Assistant Professor, Mathematics, Statistics, and Computer Science
I was exposed to computers at a young age when my father opened a computer store, and have been hooked ever since. My mother was a teacher for nearly 30 years. Put those two facts together and it is pretty clear I have the perfect job for me. I love teaching courses on all aspects of computer and data science, especially when it means I get to learn something new.
At this point, computers, algorithms, and data are an integral part of our lives, in both visible and invisible ways. Data is increasingly used to drive business forward and make decisions about our lives, and I feel privileged to be involved in the fast-paced swirl of technology.
These days most of my research comes in the form of applying data science and machine learning to text data, although I have also worked on parallel and high-performance computing. See below for more details. I am always on the lookout for motivated undergraduate researchers – please email me if you’re interested.
At heart, I am interested in producing computing tools that can make us more productive and improve our lives.
During graduate school I spent the bulk of my time in the area of parallel computing. Climate simulations, medical research, artificial intelligence, big data, and more require increasing amounts of computational power. These days performance improvements typically come in the form of utilizing many processing chips at the same time, but this kind of coordination is notoriously difficult. Hence, in graduate school I focused on designing and implementing efficient tools for finding and fixing bugs in these types of parallel programs.
While I remain interested in parallel and high-performance computing, recently I have found a fruitful line of work in text analytics. In particular, helping researchers apply machine learning and data science techniques to text data, such as analyzing foreign media news articles. I especially enjoy these projects because they often involve interdisciplinary collaboration
B.S. — Computer Science & Mathematics, Truman State University, 2012
Ph.D. — Computer Science, Washington University in St. Louis, 2017
COMP152: Object-Oriented Data Structures and Algorithms
COMP235: Introduction to Systems Programming
COMP345: Operating Systems
COMP347: Applied Machine Learning
Transforming Media Narratives on Migration: Narrative Divergence within Northern Triangle, Mexican, and US News Reporting on Migration from 1999-2019
Hinck, R. S., Kitsch, S. R., Utterback, R., and Wenzel, S.
Paper presented (by R. Hinck) at the 2021 National Communication Association Annual Conference, Seattle, WA 2021
Mexican and Northern Triangle Perspectives on Migration: Identifying and Assessing Strategic Narrative Alignment
Media Ecology & Strategic Analysis (MESA) Group (Skye Cooley, Robert Hinck, Asya Cooley, Sara Kitsch, Robert Utterback, Jared Johnson)
Prepared for the U.S. Department of Homeland Security
Jammu and Kashmir Reach Back: Media Analysis of Extremist Activities in Indian and Pakistani News
Cooley, Skye, Hinck, Robert, and Utterback, Robert
A Media Ecology & Strategic Analysis (MESA) Group Report
Efficient Race Detection with Futures
Utterback, Robert, Agrawal, Kunal, Fineman, Jeremy, and Lee, I-Ting Angelina
In Proceedings of the 24th Symposium on Principles and Practice of Parallel Programming 2019
Processor-Oblivious Record and Replay
Utterback, Robert, Agrawal, Kunal, Lee, I-Ting Angelina, and Kulkarni, Milind
ACM Trans. Parallel Comput. 2019
For more, see my publications page.