While teaching is not part of my duties as a research faculty, I engage in teaching at as often as I can. As part of two research grants I am co-principle investigator on, I teach data analytics courses on data science and predictive analytics at the Navy’s four different publicly owned shipyards. These courses are designed specifically for the shipyards and their requirements enabling them to explore data, look for patterns, identify efficiencies and improvements that can be made. I have had a lead role in developing the curriculum for the courses and traveling on site to teach the courses. In particular I have focused on determining the most effective way to teach complex topics like artificial intelligence and deep learning in a way so that students can understand when and how to effectively use these analytic methods in their day to day workflow but also understand the assumptions and pitfalls that can be associated with them.
In addition, throughout my work on research projects and in my everyday interactions with other ODU faculty, project scientists, graduate students and undergraduate students I have adopted a philosophy that improves them and me. I treat each of these interactions as a dual-sided teachable moment that can help me learn how to better meet the needs of others at our university. In particular, working at multi-disciplinary research center like VMASC, I have learned and now emphasize that different perspectives yield innovation. Knowledge of many subject areas provides a cross fertilization of ideas that frequently leads to innovation. “Eureka” moments are the product of our unconscious minds working on a problem, using previously studied materials and methods. My research projects expose students to social science, religion, foreign languages and mathematics. This enables them to develop a storehouse of knowledge with a wide range yielding particularly creative solutions to problems in academia and their lives.
As a research faculty member, I have been part of committees for dissertation projects at Old Dominion University (Computational Modeling and Simulation Engineering (CMSE) and Computer Science (CS)). As a committee member I take an active role, especially during proposal preparation as I can contribute to helping students arrive at a specific and grounded problem definition, a major challenge that most graduate students face.
Coleman, Evan. Advisor: Masha Sosonki. Completed Spring 2019. Resilience for Asynchronous Iterative Methods for Sparse Linear Systems. Computational Modeling and Simulation Engineering Department. Old Dominion University, Norfolk, VA.
Alexander Nwala. Advisor(s): Michael Nelson and Michele Wiegle. (Summer 2020). Bootstrapping Web Archive Collections from Micro-collections in Social Media. Computer Science. Old Dominion University, Norfolk, VA.
Undergraduate course, Gettysburg College, Department of Computer Science, 2014
CS 103: Introduction to Computing at Gettysburg College is a laboratory-based survey course designed primarily for non-majors. The course provides an overview of computer science and the application of computing in various fields.
Undergraduate course, Gettysburg College, Department of Computer Science, 2013
CS 216: Data Structures and Algorithms at Gettysburg College is a course that builds upon the foundational programming knowledge from earlier courses. It focuses on advanced data organization and manipulation techniques essential for efficient software development.
Undergraduate course, Gettysburg College, Department of Computer Science, 2013
CS 111: Introduction to Computer Science I at Gettysburg College is a foundational course in computer science, often referred to as a “CS1” course. It serves as the initial programming course for computer science majors and other students interested in developing programming skills.