The Data Science Institute (DSI) at Vanderbilt University hosted its Spring 2023 Final Project Presentations April 21. The event provided an opportunity for the data science teams to showcase their projects and present their findings after a semester of hard work.
The projects covered a wide range of topics, including sports analytics, EEG data modeling, legal research, deep learning for narrative arcs, dashboard creation for musicians, fMRI data processing, ancient mortar analysis, LIGO data modeling, and new language acquisition.
One of the projects presented was the Women’s basketball project, which aimed to make better use of training and competition data through improved analytics efforts, predictive models, and data visualization. The project was led by PI Tyler Clarke from Vanderbilt Athletics, and the team members were Grace Ko, Minwoo Sohn, Nikkhil Niranjan, and Sovann Chang.
Another project, the EEG Project, was led by Sasha Key from the Vanderbilt School of Medicine’s Department of Hearing and Speech Sciences. The goal of this project was to prepare EEG data for modeling with deep learning transformers to better understand responses from a human subjects trial. The team members included Dillen Cameron, Junchi Li, Jiaying Liang, Alexandra Key, Xueyuan Li, and Abbie Petulante.
The Legal Project, led by Brian Fitzpatrick with Vanderbilt Law School, aimed to train a deep learning model for a number of law-related tasks. The team members included Promod Rajaguru, Jimmy Baek, Ilayda Koca, Annabelle Abbott, and Yurui Xu.
The Narrative Arc project, led by Corey Brady, Assistant Professor of Mathematics Education and the Learning Sciences in Peabody College, used deep learning to visualize how narratives evolve and use patterns in narratives to better understand how deep learning models learn. The team members included Yunfei Lyu, Jordan Nieusma, Amogh Vig, Brian Goldsmith, Lyu Zhaozhou, Sophia Tannir, and Yuning Wu.
In collaboration with the Arts and Business Council in Nashville, the ABC Musicians data project aimed to build a dashboard to clearly convey survey results from musicians with their own businesses in Nashville. The project was led by Jonathan Harwell-Dye, Jill McMillan Palm, and Jackie Tidwell with the Nashville Business Council, and the team members included Xishan Deng, Jonathan Harwell-Dye, Kyle Spottiswood, Katherine Oung, Shalini Thinakaran, and Max Herman.
The fMRI data project, led by Katherine Aboud with the Vanderbilt Brain Institute at Peabody College, aimed to prepare fMRI data for modeling with deep learning transformers to better process fMRI data. The team members included Ricky Sun, Katherine Aboud, Merve Kasap, Jiayu Shi, Yahan Yang, Min Kyung Hong, and Abbie Petulante.
The Ancient Mortars project, led by Markus Eberl, Associate Professor of Anthropology in the College of Arts and Sciences, used deep learning and transformers to create a model to identify particles composing ancient structures. The team members included Kevin Chen, Neal Bagai, Youngbin Kwon, Arjun Gupta, and Daniel J Park.
The LIGO Data project, led by Karan Jani, Professor of Physics and Astronomy, aimed to use LIGO data for modeling with deep learning transformers to understand gravitational waves of colliding black holes. The team members included Zack Braasch, Ziyao Shang, and Youngbin Kwon.
The Spring 2023 DSI Final Project Presentations showcased an impressive array of data science projects across a range of disciplines. It was clear that each team had put in a great deal of effort and had made significant strides towards achieving their respective goals.
As the field of data science continues to grow and evolve, it is exciting to see the innovative ways that it is being applied to various fields and industries. The work of the DSI teams is a testament to the vast potential of data science to transform and improve our world.
We congratulate all the teams on their hard work and wish them the best of luck as they move forward with their projects. We look forward to seeing the continued impact of their research and the future contributions of the DSI to the field of data science.