I am a first-year Computer Science PhD student at Stony Brook University co-advised
by Professors Erez Zadok and Omkant Pandey. My research interests include
distributed systems, security, and reinforcement learning (specifically multi-armed bandits).
Other interests include proof assistants like Lean and Coq, and various math topics including
algebra, measure theory, and most recently topology.
I am originally an Arizona native born and raised in the city of Chandler.
Prior to joining the PhD program, I also completed my undergraduate degree in
Computer Science and Mathematics at Stony Brook.
Feel free to contact me: csasmith (at) cs (dot) stonybrook (dot) edu
The multi-armed bandit problem is a special case of online reinforcement learning where an
agent faced with a set of actions tries to balance the task of learning the action with the greatest
expected reward against the task of maximizing cumulative reward from actions played so far. The distributed
case often has several agents cooperating to solve the same bandit problem,
with applications in recommendation systems, medical trials, robotics, and more. This work
presents an algorithmic design for cooperative bandit problems that adapts
two state-of-the-art single-agent "Upper Confidence Bound" (UCB) algorithms, proves it outperforms the
single-agent benchmark, and most importantly is completely decentralized. To date, every other
cooperative bandit algorithm uses some global knowledge of the network to achieve its performance, which
is not always realistic given the size and nature of agent networks that may appear in practice.
I was primarily responsible for implementation, conducting comprehensive simulations, and communicating results
in the paper.
Congressional Redistricting Tool
Designed and implemented a full stack web application for a capstone project in a four-person team that
provides the user with a selection of congressional districting plans generated
on a HPC cluster by a Markov chain Monte Carlo method. Users can visually compare
districting statistics and geometry, and then optimize a chosen districting for
equal population variance between districts via a novel simulated annealing algorithm
run on the application server in real time that works with highly granular census
data. Such a tool could be used by legislators to efficiently search for more equitable
I was primarily responsible for the application design, React front end, and
Java Spring + Hibernate backend interfacing with a MySQL database. Our tool produced
significant improvements across all states in population equality variance during
the simulated annealing stage that is currently unattainable by existing methods
at the usual precinct granularity.
In an autonomous vehicle platoon, a set of vehicles coordinate with each other to travel single file with
greatly reduced following distances. Platooning promises benefits such as fuel efficiency, road safety,
reduced traffic congestion, and passenger comfort. Vehicle platoons are often envisioned as dynamic, with
vehicles fluidly joining and leaving the platoon. This dynamicism, however,
introduces a security risk as platoon members can send dangerous coordination messages to other vehicles.
Intervehicular message integrity can be verified with cryptographic techniques, but message truthfulness cannot be,
as an entity with the right keys could send a valid request to join a platoon, but not actually be in the correct
physical location to join the platoon. In this project we developed a security protocol for vehicles requesting to join
a platoon. The protocol essentially challenges the candidate vehicle to perform a set of longitudinal perturbations
(i.e. back and forth wiggles), and a verifier vehicle at the back of the platoon records the measurements of
the vehicle behind (if any), and accepts the candidate request if the recorded movements sufficiently match those in the challenge.
The project was undertaken as part of an NSF-funded REU at the University of Arizona, and I was primarily
responsible for designing the wiggle protocol and analyzing its security.