Data Science Day 2024 Agenda

Join the School of Data Science and Society for a portion or the entire day, discovering data science expertise across campus.

Registration closed at 3 p.m. on Sept. 25. Please email sdss@unc.edu with any questions.

Thursday, Sept. 26, 8:30 a.m. – 5 p.m.
Carolina Union
209 South Road, Chapel Hill, NC 27599

Agenda

All activities will be in the Carolina Union Great Hall unless otherwise listed below.

8:00 a.m. – 8:30 a.m.	Check-in, Breakfast
8:30 a.m. – 8:45 a.m.	SDSS Words of Welcome
8:45 a.m. – 9:15 a.m.	Opening Keynote: Data Science & AI for Social Good: Promoting Housing and Health Among People Experiencing Homelessness Hsun-Ta Hsu, Associate Professor, School of Social Work and School of Data Science and Society
9:15 a.m. – 10:15 a.m.	10-minute Short Talks
10:15 a.m. – 11:15 a.m.	Panel Session, How Data Science is Transforming R&D
11:15 a.m. – 12:15 p.m.	2023 Seed Grant Winner Presentations
12:15 p.m. – 12:30 p.m.	Carolina Data Science Researcher Community Hub
12:30 p.m. – 1:00 p.m.	Lunch
1:00 p.m. -1:50 p.m.	Flash Talks
1:50 p.m. – 2:50 p.m.	10-minute Short Talks, Great Hall BS/BA Information Session, GSU 2422 MADS Information Session, GSU 2420
2:50 p.m. – 4:20 p.m.	Poster Session and Researcher Networking
4:20 p.m. – 4:50 PM	Closing Keynote: AI for the Rest of Us Phaedra Boinodiris, Principal Consultant Trustworthy AI, IBM
4:50 p.m. – 5:00 p.m.	Award Announcements and Closing
8:00 a.m. – 1:30 p.m.	Researcher Resource Fair, Great Hall Lobby
2:00 p.m. – 5:00 p.m.	Student Organization Fair, Great Hall Lobby

Keynote Speakers

Morning Keynote:

Hsun-Ta Hsu, Associate Professor, School of Social Work and School of Data Science and Society, UNC-Chapel Hill

Biography:Dr. Hsun-Ta Hsu is an Associate Professor at the University of North Carolina at Chapel Hill, serving on the faculty of both the School of Social Work and the School of Data Science and Society. His research focuses on promoting health and housing outcomes among individuals experiencing homelessness through a multi-level approach, examining individual, social network, and neighborhood influences. His recent work adopts a community-engaged data science approach to address homelessness at the system level.

Keynote: This presentation, titled “Data Science & AI for Social Good: Promoting Housing and Health Among People Experiencing Homelessness,” will discuss how adopting an interdisciplinary, community-participatory data science approach can help address the housing and health needs among people experiencing homelessness.

Afternoon Keynote: AI for the Rest of Us

Phaedra Boinodiris, IBM Consulting’s Global Leader for Responsible AI; RSA Fellow, Co-founder Future World Alliance

Biography: A fellow with the London-based Royal Society of Arts, Boinodiris has focused on inclusion in technology since 1999. She currently leads IBM Consulting’s Trustworthy AI Practice, she is the AI ethics board focal for all of consulting, and leads IBM’s Trustworthy AI Center of Excellence. She is the co-author of the book ‘AI for the Rest of Us’. Boinodiris is a co-founder of the Future World Alliance, a 501c3 dedicated to curating K-12 education in AI ethics. In 2019, she won the United Nations Woman of Influence in STEM and Inclusivity Award and was recognized by Women in Games International as one of the Top 100 Women in the Games Industry as she began one of the first scholarship programs in the United States for women to pursue degrees in game design and development.

Keynote: AI is a way for us to understand and grasp knowledge, information and put data into context. It is being used globally to make all kinds of decisions that directly impact our lives and yet most understand very little about it. We start with the definition that data is an artifact of human experience. We need the widest variance of humans, irrespective of your role and skillset, developing AI so that everyone’s story is a part of the models we are building. Join IBM’s own Phaedra Boinodiris as she uses her own hand-drawn comics to illustrate what she has learned in the space of AI ethics, along with stories and examples of AI’s impact in industry and government.

Short Talks

Morning Session: 9:15-10:15 a.m.

Xiao-Ming Liu, Decoding Earth’s Critical Zone: Harnessing Data Science for Climate Resilience
Nader Mehri, Collaboration With Community-Based Organizations Increases the Distributed COVID-19 Tests
Wubin Bai, Leveraging Sensor-Algorithm Synergy for Advancing Muscle-Tracking Technology
Cody Morris, American Growth Project: Forecasting America’s Micro-Economies
Suparna Goswani, Enhancing Collaboration Through Open Data: HEAL Data Ecosystem Insights

Afternoon Session: 1:50-2:50 p.m.

Danielle Szafir, Increasing Data Utility through Adaptive Visualization
Greg Characklis, Assessing and Managing the Financial Risks of Extreme Events
Claire Doerschuk, Pneumonia and Lung Disease From Combustion Products of Burn Pits
Yifei Lou, Harvesting Sparsity and Similarity in Data Science
Ashley Avis, Bias in Artificial Intelligence/Machine Learning

Panel Discussion: How Data Science is Transforming R&D

Moderated by Rick Marks, Professor of the Practice, UNC School of Data Science and Society

Panelists:

Harlin Lee, Assistant Professor, UNC School of Data Science and Society
Youzou Lin, Associate Professor, UNC School of Data Science and Society
Adam Silva, Senior Artificial Intelligence Specialist, Duke Energy
Frances Tong, Senior Staff Data Scientist, BD

Poster Sessions

Poster presenters:

Ying Yu, Understanding Energy Poverty in the Electrification Era: A Machine Learning Approach
Michael Dunn, Aortic Root Pressure for Detecting Aortic Stenosis using Machine Learning
Chuxiangbo Wang, Linear independent component analysis in Wasserstein space
Amartya Banerjee, Trajectory Inference in Wasserstein Space
Aaron Jacobson, Spatio-Temporal Analysis of Brain Functional Connectome
Aashka Dave, Decoding Climate Data Modeling and its Impacts
Karen Medlin, Classifying Imbalanced Data
Kaitlyn Hohmeier, Application of Gromov-Wasserstein Barycenters to Classification Problems
Riley Harper, Leveraging AI and Data Visualization for Brazilian Entrepreneurs
Juan Garcia, Risk stratification through class-conditional conformal estimation
Mohammad Golam Kibria, A Novel Framework for Assessing AI Explainability in Healthcare
Katherine Conners, Short-term Repeatability of Artificial Intelligence Estimated Electrocardiographic Age
Eva Loeser, Understanding Enzymatic Processing Via a Random Order of Service Queueing Model
Saurav Raj Pandey, PedSleepMAE: Generative Model for Multimodal Pediatric Sleep Signals
Anvesh Rao Vijjini, SocialGaze: Improving the Integration of Human Social Norms in Large Language Models

A digital version of these posters will be sent to event participants who RSVP before Wednesday, Sept. 23.

Flash Talks

Flash talks will be chosen at random via a name generator during the Data Science Day session. A digital version of all flash talk presentations will be sent to event participants who RSVP before Wednesday, Sept. 23.