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@ -3,7 +3,7 @@ Title: Research
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Description: A list of my research Projects
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---
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**[Quick List of Publications](/publications)**
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**[Quick List of Publications](/publications/)**
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**Broad Research Interests:** Automated Reasoning, Artificial Intelligence, Formal Methods
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@ -45,7 +45,7 @@ Together we built [CryptoSolve](https://github.com/cryptosolvers/CryptoSolve), a
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I still help maintain the codebase, as well as contribute to our current work on Garbled Circuits. We previously presented our work
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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)).
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I've written a few [notes](termreasoning) about term reasoning.
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I've written a few [notes](termreasoning/) about term reasoning.
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Current Collaborators:
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- NRL: Catherine Meadows
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@ -77,23 +77,23 @@ my ideas.
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**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.
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[Notes and Other Goodies](reinforcementlearning) / [Github Code](https://github.com/brandon-rozek/ReinforcementLearning)
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[Notes and Other Goodies](reinforcementlearning/) / [Github Code](https://github.com/brandon-rozek/ReinforcementLearning)
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## Other
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[**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)
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[**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)
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Before this study, I worked through a great book called ["Build your own Lisp"](https://www.buildyourownlisp.com/).
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[**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.
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[**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.
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**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)**
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**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/)**
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[**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.
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[**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.
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[Modeling Population Dynamics of Incoherent and Coherent Excitation](/files/research/modellingpopulationdynamics.pdf)
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@ -4,6 +4,6 @@ description: Notes about Automated Theorem Proving
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---
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More links coming soonish:
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- [Definitional CNF](definitional-cnf)
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- [Davis Putnam](davis-putnam)
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- [Finding Counter-Models through Truth Functional Expansions](truth-functional-expansion)
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- [Definitional CNF](definitional-cnf/)
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- [Davis Putnam](davis-putnam/)
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- [Finding Counter-Models through Truth Functional Expansions](truth-functional-expansion/)
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@ -7,15 +7,15 @@ Cluster Analysis is the art of finding inherent structures in data to form group
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This is an independent study, meaning that I will be studying this topic under the direction of a professor, in this case being Dr. Denhere.
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I have provided a list of topics that I wish to explore in a [syllabus](syllabus)
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I have provided a list of topics that I wish to explore in a [syllabus](syllabus/)
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Dr. Denhere likes to approach independent studies from a theoretical and applied sense. Meaning, I will learn the theory of the different algorithms, and then figure out a way to apply them onto a dataset.
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## Readings
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There is no definitive textbook for this course. Instead I and Dr. Denhere search for materials that we think best demonstrates the topic at hand.
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I have created a [Reading Page](readings) to keep track of the different reading materials.
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I have created a [Reading Page](readings/) to keep track of the different reading materials.
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## Learning Notes
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I like to type of the content I learn from different sources. A [notes page](notes) is created to keep track of the content discussed each meeting.
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I like to type of the content I learn from different sources. A [notes page](notes/) is created to keep track of the content discussed each meeting.
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@ -3,44 +3,44 @@ title: Lecture Notes for Cluster Analysis
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showthedate: false
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---
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[Lecture 1: Measures of Similarity](lec1)
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[Lecture 1: Measures of Similarity](lec1/)
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[Lecture 2.1: Distance Measures Reasoning](lec2-1)
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[Lecture 2.1: Distance Measures Reasoning](lec2-1/)
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[Lecture 2.2: Principle Component Analysis Pt. 1](lec2-2)
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[Lecture 2.2: Principle Component Analysis Pt. 1](lec2-2/)
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Lecture 3: Discussion of Dataset
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[Lecture 4: Principal Component Analysis Pt. 2](lec4)
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[Lecture 4: Principal Component Analysis Pt. 2](lec4/)
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[Lecture 4.2: Revisiting Measures](lec4-2)
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[Lecture 4.2: Revisiting Measures](lec4-2/)
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[Lecture 4.3: Cluster Tendency](lec4-3)
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[Lecture 4.3: Cluster Tendency](lec4-3/)
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[Lecture 5: Introduction to Connectivity Based Models](lec5)
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[Lecture 5: Introduction to Connectivity Based Models](lec5/)
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[Lecture 6: Agglomerative Methods](lec6)
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[Lecture 6: Agglomerative Methods](lec6/)
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[Lecture 7: Divisive Methods Part 1: Monothetic](lec7)
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[Lecture 7: Divisive Methods Part 1: Monothetic](lec7/)
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[Lecture 8: Divisive Methods Part 2: Polythetic](lec8)
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[Lecture 8: Divisive Methods Part 2: Polythetic](lec8/)
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[Lecture 9.1: CURE and TSNE](lec9-1)
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[Lecture 9.1: CURE and TSNE](lec9-1/)
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[Lecture 9.2: Cluster Validation Part I](lec9-2)
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[Lecture 9.2: Cluster Validation Part I](lec9-2/)
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[Lecture 10.1: Silhouette Coefficient](lec10-1)
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[Lecture 10.1: Silhouette Coefficient](lec10-1/)
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[Lecture 10.2: Centroid-Based Clustering](lec10-2)
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[Lecture 10.2: Centroid-Based Clustering](lec10-2/)
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[Lecture 10.3: Voronoi Diagrams](lec10-3)
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[Lecture 10.3: Voronoi Diagrams](lec10-3/)
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[Lecture 11.1: K-means++](lec11-1)
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[Lecture 11.1: K-means++](lec11-1/)
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[Lecture 11.2: K-medoids](lec11-2)
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[Lecture 11.2: K-medoids](lec11-2/)
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[Lecture 11.3: K-medians](lec11-3)
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[Lecture 11.3: K-medians](lec11-3/)
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[Lecture 12: Introduction to Density Based Clustering](lec12)
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[Lecture 12: Introduction to Density Based Clustering](lec12/)
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@ -5,12 +5,12 @@ showthedate: false
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I didn't do the greatest job at writing a progress report every week but here on the page are the ones I did write.
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[January 29 2019](jan29)
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[January 29 2019](jan29/)
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[February 12 2019](feb12)
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[February 12 2019](feb12/)
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[February 25 2019](feb25)
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[February 25 2019](feb25/)
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[March 26 2019](mar26)
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[March 26 2019](mar26/)
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[April 2 2019](apr2)
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[April 2 2019](apr2/)
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@ -8,6 +8,6 @@ Back in the Fall of 2018, under the guidance of Ian Finlayson, I worked towards
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[Github repository](https://github.com/brandon-rozek/sloth) outlining my work.
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[Short Notes](types) on Types of Programming Languages
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[Short Notes](types/) on Types of Programming Languages
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Before this study, I worked though a book called ["Build your own Lisp"](https://www.buildyourownlisp.com/) and my implementation of a lisp like language: [Lispy Code](https://github.com/brandon-rozek/lispy)
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@ -7,7 +7,7 @@ Reinforcement learning is the art of analyzing situations and mapping them to ac
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In this independent study, I as well as Dr. Stephen Davies, will explore the Reinforcement Learning problem and its subproblems. We will go over the bandit problem, markov decision processes, and discover how best to translate a problem in order to **make decisions**.
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I have provided a list of topics that I wish to explore in a [syllabus](syllabus)
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I have provided a list of topics that I wish to explore in a [syllabus](syllabus/)
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## Readings
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@ -18,14 +18,14 @@ Reinforcement Learning: An Introduction
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By Richard S. Sutton and Andrew G. Barto
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[Reading Schedule](readings)
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[Reading Schedule](readings/)
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## Notes
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The notes for this course, is going to be an extreemly summarized version of the textbook. There will also be notes on whatever side tangents Dr. Davies and I explore.
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[Notes page](notes)
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[Notes page](notes/)
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I wrote a small little quirky/funny report describing the bandit problem. Great for learning about the common considerations for Reinforcement Learning problems.
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@ -3,13 +3,13 @@ title: Lecture Notes for Reinforcement Learning
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showthedate: false
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---
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[Chapter 1: An Introduction](intro)
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[Chapter 1: An Introduction](intro/)
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[Chapter 2: Multi-armed Bandits](bandits)
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[Chapter 2: Multi-armed Bandits](bandits/)
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[Chapter 3: Markov Decision Processes](mdp)
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[Chapter 3: Markov Decision Processes](mdp/)
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[Chapter 4: Dynamic Programming](dynamic)
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[Chapter 4: Dynamic Programming](dynamic/)
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[Chapter 5: Monte Carlo Methods](mcmethods)
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[Chapter 5: Monte Carlo Methods](mcmethods/)
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