Lecture Series

The Lecture Series will bring Data Science speakers to UCSC and CSUMB. These lectures will be open to the public and aim to increase the awareness of Data Science programs, research, pedagogy, and implementations among our community.

25-26 Series

Robert Gould - UCLA

Robert Gould is a Teaching Professor and Vice Chair of the Department of Statistics at UCLA, where he has been active in statistics education and data science education since 1994. As lead principal investigator of the Mobilize project, he is the architect of the Mobilize Introduction to Data Science course, a year-long high school course. Robert is the founder of DataFest, a 48-hour undergraduate data analysis competition sponsored by the American Statistical Association and held at 42 sites around the world. He is a Fellow of the American Statistical Association and in 2019 was awarded the CAUSE Lifetime Achievement Award for Statistics Education and the American Statistical Association Waller Distinguished Teaching Career Award. He is co-author of an introductory statistics textbook and serves as director of the Center for Teaching Statistics at UCLA.

Public Lecture, October 29th, 2025

Exploring Data Science in the High School Curriculum

Dr. Robert Gould, UCLA professor and national leader in K–12 data science education, explores the transformative potential of data science in high school mathematics. In this presentation, Dr. Gould discusses how data science can serve as a dynamic and viable 4th-year math option that prepares students for the careers of tomorrow. He addresses both the "how" and the "why" behind integrating data science into the curriculum, highlighting how this growing field supports equity, engagement, and real-world readiness for all students. The talk covers the core components of data science education—including data collection, visualization creation, and basic programming—and provides practical approaches to implementing these concepts in schools. This session is designed for teachers, school counselors, and administrators interested in transforming student learning through data science.

Gina-Maria Pomann - Duke

Gina-Maria Pomann is Associate Professor of Biostatistics and Bioinformatics and Director of the Biostatistics, Epidemiology, and Research Design (BERD) Methods Core at Duke University School of Medicine. She also serves on the Duke AI Health Faculty Council and as Associate Professor at the Duke-NUS Centre for Quantitative Medicine in Singapore. Gina-Maria is Co-Principal Investigator on multiple NIH-funded training programs, including the Quantitative Methods for HIV/AIDS Research Program and the Quantitative Team Science (QuanTS) Program, which provides open-access educational videos and a 7-month training program for quantitative scientists to improve their collaboration skills. Her research focuses on developing models for quantitative collaboration in academic medical centers, training the clinical and translational science workforce, and statistical methods including functional data analysis and predictive modeling. She is a co-founder of the biostats4you educational website and contributes to numerous workforce development programs, including the Duke AI Health Fellowship Program.

Public Lecture, September 15th, 2025

Integrating Team Science Skills to Elevate Data Science Education Outcomes

Preparing students for careers that use quantitative sciences (data science, statistics,epidemiology, computational mathematics, informatics, etc.) requires developing morethan just their technical skills. Professionals who use quantitative methods must oftencollaborate across disciplines, communicate effectively, and make sure analytic resultscan be reliably reproduced. To equip students for data driven careers in academia,industry, and government, educational approaches must integrate specialized trainingfor collaboration skills. Notably, a recent national survey of practicing biostatisticiansfound that many of these essential skills are often acquired informally on the job ratherthan through formal graduate training. To address this gap, the National Institute ofGeneral Medical Sciences is supporting a partnership between Duke University and theUniversity of Kentucky to develop the Quantitative Team Science (QuanTS) Program.The QuanTS Program provides online learning modules, structured mentor guides, andspecialized hands-on exercises to help students cultivate nine key skills identified ascritical for workforce readiness. Intentionally embedding collaborative skill developmentwithin educational curricula and workforce training not only enhances individual careerreadiness but also improves the quality and impact of collaborative biomedical research.This presentation will offer resources for educators to incorporate collaborative skillstraining into quantitative science curricula, focusing on shared experiences andidentifying opportunities for future advancements.

24-25 Series

Valerie Carr - SJSU Carr Lab Investigating Memory and the Brain

Valerie Carr earned a BS in Biological Psychology from the College of William and Mary and a PhD in Neuroscience from UCLA. As a PhD student and later as a postdoctoral fellow at Stanford University, Valerie used structural and functional MRI to investigate the neural basis of episodic memory in both younger and older adults. As a Professor at SJSU, Valerie's goal is to leverage her cognitive neuroscience background to elucidate strategies for improving brain health, memory, and executive function across the lifespan. She teaches courses relating to neuroscience, learning and memory, and computer programming. Regarding the latter, she is actively involved in pedagogical research examining optimal strategies for teaching interdisciplinary computing.

Public Lecture, April 7th, 2025

Applied Programming Experiences (APEX): Embedding interdisciplinary computing modules into introductory biology and statistics courses

Interdisciplinary professionals with both domain and computing skills are in high demand in our increasingly digital workplace. Universities have begun offering interdisciplinary computing degrees to meet this demand, but many community college students are not provided learning experiences that foster their self-efficacy in pursuing them. The Applied Programming Experiences (APEX) program aims to address this issue by embedding computing modules into introductory biology and statistics courses at community colleges. In this talk, I will describe how we developed these modules, train faculty to use them, provide continuing professional development for faculty adopting the modules, and how we assess students. Ultimately, we aim to encourage nationwide adoption of embedding computing into popular introductory community college and lower division courses.

Jacob Bien - USC Marshall School of Business

Jacob Bien is Professor of Data Sciences and Operations (Statistics group) at the Marshall School of Business, University of Southern California.

Public Lecture, February 10th, 2025

Generative AI for Data Science 101: Coding Without Learning To Codeinterdisciplinary classes across campus.

Should one teach coding in a required introductory statistics and data science class for non-major students? Many professors advise against it, considering it a distraction from the important and challenging statistical topics that need to be covered. By contrast, other professors argue that the ability to interact flexibly with data will inspire students with a lasting love of the subject and a continued commitment to the material beyond the introductory course. With the release of large language models that write code, we saw an opportunity for a middle ground, which we tried in Fall 2023 in a required introductory data science course in our school's full-time MBA program. We taught students how to write English prompts to the artificial intelligence tool Github Copilot that could be turned into R code and executed. In this talk, we report on our experience using this new approach.

23-24 Series

Eric Van Dusen - UC Berkeley

Eric Van Dusen is Outreach and Tech Lead and Lecturer at the College of Computing, Data Science, and Society at UC Berkeley.

Public Lecture, April 29th, 2024

Data Science Modules, how UC Berkeley has built interactive computing into interdisciplinary classes across campus.

This talk will illustrate the world of Data Science Modules and how UC Berkeley has seamlessly integrated interactive computing into classes across the entire campus. The core of this initiative revolves around leveraging the power of data science to enhance educational experiences, making it more hands-on and applicable to real-world scenarios. UC Berkeley's approach involves embedding short, flexible modules into existing courses across various disciplines. This method allows students from diverse academic backgrounds to gain practical skills in data analysis, visualization, and computational thinking without the need for a full data science course. Ubiquitous access to interactive computing, through Jupyter notebooks, enables students to execute code, analyze data, and visualize results all in one place. This interactive platform not only makes learning more engaging but also directly applicable to solving complex problems in fields ranging from social sciences to natural sciences and beyond. By incorporating these modules , UC Berkeley is not just teaching students how to use data science tools; it's empowering them to apply these tools to their areas of study. This interdisciplinary approach demystifies data science, making it accessible and valuable to all students, regardless of their major.

Teaching Seminar, April 30th, 2024

Teaching with the Data Science Stack

The talk explores the transformative potential of Jupyter notebook-based instruction, focusing on the integration of a set of open source tools used to teach at scale in dozens of classes at UC Berkeley. At the infrastructure level, the campus JupyterHub facilitates universal access to both Notebook and Lab environments, and an RStudio environment. This allows for a more dynamic and interactive teaching and learning experience, making it easier for students and educators to easily engage with interactive computing in any class . Additionally, the talk highlights the utility of nbgitpuller in simplifying the distribution of materials by seamlessly pulling content from Git repositories into Jupyter environments. Teaching at scale also involves the use of automated grading systems, with a focus on tools like Otter Grader and Gradescope. . This talk aims to showcase the myriad ways in which notebook-based tools and complementary technologies can revolutionize the educational landscape, particularly in STEM fields.

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