Telling a story about your analytical work is never easy. You have worked hard gathering data, analyzing it, and building models to make predictions. Now you are asked present to an executive that doesn't really understand your work. How should you structure your presentation? What tips are there for improving your chances of getting your message across. This workshop will walk through methods to improve the clarity in your message and improve the chances of the audience walking away with your key points. From structuring your presentation to the details of what to include and not include in your visualizations, you’ll be asked to rework data, build better visualizations, have the right slide titles, and put a short presentation together. This workshop will focus on presenting in a corporate environment and in environments with a non-expert audience. While not focused on academic work where every detail of your method is important, the tips and techniques discussed can help improve your conference presentations and classroom materials.
As data science continues to expand, developing an educational model that effectively engages, supports, and empowers students from various disciplines is crucial. To address this need, the NC State Data Science Academy developed the All-Campus Data Science through Accessible Project-based Teaching and Learning (ADAPT) model. Project-based learning (PBL) allows students to explore topics of interest using relevant data to create artifacts showcasing their problem-solving skills (Krajcik & Shin, 2014). This approach encourages diverse student engagement in data science education.
This half-day workshop will introduce the foundational principles of PBL through the lens of the ADAPT model. We will provide a roadmap for organizing a data science course around real-world projects that promote student agency, creativity, and practical application. Effective strategies for structuring a PBL-based course will be discussed, such as designing learning trajectories with milestones and rubrics to promote clear expectations and measurable outcomes. By setting milestones with well-aligned assessment rubrics, participants will learn to guide students through data science projects over the course of a semester. Additionally, the workshop will delve into reflecting students' identities in PBL, emphasizing how projects can connect with students' diverse backgrounds, experiences, and interests. With this approach, students can see themselves as “a person who does data science” in their respective domains.
Through examining examples of implementing the ADAPT model, interactive discussions, and hands-on activities, attendees will leave equipped with practical tools and insights to implement PBL in their own data science courses, ultimately creating a more inclusive and impactful data science learning environment.
As one of the world’s oldest and largest data archives for social and behavioral sciences, ICPSR strives to assist researchers throughout the lifecycle of their projects. Typically known for its catalog of almost 20,000 data collections for secondary analysis or the Summer Program in Quantitative Methods, ICPSR also provides resources for creating data management and sharing plans, tools to assist in primary research, and help in meeting federal data sharing requirements. This workshop will introduce (1) the variety of resources available, (2) the types of data in the catalog and how to access them (both public and restricted), and (3) the ways in which professional curation staff can add value in sharing and preserving your research data.
The workshop on Data for Good for Education is focused on growing and enabling a network of educators looking to change the world of Data Science education. Research indicates that students should have the opportunity to engage in projects and assignments that promote or explore meaningful social impacts. Whether you are just starting on the journey or have been supporting social good for years, this workshop will deepen your thinking, provide new lenses for projects, and broaden your support network for helping students engage in meaningful work from a data science perspective.
The morning sessions will feature a keynote speaker and engage the community in defining how academic institutions and educators can best engage data for good. Together we will work to understand how to further embed this work across institutions and practice. The afternoon sessions will focus on participant’s individual skills, expertise, and networks to support their own pedagogical and practical work. A panel discussion of how to engage experts from other disciplines will be followed by two hands-on sessions to develop how-to guides for more effectively sourcing and supporting social good projects.
The workshop will close with a reception and poster-session for participants to network and see what others have been doing within the realm of data for good.
The National Science Foundation has provided funding for this workshop to support participant’s travel, lodging, and conference attendance. For more information on applying for funding please contact the session co-organizer at: [email protected].
This half-day workshop will engage (prospective) educators in the design and implementation of data-enabled POGIL (Process-Oriented Guided Inquiry Learning) modules using no-code to low-code tools such as CODAP. These data-enabled POGIL modules are culturally relevant and can be used across disciplines to empower natural collaboration around real-world challenges of high social impact, specifically climate change (geosciences), criminal justice, and food and water sciences. Participants of this workshop will be trained in POGIL and CODAP and will be provided instruction materials to enable them to deliver data-centric content across disciplines that will translate into significant growth in the exposure of students to data science and their preparation for solving problems in their respective disciplines using data science. This workshop material was created through NSF grant #2304100.
As the U.S. Census Bureau’s main data dissemination tool, data.census.gov provides data from the American Community Survey, Decennial Census, our Economic programs and more. As such, the site relies on user feedback to expand its functionality and features and has made significant improvements since its launch in 2020. In using data.census.gov, students and educators alike can quickly and easily retrieve important demographic, social, and economic data that is vital for academic and research purposes.
Join U.S. Census Bureau staff as they provide a demonstration of their main data dissemination platform, data.census.gov. In this session, attendees will learn about the data availability within data.census.gov, delve into the two main ways of searching for data points on the site, and explore the table and mapping capabilities. Attendees will also receive information on newer features that improve the functionality of data.census.gov, and resources that are available for mastering different aspects of the site.
50 Years of Tree Census Data: Analyzing Species Over Time - Ella Addink (Calvin University), Noelle Haviland (Calvin University), Stacy DeRuiter (Calvin University)
Understanding Maternal Health Challenges in Ghana: A Data-Driven Approach - Morayo Akintonde (Howard University Student)
Integrating AI in Bioinformatics Education: Bridging Computational and Biomedical Sciences through Interdisciplinary Learning - Cecilia Arighi (University of Delaware), Amelia Harrison (University of Delaware), Karen Hoober (University of Delaware)
Evaluating the Performance of Predictive Clinical AI/ML Tools After Deployment - Brittany Baur (University of Michigan)
Research gaps and insights on data-based decision-making for school improvement (DBDM) - Dila Bhandari (Tribhuvan University)
Analyzing Consistency Among NHL Forwards Performance - Mirriam Chikwamu (Whitman College/ Black in AI)
Birdseye: Transforming Scholarly Research with AI-Powered Document Analysis - Jeong Hin Chin (University of Michigan), Peter Braden (University of Michigan)
External Validation and Comparison of a General Ward Deterioration Index Between Diversely Different Health Systems - Brandon Cummings (Max Harry Weil Institute for Critical Care Research and Innovation), Brittany Baur (Max Harry Weil Institute for Critical Care Research and Innovation), Sardar Ansari (Max Harry Weil Institute for Critical Care Research and Innovation)
QuestionBot: Generating Questions for Videos using GenAI - Kapotaksha Das (University of Michigan-Dearborn)
Auditing Diversity in LLM (Language Learning Models) and TTI (Text-to-Image) systems - Ndeye Diop (University of Washington)
Integrating Ethical AI Principles into Undergraduate Data Science Curriculum - Vera Dureke (Indiana University East)
Transformative Teaching: Unleashing the Power of Generative AI in Education - David Han (UTSA)
Scalable Data Science: Implementing a Workflow Using ADIOS2, MPI, MGARD, Visualization Tools, and AI Applications - Grace Han (UNC Chapel Hill), Summer Wu (UNC Chapel Hill), Ethan Klasky (University of Florida)
Data Science for Humans - Doheon Han (University of Notre Dame)
Fifty Years of Ecosystem Preserve Tree Census Data - Noelle Haviland (Calvin University), Ella Addink (Calvin University), Stacy DeRuiter (Calvin University)
Enhancing Educational Outcomes through AI-Driven Automated Feedback and Assessment - Armaan Hiranandani (Cornell University/UC Berkeley Alum), Tia Pappas (Peer)
AI-Powered Community Engagement: Advancing Health Equity in Hypertension Management within Marginalized Black Communities - Najma Hurre (UW-Madison School of Nursing/Pharmacy)
Uncovering Latent Arguments in Social Media Messaging by Employing LLMs-in-the-Loop Strategy - Tunazzina Islam (Purdue University), Dan Goldwasser (Purdue University)
eLLMinating Clutter: Fine Tuning Transformers for Text Summarization - Zaina Khalil (University of Illinois Chicago), Johnathan Garcia (California State University, Fresno), Jack Sanderson (University of Chicago), Diego Sarria (North Carolina State University)
Do Multimodal Large Language Models Understand Welding? - Grigorii Khvatskii (University of Notre Dame), Yong Suk Lee (University of Notre Dame), Corey Angst (University of Notre Dame), Nicholas Berente (University of Notre Dame), Maria Gibbs (University of Notre Dame), Robert Landers (University of Notre Dame), Nitesh Chawla (University of Notre Dame)
Engaging Students in the Data Science Classroom - Susanna Lange (University of Chicago), William Trok (University of Chicago)
Leveraging AI for Privacy Preservation and Trust Management in NextG-based IoT Networks - Tan Le (Hampton University), Van Le (Kempsville High School)
Human Motion Recognition for Next-Generation Healthcare Systems: AI-Driven Behavioral Analysis Framework - Van Le (Kempsville High School)
Patient Perceptions of Generative Artificial Intelligence Use in Healthcare Architecture Research - Natalie Leonard (University of Michigan), Upali Nanda (University of Michigan), Emerson Delacroix (University of Michigan), Matias del Campo (New York Institute of Technology), Joy Knoblauch (University of Michigan)
Neural Conformal Control for Multi-view Uncertainty Quantification in Time Series - Ruipu Li (University of Michigan), Alexander Rodriguez
Could Robots Help You Live A Longer and More Prosperous Life? - Agness Lungu (Indiana University)
Semantic Segmentation - A Comparative study of Data Processing Techniques in Distributed Environments - Naomi Maranga (University of Pennsylvania)
A Quasi-Experimental Study of New York City’s Sodium Warning Regulation and Hypertension Prevalence, 2005-2020 - Nathaniel Maxey (Center for Anti-racism, Social Justice and Public Health School of Global Public Health | New York University)
Bias Detection and Prevention in Mortgage Loan Applications - Shemaiah Mbetwa (Alabama A&M University), Opeyeoluwa Olanipekun (Student), Prosper Banda (Student)
ADAPTing Data Science Literacy Across Disciplines - Jeanne McClure (North Carolina State University), Sunghwan Byun
Using Graph Theory to predict the effect of brain integration on memory - Malak Mohamed (Berea College)
Personalized Recommendation with LLMs and Human-in-the-Loop using Multi-modal Data - Sohrab Namazi Nia (New Jersey Institute of Technology)
Auto annotation of Linguistic Features for Audio Deepfake Detection - Kifekachukwu Nwosu (University of Maryland, Baltimore County)
Measurements of High Energy Higgs Production - Ruvarashe Nyabando (Alabama A&M University)
Monitoring Dataset Shift in Clinical AI/ML Models during the Post-Deployment Phase - Connor O'Brien (Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Department of Emergency Medicine, University of Michigan), Sardar Ansari (Max Harry Weil Institute for Critical Care Research and Innovation, University of Michigan, Department of Emergency Medicine, University of Michigan)
Experiences from North Carolina State University’s Data Science Consulting Program - Shannon Ricci (North Carolina State University)
The Choice Of AI - Valencia Ross (Alabama A&M University)
The Human-Machine Symbiosis - Nathaniel Sakyi (The University of Texas at El Paso)
Designing Impactful Common Good Programs – Lauren Saloio (The Center for Data Science at the University of Massachusetts Amherst)
Automated Medical Data Entry Using LLMs - Anju Santhosh Kumar (University of Massachusetts, Amherst), Yajie Li (University of Massachusetts, Amherst), Robin Dziewietin (University of Massachusetts, Amherst)
The Data Justice Academy - Claudia Scholz (University of Virginia School of Data Science)
Alzheimer’s research on West and East African patients in comparison to America and access to medical treatment - Charlie Sconiers (Howard University)
Responsible AI Literacy : An initial discussion on perceptions, approaches and considerations - Subhasree Sengupta (Florida State University)
Impact of Biases in Training Data on Hiring Algorithms - Pearl Sikepe (Student), Pearl Sikepe (Student)
Disparities in Mental Health Resources Across the African Diaspora - Toni Smith (Howard University THREADS)
Analyzing Colombia’s Armed Conflict Using Retrieval-Augmented Generation Approach - Anna Sokol (University of Notre Dame), Matthew Sisk (University of Notre Dame), Nitesh Chawla (University of Notre Dame)
Warming makes forest trees grow earlier in the spring, but often not taller - Yiluan Song (University of Michigan)
Human-Guided Iterative Prompt Engineering for Precision Feedback Message Authoring Using LLMs - Zhiyi Sun (University of Michigan Medical School)
Healthcare Accessibility's Impact on Long COVID Prevalence - Rajan Tavathia (San Diego Supercomputer Center), ,
Statistical inference using identity-by-descent segments: perspectives on recent positive selection - Seth Temple (University of Michigan)
The Next Generation of Academic Data Science Consulting in the Age of AI - Alp Tezbasaran (North Carolina State University)
Modelling Crop Yields and Climate Change in Mexico - Aaron Toth (Calvin University), Stacy DeRuiter (Calvin University), Sung Soo Lim (Calvin University)
From Diagnostic Accuracy to Ethical Risk: SHAP Insights into Cross-Domain Healthcare AI Models - Rebecca Tsekanovskiy (Rensselaer Polytechnic Institute)
From Interests to Insights: An LLM Approach to Course Recommendations Using Natural Language Queries - Hugh Van Deventer (University of Michigan)
Ash: A Tool to Confront Zombies in the Scientific Literature - Matthew VanEseltine (University of Michigan)
AI based Neuroendoscopic Skills Evaluation - Sushil Vemuri (Texas A&M Institute of Data Science)
On the optimal prediction of extreme events in heavy-tailed time series with applications to solar flare forecasting - Victor Verma (Department of Statistics, University of Michigan)
AI-Powered Video Deepfake Detection: Combating Misinformation and Ensuring Public Trust - Akinahom Wabella
An Immersive Approach to LLM Security Education Using Open-Source RAG Tools - Dominic Wilson (University of Findlay)
End of a golden era: the uncertain future of social media research - Sidney Wong (University of Canterbury)
Examining the (In)consistencies of Privacy Disclosures and Opt-Out Controls in U.S. Commercial Banks - Lu Xian (University of Michigan)
Enhancing Algorithmic Transparency and Literacy Through an End-User Audit Intervention Tool - Lu Xian (University of Michigan), Qunfang Wu (Harvard University)
Balancing AI and Human Expertise: A Case Study of ChatGPT in Machine Learning Assessments - Jin Yoo (Purdue University Fort Wayne)
Assessing the Role of AI in Disaster Relief - Marian Zuniga (University of California, Merced)