Past Events

Lessons Learned in Deploying AI in Manufacturing

Eric Wespi (Boston Scientific)

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.

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.

Non-Parametric Estimation of Manifolds from Noisy Data

Yariv Aizenbud (Yale University)

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.

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.

Data Science @ Instacart

Jeffrey Moulton (Instacart)

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.

2021 Field of Dreams Conference

Advisory: Register here to attend the Field of Dreams!



The Field of Dreams Conference introduces potential graduate students to graduate programs in the mathematical sciences at Alliance schools as well as professional opportunities in these fields. Scholars spend time with faculty mentors from the Alliance schools, get advice on graduate school applications, and attend seminars on graduate school preparation and expectations as well as career seminars.

Tentative List of Speakers

  • Ricardo Cortez (Tulane University) (confirmed)
  • Carrie Eaton (Bates College) (confirmed)
  • Leslie McClure (Drexel University) (confirmed)
  • Shannan Paul (NUTS, ltd) (confirmed)
Institute for Mathematics and its Applications and Math Alliance logos
Field of Dreams 2021 poster

CA+ conference


This mini-conference is hosted jointly by Iowa State, Minnesota, and Wisconsin and highlights work in commutative algebra and related fields like algebraic geometry, number theory, combinatorics, and more. We have colloquium-style talks targeted at a broad audience of people interested in algebra.

Challenges in Building Intelligent Search Systems

Jiguang Shen (Microsoft Research)

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. 

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.

Predicting Tomorrow: Industrial Forecasting at Scale

Jimmy Broomfield (Target Corporation)

Have you ever wondered how supply chains make decisions about purchasing and transport? Or perhaps you've stayed up at night wondering how energy companies plan for customer demand. Time series forecasting is a major component used to help business teams solve these problems. In this talk, I'll share my career journey in the world of industrial forecasting. We'll touch on the topics of data preparation, time series models, accuracy metrics, high level architecture, and compute/time constraints.

Jimmy graduated from the University of Minnesota in 2019 with a PhD in Math and joined Ecolab's advanced analytics team where he primarily worked in the field of time series analysis and forecasting.  During his time at Ecolab, Jimmy made contributions to the enterprise's time series classification framework by introducing novel wavelet and frequency based features.  He also served as a team lead with the responsibility for architecting, building, and validating a modern supply chain forecasting system for Ecolab's industrial chemical distribution.  Jimmy recently made a career transition to the demand forecasting team at Target where he hopes to continue his journey toward understanding industrial forecasting challenges and solutions.

Data depths meet Hamilton-Jacobi equations

Ryan Murray (North Carolina State University)

Widespread application of modern machine learning has increased the need for robust statistical algorithms. One fundamental geometric quantity in robust statistics is known as a data depth, which generalizes the notion of quantiles and medians to multiple dimensions. This talk will discuss recent work (in collaboration with Martin Molina-Fructuoso) which connects certain types of data depths with Hamilton-Jacobi equations, a first-order partial differential equation that is fundamental to control theory. Computational considerations, connections to convex geometry and a number of related open problems will all be discussed.

Ryan Murray received his PhD in mathematics from Carnegie Mellon University in 2016, and was a Chowla Assistant Professor at Penn State University from 2016-2019. Since 2019 he is an assistant professor at North Carolina State University, department of mathematics.

Data Scientists under attack!! Let's help them together

Sharath Dhamodaran (OptumLabs)


Imposter Syndrome intensifies each day with growing expectations of being a data scientist. You need to have strong quantitative and technical skills (mathematics, statistics, computer science, operations research, optimization, machine learning), business knowledge and consulting skills (problem formulation and framing), relationship and communication skills (advising, negotiating, and managing expectations), computing skills (general-purpose, statistical, mathematical, databases, business intelligence, big data, cloud), and the list goes on. No one can do it all. Data Scientists that do 70% of these are the best of the best. Let me save you some anxiety by sharing my ongoing journey navigating this challenging and rewarding career.

I lead a team of data scientists focused on creating healthcare machine learning products for our internal and external customers at OptumLabs, part of UnitedHealth Group. I have 8 years of professional experience solving interesting real-world problems using data science. Outside of work, I enjoy interacting with students and professionals and helping them transition to data science. I also compete in cricket tournaments in Minnesota. 

Organizational Collaboration with Assisted Learning

Jie Ding (University of Minnesota, Twin Cities)


Humans develop knowledge from individual studies and joint discussions with peers, even though each individual observes and thinks differently. Likewise, in many emerging application domains, collaborations among organizations or intelligent agents of heterogeneous nature (e.g., different institutes, commercial companies, and autonomous agents) are often essential to resolving challenging problems that are otherwise impossible to be dealt with by a single organization. However, to avoid leaking useful and possibly proprietary information, an organization typically enforces stringent security measures, significantly limiting such collaboration. This talk will introduce a new research direction named Assisted Learning that aims to enable organizations to assist each other in a decentralized, personalized, and private manner.

Jie Ding is an Assistant Professor in Statistics and a graduate faculty in ECE at the University of Minnesota. Before joining the University of Minnesota in 2018, he received a Ph.D. in Engineering Sciences in 2017 from Harvard University and worked as a post-doctoral fellow at Information Initiative at Duke University. Before that, Jie graduated from Tsinghua University in 2012, enrolled in the Math & Physics program and the Electrical Engineering program. Jie has broad research interests in machine learning, with a recent focus on collaborative learning and privacy.