معرفی
Jeremy Teitelbaum is a Professor in the Department of Mathematics at the University of Connecticut within the College of Liberal Arts and Sciences. He serves as Director of UConn's interdisciplinary Masters Program in Data Science, a one-year professional degree program. His academic career spans both pure mathematics and data science applications.
Teitelbaum's research bridges classical algebraic number theory and modern machine learning. Initially focused on p-adic geometry, elliptic curves, modular forms, and p-adic L-functions, his work evolved significantly toward machine learning and data science. Current interests include bioinformatics, unsupervised learning (particularly clustering), and mathematical foundations of machine learning. He maintains active GitHub repositories documenting his computational work and lecture materials.
His publication trends reveal a transition from pure number theory (2000s) toward machine learning applications (2020s), with consistent mathematical rigor throughout. Keywords across his work include algebraic geometry, representation theory, p-adic analysis, and statistical learning theory, reflecting both his foundational expertise and contemporary applications.
Teitelbaum has held significant administrative roles including Dean of the College of Liberal Arts and Sciences (2008-2017) and interim Provost (2017-2018). He is a Certified Instructor for The Software Carpentry and develops extensive online educational materials, including complete video lecture series for Abstract Algebra and Transition to Higher Mathematics based on open-source textbooks.
His teaching portfolio includes graduate courses like Fundamentals of Data Science (Grad 5100) and Mathematics of Machine Learning (Math 3094), alongside core mathematics courses such as Abstract Algebra and Linear Algebra. He maintains specialized interests in mathematical visualization tools, including Bokeh library applications and linear algebra pedagogy tools.
