config.toml

@@ -18,7 +18,7 @@ mediaTypes = ['^application/json']
     author = "Brandon Rozek"
     avatar = "avatar.jpg"
     favicon = "favicon.ico"
-    description = "Computer Science PhD Candidate @ RPI, Writer of Tidbits, and Linux Enthusiast"
+    description = "PhD Student @ RPI, Writer of Tidbits, and Linux Enthusiast"
     email = "brozek@brandonrozek.com"
     identities = [
         "https://github.com/brandon-rozek",

content/research/_index.md

@@ -7,36 +7,25 @@ Description: 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](#planning-under-uncertainty)
-- [Logic](#logic)
-- [Symbolic Methods for Cryptography](#symbolic-methods-for-cryptography)
-
 ## Planning under Uncertainty
 
-My dissertation topic is on automatically finding and recognizing plans
+During my PhD I have been primarily focused on investigating planning and sequential decision
-when agents are uncertain about the environment but can compare the
+making under uncertainty:
-uncertainty between events *qualitatively*. For example, it is totally
+- I created a new framework which allows agents to make plans under *qualitative uncertainty*.
-expected when we stack a block that it stays on the top. However, there is a
+This helps in settings where the user doesn't have exact probabilities that various
-smaller likelihood that the block falls off.
+facts holds, but can instead bucket them into different likelihood values.
-How can we best make use of this qualitative uncertainty?
+This work is supervised under [Selmer Bringsjord](https://homepages.rpi.edu/~brings/).
-- Agents when operating under uncertainty will seek plans which maximize the likelihood of their goals.
+- Additionally with Selmer Bringsjord in the [RAIR Lab](https://rair.cogsci.rpi.edu/), I have looked at planning through automated reasoning.
-I designed an algorithm for recognizing these plans under qualitative possibility theory. This work is supervised under [Selmer Bringsjord](https://kryten.mm.rpi.edu/selmerbringsjord.html) (Paper to be released soon)
+I further developed [Spectra](https://github.com/rairlab/spectra) and the underlying
-- Additionally with Selmer Bringsjord in the [RAIR Lab](https://rair.cogsci.rpi.edu/), I created a framework that captures
+planning with formulas framework to show classes of uncertainty problems that
-situations where agents are able to bucket the likelihood of facts within their environment. I then provide an effective
+are easy to encode. Additionally, I wrote a QA algorithm for ShadowProver to integrate to Spectra
-techinque for using classical planners to find plans which maximize the agent's likelihood of success. ([Paper](/paper/2406.02))
+for planning under epistemic uncertatinty.
-- In the RAIR Lab, I also further developed [Spectra](https://github.com/rairlab/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](/paper/2405.01/))
 - 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://harshakokel.com/), and [Shirin Sohrabi](https://researcher.watson.ibm.com/researcher/view.php?person=us-ssohrab) at IBM I developed an algorithm
-for guiding hierarchical reinforcement agents under partial observability. Specifically,
+for guiding hiearchical reinforcement agents under partial observability when domain knowledge
-I focused on situations where the agent knows what they don't know and compiled that knowledge
+can be encoded for characterizing discovery of unknown predicates.
-so that a fully-observable non-deterministic planner can decompose the overall problem. ([Paper](/paper/2406.01))
+
 
 ## Logic
 
@@ -57,7 +46,7 @@ Related Notes:
 
 - [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
@@ -84,11 +73,6 @@ Collaborators:
 
 Group Website: [https://cryptosolvers.github.io](https://cryptosolvers.github.io)
 
----
-
-**Note:** From this point on, the projects listed happened over 5 years ago.
-
----
 
 ## Reinforcement Learning