David is a first-year Ph.D. student in Computer Science, and is advised by Professor Tina Eliassi-Rad. His research interests are fairness in machine learning, computational social science, and computational reproducibility. David applies machine learning to social issues but is also interested in identifying and measuring its limitations. In the process, he draws on domain knowledge and software-engineering best practices.
He graduated from Princeton University with a concentration in Computer Science and a certificate in Statistics and Machine Learning. There, he was fortunate enough to be advised by Matthew Salganik and wrote a senior thesis titled Successes and struggles with computational reproducibility: Lessons from the Fragile Families Challenge.
Previously, David was a software engineer at Bloomberg LP where his work focused on full-stack C++ applications, message queueing, and continuous integration. In November 2019, he co-delivered the keynote address at the RabbitMQ Summit in London. The talk is titled: “Growing a farm of rabbits to scale financial applications”. A recording is available on Youtube.