Brandon Rozek

Quick List of Publications

Broad Research Interests: Automated Reasoning, Artificial Intelligence, Formal Methods

Symbolic Methods for Cryptography

Worked with Dr. Andrew Marshall under an ONR grant in collaboration with University at Albany, Clarkson University, University of Texas at Dallas, and the Naval Research lab in order to automatically generated and verify cryptographic algorithms using symbolic (as opposed to computational) methods.

During that time period I built a free algebra library, rewrite library, parts of the crypto tool, and dabbled in Unification algorithms. You can check them out on Github.

Currently, I am an external collaborator who mainly helps maintain the codebase I started as well as contribute to our current work with Garbled Circuits. We presented our work at UNIF 2020 (slides), FROCOS 2021, and have a couple other papers in the works.

Through my collaborators, I’ve learned about term reasoning and algebras. [Notes]

Reinforcement Learning

Deep Reinforcement Learning: With Dr. Ron Zacharski I focused more on a particular instance of Reinforcement Learning where deep neural networks are used. During this time, I built out a Reinforcement Learning library written in PyTorch. This library helps me have a test bed for trying out different algorithms and attempts to create my own.

One particular problem I’m fascinated by is how to make Reinforcement Learning algoirthms more sample efficient. This means, how can we make it so that it learns more from every observation or make it so that we can achieve our goal quicker?


RL Library on Github Interactive Demonstrations Library Honors Thesis (Eagle Scholar Entry)
Honors Defense QEP Algorithm Slides More…

Reinforcement Learning: Studied the fundamentals of reinforcement learning with Dr. Stephen Davies. We went over the fundamentals such as value functions, policy functions, how we can describe our environment as a markov decision processes, etc.

Notes and Other Goodies

Github Code


Programming Languages: Studying the design of programming languages. So far I made an implementation of the SLOTH programming language, experimenting with what I want my own programming language to be syntatically and paradigm wise. SLOTH Code

Before this study, I worked though a book called “Build your own Lisp” and my implementation of a lisp like language: Lispy Code

Competitive Programming: Studying algorithms and data structures necessary for competitive programming. Attended ACM ICPC in November 2018/2019 with a team of two other students.

Cluster Analysis: The study of grouping similar observations without any prior knowledge. I studied this topic by deep diving Wikipedia articles under the guidance of Dr. Melody Denhere during Spring 2018. Extensive notes

Excitation of Rb87: Worked in a Quantum Research lab alongside fellow student Hannah Killian under the guidance of Dr. Hai Nguyen. I provided software tools and assisted in understanding the mathematics behind the phenomena.

Modeling Population Dynamics of Incoherent and Coherent Excitation

Coherent Control of Atomic Population Using the Genetic Algorithm

Beowulf Cluster: In order to circumvent the frustrations I had with simulation code taking a while, I applied and received funding to build out a Beowulf cluster for the Physics department. Dr. Maia Magrakvilidze was the advisor for this project.

High Performance Cluster for Research and Education Report (nicknamed LUNA-C)

LUNA-C Poster