8.6 KiB
| Title | Description |
|---|---|
| Research | A list of my research Projects |
Broad Research Interests: Automated Reasoning, Automated Planning, Artificial Intelligence, Formal Methods
Currently, I'm a Computer Science PhD Candidate at Rensselaer Polytechnic Institute. I enjoy using logic-based techniques and designing algorithms to solve problems.
Jump to:
Planning under Uncertainty
My dissertation topic is on automatically finding and recognizing plans when agents are uncertain about the environment but can compare the uncertainty between events qualitatively. For example, it is totally expected when we stack a block that it stays on the top. However, there is a smaller likelihood that the block falls off. How can we best make use of this qualitative uncertainty?
- Agents when operating under uncertainty will seek plans which maximize the likelihood of their goals. I designed an algorithm for recognizing these plans under qualitative possibility theory. This work is supervised under Selmer Bringsjord (Paper to be released soon)
- Additionally with Selmer Bringsjord in the RAIR Lab, I created a framework that captures situations where agents are able to bucket the likelihood of facts within their environment. I then provide an effective techinque for using classical planners to find plans which maximize the agent's likelihood of success. (Paper)
- In the RAIR Lab, I also further developed Spectra -- an automated planner built on automated theorem proving. I showed how a class of problems under uncertainty can be easily encoded and wrote a question-answer algorithm for ShadowProver so that Spectra can find plans under epistemic uncertainty. (Paper)
- With Junkyu Lee, Michael Katz, Harsha Kokel, and Shirin Sohrabi at IBM I developed an algorithm for guiding hierarchical reinforcement agents under partial observability. Specifically, I focused on situations where the agent knows what they don't know and compiled that knowledge so that a fully-observable non-deterministic planner can decompose the overall problem. (Paper)
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 Tedder, I'm currently working on building a tool that checks if matrix models of given logic satisfies relevance properties.
- 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
Note: From this point on, the projects listed happened over 5 years ago.
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.