11:30 am \u2013 2:00 pm. (Shan Lobby) Registration <\/strong><\/p>\n\n\n\n Noon\u20131:30 p.m (Platt Green Room). Connections Lunch (Registered students and invited participants) <\/strong><\/p>\n\n\n\n 1:30\u20132 p.m. (Shan Shakespeare) Afternoon Tea <\/strong><\/p>\n\n\n\n 2\u20132:15 p.m. (Shan 1430) Welcome by President Harriet B. Nembhard<\/strong>, Harvey Mudd College <\/p>\n\n\n\n 2:15\u20133:15 p.m. (Shan 1430) Keynote #1: Joseph Robertson<\/strong>, PhD, chief data scientist, Mato Ohitika Analytics LLC, Home of the Data Sovereignty Initiative <\/p>\n\n\n\n Title<\/strong>: Data Science for Social Good and the Wodakota Okiciyapi Educational AI Alliance: Data Sovereignty, Data for Good, and Building Grass Roots Community Partnerships <\/p>\n\n\n\n Abstract<\/strong>: This keynote will explore Dr. Robertson\u2019s current data science work that first originated from his doctoral work on ethically bridging science and culture to design data science workflows to meet the challenges of underrepresented communities. The Indigenous Perspective Praxis created new data science pedagogy in identifying harms in technological systems which has led to the Wodakota Okiciyapi (Alliance of Helping Others), an Artificial Intelligence (AI) educational partnership designed to assist and promote student opportunities through Mato Ohitika Analytics LLC\u2019s decolonizing internship program. Dr. Robertson is pleased to share with students his data science philosophy and work he does from data to doing. <\/p>\n\n\n\n 3:15\u20133:30 p.m. Break <\/p>\n\n\n\n 3:30\u20135 p.m. (Shan 1430) AI Panel <\/strong><\/p>\n\n\n\n Title: <\/strong>A.I. and U: The Impact(s) of the Rise of Data Science and Artificial Intelligence on Education <\/p>\n\n\n\n Abstract: <\/strong>The ubiquitous presence of data as well as the advent of artificial intelligence is having an impact on all aspects of modern life. This is also particularly true in higher education, especially in the areas of computer science and mathematics. This panel will discuss various aspects of how the rise of A.I. and the evolution of data science is impacting curriculum, enrollment, policy, personnel, practices, and more at all levels of the education enterprise. Some specifics will include: (How) Should we distinguish AI and Data Science programs? Does quantitative literacy include knowledge and competency with data and AI? How are policies around AI different in public and private institutions and in K-12 and undergraduate levels? <\/p>\n\n\n\n Facilitator:<\/strong> Talithia Williams, Professor of Mathematics, Harvey Mudd College 5\u20135:45 p.m. (Shan Shakespeare theatre) Reception<\/p>\n\n\n\n 7:30\u20138:45 a.m. (Platt Green Room) Continental Breakfast <\/strong><\/p>\n\n\n\n 9\u201310 a.m. (Shan 1430) Keynote #2: David Uminsky<\/strong>, PhD, executive director and senior research associate, Data Science Institute, University of Chicago <\/p>\n\n\n\n Title: <\/strong>Advancing Community-Centered Data Science and AI <\/p>\n\n\n\n Abstract<\/strong>: In this we will explore how data science and AI can drive meaningful change when grounded in the needs, expertise, and lived experiences of communities. We will highlight examples of collaborative work spanning human rights, environmental resilience, food and agriculture, and partnerships with Indigenous-led efforts. Through these case studies, we will demonstrate how universities researchers – faculty, staff, and students – can ensure that data science is not only technically rigorous, but also actionable and socially accountable. Time permitting we will discuss some the challenges for scaling this work and partnerships and efforts we are working on to lower the barriers to increase the impact of working in public interest data science and AI. <\/p>\n\n\n\n 10:15\u201311:30 a.m. Parallel Sessions: <\/p>\n\n\n\n Track A <\/strong>(Shan 1430): Teaching Data Science <\/strong>in 2026 <\/p>\n\n\n\n \u25cf 10:15 – 10:35: Ji Son<\/strong>, Cal State LA <\/p>\n\n\n\n Title<\/strong>: Should data science be part of introductory statistics? <\/p>\n\n\n\n Abstract<\/strong>: Introductory statistics is a ubiquitous course, taken at scale by students across disciplines, and it is often the last formal exposure many students have to statistical reasoning. Yet in many introductory statistics courses, there is a conspicuous absence of working directly with real data, and the connection of statistics to modeling. At the same time, data science has elevated the exploration and modeling of data as central practices valued across many professions. This raises an important question for educational institutions: does data science belong in introductory statistics? We argue that modeling data is not external to statistics, but rather an organizing principle that can bring coherence to the concepts typically taught in an introductory course. Using examples from the CourseKata project\u2019s modeling-first curriculum, we demonstrate how students with diverse mathematical backgrounds engage in modeling by repeatedly making connections between familiar introductory statistics concepts (e.g., mean and standard deviation) and a central modeling idea (e.g., DATA = MODEL + ERROR). Results from a performance assessment administered across nine courses at five institutions provide evidence of which data science competencies students reliably develop and where persistent challenges remain. Introductory statistics may be an opportunity hidden in plain sight to scale data science practices broadly.<\/p>\n\n\n\n \u25cf 10:35 – 10:55: Padmanabhan Seshaiyer<\/strong>, George Mason University <\/p>\n\n\n\n Title<\/strong>: Transforming Institutional Practices through Data-driven Instructional Approaches in the age of AI <\/p>\n\n\n\n Abstract<\/strong>: This presentation explores how data-driven instructional approaches are reshaping institutional practices in the age of artificial intelligence. Participants will examine the role of learning analytics, AI-enabled technologies, and evidence-based pedagogical strategies in enhancing student engagement, personalizing learning experiences, and strengthening instructional decision-making across disciplines. The talk emphasizes the intentional integration of problem-solving competencies and durable skills into interdisciplinary curricula to better prepare students for complex, real-world challenges. In addition, the presentation highlights pathways for students and faculty to participate in funded research, internships, and industry partnerships that advance data-informed teaching practices and align educational outcomes with evolving workforce needs. <\/p>\n\n\n\n \u25cf 10:55 \u2013 11:15: Rachel Levy<\/strong>, NC State University Data Science and AI Academy <\/p>\n\n\n\n Title<\/strong>: The All- Campus Data Science and AI Project based teaching and learning (ADAPT) model <\/p>\n\n\n\n Abstract<\/strong>: The NC State University Data Science and AI Academy ADAPT model has provided data science editor to students from all 12 colleges and over 150 different majors in a large public university. In this session we will discuss how the model can be modified for different learning contexts. Including K-12, professional development, and different courses. <\/p>\n\n\n\n \u25cf 11:15 \u2013 11:30: Shared Discussion Time <\/p>\n\n\n\n Track B <\/strong>(Shan B442): Strategies of Grass Roots Data Science<\/strong>: Examples of the Indigenous Perspective Praxis using Real Time Case Studies in Geographic Information Systems, Education, and Artificial Intelligence Ethics <\/p>\n\n\n\n Facilitator<\/strong>: Joseph Robertson, Mato Ohitika Analytics LLC <\/p>\n\n\n\n Abstract<\/strong>: This breakout session will be a student-centric forum to discuss data science strategies of grass roots organizing, developing workflows, and examining artificial intelligence in education. Students will examine several examples of real time data science workflows and explore Dr. Robertson\u2019s data sovereignty initiative framework to discuss and design culturally intrinsic examples of community engagement. <\/p>\n\n\n\n 11:45 a.m.\u20131:15 p.m. (Platt Greenroom) Lunch <\/strong><\/p>\n\n\n\n 1:30\u20132:30 p.m. (Shan 1430) Keynote #3: Nathan Alexander<\/strong>, PhD, assistant professor of curriculum and instruction, Howard University School of Education and Center for Applied Data Science and Analytics <\/p>\n\n\n\n Title<\/strong>: How much data <\/em>do you want for your progress? \u2014 Quantitative History and the Study of Social Problems <\/p>\n\n\n\n Abstract<\/strong>: In the early 1980s, it was James Baldwin \u2014 author and civil rights activist \u2014 who famously asked during an interview: \u201cHow much time <\/em>do you want…for your progress?\u201d The progress, or lack thereof, that Baldwin questioned had always been formulated as an issue of time, of waiting \u2014 a practice of slow-but-steady gains grounded in revisiting the myriads of society\u2019s social problems with fancy tools and new technologies. Given the expanding interest in data science and the rapid adoption of artificial intelligence (AI), this talk is prompted by Baldwin\u2019s quote and interdisciplinary research in quantitative history, ethnic studies, and the mathematical and data sciences. Specifically, I invite us to collectively consider the question: \u2018How much data <\/em>do you want for your progress?\u2019 in the study of social problems. <\/p>\n\n\n\n 2:45\u20133:45 p.m. (Shan 1430): Data Science for Health and Public Policy <\/strong><\/p>\n\n\n\n \u25cf 2:45 \u2013 3:05: Oluwatosin Babasola<\/strong>, University of Georgia <\/p>\n\n\n\n Title<\/strong>: A Phylogeny-Informed Mathematical Modeling of H5N1 Transmission Dynamics and Effectiveness of Control Measures <\/p>\n\n\n\n Abstract<\/strong>: The highly pathogenic avian influenza (HPAI) subtype H5N1 is a severe viral disease that continues to pose a significant threat to public health, and a rigorous understanding of its transmission dynamics across its major pathways is essential for developing effective control strategies. Phylogenetic analysis has suggested that H5N1 spillover occurs primarily between wild and domestic birds. However, increasing contact between these species and humans continues to increase the risk of zoonotic transmission. In this work, we developed a mathematical model to examine the transmission dynamics of H5N1 and evaluate the effectiveness of proposed control measures. The model employed a compartmental framework that included human, domestic, and wild bird populations. We then used this model to estimate the basic reproduction number for each population group and performed a sensitivity analysis to assess the contribution of parameters to the spread of the disease. Numerical simulations were also conducted to evaluate the impact of inter-species interactions on H5N1 infection in humans and to determine the effectiveness of different control measures. The results suggested that a vaccination strategy with high vaccine efficacy, combined with a vaccination rate above 50, significantly reduced the basic reproduction number. In addition, decreasing cross-species interactions led to a substantial reduction in disease transmission within the human population. Finally, an optimal control analysis indicated that a combined approach involving environmental sanitation and targeted culling of poultry was an effective strategy to control the outbreak and reduce the possibility of human spillover <\/p>\n\n\n\n \u25cf 3:05 \u2013 3:25: Manuchehr Aminian<\/strong>, Cal Poly Pomona <\/p>\n\n\n\n Title<\/strong>: Evaluating quantitative indicators for forecasting targeted mass killing events <\/p>\n\n\n\n Abstract<\/strong>: A simple, perhaps naive, goal for humanity is to stop genocides, targeted mass killings, and other atrocities from happening ever again. A step in this direction would require one to persuade policymakers when there are risks of such events, and to take appropriate action. Understanding that while qualitative work and conflict-specific expertise is an essential part of this toolkit, one should also explore if or how quantitative approaches and statistical\/machine learning may lend evidence towards evaluating definitions and producing risk scores. Toward these ends, we present ongoing work applying and analyzing data-scientific methods in two directions. The first direction involves a forecasting problem for targeted mass killing events and atrocities, identifying and analyzing predictive features, and producing risk scores. In our second direction, we propose a new set of guidelines for an operational definition of genocide. Subject matter experts in our group coded historical events, and we discuss the results: whether a quantitative model can recapitulate the subject matter experts’ coding to address whether the “actionable” part of such guidelines is indeed true. <\/p>\n\n\n\n \u25cf (CANCELLED) 3:25 \u2013 3:45: Ahmad Alkasir<\/strong>, Ellison Medical Institute\u00a0<\/p>\n\n\n\n Title<\/strong>: The role of real-world data in clinical evidence generation <\/p>\n\n\n\n Abstract<\/strong>: Real-world data (RWD)\u2014including electronic health records, claims, registries, and other routinely collected sources\u2014has the potential to transform clinical evidence generation beyond the boundaries of traditional clinical trials. As health systems, regulators, payers, and clinicians increasingly look to real-world evidence (RWE) to guide decisions, the central question is no longer whether RWD can be used, but under what conditions it can produce evidence that is reliable, timely, and fit for purpose. I will discuss the significance of RWD in creating clinical evidence outside of the traditional trial context. I’ll discuss the fundamental drawbacks that currently limit observational inference and continuous learning: missing or poorly formatted data, insufficient longitudinal follow-up across institutions and payers, and inconsistent capture of essential outcomes like death. I will then outline a set of policy and infrastructure priorities to strengthen RWD-based evidence generation, such as improving data quality at the point of care through aligned incentives, enabling nationwide exchange of longitudinal health information, expanding access to comprehensive claims and mortality data, and supporting linkages that move towards whole-person measurement. <\/p>\n\n\n\n 4\u20135 p.m. (Platt Green Room and Living Room) Student Presentation Session <\/strong><\/p>\n\n\n\n 5:15\u20136:30 p.m. (Shan Shakespeare theatre) Closing reception <\/strong><\/p>\n\n\n\n <\/p>\n\n\n\n <\/p>\n\n\n\n <\/p>\n\n\n\n <\/p>\n","protected":false},"excerpt":{"rendered":" Schedule At-a-glance Friday 11:30 am \u2013 2:00 pm. (Shan Lobby) Registration Noon\u20131:30 p.m (Platt Green Room). Connections Lunch (Registered students […]<\/p>\n","protected":false},"author":25,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"class_list":["post-2003","page","type-page","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/www.hmc.edu\/dssi\/wp-json\/wp\/v2\/pages\/2003","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.hmc.edu\/dssi\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/www.hmc.edu\/dssi\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/www.hmc.edu\/dssi\/wp-json\/wp\/v2\/users\/25"}],"replies":[{"embeddable":true,"href":"https:\/\/www.hmc.edu\/dssi\/wp-json\/wp\/v2\/comments?post=2003"}],"version-history":[{"count":14,"href":"https:\/\/www.hmc.edu\/dssi\/wp-json\/wp\/v2\/pages\/2003\/revisions"}],"predecessor-version":[{"id":2093,"href":"https:\/\/www.hmc.edu\/dssi\/wp-json\/wp\/v2\/pages\/2003\/revisions\/2093"}],"wp:attachment":[{"href":"https:\/\/www.hmc.edu\/dssi\/wp-json\/wp\/v2\/media?parent=2003"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
Panelists:<\/strong>
Ron Buckmire, Professor of Mathematics and Dean of the School of Computer Science and Mathematics, Marist University
Kathryn Leonard, Professor of Computer Science, Interim Dean of the College and Vice-President for Academic Affairs, Occidental College Rachel Levy, Professor of Mathematics and Executive Director of the Data Science and AI Academy, North Carolina State University Padmanabhan Seshaiyer, Professor of Mathematics and (former) Associate Dean for Academic Affairs, George Mason University<\/p>\n\n\n\nSaturday<\/h3>\n\n\n\n