The University of Virginia Data Science Institute granted degrees to 31 Master of Science in Data Science students in 2018, as part of the university’s final exercises May 20.
Several members of the MSDS '18 cohort were recognized for their achievements as students during the program.
Myron Chang was awarded the Wood Family Outstanding Student Award. Chang was recognized by faculty and his fellow members of the MSDS ‘18 cohort for his hard work and dedication during the program, and for his work on the capstone project, “Predicting Risk in Complex Patients.” Caitlin Dreisbach, also a 2017-18 Presidential Fellow in Data Science, was awarded the Wood Family Service Award. Dreisbach was noted for her generous participation and enthusiastic dedication to her own projects and the program in general.
In addition to being recognized at graduation, Chang and Dreisbach received a monetary award from the fund established by UVA Data Science Board member Oscar Wood. The selection criteria for the outstanding student award includes superior performance in the MSDS program and recommendations from faculty members who teach in those programs. Selection criteria for the service award includes demonstrated service to the Data Science Institute, to data science-related programs within the University, and to the data science community.
Three groups of MSDS ‘18 students were honored at graduation as recipients of awards from the 2018 Systems & Information Engineering Design Symposium (SIEDS).
Sally Gao, Kennan Grant, and Huitong Pan, working with advisors Wendy Novicoff (Public Health Sciences) and Hyojung Kang (Systems and Information Engineering), were awarded Best Poster for their research and the poster presentation of their capstone project, “Analyzing National and State Opioid Abuse Treatment Completion with Multilevel Modeling.”
Caitlin Dreisbach, Morgan Wall, and Ali Zaidi, working with advisor Abby Flower (Data Science Institute/Systems and Information Engineering), received a Best Paper award for their research and publication, “Using Autoencoders and Text Mining to Characterize Single Cell Populations in the Hippocampus and Cortex.” The researchers also presented their research at the 2018 Tom Tom Applied Machine Learning Conference in Charlottesville.
Jack Prominski and Pragati Shah, working with advisor Rafael Alvarado (Data Science Institute), received a Best Paper award for their research and publication, “Editor Matching for Academic Journals Through Rich Semantic Network Development.” Prominski also presented an independent research project on using data science to fairly redistrict congressional boundaries at the 2018 Tom Tom Applied Machine Learning Conference.
The 2018 MSDS Cohort is moving on to a variety of places and positions. From data science in Chicago to data analytics in D.C., there is no doubt that each talented and highly skilled MSDS '18 graduate will excel as a professional in the field.