Broad Research Interests: Automated Reasoning, Automated Planning, Artificial Intelligence, Formal Methods
Planning under Uncertainty
During my PhD I have been primarily focused on investigating planning and sequential decision making under uncertainty through integrative methods:
- With Selmer Bringsjord in the RAIR Lab I have looked at planning through automated reasoning. I further developed Spectra and the underlying planning with formulas framework to show classes of uncertainty problems that are easy to encode. Additionally, I wrote a QA algorithm for ShadowProver to integrate to Spectra for planning under epistemic uncertatinty.
- With Junkyu Lee, Michael Katz, Harsha Kokel, and Shirin Sohrabi at IBM I developed an algorithm for guiding hiearchical reinforcement agents under partial observability when domain knowledge can be encoded for characterizing discovery of unknown predicates. This techinque uses a fully-observable non-deterministic planner to generate a high-level policy where each high-level action is an option that a reinforcement learning agent needs to learn.
- More to come…
Logic
Underlying my work in artificial intelligence and cryptography is computational logic. In that regard, I have been able work on problems from the underlying logic formalisms, unification algorithms, to building tools for interactive theorem provers.
- With Andrew Marshall and Kimberly Cornell, we’re currently developing a new syntactic AC algorithm.
- With Thomas Ferguson and James Oswald we formalized a model theory for a fragment of the Deontic Cognitive Event Calculus.
- With James Oswald we’ve built interactive theorem provers and showed validity of large proofs in parallel using a high performance cluster.
Related Notes:
Symbolic Methods for Cryptography
Worked with Andrew Marshall and others in applying term reasoning within computational logic towards cryptography. This collaboration was previously funded under an ONR grant. We are interested in applying techniques such as unification and term rewriting to the following areas:
- Block Ciphers
- Secure Multi-party Computation
- Authentication
- Commitment Schemes
Together we built CryptoSolve, a symbolic cryptographic analysis tool, and made it publically available on GitHub. I wrote the term algebra and rewrite libraries, and contributed to the mode of operation library and some unification algorithms. I still help maintain the codebase. We previously presented our work at UNIF 2020 (slides), FROCOS 2021 (slides), WRLA 2022 (slides), and GandALF 2022.
Collaborators:
- NRL: Catherine Meadows
- UMW: Andrew Marshall
- UT Dallas: Serdar Erbatur
- SUNY Albany: Paliath Narendran and Kimberly Cornell
- Clarkson University: Christopher Lynch and Hai Lin
Group Website: https://cryptosolvers.github.io
Reinforcement Learning
During my undergraduate degree, I worked with Dr. Ron Zacharski on making deep reinforcement learning algorithms more sample efficient with human feedback.
In my experimentation, I built out a Reinforcement Learning library in PyTorch.
Links:
| RL Library on Github | Interactive Demonstrations Library | Undergraduate Honors Thesis (Eagle Scholar Entry) |
| Undergraduate Honors Defense | QEP Algorithm Slides | More… |
Dr. Stephen Davies guided my study of the fundamentals of reinforcement learning. We went over value functions, policy functions, how we can describe our environment as a markov decision processes, and other concepts.
Notes and Other Goodies / Github Code
Other Research and Academic Activities
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. LUNA-C Poster
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
Programming Languages: Back in the Fall of 2018, under the guidance of Ian Finlayson, I worked towards creating a programming language similar to SLOTH (Simple Language of Tiny Heft). SLOTH Code
Before this study, I worked through a great book called “Build your own Lisp”.
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.