I'm a Lead Machine Learning Engineer at Penn Medicine. I work on making machine learning and AI useful in healthcare.
Getting a model to work is a start. I'm interested in deploying models, fitting them into real workflows, measuring whether they help, and keeping them reliable.
I co-founded DataPhilly and help connect people working with data and AI in Philadelphia.
Now
I'm focused on getting ML and AI into production in healthcare. I build reliable applications and evaluate them. My work also includes retrieval and LLM-based systems, secure integration into clinical workflows, and applied research.
I also stay involved with DataPhilly and the local data community.
CHIME is the COVID-19 Hospital Impact Model for Epidemics. It’s an open-source tool developed collaboratively to help hospitals plan for capacity needs during the pandemic.
I co-founded DataPhilly. Through talks and workshops, I help connect people in Philadelphia who are curious about data.
Selected talks
I share what I learn through technical talks and community events. Topics include Python, real-time predictive analytics, healthcare data science, and open-source collaboration. Here are a few of my talks.
Here are my notes from PyData with links for more details. This isn't a complete list, and in some cases my notes don't really do justice to the actual talks, but I hope that these will be helpful to anyone who's feeling PyData FOMO until the videos are released.
This is the first post in a multi-part series wherein I will explain the details surrounding the language prediction model I presented in my Pycon 2014 talk. If you make it all the way through, you will learn how to create and deploy a language prediction model of your own.
A little over a year ago I was frustrated with the lack of data meetups in the Philadelphia area, so I started DataPhilly. I quickly learned that when you start a tech meetup you're going to have to do some public speaking to get the ball rolling.