Math-to-Industry Boot Camp IV

Advisory: Extended application deadline is March 22, 2019



The Math-to-Industry Boot Camp is an intense six-week session designed to provide graduate students with training and experience that is valuable for employment outside of academia. The program is targeted at Ph.D. students in pure and applied mathematics. The boot camp consists of courses in the basics of programming, data analysis, and mathematical modeling. Students work in teams on projects and are provided with training in resume and interview preparation as well as teamwork.

There are two group projects during the session: a small-scale project designed to introduce the concept of solving open-ended problems and working in teams, and a "capstone project" that is posed by industrial scientists. Last year's industrial sponsors included 3M, D-Wave Systems, Milwaukee Brewers, National Security Technologies, Schlumberger-Doll Research, and Whitebox Advisors. 

Weekly seminars by speakers from many industry sectors provide the students with opportunities to learn about a variety of possible future careers.


Applicants must be current graduate students in a Ph.D. program at a U.S. institution during the period of the boot camp.


The program will take place at the IMA on the campus of the University of Minnesota. Students will be housed in a residence hall on campus and will receive a per diem and a travel budget, as well as an $800 stipend.


To apply, please supply the following materials through the link at the top of the page:

  • Statement of reason for participation, career goals, and relevant experience
  • Unofficial transcript, evidence of good standing, and have full-time status
  • Letter of support from advisor, director of graduate studies, or department chair

Selection criteria will be based on background and statement of interest, as well as geographic and institutional diversity. Women and minorities are especially encouraged to apply. Selected participants will be contacted in April.


Name Department Affiliation
Jesse Berwald   D-Wave Systems
Nicole Bridgland   World Wide Technology
Benjamin Brubaker School of Mathematics University of Minnesota, Twin Cities
Yiqing Cai   Gro Intelligence
Sarah Chehade Department of Mathematics University of Houston
Brendan Cook   University of Minnesota, Twin Cities
William Cooper Department of Mechanical Engineering University of Minnesota, Twin Cities
Steven Dabelow Department of Applied and Computational Mathematics and Statistics University of Notre Dame
Davood Damircheli Department of Mathematics and Statistics Mississippi State University
Dilek Erkmen Department of Mathematical Science Michigan Technological University
Jonathan Hahn   World Wide Technology
Jordyn Harriger Department of Mathematics Indiana University
Brad Hildebrand   Cargill, Inc.
Jonathan Hill   ITM TwentyFirst LLC
Thomas Hoft Department of Mathematics University of St. Thomas
SeongHee Jeong   Louisiana State University
Michael Johnson Strategic Marketing and Portfolio Division Cargill, Inc.
Kiwon Lee Department of Mathematics The Ohio State University
Xing Ling Department of Mathematical Science Michigan Technological University
Sijing Liu Department of Mathematics Louisiana State University
Kevin Marshall Department of Mathematics University of Kansas
Kristina Martin Department of Supervision, Regulation, and Credit Federal Reserve Bank of Minneapolis
Vikenty Mikheev Department of Mathematics Kansas State University
Sarah Milstein   University of Minnesota, Twin Cities
Sarah Miracle Department of Computer and Information Sciences University of St. Thomas
Bibekananda Mishra Department of Mathematics University of Kansas
Whitney Moore Career Center for Science and Engineering University of Minnesota, Twin Cities
Anthony Nguyen Department of Mathematics University of California, Davis
Damilola Olabode Department of Mathematics and Statistics Washington State University
Negar Orangi-Fard Department of Mathematics Kansas State University
Samantha Pinella Department of Mathematics University of Michigan
Michelle Pinharry School of Mathematics University of Minnesota, Twin Cities
Puttipong Pongtanapaisan Department of Mathematics The University of Iowa
Matthew (Jake) Roberts Department of Mathematical Sciences Michigan Technological University
Jose Pedro Rodriguez Ayllon Department of Mathematics University of Houston
Nandita Sahajpal Department of Mathematics University of Kentucky
Fadil Santosa School of Mathematics University of Minnesota, Twin Cities
Samantha Schumacher Department of Data Science & Analysis Target Corporation
Olabanji Shonibare   Starkey Hearing Technologies
David Shuman Department of Mathematics, Statistics and Computer Science Macalester College
Matthew Sikkink Johnson Department of Mathematics University of Minnesota, Twin Cities
Daniel Spirn University of Minnesota University of Minnesota, Twin Cities
Rebeccah Stay   Cargill, Inc.
Ben Strasser Department of Mathematics University of Minnesota, Twin Cities
Rahim Taghikhani School of Mathematics and Statistics Arizona State University
Zeinab Takbiri Department of Engineering R&D and Data Science Cargill, Inc.
Tianyu Tao Department of Mathematics University of Minnesota, Twin Cities
Jing Wang   Thrivent Financials
Nathan Willis Department of Mathematics The University of Utah
Guanglin Xu Institute for Mathematics and its Application University of Minnesota, Twin Cities
Yanhua Yuan   ExxonMobil
Christina Zhao   University of Minnesota, Twin Cities
Li Zhu Department of Mathematical Sciences University of Nevada


Projects and teams

Project 1: Rail car supply forecasting

  • Mentor Zeinab Takbiri, Cargill, Inc.
  • Sijing Liu, Louisiana State University
  • Damilola Olabode, Washington State University
  • Puttipong Pongtanapaisan, The University of Iowa
  • Nathan Willis, The University of Utah

Cargill is a major grain trader in the US. We utilize over 100,000 rail cars per year to ship grains to our domestic and export customers. Cargill uses railroad-supplied cars to move a lot of these shipments of grain. The railroads require us to take on an obligation to run their cars for a year. We are looking for help in developing a supply and demand model that can determine how many cars Cargill should take on in a given year as well as a forecast of the overall market’s need for railroad owned equipment.

Project 2: Accuracy of a simple freeze-out model as a description of the QPU distribution for C4 RAN1 problems

  • Mentor Jesse Berwald, D-Wave Systems
  • Sarah Chehade, University of Houston
  • Davood Damircheli, Mississippi State University
  • Kevin Marshall, University of Kansas
  • Li Zhu, University of Nevada

A quantum processing unit (QPU) is a programmable chip that leverages superposition and entanglement, fundamental quantum mechanical properties, to solve problems. The D-Wave quantum annealing computer currently operates with a 2048-qubit QPU. Calibrating such a chip in the presence of thermal, quantum mechanical, and design-specific noise is a critical component to producing a working quantum computer. 

D-Wave Systems has developed many internal calibration tests to infer anomalies observed in the QPU. Error correction on many levels is used to mitigate these anomalies wherever possible (though thermal and quantum fluctuations will always be present). The variety of tests often requires different models and statistical methods. This project looks at a test of a specific configuration of randomly coupled qubits (C4 RAN1). Students will implement and fit a model based on observations from the QPU. A significant part of the pipeline will include a visualization component to enable easy, and deeper, analysis of anomalies if they are present. 

Project 3: Improving Mine Dispatching

  • Mentor Nicole Bridgland, World Wide Technology
  • Mentor Jonathan Hahn, World Wide Technology
  • Steven Dabelow, University of Notre Dame
  • Jordyn Harriger, Indiana University
  • SeongHee Jeong, Louisiana State University
  • Kiwon Lee, The Ohio State University

Mines have lots of moving parts, and timing of delivery between them is crucial.  Time that mining equipment spends idle represents lost production opportunity. Time trucks spend idle, while not as obviously problematic, represents at least wasted fuel if not lost production opportunity elsewhere in the mine.  Given a system of several shovels and crushers, and trucks moving material between them, how can you best decide where to send empty/loaded trucks as they become available? When equipment experiences delays, when should you reroute trucks vs simply wait it out, and how should you reroute them? The goal of this project will be to develop tools to help human dispatchers make these decisions, possibly in the form of machine-generated recommendations.

Project 4: Analogous year detection

  • Mentor Yiqing Cai, Gro Intelligence
  • Xing Ling, Michigan Technological University
  • Ben Strasser, University of Minnesota, Twin Cities
  • Rahim Taghikhani, Arizona State University
  • Tianyu Tao, University of Minnesota, Twin Cities

Gro is a data platform with comprehensive data sources related to food and agriculture. With data from Gro, stakeholders can make quicker and better decisions, which in most cases are time sensitive. In this project, the students will use data from Gro to identify analogous events. For example, people can compare and find a year with similar precipitation and soil moisture patterns to draw inferences about second and third order effects such as flooding or decreased crop planted area. This type of analysis can help quantify the impact of an event, and remedy the negative impact if it is severe and not avoidable.

Data will be provided through Gro API. Data pulled from Gro are in the format of time series, which are called data series. Different data series can come from different sources, and have different frequencies. For example, there is daily Precipitation data from TRMM, and NDVI at a frequency of 8 days (a type of vegetation index) from GIMMS MODIS.  

Goals: The deliverables of this project will be in the form of an executable model. Given a data series (or a set of data series), and a selected time period, find analogous periods in history that are most similar to this selected period. Given the project goal, it all boils down to defining similarity between a pair of data series, or concatenated data series. 

Project 5: Deblending simultaneous-source seismic signals

  • Mentor Yanhua Yuan, ExxonMobil
  • Dilek Erkmen, Michigan Technological University
  • Anthony Nguyen, University of California, Davis
  • Samantha Pinella, University of Michigan
  • Jose Pedro Rodriguez Ayllon, University of Houston
  • Nandita Sahajpal, University of Kentucky

Acquisition of seismic data in marine environment is a costly process. Traditionally, in marine seismic surveys, a boat tows a line of receivers while moving slowly. To obtain signals at the receivers, a wave source, typically an air gun, is generating a pulse with frequencies in the 10 of Hz which penetrates the earth and reflects back on the different layers of the earth. Recently, an innovation in this space was introduced that has been shown to have substantial savings and allowed for wider distances between the source and the receivers. In the new method, more than one seismic sources or air guns are fired with short or zero delays between them so that the signal generated by each source overlap at some or all receivers. The collected signals at the receivers are therefore blended together in simultaneous-source acquisition, and a “deblending” process is usually needed to separate signals from the individual sources before any further analysis. To make it easier for decoding, multiple sources are usually fired at a random time, and (or) with signatures coded differently. Based on the incoherence assumption, the deblending problem can be explored in different ways, including as signal processing problem, inversion problem, or data analytics problem. In this project, we will try these methods and look for a robust deblending algorithm to reconstruct individual source signals from encoded data.

Project 6: Accuracy and precision of Time-to-Event Models with Flexible Dimensionality

  • Mentor Jonathan Hill, ITM TwentyFirst LLC
  • Brendan Cook, University of Minnesota, Twin Cities
  • Vikenty Mikheev, Kansas State University
  • Bibekananda Mishra, University of Kansas
  • Negar Orangi-Fard, Kansas State University
  • Matthew (Jake) Roberts, Michigan Technological University

Medical underwriting is expensive and time-consuming, involving trained underwriters who manually review medical history and long delays waiting for documentation. For these reasons, researchers in life insurance and related industries are fervently searching for methods to estimate mortality risk faster and at lower cost.

One proposed solution is to use a smaller set of medical features than what is typically collected in underwriting. These features could be collected through a questionnaire and used to generate a rapid estimate of mortality risk. This solution could have additional value in cases of full underwriting where some medical data is missing. A key objective will be quantifying the increase in uncertainty, or decrease in precision, as a consequence of using a smaller feature set.

During this week-long project, you will take a crash course in survival analysis, explore models for time-to-event data (including traditional and machine learning approaches), determine appropriate metrics, engineer features, and compete to create the best possible model of mortality risk. If time allows, there may be opportunity to develop novel modelling techniques.

We will be using a unique world-class dataset on senior life outcomes provided by ITM TwentyFirst, a Minneapolis-based life settlements servicing company.

Start date
Monday, June 24, 2019, 8 a.m.
End date
Friday, Aug. 2, 2019, 5 p.m.

University of Minnesota