Past events

Data science seminars hosted by the The Institute for Mathematics and Its Applications (IMA) take place on Tuesdays from 1:25 p.m. - 2:25 p.m.

This week, Matthew Hirn (Michigan State University), will be giving the lecture.

View the full list of IMA data science seminars.

Data science seminars hosted by the The Institute for Mathematics and Its Applications (IMA) take place on Tuesdays from 1:25 p.m. - 2:25 p.m.

This week, Xiuyuan Cheng (Duke University), will be giving the lecture.

View the full list of IMA data science seminars.

Data science seminars hosted by the The Institute for Mathematics and Its Applications (IMA) take place on Tuesdays from 1:25 p.m. - 2:25 p.m.

This week, Gal Mishne (University of California, San Diego), will be giving the lecture.

View the full list of IMA data science seminars.

The University of Minnesota Data Science in Multi-Messenger Astrophysics program is organizing the second annual Women in Data Science Symposium. Due to the ongoing Covid-19 pandemic, the symposium this year will take the form of a virtual panel discussion. The panelists will be four women data science leaders in academia, research, and industry. The event will be moderated by two graduate student DSMMA trainees. 

Join them to hear about the multifaceted career opportunities that await you!  Listen as successful women discuss their career paths and challenges they have faced. Exchange ideas, discover new opportunities and learn about the future of the field. 

Please sign up for the Women in Data Science Workshop scheduled for Saturday, November 21 from 10:00 a.m.-12:30 p.m. For more details about the program, visit the Women in Data Science Symposium webpage.

Grad students! Please join us for the Computer Science & Engineering Graduate Town Hall on Friday, November 20 from 1:30 - 2:30 p.m. Central.

The intention is to give you a chance to ask questions, provide feedback, and interact directly with the department leadership regarding your graduate experience. You can join the meeting via this Zoom link.

You can use the link below to RSVP as well as provide feedback regarding your experiences in your courses and with the department as a Computer Science or Data Science graduate student. Please note that you can remain anonymous to provide feedback: z.umn.edu/csgrad_townhall

We look forward to reading your comments and seeing you at the Graduate Town Hall!

Prospective students can RSVP for an information session to learn about the following graduate programs:

• Computer Science M.S.
• Computer Science MCS
• Computer Science Ph.D.
• Data Science M.S.
• Data Science Post-Baccalaureate Certificate

During the information session, we will go over the following:

• Requirements (general)
• Applying
• Prerequisite requirements
• What makes a strong applicant
• Funding
• Resources
• Common questions
• Questions from attendees

Data science seminars hosted by the The Institute for Mathematics and Its Applications (IMA) take place on Tuesdays from 1:25 p.m. - 2:25 p.m.

This week, Alexander Cloninger (University of California, San Diego), will be giving a lecture titled "Fast Statistical and Geometric Distances Between Families of Distributions".

Registration is required to access the Zoom webinar.

Abstract

Detecting differences and building classifiers between a family of distributions, given only finite samples, has had renewed interest due to data science applications in high dimensions.   Applications include survey response effects, topic modeling, and various measurements of cell or gene populations per person.  Recent advances have focused on kernel Maximum Mean Discrepancy and Optimal Transport.  However, when the family of distributions are concentrated near a low dimensional structure, or when the family of distributions being considered is generated from a family of simple group actions, these algorithms fail to exploit the reduced complexity.  In this talk, we'll discuss the theoretical and computational advancements that can be made under these assumptions, and their connections to harmonic analysis, approximation theory, and group actions. Similarly, we'll use both techniques to develop methods of provably identifying not just how much the distributions deviate, but where these differences are concentrated. We'll also focus on applications in medicine, generative modeling, and supervised learning.

View the full list of IMA data science seminars.

The UMN Machine Learning Seminar Series brings together faculty, students, and local industrial partners who are interested in the theoretical, computational, and applied aspects of machine learning, to pose problems, exchange ideas, and foster collaborations. The talks are every Wednesday from 3:30 p.m. - 4:30 p.m. during the Fall 2020 semester.

This week's speaker, Edward McFowland III  (University of Minnesota Carlson School of Management) will be giving a talk about the intersection of information systems, machine learning, and public policy—include the development of computationally efficient algorithms for large-scale statistical machine learning and “big data” analytics.

Are you a student with an interest in data science? The University of Minnesota is hosting a new program in Data Science in Multi-Messenger Astrophysics, geared to prepare graduate (M.S. and Ph.D) students for careers in data science, in both academia and industry.

In addition to providing research opportunities at the frontier of astrophysics, this program also includes opportunities for developing professional skills, internships, outreach activities, and others.

If you would like to learn more about this program, please sign up for one of two information sessions:

• Tuesday, October 13 at 12:30 p.m. Central
• Tuesday, November 10 at 5:00 p.m. Central

Join us to hear about the multitude of career opportunities that await you!

Data science seminars hosted by the The Institute for Mathematics and Its Applications (IMA) take place on Tuesdays from 1:25 p.m. - 2:25 p.m.

This week, Naoki Saito (University of California, Davis), will be giving a talk called "Natural Graph Wavelet Packets".

Registration is required to access the Zoom webinar.

Abstract

I will discuss how to build a smooth multiscale wavelet packet dictionary for graph signal processing. Our approach utilizes the dual geometry of an input graph organized by new non-trivial eigenvector distances. More precisely, we construct a dual graph where each node represents a Laplacian eigenvector of the input graph and each weight is an affinity measure between the corresponding pair of the graph Laplacian eigenvectors, which is typically the inverse of the non-trivial distance between them. Once such a dual graph is formed, we bipartition the dual graph and construct tree structured subspaces. Finally, we generate smooth localized wavelet packet vectors (and the expansion coefficients of an input graph signal) on each such subspace that corresponds to a collection of the graph Laplacian eigenvectors. This can be viewed as a graph version of the "Shannon" wavelet packet dictionary. Using the best-basis algorithm or its variants on this graph wavelet packet dictionary, one can select a graph orthonormal basis suitable for a given task such as efficient approximation, denoising, classification.

I will also demonstrate the effectiveness of our graph wavelet packet dictionary compared to some other graph bases (e.g., graph Haar basis, graph Walsh basis, etc.) using both synthetic and real datasets.

View the full list of IMA data science seminars.