Machine Learning & AI Topics
Production machine learning systems, model development, deployment, and operationalization. Covers ML architecture, model training and serving infrastructure, ML platform design, responsible AI practices, and integration of ML capabilities into products. Excludes research-focused ML innovations and academic contributions (see Research & Academic Leadership for publication and research contributions). Emphasizes applied ML engineering at scale and operational considerations for ML systems in production.
Responsible AI: Fairness, Bias, and Interpretability
Building ML and AI systems that are fair, explainable, and safe. Covers identifying and mitigating bias, fairness metrics and tradeoffs, model interpretability and explainability techniques, label-bias feedback loops, and responsible and safe development practices for production models. Emphasizes accountability and transparency as first-class design constraints.
Model Evaluation and Validation
Measuring whether a model is good enough to trust and ship. Covers metric selection for classification, regression, and ranking (precision/recall, ROC-AUC, calibration, RMSE), offline validation design, evaluation-metric-to-business-objective alignment, and production safety guardrails. Emphasizes choosing metrics that reflect real objectives and avoiding misleading evaluations.
Data Preparation and Class Imbalance for ML
Preparing training data and handling skewed or shifting distributions. Covers preprocessing and cleaning for model input, data augmentation, distribution shift, class imbalance techniques (resampling, reweighting, threshold tuning), and cold-start scenarios where labeled data is scarce. Focuses on the data-side decisions that determine whether a model can learn at all.
Classical Machine Learning Algorithms
Foundational non-deep-learning models and when to reach for each. Covers linear and logistic regression, decision trees and ensemble methods (random forests, gradient boosting), support vector machines, k-nearest neighbors, and clustering, including their assumptions, strengths, and failure modes. Focuses on algorithm selection and the numerical and implementation considerations behind these workhorse models.
Feature Engineering and Feature Stores
Transforming raw data into predictive model inputs and serving those features reliably. Covers feature creation and selection, encoding high-cardinality and categorical variables, representation learning, and the design of feature stores for training/serving consistency. Emphasizes features as a primary lever on model quality and the operational challenges of keeping them fresh and consistent.