Past Events

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.

Research and Opportunities in the Mathematical Sciences at Oak Ridge National Laboratory

Juan Restrepo (Oregon State University)


I will present a general overview of  Oak Ridge National Laboratory research in mathematics and computing. A brief description of my own initiatives and research will be covered as well.  I will also describe opportunities for students, postdocs, and professional mathematicians.

Dr. Juan M. Restrepo is a Distinguished Member of the R&D Staff at Oak Ridge National Laboratory. Restrepo is  a fellow of SIAM and APS. He holds professorships at U. Tennessee and Oregon State University. Prior to ORNL, he was a professor of mathematics at Oregon State University and at the University of Arizona. He has been a frequent IMA visitor.

His research focuses on data-driven methods for dynamics, statistical mechanics, transport in ocean and uncertainty quantification in climate science.

Scalable and Sample-Efficient Active Learning for Graph-Based Classification

Kevin Miller (University of California, Los Angeles)

Active learning in semi-supervised classification involves introducing additional labels for unlabelled data to improve the accuracy of the underlying classifier. A challenge is to identify which points to label to best improve performance while limiting the number of new labels; this is often reflected in a tradeoff between exploration and exploitation, similar to the reinforcement learning paradigm. I will talk about my recent work designing scalable and sample-efficient active learning methods for graph-based semi-supervised classifiers that naturally balance this exploration versus exploitation tradeoff. While most work in this field today focuses on active learning for fine-tuning neural networks, I will focus on the low-label rate case where deep learning methods are generally insufficient for producing meaningful classifiers.  

Kevin Miller is a rising 5th year Ph.D. candidate in Applied Mathematics at the University of California, Los Angeles (UCLA), studying graph-based machine learning methods with Dr. Andrea Bertozzi. He is currently supported by the DOD’s National Defense Science and Engineering Graduate (NDSEG) Fellowship and was previously supported by the National Science Foundation's NRT MENTOR Fellowship. His undergraduate degree was in Applied and Computational Mathematics from Brigham Young University, Provo. His research focuses on active learning and uncertainty quantification in graph-based semi-supervised classification.

Long-term Time Series Forecasting and Data Generated by Complex Systems

Kaisa Taipale (CH Robinson)

Data science, machine learning, and artificial intelligence are all practices implemented by humans in the context of a complex and ever-changing world. This talk will focus on the challenges of long-term, seasonal, multicyclic time series forecasting in logistics. I will discuss algorithms and implementations including STL, TBATS, and Prophet, with additional attention to the data-generating processes in trucking and the US economy and the importance in algorithm selection of understanding these data-generating processes. Subject matter expertise must always inform mathematical exploration in industry and indeed leads to asking much more interesting mathematical questions.

Standardizing the Spectra of Count Data Matrices by Diagonal Scaling

Boris Landa (Yale University)

A longstanding question when applying PCA is how to choose the number of principal components. Random matrix theory provides useful insights into this question by assuming a “signal+noise” model, where the goal is to estimate the rank of the underlying signal matrix. If the noise is homoskedastic, i.e. the noise variances are identical across all entries, the spectrum of the noise admits the celebrated Marchenko-Pastur (MP) law, providing a simple method for rank estimation. However, in many practical situations, such as in single-cell RNA sequencing (scRNA-seq), the noise is far from being homoskedastic. In this talk, focusing on a Poisson data model, I will present a simple procedure termed biwhitening, which enforces the MP law to hold by appropriately scaling the rows and columns of the data matrix. Aside from the Poisson distribution, this procedure is extended to families of distributions with a quadratic variance function. I will demonstrate this approach on both simulated and experimental data, showcasing accurate rank estimation in simulations and excellent fits to the MP law for real scRNA-seq datasets.

Boris Landa is a Gibbs Assistant Professor in the program for applied mathematics at Yale University. Previously, he completed his Ph.D. in applied mathematics at Tel Aviv University under the guidance of Prof. Yoel Shkolnisky. Boris's research is focused on theory and methods for processing large datasets corrupted by noise and deformations, with applications in the biological sciences.