Teaching
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.
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Working With Graduate Students
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.
Students working under my mentorship on research projects attend project meetings and are viewed as equals to collaborators and co-principle or principle investigators. This broadens their exposure to the intricacies of managing funded research projects and gives them a front row seat to how obstacles emerge and effective ways to overcome them. In addition, I work with them to publish book chapters, journal articles and peer-reviewed conference papers. During the process of writing up and publishing research with students I spend one-on-one time providing feedback on their research methodologies, result presentation and technical writing. It is incredibly rewarding to see them hone their skills over time based on my feedback. In almost every case the student ultimately reaches a point where they do not need significant feedback. During my time at ODU I have co-authored 22 pieces of research (13 journal publications, 8 peer-reviewed conference papers and 1 book chapter) with 5 different ODU graduate students. The students are always extremely appreciative of my mentorship and as a testament to our collaborations 3 of them have written testimonial letters describing how influential I have been to their work. Several excepts from those testimonials are below while the full letters can be found at the end of this document.
Serving on Dissertation Committees
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.
While serving on the dissertation committee of one doctoral student, Alexander Ngwala, I have been able to partially support his work with funding from the Navy International Program Office (NIPO) International Program Opportunity Engagement for Technology (IPOET) research project where I am a co-principal investigator. Alexander’s dissertation, Bootstrapping Web Archive Collections from Micro-collections in Social Media, requires archiving and extracting metadata about web and social media content. Working with the student’s advisor we were able to find a path where my project could fund the student and his research on archiving and extracting metadata about web and social media content could be integrated into my project’s deliverables. Getting to spend one-on-one time with Alexander and ensuring that I fully understood all the subtleties to his research has been an incredibly rewarding experience. Furthermore, it has given his work wider exposure and helped him understand the process of moving basic research into a production setting.
Doctoral Thesis: Committee Member
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.
Student’s Advised Under Projects
Graduate Students
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Grygorian, Gayane (ongoing). Modeling, Simulation and Visualization Engineering Dept. Old Dominion University, Norfolk, VA.
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Vernon-Bido, Daniele (ongoing). Modeling, Simulation and Visualization Engineering Dept. Old Dominion University, Norfolk, VA.
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Kavak, Hamdi (2013-2018). Modeling, Simulation and Visualization Engineering Dept. Old Dominion University, Norfolk, VA.
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Lynch, Christopher. (2019), Modeling, Simulation and Visualization Engineering Dept. Old Dominion University, Norfolk, VA.
Undergraduate Students
- O’Brien, Kevin (2020). Computer Science Dept. Old Dominion University, Norfolk, VA.
High School Students
- Jenkins, Bakari (2016), Pruden Center, Suffolk, VA.
Previous Teaching Experience
Finally, from 2013-2014 I was a visiting professor and a fulltime faculty member of the Computer Science Department at Gettysburg College. During my year at Gettysburg I received very complimentary teaching evaluations from my students. Evaluations were done on a five point scale (5 – excellent, 4 – very good, 3 – good, 2 – fair, 1, poor). The average evaluation across a set of 8 questions for all the students in each of the three courses (6 sections) that I taught were: (a) CS 111 - 4.46 / 5.0; (b) CS 216 – 4.51 / 5.0; and (c) CS 101 - 4.52 / 5.0. The evaluations are available for download here. In addition, I am rated with a perfect score of 5.0 on the student sourced website ratemyprofessor.com.
The teaching experience at Gettysburg College taught me to encourage students to appreciate profound ideas from different cultures and disciplines and to use them in new, creative and interesting ways. Ultimately, I learned the following principles which guided my approach to teaching (in the classroom and outside it):
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Always strive to keep non-majors engaged. Learning data science is a difficult accomplishment for majors and non-majors alike, but majors have a motivational advantage over their non-major peers in introductory courses. Majors have chosen to pursue data science; whereas, non-majors are required to do so regardless of their opinions of its value or utility.
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Always practice and profess the excitement of discovery. The beauty of data science is the range of opportunities it presents for discovery and the applications it has across all disciplines. I strive to awake all students I interact with to the rare opportunities pursuing data science can unlock.
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Always invite participation. Students are at their best when they feel comfortable. I want to create an environment where students know I am excited to teach and they feel encouraged to participate.
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Always be mindful of incorporating research while providing instruction. One of the great things about being in a field as underdeveloped as data science is that current research problems can be made accessible to students even at the introductory levels. While most of education is about learning what is known, students often find it more interesting to learn about what is not known yet.
Teaching and mentorship, even when they have not occurred in a formal listed course setting, are a passion of mine. The most ground-breaking research results bring me the same joy and exhilaration as watching a student reach a point where they no longer need extensive feedback to be an effective researcher and technical writer. While I am fortunate to have found something I am so passionate about, I feel that I have a responsibility to continue to work hard for the ODU students I interact with. It is a great gift and a great responsibility to share the discipline I love with others.
Summary of Teaching Experience (outside of Old Dominion University)
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Spring 2014 – Gettysburg College - CS 103: Introduction to Computing
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Fall 2013 – Gettysburg College - CS 111: Introduction to Computer Science I
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Fall 2013 – Gettysburg College - CS 216: Data Structures
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Fall 2005 – Spring 2012 – University of Virginia – Served as a teaching assistant for more than 7 different courses and served as a lecturer for 2 courses. Also received the School of Engineering’s Outstanding Teaching Assistant Teaching Award and was a 2-time Semifinalist for the University of Virginia’s Super Teaching Assistance Fellowship.