Closed-Loop Optimization in Laboratory Automation
Building systems that learn from experimental results and automatically improve processes—covering optimization algorithms, feedback architectures, and practical implementation patterns.
Instrument Agents: AI Systems That Control Lab Equipment
Designing AI agents that can understand, plan, and execute laboratory instrument operations—covering agent architectures, tool abstraction patterns, and safety boundaries.
Text-to-Protocol: From Natural Language to Executable Lab Procedures
Designing systems that convert natural language instructions into structured, validated laboratory protocols—covering representation formats, LLM pipelines, and safety verification.
AI in Laboratory Automation: Current State, Limitations, and the Path Forward
A comprehensive look at where AI stands in lab automation today—the promising advances, the persistent challenges, and the gap between research demos and production-ready systems.
Building AI-Ready Data Infrastructure in Industrial Software
A practical guide to collecting, structuring, and leveraging data from distributed industrial systems—where each PC runs different environments and logs are your only starting point.
Large Language Models in Lab Automation: From Natural Language to Robot Control
Exploring how LLMs are transforming laboratory automation—from interpreting human commands to orchestrating robotic workflows—and the practical considerations for deployment in air-gapped environments.
Bringing Vision-Language Models to Lab Automation: Challenges and Possibilities
Exploring how VLMs could transform laboratory automation, and the practical constraints of deploying AI in air-gapped industrial environments.
Implementing AI Services in Offline Industrial Environments
A practical guide to deploying AI capabilities on air-gapped systems—from local inference engines to edge-optimized models and hybrid architectures.
Clean Code Part 4: Concurrency and Refactoring
Advanced clean code topics covering concurrent programming, successive refinement through real-world examples, and the comprehensive list of code smells and heuristics.
Clean Code Part 3: Testing and Class Design
Advanced clean code concepts covering boundaries with third-party code, unit testing best practices, class design principles, and system architecture.
Clean Code Part 2: Structure and Formatting
Code organization principles from Clean Code. Covers formatting rules, objects vs data structures, and robust error handling strategies.
SDP: Software Development Process - OMSCS 2025 Fall
Key software engineering concepts from Georgia Tech OMSCS SDP (CS 6300): lifecycle models, UML, testing, Android development, and team collaboration.
Clean Code Part 1: The Fundamentals
Core principles of clean code from Robert C. Martin's classic book. Covers what clean code means, naming conventions, function design, and the proper use of comments.
Lessons from Integrating Hardware SDKs in Industrial Robotics
Practical considerations and patterns learned from working with vendor-provided SDKs—both .NET assemblies and COM components—in laboratory automation.
Why Some Industrial Software Can't Use the Cloud
Reflections on building software in air-gapped environments where AWS, Kubernetes, and modern cloud infrastructure simply aren't an option.
Service Logging Best Practices: A Complete Guide to Production-Ready Logging
A practical guide to structured logging, correlation IDs, centralized pipelines, and safe operational logging in production systems.
REST API Design Best Practices: Building APIs That Scale
A practical guide to designing clear, consistent, and secure REST APIs with real-world patterns and examples.
.NET Framework 4.8 WebForms: A Practical Guide
A practical guide to WebForms fundamentals, performance, security, and migration planning for enterprise apps.
AI4R: Artificial Intelligence for Robotics - OMSCS 2025 Spring
Key robotics AI concepts from Georgia Tech OMSCS AI4R (CS 7638): localization, planning, perception, control, and SLAM.
ML4T: Machine Learning for Trading - OMSCS 2024 Fall
Key ML-for-trading concepts from Georgia Tech OMSCS ML4T (CS 7646): portfolio theory, signals, risk, and evaluation.
Unsupervised Learning: K-means Clustering and Anomaly Detection
Explore core concepts of unsupervised learning, including K-means clustering, optimization strategies, and how anomaly detection systems are designed and evaluated.
Decision Trees and Ensembles: From Splits to Random Forests and XGBoost
A complete guide to decision trees, covering entropy, information gain, one-hot encoding, regression trees, and ensemble methods like Random Forest and XGBoost.
Practical Advice for Applying Machine Learning
Learn how to make decisions, evaluate models, handle bias and variance, and manage real-world ML workflows with cross-validation, error analysis, and transfer learning.
Training Neural Networks: Activation Functions, Backpropagation, and TensorFlow Implementation
Explore how neural networks are trained with gradient descent, softmax, and backpropagation using TensorFlow. Understand activation functions and multiclass classification techniques.
Understanding Neural Networks: From Biology to TensorFlow
A comprehensive guide to neural networks, forward propagation, TensorFlow implementation, and efficient matrix computations.
Logistic Regression: From Sigmoid to Regularization
A comprehensive breakdown of logistic regression, sigmoid function, loss functions, and regularization for classification tasks.
Regression with Multiple Input Variables
Deep dive into multiple linear regression, vectorization, gradient descent, feature scaling, and polynomial regression.
Introduction to Machine Learning
Overview of supervised and unsupervised learning, linear regression, and gradient descent