Internal Funding

Data Science Workshops


The Data Science Workshop program is an internal seed funding program offered by the Office of Research and Innovation. The primary goal of the Data Science Workshop program is to develop new collaborations between NC State data science methods and applications researchers. The goal is to plan and execute a small workshop that brings together researchers from both inside and outside NC State to define an innovative interdisciplinary research program that has strong potential for significant future external support from government agencies, corporations, industrial consortia or foundations.


We received 9 applications from faculty and staff in the Colleges of Agriculture and Life Sciences, Education, Engineering, Humanities and Social Sciences, Management, and Sciences, the Libraries, and the Office of Research and Innovation.


Promoting Youth Critical Data Literacy Through Computing and Community Storytelling With Data. PIs: Shiyan Jiang (Teacher Education and Learning Science), Bita Akram (Computer Science).

In the era of data revolution and datafication, critical data literacy, the capacities for interpreting and using data from critical perspectives, is needed by data science professionals and citizens alike in order to be informed and engaged in our communities. Democratic participation now entails both asking questions about data (e.g., Who is included? Who is left out? Is there discriminatory bias?) and answering questions with data in ways that examine power relations in society. However, opportunities to develop computational and media literacy skills to wrangle data, build dynamic data visualizations, and tell compelling stories about important social and scientific issues are rare for youth. This project will organize a workshop that brings teachers who are interested in integrating data-related activities into classrooms together. The theme of the workshop is fostering youth critical data literacy. Drawing on insights from the workshop, we will develop technology prototypes for supporting youth’s understanding of decision-making bias in generating data, processing data, and sharing data insights that often result in perpetuating systematic social inequities.

Machine Learning-Based Mathematical Representation of Model Uncertainty for Bayesian Inverse Uncertainty Quantification. PIs: Xu Wu (Nuclear Engineering), Ralph Smith (Mathematics).

The primary objective of this project is to leverage the recent development in Artificial Intelligence (AI) and Machine Learning (ML) for scientific computing to provide a more comprehensive mathematical representation of model uncertainty during Bayesian inverse Uncertainty Quantification (UQ). Inverse UQ is the process of quantifying the input uncertainties based on model fits to measured data, which is a crucial step to improve computational model accuracy. Model uncertainty has often been ignored due to the lack of satisfactory methods to represent it, which can cause over-fitting in inverse UQ. We plan to adopt Physics-Informed Machine Learning (PIML) and deep neural networks to build metamodels to characterize the model uncertainty. We will organize a workshop in coordination with the American Nuclear Society Meetings to build on keen interest from both the domestic and international nuclear communities to see progress in scientific ML-assisted inverse UQ. The proposed work can benefit the development of advanced nuclear reactors by enhancing the predictive capability of simulation models.

Think and Do: A Workshop to Advance Open Climate Data Science in North Carolina. PIs: Kathie Dello (State Climate Office) and Jessica Matthews (North Carolina Institute for Climate Studies).

The impacts of climate change touch every corner of North Carolina, from Murphy to Manteo. Resource managers are faced with myriad challenges posed by a hotter, wetter, and more humid climate. Climate science research, when conducted and produced alongside stakeholders (i.e., coproduction), has the opportunity to positively impact communities in North Carolina and beyond as they prepare for, respond to, and adapt to climate change. We will hold an Open Climate Data Science Workshop to initiate conversations between NC State and the broader NC community while facilitating open data science training, building trust in comprehensive data-driven approaches for addressing climate change, and developing collaboration mechanisms for climate change resilience projects. To build capacity for open climate data science in North Carolina, we will incorporate two parallel hands-on workshop tracks: (1) a beginner track where attendees can learn fundamental skills for accessing open climate data, open science practices, and use open source tools to conduct basic statistical analyses (e.g., linear models) and (2) an advanced track where attendees will learn advanced statistical modeling (e.g., machine learning) with climate data and how to access and analyze climate data on cloud platforms. We aim to coordinate a centralized initiative at NC State to support open climate data science across North Carolina, including collaboration proposals developed during the workshop.