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This thesis is organized as follows: Chapter 1 introduces the background, motivation, objectives, and contributions of this thesis. Chapter 2 presents a review of existing online impedance extraction approaches. Chapter 3 proposes the improved measurement setup of the inductive coupling approach and introduces the theory behind time-variant online impedance extraction. Chapter 4 develops a three-term calibration technique for the proposed measurement setup to deembed the effect of the probe-to-probe coupling between the inductive probes with the objective to improve the accuracy of online impedance extraction. Chapter 5 discusses the additional measurement setup consideration in industrial applications where significant electrical noise and power surges are present. Chapter 6 discusses and demonstrates the application of the inductive coupling approach in online detection of the incipient stator faults in the inverter-fed induction motor. Chapter 7 further extends the application of this approach for non-intrusive extraction of the voltage-dependent capacitances of the silicon carbide (SiC) power metal-oxide-semiconductor field-effect transistor (MOSFET). Finally, Chapter 8 concludes this thesis and proposes future works that are worth exploring.

Uplift modeling is a causal learning technique that estimates subgroup-level treatment effects. It is commonly used in industry and elsewhere for tasks such as targeting ads. In a typical setting, uplift models can take thousands of features as inputs, which is costly and results in problems such as overfitting and poor model interpretability. Consequently, there is a need to select a subset of the most important features for modeling. However, traditional methods for doing feature selection are not fit for the task because they are designed for standard machine learning models whose target is importantly different from uplift models. To address this, we introduce a set of feature selection methods explicitly designed for uplift modeling, drawing inspiration from statistics and information theory. We conduct empirical evaluations on the proposed methods on publicly available datasets, demonstrating the advantages of the proposed methods compared to traditional feature selection. We make the proposed methods publicly available as a part of the CausalML open-source package.

CausalML is a Python implementation of algorithms related to causal inference and machine learning. Algorithms combining causal inference and machine learning have been a trending topic in recent years. This package tries to bridge the gap between theoretical work on methodology and practical applications by making a collection of methods in this field available in Python. This paper introduces the key concepts, scope, and use cases of this package.

In machine learning applications for online product offerings and marketing strategies, there are often hundreds or thousands of features available to build such models. Feature selection is one essential method in such applications for multiple objectives: improving the prediction accuracy by eliminating irrelevant features, accelerating the model training and prediction speed, reducing the monitoring and maintenance workload for feature data pipeline, and providing better model interpretation and diagnosis capability. However, selecting an optimal feature subset from a large feature space is considered as an NP-complete problem. The mRMR (Minimum Redundancy and Maximum Relevance) feature selection framework solves this problem by selecting the relevant features while controlling for the redundancy within the selected features. This paper describes the approach to extend, evaluate, and implement the mRMR feature selection methods for classification problem in a marketing machine learning platform at Uber that automates creation and deployment of targeting and personalization models at scale. This study first extends the existing mRMR methods by introducing a non-linear feature redundancy measure and a model-based feature relevance measure. Then an extensive empirical evaluation is performed for eight different feature selection methods, using one synthetic dataset and three real-world marketing datasets at Uber to cover different use cases. Based on the empirical results, the selected mRMR method is implemented in production for the marketing machine learning platform. A description of the production implementation is provided and an online experiment deployed through the platform is discussed.