I am a NYC-based software engineer. I’m currently at Uber as a member of Observability Applications.
I’m interested in research engineering and machine learning infrastructure engineering positions.
More broadly, I’m especially interested in opportunities that operate at the intersection of research and engineering. To that end, the roles that most excite me provide opportunities to explore my interests in AI/ML; how they might be implemented or productized both scalably and performantly; and bring those research and modelling findings into production.
If you and your team are working on projects that I’d make a good addition for, let’s talk.
My research interests lie in the fields of machine learning, knowledge representation, and decision theory.
Specifically, I am drawn to the following questions:
- What is knowledge? What does it mean to know something? Can a machine that achieves near-human performance at some task be said to truly know and understand that task beyond a behavioristic interpretation thereof?
- How do we model and represent that knowledge to achieve precision, recall, and performance on par with – possibly even beyond – humans?
- Traditional machines reason with “explicit” logic – conditionals, pattern-matching, etc. How can we, as engineers, design and implement systems that can reason about ambiguity and uncertainty?