The Actor Pattern for AI Interaction: A Design Analysis
An in-depth analysis of the actor pattern in AI interaction architectures, exploring computational boundaries and intelligent design.
Professional Portfolio
This section showcases professional work, research projects, and technical contributions. It includes independent research and open source projects that explore AI/ML systems and a variety of other topics. Most of this content is refreshed using AI, and the original work is available on my blog. View my full resume here.
Documents in Professional Portfolio (16)
A Multi-Modal Cognitive Planning Architecture for AI-Driven Task Execution
A novel cognitive planning architecture implementing multiple distinct planning strategies within a unified framework, exploring how different cognitive modes can be operationalized in AI systems.
Quadratic Quasi-Newton (QQN): A Hybrid Optimization Method with Normalized Line Search
Novel optimization algorithm hybridizing L-BFGS with gradient descent through quadratic interpolation and magnitude-based normalization
Rediscovering MindsEye: A Case Study in Algorithmic Bias and Overlooked Innovation
Analysis of the overlooked MindsEye deep learning framework and its implications for training data bias in AI systems
MindsEye Reference Counting Analysis
An in-depth analysis of MindsEye's sophisticated reference counting approach for GPU memory management in Java ML frameworks
MindsEye's Modular Optimization Architecture: A Technical Analysis
Technical analysis of MindsEye's modular optimization architecture, examining its four-layer decomposition and innovative approaches to machine learning framework design.
reSTM: A REST-Based Distributed Software Transactional Memory Platform
A novel distributed software transactional memory platform providing ACID guarantees across clusters through REST-friendly HTTP protocol
Scalable Implementation of 2D Convolution Layers in Differentiable Neural Networks: A Multi-Tiered Approach with Dynamic Partitioning
A comprehensive methodology for implementing scalable 2D convolution layers in neural networks, addressing GPU memory constraints through dynamic partitioning
Hybrid Memory Management for Java-Based Deep Learning Systems: A Reference Counting Approach with Static Analysis
A comprehensive research paper presenting MindsEye, a hybrid memory management system combining reference counting with Java's garbage collection for deep learning applications.
Probabilistic Decision Trees: A Cross-Entropy Approach to Joint Distribution Modeling
Novel extension to decision tree methodology that models joint probability distributions using cross-entropy optimization between prior and posterior distributions.
Efficient Storage and Hierarchical Compression of Large-Scale N-gram Language Models
A novel approach to compressing large-scale n-gram language models using hierarchical structural expectations
Trust Region Methods for Neural Network Optimization
Comprehensive software framework for implementing trust region methods in neural network optimization with Java MindsEye library
Symmetric Textures: Neural Art Generation with Geometric Constraints
Novel technique for generating mathematically symmetric textures using neural networks with geometric constraints, exploring Euclidean, spherical, and hyperbolic geometries.
PromptOptimization: A Genetic Algorithm Framework for Automated Prompt Engineering in Large Language Models
A comprehensive framework for automated prompt optimization using genetic algorithms, enabling systematic improvement of Large Language Model prompts through evolutionary computation.
Recursive Subspace Optimization (RSO): A Layer-Wise Meta-Learning Approach for Deep Networks
A novel optimization algorithm that improves deep neural network training by decomposing gradients into layer-wise components and using meta-optimization to find optimal combinations.
Alternative Loss Functions in Regression: A Visual Guide
Explore alternative loss functions for regression beyond least-squares, including zero-loss zones, robust methods, and practical applications in engineering and ML.