Distinguished Lecturer Series

 

We are pleased to announce that our Distinguished Lecturer in Mathematics and Statistics for the 2021-22 academic year, Dr. Lauren Klein of Emory University, will be presenting on Zoom two distinguished lectures: “Data Feminism in Action” on Wednesday, March 2, from 3-4 p.m. and “Data Feminism” on Thursday, March 3, from 12:45-1:45 p.m.


Dr. Lauren Klein is Winship Distinguished Research Professor and Associate Professor in the departments of English and Quantitative Theory & Methods at Emory University. Klein works at the intersection of digital humanities, data science, and early American literature, with a focus on issues of gender and race. She is the author of “An Archive of Taste: Race and Eating in the Early United States” (2020) and, with Catherine D’Ignazio, “Data Feminism” (2020). With Matthew K. Gold, she edits “Debates in the Digital Humanities,” a hybrid print-digital publication stream that explores debates in the field as they emerge.

 

Talk #1: Data Feminism in Action (Virtual)

Date: Wednesday, March 2

Time: 3:00 - 4:00 pm

In-Person Viewing Location: SLC 120

Zoom Link: https://minnstate.zoom.us/j/91571903912

Abstract: What is feminist data science? How is feminist thinking being incorporated into data-driven work? How are scholars in the humanities and social sciences bringing together data science and feminist theory in their research? Drawing from her recent book, “Data Feminism,“ coauthored with Catherine D’Ignazio, Klein will present a set of principles for doing data science that are informed by the past several decades of intersectional feminist activism and critical thought. To illustrate these principles, as well as some of the ways that scholars and designers have begun to put them into action, she will discuss a range of recent research projects including several of her own: 1) a thematic analysis of a large corpus of nineteenth-century newspapers that reveals the invisible labor of women newspaper editors; 2) the development of a model of lexical semantic change that, when combined with network analysis, tells a new story about Black activism in the nineteenth-century United States; and 3) an interactive book on the history of data visualization that shows how questions of politics have been present in the field since its start. Taken together, these examples demonstrate how feminist thinking can be operationalized into more ethical, more intentional, and more capacious data practices. 

 

Talk #2: Data Feminism (Virtual)

Date: Thursday, March 3

Time: 12:45 – 1:45 pm

In-Person Viewing Location: Kryzsko Ballroom (registration required)

Zoom Link: https://minnstate.zoom.us/j/96504864291

Abstract: As data are increasingly mobilized in the service of governments and corporations, their unequal conditions of production, their asymmetrical methods of application, and their unequal effects on both individuals and groups have become increasingly difficult for data scientists to ignore. But it is precisely this power that makes it worth asking: Data science by whom? Data science for whom? Data science with whose interests in mind? These are some of the questions that emerge from what we call data feminism, a way of thinking about data science and its communication that is informed by the past several decades of intersectional feminist activism and critical thought. Illustrating data feminism in action, this talk will show how challenges to the male/female binary can help to challenge other hierarchical (and empirically wrong) classification systems; it will explain how an understanding of emotion can expand our ideas about effective data visualization; how the concept of invisible labor can expose the significant human efforts required by our automated systems; and why the data never, ever “speak for themselves.” The goal of this talk is to model how scholarship can be transformed into action: how feminist thinking can be operationalized in order to imagine more ethical and equitable data practices.

Departmental Seminar

Applying unsupervised and supervised learning methods to minimize risk to bald eagles from industrial wind turbines

Dr. Silas Bergen
Winona State University

Abstract:  In this talk, I describe a collaboration with research wildlife biologists and statisticians to analyze over 2 million data points collected from GPS telemetry devices attached to bald eagles. My research project involved two phases.  In the first phase, I applied unsupervised learning methods to identify distinct bald eagle behavioral flight modes using the flight data obtained from the GPS observations.  In the second phase, I applied supervised learning methods to classify behavior risks using environmental data to understand how bald eagle flight related to underlying land features and topography.   The intent of this project was to understand land types where bald eagles might be at greater risk of collision with industrial wind turbines to inform placement of wind farms.  This majority of the talk will be accessible to the general public; the entirety will be accessible to 2nd- or 3rd-year statistics/data science majors.

Wednesday, January 19th,

12:00- 12:50 PM

Science Laboratory Center/SLC 120


Math/Stat Club Study Night

 


Math Education Panel

12:00 - 12:50 PM, Wednesday, December 1, SLC 120

Refreshments served beforehand in SLC Atrium. 

Mathematics Teaching Panel  


Scott Halverson
Winona Sr. High School

Scott Mlynczak
Winona Sr. High School

Conager Mrozek
WSU Recent Alumnus




Student Internship Presentations

12:00 - 12:45 PM, Wednesday, Nov 17, SLC120

Fastenal Internship

Kailee Brower

I completed my internship at Fastenal working with the Business Data and Strategy Support group. Fastenal started out distributing fasteners, but today distributes many different manufacturing and industrial resources. During this internship, I learned about Power Query, M code, PeopleSoft queries, excel functions, data visualization techniques and data management. I also learned how a large business worked, how to communicate in a business setting, and time management skills. I continue to work at Fastenal and I’m excited to learn more and expand my knowledge in applications for data analysis.

       CROSS Service Internship

Kyle Maciej

My internship took place at a non-profit called CROSS. CROSS provides services to people who are less fortunate. CROSS collects a lot of data and currently has limited resources to look at all this data. The duties of my internship included helping to make sense of all the data that CROSS collected and help them figure out what to do moving forward. I cleaned data and created basic data visualizations so I could provide valuable information to CROSS. Overall, my internship was a good experience to apply what I had learned from the classroom into the real world and solve real world problems.