I currently work for Riverside Research on their geospatial intelligence programs. I recently graduated from the University of Rochester with a Computer Science B.S., Applied Mathematics B.S., and Physics B.A., and Engineering Science B.A.. I find theoretical work in mathematics and computer science fascinating. In my personal time I read on results in complexity theory, logic, number theory, and information theory such as the Completeness and Incompleteness Theorems of First and Second Order Logic, the Source Coding Theorem, Cook's Theorem, and the Prime Number Theorem.
While some of the theoretical results mentioned above are absolutely fascinating, I am also motivated by improving the lives of other as effectively as I can. I consider myself part of the effective altruist movement, dedicated to improving the lives of others as effectively as I can. I annually contribute 10% of my income to Givewell, an organization dedicated to improving the wellbeing of humankind through funding the most efficient charities.
I currently work for Riverside Research on their satellite planning tool the Collection Planning Suite (CPS). I am continuing work done as an intern at Riverside and also working on other aspects of development in Riverside's geospatial intelligence programs.
From May 2020-Sep 2021 I worked part with Riverside Research as a Machine Learning Intern in the Modeling & Application Development group. My work has focused on using mixed integer programming (MIP) and ML tools to improve Riverside's satellite scheduling system called the Collection Planning Suite.
In Summer 2020 I delved into the world of state of the art reinforcement learning. For anyone else interested in this area I highly reccomend YouTube courses by Sergey Levine and David Silver.
I ended up focusing my research on reinforcement learning in multiplayer competitve environments. I developed a couple novel algorithms based off of ideas in meta learning and game theory. Read more about this research here!
I received a DAAD scholarship to do computer science research at the Technical University of Hamburg in Summer 2019. My research focused on the ‘informative path planning’ problem. The goal of the research was to develop and compare sampling based path planning algorithms for 2D field exploration (e.g. temperature field or topography map). The fields were modelled using Gaussian Processes and Gaussian Markov Random Fields and then various sampling algorithms were implemented (in Python) to efficiently plan informative exploratory paths.
Lab teaching assistant for Intro to Computer Science course in Java (CSC 171), teaching fundamentals of CS and helping on programming projects.
Teaching assistant for advanced Computer science course, Design & Analysis of Efficient Algorithms (CSC 282), leading problem sessions.
Teaching assistant for Chemical Principles for Engineers course (CHM 137) and Electricity and Magnetism (PHY 122), leading weekly recitations and workshops.