Teaching

I am an instructor at the University of Wisconsin-Milwaukee (UWM), where I teach courses that blend my expertise in data science, machine learning, and healthcare informatics. Currently, I teach two key courses: Big Data Analysis and Introduction to Healthcare Informatics. These courses are designed to provide students with both theoretical knowledge and hands-on experience, preparing them to tackle real-world challenges in healthcare and data-driven industries.

Big Data Analysis

Big data isn’t just a buzzword—it’s a force driving innovation across industries. In this course, I guide students through the fascinating world of large-scale data processing and advanced analytics, where they learn to extract valuable insights from vast datasets.

The course starts with foundational principles of distributed systems and data frameworks such as Apache Hadoop and Spark. We focus on building scalable data pipelines, efficient data storage strategies, and parallel computing techniques. Students also dive into machine learning for big data, discovering how predictive models can drive decision-making in domains like healthcare, finance, and technology.

Key topics include: • Introduction to distributed systems and frameworks (Hadoop, Spark) • Data pipeline architecture and scalable processing • Cleaning, transforming, and managing large datasets • Machine learning techniques for high-dimensional data • Real-world case studies and hands-on projects

By working on projects with real-world datasets, students gain experience in solving data challenges that span various industries, including healthcare, e-commerce, and logistics.

Introduction to Healthcare Informatics

Healthcare today relies heavily on data-driven innovation to enhance patient care, streamline operations, and support evidence-based decisions. In this course, I introduce students to the dynamic field of healthcare informatics, where technology meets medicine to drive better outcomes.

We explore how Electronic Health Records (EHR), health data standards, and clinical decision support systems (CDSS) shape modern healthcare. Students learn how data interoperability—through standards like HL7 and FHIR—enables efficient information sharing across hospitals and healthcare organizations. The course also emphasizes ethical data use, privacy, and security, which are critical concerns in healthcare data management.

Students engage in case studies that cover real-world innovations, including AI-driven diagnostics, predictive models for patient care, and analysis of social determinants of health to understand how external factors influence patient outcomes.

Key areas of focus include: • Understanding healthcare data ecosystems (EHRs, registries, health information exchanges) • Clinical decision support and AI in healthcare • Health data privacy, security, and ethical considerations • Standards and interoperability (HL7, FHIR) for data exchange • Analyzing social determinants of health for improved care strategies

This course equips students to become leaders in healthcare technology, bridging the gap between clinical practice and data science through hands-on projects and applied learning.