Past events

Industrial Problems Seminar: Certified Robustness against Adversarial Attacks in Image Classification

In collaboration with the Minnesota Center for Industrial Mathematics, the Industrial Problems Seminars are a forum for industrial researchers to offer a first-hand glimpse into industrial research. The seminars take place Fridays from 1:25 p.m. - 2:25 p.m.

This week's speaker, Fatemeh Sheikholeslami (Bosch Center for Artificial Intelligence), will be giving a talk titled "Certified Robustness against Adversarial Attacks in Image Classification."

Registration is required to access the Zoom webinar.

Abstract

Researchers have repeatedly shown that it is possible to craft adversarial attacks, i.e., small perturbations that significantly change the class label, on deep classifiers and considerably degrade their performance. This fragility can significantly hinder the deployment of deep learning-based methods in safety-critical applications. To address this, adversarial attacks can be defended against either by building robust classifiers or, by creating classifiers that can detect the presence of adversarial perturbations. I will talk about a couple of algorithms that we have developed at BCAI which provide certified defenses against different threat models.

Biography

Fatemeh Sheikholeslami received her PhD in Electrical Engineering from University of Minnesota in 2019, under the supervision of Professor Georgios Giannakis. She is currently a Machine Learning Research Scientist at Bosch Center for Artificial Intelligence with the Safe and Robust Deep Learning group.

UMN Machine Learning Seminar: Machine Learning for Sparse Nonlinear Modeling and Control

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 Thursday from 12 p.m. - 1 p.m. during the Fall 2021 semester.

This week's speaker, Steven L. Brunton (University of Washington), will be giving a talk titled "Machine Learning for Sparse Nonlinear Modeling and Control."

Abstract

This work describes how machine learning may be used to develop accurate and efficient nonlinear dynamical systems models for complex natural and engineered systems. We explore the sparse identification of nonlinear dynamics (SINDy) algorithm, which identifies a minimal dynamical system model that balances model complexity with accuracy, avoiding overfitting. This approach tends to promote models that are interpretable and generalizable, capturing the essential “physics” of the system. We also discuss the importance of learning effective coordinate systems in which the dynamics may be expected to be sparse. This sparse modeling approach will be demonstrated on a range of challenging modeling problems in fluid dynamics, and we will discuss how to incorporate these models into existing model-based control efforts.

Biography

Steven L. Brunton is a Professor of Mechanical Engineering at the University of Washington. He is also Adjunct Professor of Applied Mathematics and Computer science, and a Data Science Fellow at the eScience Institute. Steve received the B.S. in mathematics from Caltech in 2006 and the Ph.D. in mechanical and aerospace engineering from Princeton in 2012. His research combines machine learning with dynamical systems to model and control systems in fluid dynamics, biolocomotion, optics, energy systems, and manufacturing. He is a co-author of three textbooks, received the University of Washington College of Engineering junior faculty and teaching awards, the Army and Air Force Young Investigator Program (YIP) awards, and the Presidential Early Career Award for Scientists and Engineers (PECASE).

Last day to cancel full semester classes without college approval and receive a "W"

The last day to cancel full semester classes without college approval and receive a "W" is Monday, November 15.

View the full academic schedule on One Stop.
 

Industrial Problems Seminar: Lessons Learned in Deploying AI in Manufacturing

In collaboration with the Minnesota Center for Industrial Mathematics, the Industrial Problems Seminars are a forum for industrial researchers to offer a first-hand glimpse into industrial research. The seminars take place Fridays from 1:25 p.m. - 2:25 p.m.

This week's speaker, Eric Wespi (Boston Scientific), will be giving a talk titled "Lessons Learned in Deploying AI in Manufacturing."

You may attend the talk either in person in Walter 402 or registering via Zoom.

Abstract

Implementing AI models in a manufacturing environment can present several challenges. In this session we will discuss both technical and cultural considerations for the deployment of AI-based machine vision in a regulated industry. Topics include supporting data architecture, messaging to senior leadership, addressing uncertainty about black-box models, make/buy decisions, and talent acquisition and retention.

Biography

Eric Wespi is a Data Science Fellow at Boston Scientific. He manages a Data Science team within the Process Development organization and leads efforts to implement AI-based computer vision in manufacturing facilities globally. Eric has worked at Boston Scientific for 6 years, prior to which he held various engineering roles at Intel. He has a bachelor’s degree in Chemical Engineering from the University of Minnesota and an MBA from Arizona State University. In his spare time Eric enjoys spending time with his family, cooking, and various other outdoor activities.

UMN Machine Learning Seminar: Diametrical Risk Minimization - Theory and Computations

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 Thursday from 12 p.m. - 1 p.m. during the Fall 2021 semester.

This week's speaker, Johannes Royset (Naval Postgraduate School), will be giving a talk titled "Diametrical Risk Minimization: Theory and Computations."

Abstract

The theoretical and empirical performance of Empirical Risk Minimization (ERM) often suffers when loss functions are poorly behaved with large Lipschitz moduli and spurious sharp minimizers. We propose and analyze a counterpart to ERM called Diametrical Risk Minimization (DRM), which accounts for worst-case empirical risks within neighborhoods in parameter space. DRM has generalization bounds that are independent of Lipschitz moduli for convex as well as nonconvex problems and it can be implemented using a practical algorithm based on stochastic gradient descent. Numerical results illustrate the ability of DRM to find quality solutions with low generalization error in sharp empirical risk landscapes from benchmark neural network classification problems with corrupted labels.

Biography

Dr. Johannes O. Royset is Professor of Operations Research at the Naval Postgraduate School. Dr. Royset's research focuses on formulating and solving stochastic and deterministic optimization problems arising in data analytics, sensor management, and reliability engineering. He was awarded a National Research Council postdoctoral fellowship in 2003, a Young Investigator Award from the Air Force Office of Scientific Research in 2007, and the Barchi Prize as well as the MOR Journal Award from the Military Operations Research Society in 2009. He received the Carl E. and Jessie W. Menneken Faculty Award for Excellence in Scientific Research in 2010 and the Goodeve Medal from the Operational Research Society in 2019. Dr. Royset was a plenary speaker at the International Conference on Stochastic Programming in 2016 and at the SIAM Conference on Uncertainty Quantification in 2018. He has a Doctor of Philosophy degree from the University of California at Berkeley (2002). Dr. Royset has been an associate or guest editor of SIAM Journal on Optimization, Operations Research, Mathematical Programming, Journal of Optimization Theory and Applications, Naval Research Logistics, Journal of Convex Analysis, Set-Valued and Variational Analysis, and Computational Optimization and Applications. He is the author of about 100 papers and two books.

IMA Data Science Seminar: Non-Parametric Estimation of Manifolds from Noisy Data

The Institute for Mathematics and Its Applications (IMA) Data Science Seminars are a forum for data scientists of IMA academic and industrial partners to discuss and learn about recent developments in the broad area of data science. The seminars take place on Tuesdays from 1:25 p.m. - 2:25 p.m.

This week's speaker, Yariv Aizenbud (Yale University), will be giving a talk titled "Non-Parametric Estimation of Manifolds from Noisy Data."

You may attend the talk either in person in Walter 402 or registering via Zoom.

Abstract

A common task in many data-driven applications is to find a low dimensional manifold that describes the data accurately. Estimating a manifold from noisy samples has proven to be a challenging task. Indeed, even after decades of research, there is no (computationally tractable) algorithm that accurately estimates a manifold from noisy samples with a constant level of noise.

In this talk, we will present a method that estimates a manifold and its tangent in the ambient space. Moreover, we establish rigorous convergence rates, which are essentially as good as existing convergence rates for function estimation.

This is a joint work with Barak Sober.

Biography

Yariv Aizenbud is a Gibbs assistant professor of applied mathematics at Yale University. Previously, he completed his Ph.D. at Tel-Aviv University. His research is focused on statistical recovery of geometric structures. from data. The applications for his research include computational biology, manifold learning, and numerical linear algebra.

Spring semester registration begins

Spring semester registration begins for students admitted to degree or certificate programs on Tuesday, November 9.

View the full academic schedule on One Stop.
 

Industrial Problems Seminar: Data Science @ Instacart

In collaboration with the Minnesota Center for Industrial Mathematics, the Industrial Problems Seminars are a forum for industrial researchers to offer a first-hand glimpse into industrial research. The seminars take place Fridays from 1:25 p.m. - 2:25 p.m.

This week's speaker, Jeffrey Moulton (Instacart), will be giving a talk titled "Data Science @ Instacart."

You may attend the talk either in person in Walter 402 or registering via Zoom.

Abstract

Jeff will talk about what it's like to work as a data scientist in tech and go over a couple examples of the types of problems that arise in digital advertising.

UMN Machine Learning Seminar: Deep Graph Learning for Drug Property Prediction

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 Thursday from 12 p.m. - 1 p.m. during the Fall 2021 semester.

This week's speaker, Junzhou Huang (University of Texas at Arlington), will be giving a talk titled "Deep Graph Learning for Drug Property Prediction."

Abstract

Graphs are powerful mathematical structures to describe relations or interactions among objects in different fields, such as biology, social science, economics and so on. Recent technological innovations are enabling scientists to capture enormous graph-structured data at increasing speed and scale. Thus, a compelling need exists to develop novel learning tools to foster and fuel the next generation of scientific discovery in graph data related research. However, the major computational challenges are due to the unprecedented scale and complexity of complex graph data analytics. There is a critical need for large-scale learning strategies with theoretical guarantees to bridge the gap and facilitate knowledge discovery from complex graph data. This talk will introduce our recent work on developing novel deep graph learning methods to efficiently and effectively process atom graph data for predicting the chemical or biological properties of drug molecules.

Biography

Dr. Junzhou Huang is a professor in the department of computer science and engineering at the University of Texas, Arlington. He received the Ph.D. degree in Computer Science at Rutgers, The State University of New Jersey. His major research interests include machine learning, computer vision, computational pathology, computational drug discovery and clinical science. He was selected as one of the 10 emerging leaders in multimedia and signal processing by the IBM T.J. Watson Research Center in 2010. His work won the MICCAI Young Scientist Award 2010, the FIMH Best Paper Award 2011, the STMI Best Paper Award 2012, the MICCAI Best Student Paper Award 2015, the 1st place of the Tool Presence Detection Challenge at M2CAI 2016, the 6th place in the 3D Structure Prediction Challenge and the 1st place in the Contact and Distance Prediction Challenge at CASP14, 2020 and the Google TensorFlow Model Garden Award 2021. He received the NSF CAREER Award 2016.

Industrial Problems Seminar: Challenges in Building Intelligent Search Systems

In collaboration with the Minnesota Center for Industrial Mathematics, the Industrial Problems Seminars are a forum for industrial researchers to offer a first-hand glimpse into industrial research. The seminars take place Fridays from 1:25 p.m. - 2:25 p.m.

This week's speaker, Jiguang Shen (Microsoft Research), will be giving a talk titled "Challenges in Building Intelligent Search Systems."

Registration is required to access the Zoom webinar.

Abstract

Intelligent search, powered by natural language processing (NLP) algorithms, helps individuals and enterprise customers find useful information they need at an unprecedented scale. Compared to the traditional web search engines, there are a lot of new challenges in this rising popular domain. In this talk, I will talk about my experience working at the public web search engine Microsoft Bing and the latest work we have done at Microsoft Research & Incubation on building intelligent search systems over enterprise documents.

Biography

Jiguang Shen received his Ph.D. in Applied Mathematics from University of Minnesota in 2017, under the supervision of Professor Bernardo Cockburn. He is currently a Senior Applied Science Manager at Microsoft working on building search and ranking systems.