Past events

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, Amir Sagiv (Columbia University), will be giving a lecture titled "An Optimal Transport Perspective on Uncertainty Propagation".

Registration is required to access the Zoom webinar.

Abstract

In many scientific areas, a deterministic model (e.g., a differential equation) is equipped with parameters. In practice, these parameters might be uncertain or noisy, and so an honest model should provide a statistical description of the quantity of interest. Underlying this computational question is a fundamental one - If two "similar" functions push-forward the same measure, are the new resulting measures close, and if so, in what sense? I will first show how the probability density function (PDF) can be approximated, using spectral and local methods, and present applications to nonlinear optics. We will then discuss the limitations of PDF approximation, and present an alternative Wasserstein-distance formulation of this problem, which yields a much simpler theory.

Biography

Amir Sagiv is a Chu Assistant Professor of Applied Mathematics at Columbia University. Before that, Amir completed his Ph.D. in Applied Mathematics at Tel Aviv University.

View the full list of IMA data science seminars.

The computer science colloquium takes place on Mondays from 11:15 a.m. - 12:15 p.m.

This week's talk is part of the Cray Distinguished Speaker Series. This series was established in 1981 by an endowment from Cray Research and brings distinguished visitors to the Department of Computer Science & Engineering every year. 

This week's speaker is Miryung Kim from the University of California, Los Angeles.

Abstract

We are at an inflection point where software engineering meets the data-centric world of big data, machine learning, and artificial intelligence.   As software development gradually shifts to the development of data analytics with AI and ML technologies, existing software engineering techniques must be re-imagined to provide the productivity gains that developers desire. We conducted a large scale study of almost 800 professional data scientists in the software industry to investigate what a data scientist is, what data scientists do, and what challenges they face. This study has found that ensuring correctness is a huge problem in data analytics.

We argue for re-targeting software engineering research to address new challenges in the era of data-centric software development. We showcase a few examples of my group's research on debugging and testing of data-intensive applications: e.g., data provenance, symbolic-execution based test generation, and fuzz testing in Apache Spark. We then conclude with open problems in software engineering to meet the needs of AI and ML workforce.

Biography

Miryung Kim is a Full Professor in the Department of Computer Science at the University of California, Los Angeles. She is known for her research on code clones---code duplication detection, management, and removal solutions. Recently, she has taken a leadership role in defining the emerging area of software engineering for data science. She received her B.S. in Computer Science from Korea Advanced Institute of Science and Technology and her M.S. and Ph.D. in Computer Science and Engineering from the University of Washington.  She received various awards including an NSF CAREER award, Google Faculty Research Award, Okawa Foundation Research Award, and Alexander von Humboldt Foundation Fellowship. She was previously an assistant professor at the University of Texas at Austin and also spent time as a visiting researcher at Microsoft Research. She is the lead organizer of a Dagstuhl Seminar on SE4ML---Software Engineering for AI-ML based Systems. She is a Keynote Speaker at ASE 2019, a Program Co-Chair of ESEC/FSE 2022, and an Associate Editor of IEEE Transactions on Software Engineering.

Don't miss Jie Ding, data science graduate faculty member and assistant professor in the School of Statistics, for the first session of the Machine Learning Seminar Series.

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, Dimitris Giannakis (Courant Institute of Mathematical Sciences), will be giving a lecture titled "Quantum Compiler for Classical Dynamical Systems".

Registration is required to access the Zoom webinar.

Abstract

We present a framework for simulating a measure-preserving, ergodic dynamical system by a finite-dimensional quantum system amenable to implementation on a quantum computer. The framework is based on a quantum feature map for representing classical states by density operators (quantum states) on a reproducing kernel Hilbert space (RKHS), H, of functions on classical state space. Simultaneously, a mapping is employed from classical observables into self-adjoint operators on H such that quantum mechanical expectation values are consistent with pointwise function evaluation. Meanwhile, quantum states and observables on H evolve under the action of a unitary group of Koopman operators in a consistent manner with classical dynamical evolution. To achieve quantum parallelism, the state of the quantum system is projected onto a finite-rank density operator on a 2^N-dimensional tensor product Hilbert space associated with N qubits. In this talk, we describe this "quantum compiler" framework, and illustrate it with applications to low-dimensional dynamical systems.

Biography

Dimitris Giannakis is an Associate Professor of Mathematics at the Courant Institute of Mathematical Sciences, New York University. He is also affiliated with Courant's Center for Atmosphere Ocean Science (CAOS). He received BA and MSci degrees in Natural Sciences from the University of Cambridge in 2001, and a PhD degree in Physics from the University of Chicago in 2009. Giannakis' current research focus is at the interface between operator-theoretic techniques for dynamical systems and machine learning. His recent work includes the development of techniques for coherent pattern extraction, statistical forecasting, and data assimilation based on data-driven approximations of Koopman operators of dynamical systems. He has worked on applications of these tools to atmosphere ocean science, fluid dynamics, and molecular dynamics.

View the full list of IMA data science seminars.

Welcome back! The spring 2021 semester begins on Tuesday, January 19.

View the full academic schedule on One Stop.

The University of Minnesota will be closed in observance of Martin Luther King, Jr. Day.

View the full schedule of University holidays.

Data matters! That’s the theme for this year’s UMN Day of Data, which explores the ways that data is used to address local and global issues including racial justice, epidemiology/health, climate, privacy and more. The event is open to all students, faculty, staff, and alumni from all University of Minnesota campuses, and attendance is free. Participants are invited to attend as many or few sessions as you like. All sessions will take place virtually.

Data science graduate faculty member Professor Ansu Chatterjee will give a presentation on data and climate change, beginning at 10:00 a.m. on Thursday, January 14. Don't miss out!

Wednesday, January 13 - Friday, January 15
Morning session: 10:00 a.m. - 12:00 p.m.
Afternoon session: 1:00 p.m. - 3:00 p.m.

More information and registration: z.umn.edu/dayofdata

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, Stefano Martiniani  (University of Minnesota Department of Chemical Engineering and Materials Science) will be giving a talk about design of novel theoretical and computational frameworks to address open problems in science and engineering. His approaches draw primarily from statistical physics, dynamical systems, and machine learning.

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, Barak Sober (Duke University), will be giving the lecture.

View the full list of IMA data science seminars.