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Monday, October 28
 

9:00am EDT

Analytical Storytelling (aka, how to present to a non-expert audience)
Monday October 28, 2024 9:00am - 12:00pm EDT
Room D (3rd Floor)
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.
Speakers
DH
Monday October 28, 2024 9:00am - 12:00pm EDT
Room D (3rd Floor) The Michigan League
  Workshop

9:00am EDT

Engaging Minds with Project-Based Learning in Data Science
Monday October 28, 2024 9:00am - 12:00pm EDT
Vandenberg
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.
Speakers
JM
KD
Monday October 28, 2024 9:00am - 12:00pm EDT
Vandenberg The Michigan League
  Workshop

9:00am EDT

Exploring and accessing data through the Inter-university Consortium for Political and Social Research (ICPSR)
Monday October 28, 2024 9:00am - 12:00pm EDT
Ballroom
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.
Speakers
LH
AP
Monday October 28, 2024 9:00am - 12:00pm EDT
Ballroom The Michigan League
  Workshop

9:00am EDT

Workshop on Data for Good for Education
Monday October 28, 2024 9:00am - 4:30pm EDT
Hussey
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: karl.schmitt@trnty.edu.
Speakers
KS
NA
AD
LH
Monday October 28, 2024 9:00am - 4:30pm EDT
Hussey The Michigan League
  Workshop

1:30pm EDT

DEDICATE: Creating Accessible POGIL-based Data Science Training for Social Impact Education
Monday October 28, 2024 1:30pm - 4:30pm EDT
Vandenberg
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.
Speakers
RU
EB
MB
RD
RR
Monday October 28, 2024 1:30pm - 4:30pm EDT
Vandenberg The Michigan League
  Workshop

1:30pm EDT

Unlocking Census data through data.census.gov
Monday October 28, 2024 1:30pm - 4:30pm EDT
Room D (3rd Floor)
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.

Speakers
TW
Monday October 28, 2024 1:30pm - 4:30pm EDT
Room D (3rd Floor) The Michigan League
  Workshop

6:00pm EDT

Welcome Reception
Monday October 28, 2024 6:00pm - 8:00pm EDT
Hussey
Monday October 28, 2024 6:00pm - 8:00pm EDT
Hussey The Michigan League
  Event
 
Tuesday, October 29
 

8:15am EDT

Welcome and Announcements
Tuesday October 29, 2024 8:15am - 9:00am EDT
Ballroom
Welcome and Opening Remarks:

Micaela Parker - Founder and Executive Director of the Academic Data Science Alliance
HV Jagadish - Edgar F Codd Distinguished University Professor and Bernard A Galler Collegiate Professor; MIDAS Director, EECS, College of Engineering - University of Michigan
Ravi Pendse - Vice President for Information Technology and Chief Information Officer - University of Michigan

Tuesday October 29, 2024 8:15am - 9:00am EDT
Ballroom The Michigan League
  Announcements

9:00am EDT

Keynote - Janet Haven - Data and Society
Tuesday October 29, 2024 9:00am - 10:00am EDT
Ballroom
Speakers
JH
Tuesday October 29, 2024 9:00am - 10:00am EDT
Ballroom The Michigan League
  Keynote

10:00am EDT

Break
Tuesday October 29, 2024 10:00am - 10:15am EDT
The Michigan League
Tuesday October 29, 2024 10:00am - 10:15am EDT
The Michigan League The Michigan League
  Break

10:15am EDT

Expanding Beyond Natural Language Processing: Harnessing Large-Scale Models for Real-Time Series Data Prediction in Advancing Wireless Communications and Networks towards the Next Generation
Tuesday October 29, 2024 10:15am - 11:15am EDT
Vandenberg
In this demonstration/tutorial, we focus on utilization of large-scale models (LLM) for real-time data modeling and prediction, particularly within the context of future wireless communications and networks. This research, carried out at the Center for Vehicle Communications and Networks (CVCC) located at the University of Michigan, Dearborn, has been supported by local autmobile industry companies in recent years.

The Transformer, pivotal in GPT models, excels in processing sequential data like text. It converts text into numerical tokens, adds positional encodings for token order, and employs attention mechanisms for focused processing. Multi-Head Attention allows for simultaneous consideration of various data relationships. With a layered structure, the model efficiently captures complex patterns. Predictions involve generating a probability distribution over the vocabulary for each position.
Transformers, naturally but non-trivia, excel in time series modeling. The incorporation of Language Models such as GPT-3 or BERT enriches real-time data exploration in wireless communications and networks, elevating capabilities in analysis, modeling, prediction, and pattern recognition. Research areas that will be discussed in detail encompass generative wireless channel modeling utilizing experimental data across microwave and millimeter bands, a C-V2X beamforming forecast model derived from field-tested data at UM-Dearborn, and projections concerning traffic and active user behavior within an innovative AI driver protocol for Internet of Things (IoT) applications.
Speakers
WX
Tuesday October 29, 2024 10:15am - 11:15am EDT
Vandenberg The Michigan League
  Session, Short Demo/Tutorial

10:15am EDT

Responsible AI Framework, Practice and Policy
Tuesday October 29, 2024 10:15am - 11:15am EDT
Ballroom
AI's power needs to be coupled with a great sense of responsibility of those who develop AI or use AI. Responsible AI, however, is a complex concept that consists of theoretical framework and practical consideration, grassroots efforts and best practices, and top-down policy and regulation. The Michigan Institute for Data Science (MIDAS) and the Microsoft Responsible AI Office have started a joint funding program aiming to support academic research on developing frameworks, practical approaches and policies to enable responsible AI. This session will feature the awardees of the first round of this funding program, who use interdisciplinary research methods from social science, computer science and so on to address significant research challenges in responsible AI. We hope that their presentations will stimulate new ideas and collaboration for further research under this theme.

Speakers:
  • Evaluating GenAI and Team-based Solutions to Reverse the Decline of Online Knowledge Communities
  • Innovating, Applying, and Educating on Fairness and Bias Methods for Educational Predictive Models
  • Advancing Responsible AI by Rethinking the Roles of Marginalized Communities in the Innovation Lifecycle: Developing the UBEC Approach
  • A Joint Human-AI Framework for Responsible AI 

Speakers
CS
JL
CB
SP
Tuesday October 29, 2024 10:15am - 11:15am EDT
Ballroom The Michigan League
  Session, Short Talks

10:15am EDT

The Rise of Robo-Umpires in Baseball, a sample lesson plan
Tuesday October 29, 2024 10:15am - 11:15am EDT
Hussey
The Rise of the Robo-Umpire in Baseball
A Lesson Plan

In this session, we will take raw pitch data from the 2024 World Series (happening simultaneously with the Conference) and develop a robo-umpire capable of calling balls and strikes with precision. The accuracy of the human umpires will be analyzed and the cost of their mistakes quantified. Should human umpires be replaced with a robot?
Speakers
AS
Tuesday October 29, 2024 10:15am - 11:15am EDT
Hussey The Michigan League
  Session, Short Demo/Tutorial - MOVE

11:20am EDT

Short Talks - Microtargeting in Social Media, Tracked Experiments for Reinforcement Learning
Tuesday October 29, 2024 11:20am - 12:20pm EDT
Hussey
Analyzing Microtargeting on Social Media
Tunazzina Islam (Purdue University), Dan Goldwasser (Purdue University)
The landscape of social media is highly dynamic, with users generating and consuming a diverse range of content. Various interest groups, including politicians, advertisers, and stakeholders, utilize these platforms to target potential users to advance their interests by adapting their messaging. This process, known as microtargeting, relies on data-driven techniques that exploit the rich information collected by social networks about their users. Microtargeting is a double-edged sword; while it enhances the relevance and efficiency of targeted content, it also poses challenges. There is the risk of influencing user behavior and perceptions, fostering echo chambers and polarization. Understanding these impacts is crucial for promoting healthy public discourse in the digital age and maintaining a cohesive society. Our work focuses on developing an organizing framework for a better understanding of microtargeting and activity patterns in social media on contentious topics. In this tutorial, we will present the challenges we face in our work and how we address these challenges by developing computational approaches for (1) characterizing user types and their motivations for engaging with content, (2) analyzing the messaging based on topics relevant to the users and their responses to it, and (3) delving into a deeper understanding of the themes and arguments involved in the content. We dive into the cutting-edge realm of social media microtargeting, a strategy that powerfully shapes public discourse, in our engaging tutorial at ADSA 2024. This tutorial, tailored for researchers and practitioners, offers computational methods, NLP tools, and analytical frameworks to explore online messaging dynamics.


Open RL Benchmark: Comprehensive Tracked Experiments for Reinforcement Learning
​Md Masudur Rahman (Purdue University)
In many Reinforcement Learning (RL) papers, learning curves are useful indicators for measuring the effectiveness of RL algorithms. However, the complete raw data of the learning curves are rarely available. As a result, it is usually necessary to reproduce the experiments from scratch, which can be time-consuming and error-prone. We present Open RL Benchmark, a set of fully tracked RL experiments that include not only the usual data, such as episodic return, but also all algorithm-specific and system metrics. Open RL Benchmark is community-driven: anyone can download, use, and contribute to the data. At the time of writing, more than 25,000 runs have been tracked, for a cumulative duration of more than eight years. Open RL Benchmark covers a wide range of RL libraries and reference implementations. Special care is taken to ensure that each experiment is precisely reproducible by providing not only the full parameters but also the versions of the dependencies used to generate it. Additionally, Open RL Benchmark comes with a command-line interface (CLI) for easily fetching and generating figures to present the results. In this document, we include two case studies to demonstrate the usefulness of Open RL Benchmark in practice. To the best of our knowledge, Open RL Benchmark is the first RL benchmark of its kind, and we hope that it will improve and facilitate the work of researchers in the field.
Speakers
TI
DG
MM
Tuesday October 29, 2024 11:20am - 12:20pm EDT
Hussey The Michigan League
  Session, Short Talks

11:20am EDT

Short Talks: Labor in DS, Learning through Competitions
Tuesday October 29, 2024 11:20am - 12:20pm EDT
Ballroom
Labor in Data Science and AI
​Satadisha Saha Bhowmick (University of Chicago)
When thinking about human-centered AI and automation, it is imperative to be mindful of the labor that goes behind the commercial proliferation of large-scale AI systems. As technologies like large language models, text-to-image models and other forms of generative AI are rapidly introduced within a number of production pipelines, in a post-pandemic reality, we need more academic interest on the potential devaluation or displacement of human labor as a consequence, and what can be done to mitigate that.

Recent literature has attempted to recognize human interventions at different stages of the commercial AI lifecycle, starting from data preparation and software development to the often hidden labor associated with ‘patchwork’- calibration and troubleshooting of AI technologies deployed in the market. Prescient inquiries have also been made on the potential encroachment of models like MidJourney, DALLE or Stable Diffusion upon creative labor. In this proposed session, we attempt to raise further questions on whether tolerability around plagiarism or copyright for AI produced art or products need to be redefined. We also seek to inquire about the requirement of a robust economic model to properly remunerate workers whose labor gets crowdsourced to train and fine-tune AI systems that seek to replace them.
 Since the current generation of educators influence the next generation of researchers, it is also essential to initiate dialogue with them around how principles of ethical labor can be integrated into AI pedagogy and learning and introduced early on in the academic journey of AI and data practitioners.

Enhancing Learning through Data Science Competitions: Insights from a Hackathon
Zarifa Zakaria (Data Science Academy, North Carolina State University)
Data science competitions, such as hackathons, offer dynamic, hands-on learning experiences that can significantly enhance students' skills and knowledge. This presentation explores how a data science competition facilitated collaborative learning, skill acquisition, and the application of known skills in new contexts. To investigate these phenomena, I conducted a detailed study of one hackathon, video documenting participant groups from beginning to end. Additionally, I collected surveys to gauge participants' prior experiences and motivations towards data science, examining how these factors influenced their engagement during the event. A critical aspect of this study focuses on the instructional structure of the competition as well. Despite often receiving minimal instructions, participants frequently benefit from specific content types provided by organizers. This research also aims to identify characteristics of minimal instructions that are promising in fostering an effective learning experience. In this presentation, I will detail my study's methodology and share insights on how to study learning experiences during short-term data competitions. I will also share preliminary results, highlighting key findings on student collaboration and learning processes from my study. By understanding these elements, we can better design data science competitions to maximize educational outcomes and student engagement.
Speakers
SS
ZZ
Tuesday October 29, 2024 11:20am - 12:20pm EDT
Ballroom The Michigan League
  Session, Short Demo/Tutorial

11:20am EDT

The Virginia Model of Data Science: Humans, Data, Machines
Tuesday October 29, 2024 11:20am - 12:20pm EDT
Vandenberg
This session will introduce the Virginia model of data science, also known as the 4+1 model, a broad framework designed to shape conversions about teaching and doing research in the field. We will review the history of predecessor definitions and models, such as Conway's Venn diagram and CRISP DM, as well as Donoho's more controversial model of "greater data science." We will then demonstrate the derivation of an invariant structure from a persistent theme in these models--the concept of a data science pipeline as a way of organizing thinking around the field. Among the topics covered will be the ongoing tensions in the field, exhibited by differences between earlier models, the subfield of data design, and the influence of the model on how data science is taught at UVA. Of particular interest to us is design, which relates to both visualization and data as a form of representation. We hope to engage a lively discussion.
Speakers
RA
PA
Tuesday October 29, 2024 11:20am - 12:20pm EDT
Vandenberg The Michigan League
  Session, Topical Session

12:20pm EDT

Lunch
Tuesday October 29, 2024 12:20pm - 1:30pm EDT
On Your Own
Tuesday October 29, 2024 12:20pm - 1:30pm EDT
On Your Own
  Lunch

1:30pm EDT

Lightning Talks
Tuesday October 29, 2024 1:30pm - 2:30pm EDT
Ballroom
Lightning Talks:
  1. Human in the Analytical Loop for Health Care Fraud Detection - Tahir Ekin (Texas State University)
  2. Algorithmic Audits: AI Quality Control with a Human Touch - Petula Brown (Univ. of Michigan)
  3. Pedagogy, Peer Networks, and Professional Identity: How Social Capital Influences the Data Science Identity of Students - Tom Leppard (North Carolina State University)
  4. The Intersection of Artificial Intelligence and Healthcare Education - Clara Linjewile (Michigan State University)
  5. Assessing the Impact of Large Language Models on Learning Data Visualization Libraries - Samira Shirazy (University of Washington)
  6. Human-Centric AI in Healthcare: Using AI to Improve Cancer Diagnosis While Keeping Human Oversight - Nasrin Ali (ColorStack, Rewriting the Code, George Mason University)
  7. Leveraging Data and AI for Equitable Social Impact: Bridging Gaps and Empowering Communities - Amen Divine Ikamba (Reboot Scholar)
  8. Evolving Data Science in the Era of Generative AI: Keeping Humans at the Core - Shubham Ranjan, Product Manager, Developer Experience (MongoDB) [Sponsored

Tuesday October 29, 2024 1:30pm - 2:30pm EDT
Ballroom The Michigan League
  Lightning Talks

2:30pm EDT

Critical Making as Pedagogical Approach to the Data Science Classroom
Tuesday October 29, 2024 2:30pm - 3:30pm EDT
Hussey
A data physicalization is a material artifact where data is intentionally encoded. It represents data through physical, tangible forms, such as creating a three-dimensional wooden model of a state's population density by county to enable users to explore and interact with the data in a more intuitive and engaging manner. As a result, data physicalizations keep humans in the loop by engaging their audience and communicating data using tangible representations. While physicalizations can historically be traced back to ancient and premodern examples such as Mesopotamian clay tokens and medieval tally sticks, there are innovative opportunities for instructors to explore the intersection of data science and critical making to convey data. Most importantly, while there are still challenges, introducing students to physicalizations allows them to experience data as a medium, addressing accessibility and multimodal learning styles – particularly kinesthetic and haptic learners. In this interactive session, participants will be introduced to data physicalization as both a form of critical making and a pedagogical tool that can be used to curate, analyze, and interpret data while keeping accessibility in mind. The session will include a brief history of data physicalization, a presentation on data physicalization as alternative pedagogical method for teaching data science, and a hands-on workshop for participants to create examples of physicalizations that can be scaled to their workshops and classrooms.
Speakers
JH
ZZ
JK
Tuesday October 29, 2024 2:30pm - 3:30pm EDT
Hussey The Michigan League
  Session, Short Demo/Tutorial

2:30pm EDT

DSXP Data Science Experiential Pathways: Scalable Intersegmental Workforce Training Program
Tuesday October 29, 2024 2:30pm - 3:30pm EDT
Ballroom
Real-world data science experiences addressing societal challenges offer highly engaging learning opportunities for students at all levels. These projects foster key data science skills like data visualization, cleaning, analysis, decision-making, inference, and effective communication. Teamwork, near-peer mentoring, and collaboration with experts from education, industry, government, and nonprofits further enhance this multidisciplinary learning approach. This short talks session showcases an integrated "Data Science Experiential Pathways" (DSXP) model, pilot-tested in 2024 with high schools, community colleges, and universities. Co-chaired by Anthony Suen (UC Berkeley Data Science Discovery program) and Judy Cameron (DataJam Director, University of Pittsburgh), the session will feature: 1. Judy Cameron discussing DataJam, a decade-long national project-based data science program and competition for high school and community college students, 2. Luella Fu (SFSU professor) on a DataJam Mentor training program for undergraduates, 3. Kyla Oh and/or Denise Hum (Laney & Skyline Community Colleges faculty) sharing their experiences advising DataJam teams, 4. Anthony Suen on his Data Science Discovery program's role in facilitating student and mentor exchanges within DataJam and setting up Discovery Projects in domain areas like transportation, and 5. Sarah Stone (Director of Data Science for Social Good at University of Washington) shares her experience on leading intensive summer research fellowships. This scalable, integrated DSXP model holds the potential to transform data science education and workforce training, addressing societal challenges while preparing future data scientists.
Speakers
JC
AS
Tuesday October 29, 2024 2:30pm - 3:30pm EDT
Ballroom The Michigan League
  Session, Short Talks

2:30pm EDT

Embedding Data Ethics and Justice Framework into the Data Science and Library and Information Science Curriculum
Tuesday October 29, 2024 2:30pm - 3:30pm EDT
Vandenberg
In the evolving landscape of our data-driven society, integrating ethical considerations with practical problem-solving in data workflows is crucial in both academic and industry contexts. This session will share our ongoing curriculum enhancement project at Indiana University Indianapolis, which incorporates a data ethics and data justice framework into the pedagogical strategies and teaching practices of undergraduate Data Science (DS) and master's level Library and Information Science (MLIS) curricula. Utilizing the Data Science Ethos Lifecycle, our goals are to: 1) update Program Learning Outcomes (PLOs) for DS to emphasize the human and sociotechnical dimensions of data work; and 2) create sample assignments, to be piloted in Spring 2025, that synergize ethics with technical content. We will present our updated PLOs and sample assignments for courses such as Data Policy and Governance, Social Media Data, and Public Library Management, and discuss: 1) using Data Science Ethos Lifecycle to develop a data science education curriculum that integrates ethical considerations with technical skills, transitioning from traditional compliance-focused education to a practical, problem-solving approach; 2) encouraging and assisting instructors from diverse backgrounds to incorporate a focus on critical thinking and ethical reasoning into their courses and assignments; and 3) enhancing career opportunities for students, particularly those from underrepresented groups and those with lower tech literacy, by offering enriched learning experiences. This session will highlight the role of human-centric education in preparing future data professionals who are not only skilled in technical aspects but also adept at addressing ethical challenges in the field.
Speakers
DY
AC
Tuesday October 29, 2024 2:30pm - 3:30pm EDT
Vandenberg The Michigan League
  Session, Topical Session

3:30pm EDT

Break
Tuesday October 29, 2024 3:30pm - 4:30pm EDT
The Michigan League
Tuesday October 29, 2024 3:30pm - 4:30pm EDT
The Michigan League The Michigan League
  Break

4:30pm EDT

Cocktail Hour
Tuesday October 29, 2024 4:30pm - 5:30pm EDT
Ballroom
Tuesday October 29, 2024 4:30pm - 5:30pm EDT
Ballroom The Michigan League
  Event

5:00pm EDT

Fireside Chat - Agus Sudjianto (Senior Vice President of Risk and Technology - H2O.ai) and Doug Hague (University of North Carolina, Charlotte)
Tuesday October 29, 2024 5:00pm - 6:00pm EDT
Ballroom
We will discuss our eerily common journeys from our early training in physics to engineering PhDs to system thinking education. Our backgrounds influenced our work in the development of what is now called Model Risk Management (MRM). We'll talk about how the MRM regulations and practice have co-evolved and deep dive into how data science is critical to managing the risk of LLMs and Generative AI.
Speakers
AS
DH
Tuesday October 29, 2024 5:00pm - 6:00pm EDT
Ballroom The Michigan League
  Event
 
Wednesday, October 30
 

8:15am EDT

Announcements and Data Science Poetry
Wednesday October 30, 2024 8:15am - 9:00am EDT
Ballroom
  1. Morning announcements
  2. Data Science Poetry
  3. Human-Centered AI: Connecting People, Information, and AI - Andrea Forte, Dean of the U-M School of Information. https://www.si.umich.edu/people/leadership-team

Wednesday October 30, 2024 8:15am - 9:00am EDT
Ballroom The Michigan League
  Announcements

9:00am EDT

Student Keynote: Considerations of Children and Adolescents in Data and Artificial Intelligence (The Kids are AI-ght?)
Wednesday October 30, 2024 9:00am - 9:30am EDT
Ballroom
Abstract:
Big data and artificial intelligence have permeated discussions not only within business circles or academic spheres, but the considerations of people in all walks of life. Despite the vast discourse from talks, panels, journals, think pieces, and podcasts on these compelling new technologies, a crucial demographic had been surprisingly overlooked: children and adolescents.

Decision-makers in business and government frequently display a lack of understanding and overlook the consequences of their actions on this vulnerable group, as evidenced by recent press releases, business memos, and legislation.

There is an urgent need to develop ethical policies that prioritize the protection of those providing data, especially minors who are particularly at risk. The dangers inherent in the handling of data and the use of AI are amplified when the humans involved are vulnerable, such as minorities, the poor, and crucially – the youth. If children are not being explicitly protected, then they are implicitly being left behind.

This presentation will address the significant gap in research, legislation, and policy concerning the effects of data and AI on children and explore the necessity of safeguarding this critical population.
Speakers
AV

Alexandra Veremeychik

Montgomery College
Wednesday October 30, 2024 9:00am - 9:30am EDT
Ballroom The Michigan League
  Keynote, Student Keynote

9:30am EDT

Student Keynote: Strengthening AI Models for Spoofed Audio Detection: An Interdisciplinary Approach Incorporating Linguistic Knowledge
Wednesday October 30, 2024 9:30am - 10:00am EDT
Ballroom
Abstract:
Deepfakes—misleading content generated or manipulated using AI methods—have proliferated as vehicles for deception and fraud, posing ever-increasing threats to individuals and institutions. Audio deepfakes in particular are overlooked in existing literature compared to video and image counterparts (Khanjani et al., 2023). Our multidisciplinary team of data scientists and sociolinguists—experts in a subdiscipline of linguistics that deals with how human language varies, socially and stylistically—offers a novel approach to detecting audio deepfakes and other spoofed audio attacks by incorporating insights about spoken human language into machine learning techniques.

This talk shares results from four years of our ongoing research and outlines novel pathways for interdisciplinary collaboration to address deepfakes as a pressing societal problem. Findings demonstrate how audio representations, manually extracted by sociolinguists, increase the detection performance significantly at the scale of all types of spoofed audio attacks, when combined with machine learning models (Khanjani et al., 2023). Additionally, when AI models are used to automatically extract Audio Linguistic Representations designed for anti-Spoofing (ALiRaS), under expert supervision, the performance of the common baselines significantly increased. Overall, the talk demonstrates that leveraging human expert knowledge is crucial in creating robust audio representations used in spoofed audio detection for strengthening AI solutions.
Speakers
ZK

Zahra Khanjani

University of Maryland Baltimore County
Wednesday October 30, 2024 9:30am - 10:00am EDT
Ballroom The Michigan League
  Keynote, Student Keynote

10:00am EDT

Break
Wednesday October 30, 2024 10:00am - 10:15am EDT
The Michigan League
Wednesday October 30, 2024 10:00am - 10:15am EDT
The Michigan League The Michigan League
  Break

10:15am EDT

AI for Social Impact: Bridging Methodological Progress and Real-World Deployment
Wednesday October 30, 2024 10:15am - 11:15am EDT
Hussey
In this session, we will explore challenges in transitioning AI advancements from research to practical social impact applications. We will share insights and case studies from our work in public health and conservation, highlighting successful strategies to overcome barriers, with a special focus on gaps in AI supporting high-stakes decisions. The session will begin with a brief presentation framing the topic's importance, then discuss advances in machine learning, multi-agent systems, uncertainty quantification, and data quality assurance, and how these have been successfully deployed to support the Centers for Disease Control and Prevention (CDC) and conservation NGOs. This will be followed by an interactive discussion with the audience aiming to foster collaboration, share experiences, and generate practical solutions.
Speakers
AR
EB
Wednesday October 30, 2024 10:15am - 11:15am EDT
Hussey The Michigan League
  Session, Topical Session

10:15am EDT

K-12, Community College, & Beyond: Developing Inclusive Ramps and Pathways to Data Science
Wednesday October 30, 2024 10:15am - 11:15am EDT
Ballroom
In an era where data science skills are increasingly in demand across industries, it's imperative to ensure equitable access to data science education. This session will delve into the barriers that students face when pursuing data science courses and discuss strategies for creating inclusive ramps and pathways to make these courses accessible to all.

Experts will discuss the need for collaborative work between K-12, community colleges, and four-year schools to make students aware of the emerging field of data science, and existing projects that seek to address alignment challenges. We will also discuss a new white paper organized by ADSA members on the topic. Join us for an engaging and thought-provoking discussion on how we can collectively work towards dismantling barriers and creating pathways for all students to thrive in data science.
Speakers
ZD
RS
PS
AZ
Wednesday October 30, 2024 10:15am - 11:15am EDT
Ballroom The Michigan League
  Session, Panel

10:15am EDT

Responsible AI: Building a Tool for Transparent and Ethical AI Implementation
Wednesday October 30, 2024 10:15am - 11:15am EDT
Vandenberg
Artificial intelligence (AI) can be used in libraries and archives as a powerful tool for enhancing metadata, improving search and discovery, recommending resources, powering library chatbots, and more. However, AI systems may incorporate surveillance technologies that threaten user privacy, and AI often reflects the biases of our society due to biased training data. This 60 minute short demonstration will feature a tool for responsible implementation of AI in libraries and archives, which has been developed as an outcome of the IMLS-funded Responsible AI project. This grant examines this tension between innovating library services and protecting library communities. The Responsible AI team will lead participants through a demonstration of the tool and solicit feedback from the data science community on features and limitations, with the potential for discussion informing AI software development and technology implementation.
Speakers
YS
NB
Wednesday October 30, 2024 10:15am - 11:15am EDT
Vandenberg The Michigan League
  Session, Short Talks

11:20am EDT

Art, artists and the concepts of intelligence, artificiality and realness.
Wednesday October 30, 2024 11:20am - 12:20pm EDT
Ballroom
The panel discussion, titled "Art, artists and the concepts of intelligence, artificiality, and realness," brings together leading artists, researchers, and scientists to explore the intersection of art, data, and artificial intelligence. The conversation delves into how artists and their creative processes are critical in how we think about intelligence and how technological innovations are transforming the landscape of art and creativity. The panelists will discuss the role of imagination in advancing technology, the ethical implications of AI-generated art, and the collaborative potential between human creativity and data science. This thought-provoking dialogue aims to redefine the boundaries of creativity and imagination in a future of emergent technologies and research.

Dasan Ahanu (https://www.dasanahanu.com/) is a poet, cultural organizer, performing artist, and scholar. He is a visiting lecturer at UNC-Chapel Hill, an alumnus of Harvard University's Nasir Jones Fellowship, and North Carolina's 2023 Piedmont Laureate for poetry. A respected recording artist, Dasan has collaborated with many Jazz, Soul, and Hip-Hop artists in North Carolina. He has published extensively, performed nationwide, and authored six poetry collections. He currently serves as artist-in-residence with NC State Live at NC State University.

Sophia Brueckner (https://www.sophiabrueckner.com/) is a futurist artist/designer/engineer who researches how technology shapes us. Inseparable from computers since the age of two, she believes she is a cyborg. As a software engineer at Google, she designed and built products used by tens of millions. At the Rhode Island School of Design and the MIT Media Lab, she researched the simultaneously empowering and controlling aspects of technology with a focus on tangible and social interfaces.

Rachel (Ray) Levy (https://raylevy.org/) is a regular supporter of poetry and art engagement at ADSA. She is the Executive Director of the NC State Data Science and AI Academy, an all-university effort to catalyze and network interdisciplinary activity. Her disciplinary background is applied mathematics and her recent research has focused on data-rich project-based educational experiences from Kindergarten to Industry. She is a Fellow of the Society for Industrial and Applied Mathematics, a writer, policymaker, public speaker and a dancer.

Marcel Fable Price (https://www.marcelfableprice.com/) is a multi-hyphenate, self-directed, intuitive creative whose primary medium lies between oratory expression and creative writing. His work, a kaleidoscope of personal experiences, forms stained glass examples of transformation, connecting with readers in a deeply resonant way. Fable believes the mortar to our humanity is shared experience, and without it, our individual gospels remain surface hymns. His poetry has been used for the inauguration of state representatives and even a Senate Majority Leader. His work has appeared in The Missouri Review, Button Poetry, Write About Now, and has been featured by PBS, The Frey Foundation, Mental Health America, and Habitat for Humanity. His first full-length collection, New American Monarch, is available for pre-order and releases in October 2024.
Speakers
DA
SB
RL
MF
Wednesday October 30, 2024 11:20am - 12:20pm EDT
Ballroom The Michigan League
  Session, Interactive/Art

11:20am EDT

Bridging Data Science and Societal Impact: Open Data Tools
Wednesday October 30, 2024 11:20am - 12:20pm EDT
Vandenberg
Grounding data science in societal impact and real-world problems is core to our mission at the University of Chicago Data Science Institute. We integrate community-centered data science across research and educational programs, with the goal of creating student education and career pathways in social impact data science, while meeting the data and technical needs of our organizational partners working at the frontlines of change. For example, in the Data Science Clinic, students work with real-world clients, including social impact organizations, debating data science ethics and developing critical thinking alongside technical skills. Many collaborations emerge from the 11th Hour Project, for which the DSI serves as a centralized hub for nonprofit grantees in four main impact areas: energy, food and agriculture, human rights, and marine technology. We build long-running partnerships with these organizations that can span across multiple years. Recent examples include the interactive data tool PalmWatch, developed in collaboration with Inclusive Development International, which examines the environmental harm caused by palm oil production in countries around the world; and the Grocery Gap Atlas, in partnership with RAFI-USA, a tool that visualizes inequities in food access and corporate concentration in the grocery market at the census tract-level nationwide. Highlighting these projects as case studies, we will discuss why partnerships with social impact organizations are important to data science researchers and our communities, and what makes a successful data science project partnership.
Speakers
DH
LG
SP
Wednesday October 30, 2024 11:20am - 12:20pm EDT
Vandenberg The Michigan League
  Session, Topical Session

11:20am EDT

Connecting the Dots: AI Education and Training with AI and Data Science Jobs
Wednesday October 30, 2024 11:20am - 12:20pm EDT
Hussey
The rapid rise of AI related jobs in the government and private sectors requires a careful classification of the knowledge, skills, and ability (KSA) that candidates need for filling such jobs and a description of suitable academic training. The ADSA community will collaborate with employers to first develop a taxonomy of learning objectives and competencies developed by AI and data science related educational efforts. We will concurrently develop a mapping of these to the KSA required by prospective employers.

Desired Outcomes:

I. From the training side (Taxonomy):
A taxonomy of topics and student learning objectives (SLO) of programs in data science and AI at different levels – certificate, bachelors, graduate certificates, master’s, and PhD.

II. From the employer Side (KSA):
A mapping between knowledge and skills for different data science and AI job categories to the created taxonomy. Establish competency levels and mechanisms for evaluation in the described knowledge and skills underlining the importance of education on “responsible data/AI citizens” in the face of algorithm bias and security issues around data.

Speakers
AP
ND
Wednesday October 30, 2024 11:20am - 12:20pm EDT
Hussey The Michigan League
  Session, Topical Session

12:20pm EDT

Lunch
Wednesday October 30, 2024 12:20pm - 1:30pm EDT
On Your Own
Wednesday October 30, 2024 12:20pm - 1:30pm EDT
On Your Own
  Lunch

1:30pm EDT

Lightning Talks
Wednesday October 30, 2024 1:30pm - 2:30pm EDT
Ballroom
Lightning Talks:
  1. Explaining (Sarcastic) Utterances to Enhance Affect Understanding in Multimodal Dialogues - Shivani Kumar (University of Michigan)
  2. What is Data Activism? Why should you care? - Claudia Scholz (University of Virginia School of Data Science)
  3. Using Data Science to Promote Equity in Healthcare - Ron Ozminkowski, PhD (Senior Vice President, Commercial Analytics, Aon plc)
  4. Can LLMs Assist Annotators in Identifying Morality Frames? - Case Study on Vaccination Debate on Social Media - Tunazzina Islam (Purdue University)
  5. Differential use of generative AI by higher education students: Early warning signs for exacerbated disparities - Elyse Thulin (University of Michigan, ISR, HBHE, Institute for Firearm Injury Prevention)
  6. AI at the Speed of Trust - Ayush Mathur (Elevance Health)
  7. How Underrepresented Teens Use Data Science and Journalism to Achieve Social Good: An Overview of DataWrite and Youth in Data Science - Rajan Tavathia (DataWrite, Inc.)
Wednesday October 30, 2024 1:30pm - 2:30pm EDT
Ballroom The Michigan League
  Lightning Talks

2:30pm EDT

Algorithmic impact assessments of AI in policing: Who watches the watchers?
Wednesday October 30, 2024 2:30pm - 3:30pm EDT
Hussey
The proliferation of AI technology in the domain of policing continues to increase despite public criticism from advocacy groups, local communities, and activist organisations. This session invites concerned researchers and practitioners to discuss possibile avenues to conduct algorithmic impact asessments of the use of AI algorithms in the policing domain. How can we wholistically approach the continuing developements such as in the use of facial recognition, automated license plate readers, biometric data collection, acoustic gunshot detection, predictive policing, LLM-generated police reports and so on? What are the risks and limits invovled in choosing to or not to work with police in order to conduct such audits? What kinds of data should be used, and how should we be critical of their provenance and bias while attempting to make use of them? What analytic frameworks e.g. causal analysis, statistical methods, or other forms of measurement should be used?


To scaffold the discussion, we will begin with a brief presentation on some of the ongoing work conducted at the Blue Data Lab on related problems and questions. The discussion will focus on developing new methodological approaches to the above, potential collaborations across discplines, and generating new questions and ideas.
Speakers
TA
Wednesday October 30, 2024 2:30pm - 3:30pm EDT
Hussey The Michigan League
  Session, Topical Session

2:30pm EDT

Healthcare AI Infrastructure and Governance, considerations from the Clinical Intelligence Committee
Wednesday October 30, 2024 2:30pm - 3:30pm EDT
Room D (3rd Floor)
As healthcare organizations rapidly expand their involvement in artificial intelligence (AI), there remain unique considerations to how the effectively and safely build these efforts. Our session plans to present and discuss important considerations such as governance and infrastructure and present an engaging discussion with the audience.
Speakers
MB
MS
SM
Wednesday October 30, 2024 2:30pm - 3:30pm EDT
Room D (3rd Floor) The Michigan League
  Session, Short Talks

2:30pm EDT

Mitigating Algorithmic Harm: Choreographic Interventions for Data Professionals
Wednesday October 30, 2024 2:30pm - 3:30pm EDT
Vandenberg
In this demonstration, we invite practitioners and researchers involved in algorithm design, implementation, or application (ADIA) to explore movement improvisation and scores – two movement arts methods – as techniques to integrate ethical principles for mitigating algorithmic harm into our day-to-day workflows. We will introduce findings from recent research on developing interventions centering these two movement arts methods and how they support integrating ethical principles into decision-making in ADIA. We then invite attendees to participate in a demonstration of one of the interventions we developed. We will conclude with a collective reflection on the agency and responsibility that we may or may not have in mitigating algorithmic harms at specific points in our ADIA workflows, and how the intervention we demonstrated may be useful to us and other data professionals for mitigating algorithmic harm as we return to our day-to-day ADIA work.
Speakers
AS
LH
Wednesday October 30, 2024 2:30pm - 3:30pm EDT
Vandenberg The Michigan League
  Session, Short Demo/Tutorial

3:45pm EDT

Stories in Concert: take a qualitative journey of information gathering and meaning making
Wednesday October 30, 2024 3:45pm - 4:45pm EDT
Hussey
This session is intended to bring conference participants together in an open and welcoming space to explore ideas with our artist in residence that can inform your work in data science and AI.
What if you blended the spontaneity of jazz improvisation with the art of poetry and the intentionality of music composition? What if this exploration in craft included exploring how that creative output operates in community with each other? Poetry can be as emotional, inspirational, educational, creative, and expressive as your imagination. Participants in this workshop will explore poetry's rhythmic and dynamic possibilities, drawing inspiration from qualitative data, personal stories, and lived experiences. This interactive session invites participants to experiment with rhythm, sound, and narrative, crafting unique compositions that resonate with individual and collective voices.
Speakers
DA
Wednesday October 30, 2024 3:45pm - 4:45pm EDT
Hussey The Michigan League
  Participatory, Interactive/Art

4:45pm EDT

Break
Wednesday October 30, 2024 4:45pm - 5:30pm EDT
The Michigan League
Wednesday October 30, 2024 4:45pm - 5:30pm EDT
The Michigan League The Michigan League
  Break

5:00pm EDT

Poster Session and Happy Hour
Wednesday October 30, 2024 5:00pm - 7:00pm EDT
Ballroom
Join us for our annual poster session, with over 65 posters from undergraduate students, graduate students, staff, and faculty representing over 40 institutions.

View the Full List of ADSA'24 Posters
Wednesday October 30, 2024 5:00pm - 7:00pm EDT
Ballroom The Michigan League
  Poster Session
 
Thursday, October 31
 

8:15am EDT

Announcements and Data Science Poetry
Thursday October 31, 2024 8:15am - 9:00am EDT
Ballroom
Thursday October 31, 2024 8:15am - 9:00am EDT
Ballroom The Michigan League
  Announcements

9:00am EDT

Keynote - Maggie Levenstein - Inter-university Consortium for Political and Social Research (ICPSR)
Thursday October 31, 2024 9:00am - 10:00am EDT
Ballroom
Speakers
ML
Thursday October 31, 2024 9:00am - 10:00am EDT
Ballroom The Michigan League
  Keynote

10:00am EDT

Break
Thursday October 31, 2024 10:00am - 10:15am EDT
The Michigan League
Thursday October 31, 2024 10:00am - 10:15am EDT
The Michigan League The Michigan League
  Break

10:15am EDT

Best Practices for Teaching Ethics in Data Science
Thursday October 31, 2024 10:15am - 11:15am EDT
Ballroom
There is a growing awareness about the ethical and societal implications related to the use of data in AI and Generative AI. It is therefore critical to integrate Ethics in the curriculum of students in Data Science and Computer Science. As future practitioners, students need to be equipped with the language and critical thinking skills that enable them to engage in the discussions around the inherent normativity in building socio-technical systems. However, there are serious challenges in the implementation of normative elements in the curriculum of Data Science and Computer Science, namely the contrasting nature of these disciplines, poor collective knowledge and best practices, and lack of support and training for instructors in technical fields who wish to engage in these discussions. The session on Methods for Teaching Ethics in Data Science aims to foster a reflection on the challenges associated with teaching Ethics in technical or quantitative domains, sharing experiences and progress made, and establishing core best practices. Our goal is to build a community empowering data science instructors in teaching the new generation of data scientists and tech practitioners. Building on the first workshop on methods for teaching ethics in data science which tool place in May 2023, we propose the second edition of the workshop with one 30 minute plenary talk (by Dr. Benedetta Giovanola, Professor, University of Macerata) followed by 10-15 min talks on teaching AI Ethics selected by a technical program committee (see draft CFP below). We will publish the proceedings online through ADSA.
Speakers
AM
SH
Thursday October 31, 2024 10:15am - 11:15am EDT
Ballroom The Michigan League
  Session, Short Talk - CLUSTER

10:15am EDT

Impacts of Data Science for Social Good Training Programs on Student Experiences and Workforce Demands
Thursday October 31, 2024 10:15am - 11:15am EDT
Vandenberg
Higher education institutions need to produce individuals with data science acumen to meet the demands of a 21st century data science workforce. While most organizations and agencies have access to data, many cannot capitalize and benefit from it, as they lack access to staff with interdisciplinary skillsets. Higher education institutions tend to operate in disciplinary silos and often prepare a workforce that is accustomed to sourcing solutions for specific disciplinary problems as opposed to interdisciplinary problems relevant to current technological and societal contexts. Furthermore, the best talents from data science programs often choose to work for large technology-oriented companies. Thus, it is very difficult for small, mid-sized companies and public sector organizations to gain access to graduates with data science skills. Several universities across the U.S. created Data Science for Social Good (DSSG) programs to address the data science talent needs of public sector organizations. However, the impacts of DSSG programs on student experiences and workforce demands are understudied. Florida DSSG program has been conducting studies on student experiences and how those experiences lead to skillsets that match workforce demands. During the session, we will present our study findings and conduct live reflection activities using the Riff AI tool to gather ADSA participants’ perspectives on the data science workforce, and STEM student educational and co-curricular programs.
Speakers
KU
DR
AP
Thursday October 31, 2024 10:15am - 11:15am EDT
Vandenberg The Michigan League
  Session, Topical Session

11:20am EDT

A New Era for Social Media Data Collecting, Sharing, and Archiving
Thursday October 31, 2024 11:20am - 12:20pm EDT
Hussey
Radical changes in platform and regulatory policy have impacted how researchers collect, analyze, and share social media data. Elon Musk bought Twitter and shut down the academic API. Reddit curtailed large-scale access to its data and then went public. Meta sunset CrowdTangle. The European Union demanded that platforms provide researchers access to data. And yet, social media remains an invaluable resource for science. In this session, we will discuss the legal, ethical, and computation challenges social media data collection, sharing, analysis, and archiving pose. Our speakers/panelists include researchers who have used authorized APIs and other data collection approaches, staff from the Social Media Archive at ICPSR who support users in sharing and reusing data, and platform insiders who are responsible for complying with data sharing regulations.
Speakers
LH
JP
MB
Thursday October 31, 2024 11:20am - 12:20pm EDT
Hussey The Michigan League
  Session, Panel

11:20am EDT

Container-driven Reproducible Research Made Simple
Thursday October 31, 2024 11:20am - 12:20pm EDT
Vandenberg
Container-driven Reproducible Research Made Simple
​Ronaldas Lencevicius (University of California, Santa Barbara)
Scholarship in data science should consists of a complete software development environment along with instructions for all the results and figures. However, fully specifying and reproducing an arbitrary data science workflow can often be challenging, especially with the increasing complexity of software dependencies and computational infrastructure. Furthermore, reproducibility that relies on documentation or language specific tools can involve specialized adjustments and tweaking that many researchers may not have the time or background for. To address this common deficiency, we introduce a computational research framework to the data science community that can specify complex computational environments using an OS-level virtualization technology called containers. We show that the container-driven reproducibility approach balances flexibility and ease of use through Visual Studio Code, a popular code editor. In addition, to alleviate the steep initial learning curve of containers, we introduce a code-generating template repository for further simplifying the initial setup of Python and/or R-based workflows commonly used in data science.
Speakers
RL
Thursday October 31, 2024 11:20am - 12:20pm EDT
Vandenberg The Michigan League
  Session

12:20pm EDT

Lunch
Thursday October 31, 2024 12:20pm - 1:30pm EDT
On Your Own
Thursday October 31, 2024 12:20pm - 1:30pm EDT
On Your Own
  Lunch

1:30pm EDT

Lightning Talks
Thursday October 31, 2024 1:30pm - 2:30pm EDT
Ballroom
Lightning Talks:
  1. Data Science + Environmental Justice = Experiential Nugget on Air Pollution - Ravanasamudram Uma (North Carolina Central University)
  2. Generative AI for Social Science Data Archiving at ICPSR - Murali Mani (University of Michigan Flint)
  3. Data Analytics Program at Washington State University - Sergey Lapin (Washington State University Everett)
  4. Non-Invasive and Explainable Assessment of Burn Depth for Surgical Decision-Making Using a Vision-Language Model and Ultrasound Imaging - Md Masudur Rahman (Purdue University)
  5. Administrative checkpoints, burdens and human-centered design: Increasing interview access to raise SNAP participation - Jae Yeon Kim (Johns Hopkins University)
  6. Assessing the Impact of Large Language Models on Learning Data Visualization Libraries - Saara Uthmaan (University of Washington)
Thursday October 31, 2024 1:30pm - 2:30pm EDT
Ballroom The Michigan League
  Lightning Talks

2:30pm EDT

Generative AI and the future of scientific code
Thursday October 31, 2024 2:30pm - 3:30pm EDT
Vandenberg
Scientific software is at the heart of data-intensive research projects in nearly every domain. With the increasing availability of generative AI tools like GitHub Copilot and ChatGPT, the practice of programming for research is sure to be affected. Because scientific conclusions frequently depend on code for data analysis, collection, visualization and simulation, the potential impacts of these tools on data-intensive research may be substantial. In this structured discussion, we'll together address the question: How does the use of generative AI code tools change the work involved in writing, using, and maintaining code for data-intensive science? We'll explore how the skills needed to develop, maintain and use high-quality scientific code may be changing. We'll discuss the potential impacts of increased reliance on generative AI code tools on the validity, correctness, and maintainability of scientific code. Finally, we'll also brainstorm what kind of training, practices, and tooling might help scientists best take advantage of generative AI software tools while mitigating risks.
Speakers
EO
Thursday October 31, 2024 2:30pm - 3:30pm EDT
Vandenberg The Michigan League
  Session, Topical Session

2:30pm EDT

Maximizing the Impact of Data Science and AI Research Institutes
Thursday October 31, 2024 2:30pm - 3:30pm EDT
Ballroom
Interdisciplinary research institutes have been playing significant roles in academic research. Data science and AI institutes, however, often face unique challenges because of the nature of data science and AI - they are not only fields of inquiry, but are also becoming essential methodologies for all research fields. As institutions grapple with this reality, they are setting up data science and AI institutes in many different ways, with different charges, funding models, operation models, and relationships with the rest of the campus. A group of leaders of data science and AI institutes at research universities have carried out a study to understand how to best position such institutes on campus, how these institutes can add the most value to the universities, and the sustainability strategies of these institutes. In this session, we will present our findings and invite audience members to share their thoughts, all for the purpose of maximizing the impact of such institutes in academic research.
Speakers
JL
BM
Thursday October 31, 2024 2:30pm - 3:30pm EDT
Ballroom The Michigan League
  Session, Topical Session

3:30pm EDT

Break
Thursday October 31, 2024 3:30pm - 3:45pm EDT
The Michigan League
Thursday October 31, 2024 3:30pm - 3:45pm EDT
The Michigan League The Michigan League
  Break

3:45pm EDT

Best Practices to Upskill Everyone to Broaden Participation in Data Science
Thursday October 31, 2024 3:45pm - 4:45pm EDT
Vandenberg
This session aims to share best practices for upskilling students from diverse majors in using data science to address problems within their respective disciplines. A significant challenge in this endeavor is achieving proficiency in programming-based tools, which often act as a barrier to student success. Non-programming tools, while more accessible and user-friendly for beginners, have inherent limitations such as the size of the datasets they can handle, limiting their effectiveness for more complex analyses. This session will explore the best practices for creating a data analytic pathway that transitions from no-code to low-code to high-code tools. The co-chairs will share their experiences in upskilling students from non-computing disciplines, beginning with spreadsheets, CODAP, and Weka, before transitioning to provide a gentle introduction to accessible high-code tools. The session will include audience participation through table discussions and share-outs, focusing on the following prompts:
1. What tools have you used, and what limitations or obstacles have your students encountered with those tools?
2. What resources do you believe are necessary or helpful to enable your students to overcome these obstacles? (Basically, your wish list.)
3. What ideas have you implemented, or do you have, to bring a wider group of students, teachers, and others into data science?
This work is supported through NSF grant #2245958.
Speakers
RU
AT
Thursday October 31, 2024 3:45pm - 4:45pm EDT
Vandenberg The Michigan League
  Session, Topical Session

3:45pm EDT

Data-driven Approaches as a Revolution for Space Weather Forecasting
Thursday October 31, 2024 3:45pm - 4:45pm EDT
Hussey
The session will feature leaders in AI/ML methods for space weather forecasting, demonstrating the promises and opportunities for space science and AI researchers.

Session Chair: Lulu Zhao (University of Michigan - Climate and Space Sciences and Engineering)
  1. Data Quality Issues in Flare Prediction Using Machine Learning Models - Ke Hu (University of Michigan)
  2. Predicting Solar Energetic Particle Events Using Machine Learning Algorithms with Flare Features - Chia-Yun Li (University of Michigan)
  3. An Overview of Surrogate Models for Synthetic White Light Images in the Space Weather Modeling Framework - Aniket Jivani (University of Michigan)
  4. Regression Estimate Recalibration using Kernelized Stein Discrepancy Scores: Applications in Space Weather - Matthew McAnear (University of Michigan)
  5. Global Geomagnetic Perturbation Forecasting with Quantified Uncertainty using Deep Gaussian Process - Hongfan Chen (University of Michigan)

Speakers
YC
TG
SZ
Thursday October 31, 2024 3:45pm - 4:45pm EDT
Hussey The Michigan League
  Session, Topical Session

5:30pm EDT

Closing Party - Wear Your Halloween Costume!
Thursday October 31, 2024 5:30pm - 8:00pm EDT
The Black Pearl
Thursday October 31, 2024 5:30pm - 8:00pm EDT
The Black Pearl
  Event
 
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