config.toml

@@ -84,6 +84,13 @@ enableGitInfo = true
     url = "/research/"
     weight = 20
 
+[[menu.main]]
+    identifier = "ta"
+    name = "TA"
+    pre = "<i class='fa fa-chalkboard-teacher fa-lg'></i>"
+    url = "/ta/"
+    weight = 30
+
 [[menu.main]]
     identifier = "community"
     name = "Community"

content/blog/ice-hockey.md

@@ -8,6 +8,17 @@ medium_enabled: false
 ---
 
 
+{{< unsafe >}}
+<style> 
+main img {
+    display: block;
+    margin-left: auto;
+    margin-right: auto;
+}
+</style>
+{{< /unsafe >}}
+
+
 ![Action shot of ice hockey players](/files/images/blog/20231126210415162.jpg)
 
 ---

content/community/logic-group.md

@@ -15,8 +15,6 @@ given: [https://rairlab.github.io/logic-group/](https://rairlab.github.io/logic-
 
 I presented the following:
 
-01/24/2024: An original talk titled "Spectra: STRIPS-Inspired AI Planner based on Automated Reasoning"
-
 10/25/2023: An original talk titled "Introduction to Goal Recognition as Planning"
 
 10/18/2023: Workshop along with James Oswald titled "RAIR Lab Software Overview"

content/menu/beef-bean-burger.md

@@ -5,7 +5,7 @@ hideDate: true
 draft: false
 ---
 
-## Ingredients:
+Ingredients:
 - Ground Beef
 - 1/2 Onion Finely Chopped
 - 1 Egg
@@ -17,7 +17,7 @@ draft: false
 - 1 green pepper
 - 1 can refried beans
 
-## Recipe:
+Recipe:
 1. Set out a large mixing bowl.
 2. Finely chop or use a food processor on the green pepper and the onion
 3. Add all the ingredient into the bowl and use your hands to mix the ingredients well until it's all evenly spread out

content/research/_index.md

@@ -5,49 +5,35 @@ Description: A list of my research Projects
 
 **[Quick List of Publications](/publications/)**
 
-**Broad Research Interests:** Automated Reasoning, Automated Planning, Artificial Intelligence, Formal Methods
+**Broad Research Interests:** Automated Reasoning, Artificial Intelligence, Formal Methods
 
-## Planning under Uncertainty
+## Logic-Based AI
+Working with [Dr. Selmer Bringsjord](https://homepages.rpi.edu/~brings/) and others in the [RAIR Lab](https://rair.cogsci.rpi.edu/) to
+design and implement artificial intelligent agents using computational logic. I'm particularly interested in:
+- Explainability through verifiable chains of inference
+- Defeasible reasoning under uncertainty
+- Reasoning about agents and their cognitive states
 
-During my PhD I have been primarily focused on investigating planning and sequential decision
+[Notes on Automated Theorem Proving](atp)
-making under uncertainty through integrative methods:
+
-- With [Selmer Bringsjord](https://homepages.rpi.edu/~brings/) in the [RAIR Lab](https://rair.cogsci.rpi.edu/) I have looked at planning through automated reasoning.
+## Integrated Planning and Reinforcement Learning
-I further developed [Spectra](https://github.com/rairlab/spectra) and the underlying
+Working with [Junkyu Lee](https://researcher.ibm.com/researcher/view.php?person=ibm-Junkyu.Lee),
-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](https://researcher.ibm.com/researcher/view.php?person=ibm-Junkyu.Lee),
 [Michael Katz](https://researcher.watson.ibm.com/researcher/view.php?person=ibm-Michael.Katz1),
-[Harsha Kokel](https://research.ibm.com/people/harsha-kokel), and [Shirin Sohrabi](https://researcher.watson.ibm.com/researcher/view.php?person=us-ssohrab) at IBM I developed an algorithm
+and [Shirin Sohrabi](https://researcher.watson.ibm.com/researcher/view.php?person=us-ssohrab)
-for guiding hiearchical reinforcement agents under partial observability when domain knowledge
+on extending and relaxing assumptions within their existing
-can be encoded for characterizing discovery of unknown predicates. This techinque
+[Planning Annotated Reinforcement Learning Framework](https://prl-theworkshop.github.io/prl2021/papers/PRL2021_paper_36.pdf) developed at IBM Research.
-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
+In this framework, automated planning is used on a higher-level version of the overall
-
+problem with a surjective function mapping RL states to AP states. The agent is
-Underlying my work in artificial intelligence and cryptography 
+based on the options framework in Hiearchical Reinforcement Learning where options
-is computational logic. In that regard, I have been able
+are defined as the grounded actions in the planning model.
-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 formaliezd 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:
+More to come...
-
-- [Automated Theorem Proving](atp/)
-- [Term Reasoning](termreasoning/)
-
 
 ## Symbolic Methods for Cryptography
-Worked with [Andrew Marshall](https://www.marshallandrew.net/) and others in applying term reasoning within computational logic
+Working with [Dr. Andrew Marshall](https://www.marshallandrew.net/) 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
@@ -56,17 +42,17 @@ as unification and term rewriting to the following areas:
 - Commitment Schemes
 
 Together we built [CryptoSolve](https://github.com/cryptosolvers/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
+I still help maintain the codebase, as well as contribute to our current work on Garbled Circuits. We previously presented our work
-at  [UNIF 2020](https://www3.risc.jku.at/publications/download/risc_6129/proceedings-UNIF2020.pdf#page=58) ([slides](/files/research/UNIF2020-Slides.pdf)), [FROCOS 2021](https://link.springer.com/chapter/10.1007/978-3-030-86205-3_14) ([slides](/files/slides/FROCOS2021.pdf)), [WRLA 2022](http://sv.postech.ac.kr/wrla2022/assets/files/pre-proceedings-WRLA2022.pdf#page=12) ([slides](/files/slides/wrla2022-slides.pdf)),
+at  [UNIF 2020](https://www3.risc.jku.at/publications/download/risc_6129/proceedings-UNIF2020.pdf#page=58) ([slides](/files/research/UNIF2020-Slides.pdf)), [FROCOS 2021](https://link.springer.com/chapter/10.1007/978-3-030-86205-3_14) ([slides](/files/slides/FROCOS2021.pdf)), and [WRLA 2022](http://sv.postech.ac.kr/wrla2022/assets/files/pre-proceedings-WRLA2022.pdf#page=12) ([slides](/files/slides/wrla2022-slides.pdf)).
-and [GandALF 2022](/paper/2209.01/).
 
+I've written a few [notes](termreasoning/) about term reasoning.
 
-Collaborators:
+Current Collaborators:
 - NRL: Catherine Meadows
-- UMW: [Andrew Marshall](https://www.marshallandrew.net/)
+- UMW: [Andrew Marshall]((https://www.marshallandrew.net/))
 - UT Dallas: Serdar Erbatur
-- SUNY Albany: [Paliath Narendran](https://www.cs.albany.edu/~dran/) and Kimberly Cornell
+- SUNY Albany: [Paliath Narendran](https://www.cs.albany.edu/~dran/), Kim Cornell
-- Clarkson University: [Christopher Lynch](https://people.clarkson.edu/~clynch/) and Hai Lin
+- Clarkson University: [Christopher Lynch](https://people.clarkson.edu/~clynch/)
 
 
 Group Website: [https://cryptosolvers.github.io](https://cryptosolvers.github.io)
@@ -74,10 +60,10 @@ Group Website: [https://cryptosolvers.github.io](https://cryptosolvers.github.io
 
 ## Reinforcement Learning
 
-During my undergraduate degree, I worked with [Dr. Ron Zacharski](http://zacharski.org/)
+**Deep Reinforcement Learning:** With [Dr. Ron Zacharski](http://zacharski.org/) I focused on how to make deep reinforcement learning
-on making deep reinforcement learning algorithms more sample efficient with human feedback.
+algorithms more sample efficient. That is, how can we make it so that the RL agent learns more from every observation to make it so that
-
+we achieve our goal faster. With that goal in mind, I built out a Reinforcement Learning library written in PyTorch to help benchmark
-In my experimentation, I built out a Reinforcement Learning library in PyTorch.
+my ideas.
 
 
 *Links:*
@@ -89,14 +75,23 @@ In my experimentation, I built out a Reinforcement Learning library in PyTorch.
 
 
 
-[Dr. Stephen Davies](http://stephendavies.org/) 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.
+**Reinforcement Learning:** Studied the fundamentals of reinforcement learning with [Dr. Stephen Davies](http://stephendavies.org/). 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](reinforcementlearning/) / [Github Code](https://github.com/brandon-rozek/ReinforcementLearning)
 
 
 
 
-## Other Research and Academic Activities
+## Other
+
+[**Programming Languages:**](proglang/) 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](https://github.com/brandon-rozek/SLOTH)
+
+Before this study, I worked through a great book called ["Build your own Lisp"](https://www.buildyourownlisp.com/).
+
+
+[**Competitive Programming:**](progcomp/) 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](clusteranalysis/)**
 
 [**Excitation of Rb87**](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.
 
@@ -105,16 +100,6 @@ In my experimentation, I built out a Reinforcement Learning library in PyTorch.
 [Coherent Control of Atomic Population Using the Genetic Algorithm](/files/research/coherentcontrolofatomicpopulation.pdf)
 
 
+
 [**Beowulf Cluster:**](lunac) 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](/files/research/LUNACposter.pdf)
 
-**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](clusteranalysis/)**
-
-[**Programming Languages:**](proglang/) 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](https://github.com/brandon-rozek/SLOTH)
-
-Before this study, I worked through a great book called ["Build your own Lisp"](https://www.buildyourownlisp.com/).
-
-[**Competitive Programming:**](progcomp/) Studying algorithms and data structures necessary for competitive programming. Attended ACM ICPC in November 2018/2019 with a team of two other students.
-
-
-
-

themes/pulp

@@ -1 +1 @@
-Subproject commit 7b7136151bf266d421258a377d0f2ab9a9f58b62
+Subproject commit e27a7a1f761ef008f518dc1e5f5ebb80f115be79