and healthcare. Specifically, I focus on developing AI systems that are (1) resource-efficient, requiring less data and supervision by medical professionals, and (2) trustworthy, exhibiting robust and fair performance to allow for their responsible deployment in a clinical setting.
I am currently a senior AI engineer at Vicarious Surgical. Previously, I was a postdoctoral scholar at Caltech working on AI systems for surgical applications. I completed my PhD at the Computational Health Informatics Lab., at the University of Oxford. There, I designed deep learning algorithms that do 'more with less' in the context of cardiac data. Before this, I completed my undergraduate degree in biomedical engineering at The Johns Hopkins University. In the past couple of years, I have also had the privilege to work alongside colleagues and mentors at Ford Motor Company, the Mayo Clinic, Merck & Co., and Flatiron Health.
As a postdoctoral scholar, I design deep learning algorithms that exploit intraoperative videos to quantify the core elements of surgery (think: what and how surgery is performed). In doing so, we can provide surgeons with feedback about their performance as a means to modulate their behaviour.
In the summer of 2021, I interned at Flatiron Health where I designed a natural language processing system that infers a clinical variable based on oncology patient visits. In the process, I also developed a framework for evaluating machine learning models in the absence of ground-truth labels.
Throughout my PhD, I focused on designing clinical deep learning algorithms that are less dependent on data, labels, and medical supervision. This involved leveraging generative modelling, self-supervised learning, and continual learning.
In the summer of 2020, I was fortunate to work alongside Antong Chen at Merck & Co. where I designed a meta-learning framework that leverages cardiac MRI data across medical centres in order to delineate the structures of the heart (segmentation) in a data-efficient manner. Our work can be found here.
In the summer of 2019, I had the pleasure of working with Kenneth Fetterly and Zachi Attia within the Department of Cardiovascular Medicine at the Mayo Clinic where I implemented self-supervised algorithms for coronary angiograms (read: X-ray videos of the arteries surrounding the heart).
In the summer of 2017, I interned at the Machine Learning Center of Excellence at Ford Motor Company, under the supervision of Kurt Godden and K.P. Unnikrishnan, where I used diagnostic trouble codes from vehicles to predict whether such vehicles will malfunction in the near future.
In the summer of 2016, I performed research at the Musculoskeletal Lab at Imperial College London, under the supervision of Alison McGregor, to identify how pressure is distributed within lower-limb prosthetic cuffs worn by amputee patients.
During my undergraduate studies, I conducted research at the Neuroengineering Lab. at The Johns Hopkins University to design and manufacture an upper-limb prosthetic device for data collection purposes.
I also led a team of engineers in the design of a surgical device that repositions patients during time-sensitive intra-operative complications. You can find an article on that here.
Feel free to reach out if you would like to chat.