Research & Publications
Academic publications, research papers, and scholarly contributions in computer science
Research & Publications
Academic contributions exploring cryptography, machine learning security, formal verification, and theoretical computer science.
Research Focus Areas
Course Assignments & Technical Work
Model-based RL, TD learning, and Feature-based Q-learning
Propositional Logic, Herbrand Semantics, and CNF
Collaboration & Contact
I'm always interested in discussing research ideas, potential collaborations, and opportunities to apply machine learning and cryptography to meaningful problems.
Academic Projects & Assignments
Course Assignments
Intelligent Systems
- Assignment 3: Problem Solving - Model-based RL, TD learning, and feature-based Q-learning
- Assignment 4: Problem Solving - Probabilities, Bayesian Networks, and Inference
- Assignment 3: Reinforcement Learning - Q-Learning and RL applications
Logic for Computer Scientists
- Homework 3: Logic Problems - Propositional logic, Herbrand semantics, and CNF
- Homework 3: Solutions - Complete solutions with explanations
Research Projects
Master’s Theorem in Algorithm Analysis
Status: Completed | Focus: Algorithm Complexity
Perfected the Master’s theorem for analyzing divide-and-conquer algorithms, developing a comprehensive understanding of recursive algorithm complexity.
Achievements:
- Rigorous mathematical proofs and derivations
- Practical applications to common algorithms (merge sort, binary search, Strassen’s algorithm)
- Educational materials for teaching complexity analysis
- Case studies demonstrating theorem application
Technical Skills: Algorithm Analysis, Computational Complexity, Mathematical Proof
Q-Learning Pac-Man Demonstration
Platform: GitHub | Status: Open Source
Implemented a reinforcement learning agent that learns to play Pac-Man using Q-Learning, demonstrating convergence and optimization in a classic game environment.
Technical Implementation:
- Q-Learning algorithm with epsilon-greedy exploration
- State space representation and feature engineering
- Reward shaping for efficient learning
- Visualization of learning progress and policy evolution
Results:
- Agent successfully learns optimal navigation strategies
- Demonstrated balance between exploration and exploitation
- Published with comprehensive documentation and tutorials
Technologies: Python, Reinforcement Learning, Q-Learning, Game AI
GitHub profile — code and coursework
Kaggle Competition Participation
Machine Learning Competitions
Platform: Kaggle | Status: Ongoing
Active participation in Kaggle competitions, applying machine learning techniques to diverse real-world datasets.
Competitions & Approaches:
- Data preprocessing and feature engineering
- Model selection and hyperparameter tuning
- Ensemble methods and stacking
- Cross-validation and performance optimization
Skills Developed:
- End-to-end ML pipeline development
- Working with messy, real-world data
- Model interpretability and evaluation
- Collaborative problem-solving
Educational & Outreach
Reinforcement Learning Video Series
Platform: YouTube | Format: Educational Shorts
Created a series of video shorts explaining reinforcement learning concepts in accessible, intuitive ways.
Topics Covered:
- Q-Learning fundamentals
- Exploration vs. exploitation trade-off
- Temporal difference learning
- Deep Q-Networks (DQN)
- Practical RL applications
Impact: Making complex AI concepts accessible to broader audiences, supporting self-directed learners
Educational shorts cover Q-learning basics through DQN-style ideas; links to the channel can be shared on request for courses and collaborators.
Software Development
Propositional Logic Chrome Extension
Platform: Chrome Web Store | Status: Published
Developed a browser utility for working with propositional logic, bridging formal logic with practical web development.
Features:
- Truth table generation
- Logical equivalence checking
- Formula simplification
- CNF/DNF conversion
- Interactive logic gate visualization
Use Cases: Logic education, formal verification, discrete mathematics coursework
Technologies: JavaScript, Chrome Extension API, Logic Programming
The extension was published for logic coursework; store listing and updates are maintained as the tool evolves.
Research Interests
Current Focus Areas
Machine Learning Applications
- Reinforcement learning in complex environments
- Transfer learning and domain adaptation
- Interpretable AI and explainability
Social & Environmental Impact
- ML for social good and community development
- Environmental monitoring and sustainability
- Healthcare accessibility and outcome prediction
Algorithmic Foundations
- Complexity theory and optimization
- Algorithm design for resource-constrained environments
- Theoretical guarantees in machine learning
Future Directions
Study Abroad Research (Australia)
- International collaboration opportunities
- Cross-cultural perspectives on AI ethics
- Environmental ML applications in unique ecosystems
Open Questions
- How can we make ML more accessible and interpretable?
- What are the most pressing social challenges that ML can address?
- How do we ensure AI systems are fair, transparent, and beneficial?
Collaboration & Contact
I’m always interested in discussing research ideas, potential collaborations, and opportunities to apply machine learning to meaningful problems.
Areas of Interest for Collaboration:
- Machine learning for social impact
- Environmental sustainability applications
- Healthcare ML research
- Algorithm analysis and optimization
- International research partnerships
Email: scott.weeden@gmail.com
GitHub: github.com/sweeden-ttu
LinkedIn: linkedin.com/in/weedens
For coursework, projects, and code samples, see repositories on GitHub and the Projects section of this site.