Teaching

University of Washington

IMT 573: Data Science I: Theoretical Foundations (Autumn 2020)

Introduces technically focused theoretical foundations of “Data Science.” Provides an overview of key concepts, focusing on foundational concepts such as exploratory data analysis and statistical inference.

Virginia Tech

CS/CMDA/STAT 3654: Intro Data Analytics & Visualization (Spring 2020)

This class covers basic principles and techniques in data analytics. Students will learn methods for the collection of, storing, accessing, processing, and analyzing standard-size and large datasets, data visualization, and identifying sources of bias. We will also cover applications of these concepts to real-world case studies.

CS 4984: Data Science & Analytics Capstone (Spring 2019)

Researchers across disciplines are excited by the prospect of “data-driven science”. This advanced project-based course is geared towards deriving valuable insights from data. Students are expected to integrate software engineering and data analytics skills acquired in previous courses. Team-based capstone data project will work on real-world challenges that surface on online social platforms.

CS 5984: Social Computing (Fall 2018)

Social computing is a research area that is at the intersection of computational systems and social behavior. This course is geared toward developing a broad understanding of today’s online social systems. From Twitter to Facebook and all the way back to email, social computing is one of the biggest forces on the internet. In this class, we will explore how and why social computing works.

CS 6724: Computational Social Science (Spring 2018)

Computational social science is an emerging field which provides the opportunity to empirically study human behavior by applying computational methods, algorithms, and models on “big social data”. In this course, students will adopt an interdisciplinary approach to empirically study different social phenomena with applications to social science fields, such as political science, sociolinguistics and sociology. The empirical approach will span a variety of quantitative methods, including applying existing machine learning tools and natural language processing techniques.

CS 4984: Social Computing Capstone (Fall 2017)

Social computing is a research area that is at the intersection of computational systems and social behavior. This project based course is geared toward developing a broad understanding of today’s online social systems. Team-based projects will focus on studying real-world challenges and opportunities in current social media platforms by analyzing the vast amounts of data people leave behind in these platforms, applying quantitative methodologies to investigate and model this data, and building social tools that can augment current social computing systems.