Tiwahe

This blog focuses on my journey with Tribal allies whom I first met at the ESIIL Innovation Summit in May 2023. At that time, I joined the Maka Sitomniya Working Group, which was eventually funded as one of the ESIIL Working Groups for in-person and virtual meetings over the next few years. I thought that I could share my wisdom and offer advice as a retired faculty–Professor Emeritus no less–with decades of experience in collaboration, working with data, and coding. Yes, there is that, but … I have come to realize that I have much to learn, and that listening is better than advising. While I sort of knew this intellectually, it is hard to let go of the ego trip of being an expert. Members of our working group “family” have helped me discover that I am a beginner, tiwahe, an innocent in so many ways.

Data Evolve

The noun data implies it is somehow static. However, data evolve in multiple ways. I started thinking about how data evolve in our lives as a verb when I met Jhon Goes-in-Center at the Exploring Data Sovereignty Workshop in Feb 2024. He was musing on how difficult it is to understand the noun data when his Lakota language is woven with verbs. Later, when we were looking out at the huge cottonwood trees surrounding the hogan where we were meeting, he talked about how everything is related by evolution–how these very trees have evolved to be successful in this seemingly dry land just a short walk from the Rio Grande River.

Jaron Lanier–There is no AI

Quotes below are from Jaron Lanier’s article There Is No AI (The New Yorker 20 Apr 2023) and an UnHerd interview by Flo Read: Jaron Lanier: How humanity can defeat AI (YouTube 2023).

Data Sovereignty

I spent a week (24-26 May 2023) in Stat 678, Introduction to Statistical Consulting, having students think and write about data sovereignty as it relates to their Statistics masters option degree programs in Statistics and Data Science or Applied Statistics.

Useful Data Science Quotes

Statistics is the mathematical language of scientific experimentation. It embodies the framing of key questions, design of experiments, analysis of data, and interpretation of results. As such, there are many quotations that have come along over the years. This is my brief attempt to compile some quotes that capture the flavor of the science of statistics.

What is Data Science?

Data includes numbers, text, images, graphs, sounds, code, and metadata. Data literacy is the ability to read, work with, analyze and communicate with data. Data science is the study, development, or application of methods that reveal new insights from data.

Data Sciences Collaboratory

This was an idea for a collaborative environment for data science activity. It has been successful at various other institutions, including Stanford, Columbia and U VA. See the Harvard Data Science Review of Columbia’s Collaboratory.

My Diversity Story

This account is inspired by my 2015-16 role chairing the search committee for the UW-Madison Vice Provost for Diversity / Chief Diversity Officer. As the only white male on the committee, and as a professor and department chair in a white male dominated institution and academia, I have certain historic privileges, special knowledge, open doors and connections. I also have responsibility to lead this search with inclusion. It seemed useful, therefore, to contemplate the long arc of diversity in my life, and to share this story with the search committe and with others who are curious about my leadership grounding. Later I realized that my white privilege led me down this reveal, and that other members of the committee were not interested in sharing their stories.

Data Science Across the Liberal Arts

Data science is the study of the generalizable extraction of knowledge from data, and the liberal arts are those subjects or skills considered essential for a citizen to know in order to take an active part in civic life. Today, liberal arts education requires training in data science, and data science is intimately tied with skills across the liberal arts landscape. UW-Madison needs to meet the challenge of data science across the liberal arts, and the Sloan Foundation can be a key player in this transformation. Put simply, we need to train a new generation that can tell meaningful stories with data.

Diversity, Food Security and Learning Security

Food security is now a well established term, referring to both the availability of food in a community as well as engagement in the process of creating, selling and consuming food. Food security began as an elite concept among whites, but in recent years has been adopted by a quite diverse, multi-ethnic community. This was evident at the 2011 Community Food Security Conference in Oakland, CA (https://foodsecurity.org), which was attended by my wife, Sharon Lezberg, who works in this area. Food security now goes well beyond food itself to embrace the fabric of communities.

Strategic Planning–Data, Models and Statistics

We now live in an information age with access to huge amounts of data in our daily lives through IT advances, but with great uncertainty about what these data actually mean. These data arise from business, manufacturing, finance, science, medicine, engineering, political science and a great many other fields. Every UW student must be quantitatively literate to survive and thrive in their future careers, regardless of area. With few exceptions, to advance and remain competitive campus researchers must have the skills and tools to analyze and interpret the massive data streams that permeate the contemporary research world. Training in statistical methods, and collaboration with statisticians, is central to this vital part of the University of Wisconsin-Madison mission.

Past is Prologue

The significance of this date is my official arrival at UW-Madison. Posts here may be added in retrospect to reflect when the material was created. This site was built based on https://github.com/barryclark/jekyll-now. The quote on my home page came from Jason Tinant.