April 30: Live interview on ABC-7 WJLA about our work modeling COVID-19 [video]
I am a PhD student in Computer Science at Harvard University, studying how machine learning and optimization can solve problems for social good. My work currently focuses on infectious disease, modeling non-pharmacological interventions for preventing the spread of COVID-19 and building predictive models of medication adherence for TB patients in India. I have also designed machine learning models for detecting sobriety using mobile health data (check out the dataset!). Before computer science, I studied computational genetics as a Pelotonia fellow.
In the Summer of 2018, I interned with Spatial.AI, a tech startup breaking into the space of geosocial data. There, I designed NLP pipelines with Python to compute “social scores” for cities using geotagged social media. I also built predictive models based on these social scores and demographic data to estimate success of retail store fronts.
I was hired in 2014 by a bussing company that decided it was time to stop managing all of their data with Excel. I built them a Microsoft Access Database to handle hundreds of assets and equipped it with a custom schedule system for drivers. Little did I know that this would spark my love of computer science! I still provide technical support and regular updates for my best customers, and probably will until Microsoft goes out of business.
Everyone needs a way to relax. I do it by beating a (my own) drum. And sometimes I even get the chance to do it on stage! Music is a life-long passion of mine that I am always looking to exercise.
Through social networks, mobile devices, and the growing internet-of-things, humans produce a massive amount of data daily. I want to apply large-scale data analysis and machine learning to leverage and make insights on that human-generated data to effect social good.