Statistics: A Lighthouse in an Ocean of Data
Dr. Tianxi Li, a new assistant professor at the School of Statistics, explores the unknown through an inquisitive statistical lens. His current work utilizes machine learning as a way to analyze interactions within complex systems, from biological functions to global networks.
Alongside his research, Li is teaching a course on machine learning this semester. Students will receive plenty of opportunities to use analytical & critical thinking, while gaining a good sense of and interpretation of data. Ultimately, Li’s course aims to provide students with a “deep understanding of uncertainty in data analysis.”
What brought you to the University of Minnesota?
The University's robust research environment and its emphasis on innovative methodologies presented an unparalleled platform to not only share my expertise but also collaboratively explore the frontiers of my domain knowledge, statistics. The School of Statistics has a stellar reputation for pioneering cutting-edge statistical research. I know I will be surrounded by a cadre of esteemed peers and bright students, assuring me of a vibrant academic ecosystem where ideas can be exchanged, tested, and refined.
Furthermore, UMN and CLA's dedication to multidisciplinary collaboration means that my research can have real-world applications and impact, ensuring that I contribute significantly to both the academic community and society at large.
What are your areas of specialty? How did you become interested in what you study and teach?
My expertise lies in statistics. Originally, during my college years in China, I planned to take mathematics as my major with aspirations of carving a career in finance. However, a pivotal moment came during one summer when I enrolled in a course on applied statistics and data analysis, instructed by Prof. Jay Emerson from Yale's Department of Statistics. It was then that I became captivated by statistics, recognizing it as a powerful tool to extract profound insights from data and aid individuals across various disciplines. That experience served as my gateway into the realm of statistics.
What questions and ideas are you most interested in exploring right now? What problems does your work seek to address?
My main research interest is statistical machine learning in complex networks. When we talk about complex systems, we're referencing systems where individual entities interact in fascinating ways. Think of the diverse ecosystems around us, the intricate dynamics of the global economy, the web of international relations, the marvel of biological systems, the vastness of the internet, and the rich tapestry of human society—these are all examples that surround us daily.
Diving deep into understanding the interaction mechanisms within these systems is a riveting challenge spanning countless fields. What's absolutely mind-blowing is that the collective behaviors of these systems can't be fully grasped just by looking at individual components. Given the layers of complexity and the inevitable noise in data, it's no surprise that these systems have emerged as prime subjects for statistical inquiry.
My current adventure revolves around deciphering interaction mechanisms based on network structures and other keen observations. For instance, have you ever wondered how researchers collaborate or how the tapestry of collaboration changes across different disciplines? And let's not stop there! We're keen to unearth those elusive social factors influencing hiring dynamics in academia—what's the hidden story behind hiring data among academic institutions? Plus, we're diving deep into understanding how our living environments directly influence co-functioning patterns in our brains.
What courses are you currently teaching or looking forward to teaching soon? What's special about them?
I am excited to announce that I will be teaching STAT 4501: Statistical Machine Learning I this upcoming fall semester. This course is tailored for undergraduates and dives deep into the foundational statistical concepts of machine learning methodologies.
While machine learning courses in engineering touch more on the applications, we will put more focus on the statistical principles to handle data uncertainty and randomness. We will journey through many topics, starting with the foundational linear regression and culminating in the cutting-edge realm of deep learning.
What are you most excited about right now?
I am particularly thrilled about the rise of statistics and data science in the modern era. As we find ourselves submerged in an ocean of data, these disciplines emerge as the lighthouses, guiding us through complicated problems. They are not just about number-crunching; they represent a new language of understanding vast streams of data and transforming them into actionable insights. This trend is reshaping our world and redefining our approach to challenges. As we stand on the cusp of this data-driven revolution, the promise is clear: a future where decisions are informed by data.
This story was edited by an undergraduate student in CLA.