Math-to-Industry Boot Camp VI

June 21 - July 30, 2021

Content

Overview

Advisory:

Application deadline is March 7, 2021

Poster:

2021_SummerBootCamp_Poster.pdf

Organizers:

Thomas Hoft	University of St. Thomas
Daniel Spirn	University of Minnesota, Twin Cities

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. Recent industrial sponsors included D-Wave Systems, Exxonmobil, Los Alamos National Laboratories, Milwaukee Brewers, Starbucks.

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

Eligibility

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

Logistics

The program will take place online. Students will receive a $800 stipend.

Applications

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.

Participants

Name	Department	Affiliation
Douglas Armstrong	Department of Data Science	Securian Financial
Yuchen Cao	Department of Mathematics	University of Central Florida
Samara Chamoun	Department of Mathematics	Michigan State University
Ana Chavez Caliz	Department of Mathematics	Pennsylvania State University
Alexander Estes	Institute for Mathematics and its Applications	University of Minnesota, Twin Cities
Raymond Friend Jr	Department of Mathematics	Pennsylvania State University
Ghodsieh Ghanbari	Department of Mathematics and Statistics	Mississippi State University
Marc Haerkoenen	School of Mathematics	Georgia Institute of Technology
Tony Haines	Department of Computational and Applied Mathematics	Old Dominion University
Natalie Heer		CH Robinson
Thomas Hoft	Department of Mathematics	University of St. Thomas
Alicia Johnson	Department of Mathematics, Statistics, and Computer Science	Macalester College
Malick Kebe	Department of Mathematics	Howard University (Washington, DC, US)
Juergen Kritschgau	Department of Mathematics	Iowa State University
Marshall Lagani	Department of Data Science	Securian Financial
Kevin Leder	Department of Industrial System and Engineering	University of Minnesota, Twin Cities
Ivan Marin		Cargill, Inc.
Francisco Martinez Figueroa	Department of Mathematics	The Ohio State University
Avishek Mukherjee	Department of Mathematical Sciences	University of Delaware (Newark, DE, US)
Muharrem Otus	Department of Mathematics	University of Pittsburgh
Smita Praharaj	Department of Mathematics	University of Missouri
Tanmay Raj		Cargill, Inc.
Abba Ramadan	Department of Applied Mathematics	University of Kansas
Samanwita Samal	Department of Mathematics	Indiana University
Natalie Sheils		UnitedHealth Group
Blerta Shtylla		Pfizer
David Shuman	Department of Mathematics, Statistics and Computer Science	Macalester College
Lauren Snider	Department of Mathematics	Texas A & M University
Daniel Spirn	University of Minnesota	University of Minnesota, Twin Cities
Elizabeth Sprangel	Department of Mathematics	Iowa State University
Kaisa Taipale	Contractual Pricing Group	CH Robinson
Sijie Tang	Department of Mathematics	University of Wyoming
Cameron Thieme	Department of Mathematics	University of Minnesota, Twin Cities
Shuxian Xu	Department of Mathematics	University of Pittsburgh
Lei Yang	Department of Mathematics	Northeastern University
Grace Zhang	School of Mathematics	University of Minnesota, Twin Cities
Miao Zhang	Department of Mathematics	Louisiana State University
Jennifer Zhu	Department of Mathematics	Texas A & M University
Ahmed Zytoon	Department of Mathematics	University of Pittsburgh

Projects and Teams

[+] Team 1 - Cargill: Hydroelectric Energy Generation Optimization

Mentor Ivan Marin, Cargill Corporation
Mentor Tanmay Raj, Cargill Corporation
Ana Chavez Caliz, Pennsylvania State University
Francisco Martinez Figueroa, Ohio State University
Juergen Kritschgau, Iowa State University
Avishek Mukherjee, University of Delaware
Smita Praharaj, University of Missouri
Cameron Thieme, University of Minnesota
Jennifer Zhu, Texas A & M University

The increased penetration of variable renewable energy (VRE) and phase-out of nuclear and other conventional electricity generation sources will require an additional flexibility in the power grid and a demand to lower the gap between the generation and demand, and how this can influence the energy pricing in the short and long term. Clean water is essential for hydropower generation, and the main source of electrical power generation in Brazil. Due to the limited water resources and the variability of precipitation, there is a need to investigate an optimal management of these resources in order to meet the power grid demand, and predict the power generation capacity, given the historical rain patterns, reservoir water levels and energy demands.

[+] Team 2 - Securian Financial: Predicting Group Life Client Mortality During a Pandemic

Mentor Douglas Armstrong, Securian Financial
Yuchen Cao, University of Central Florida
Samara Chamoun, Michigan State University
Marc Haerkoenen, Georgia Institute of Technology
Abba Ramadan, University of Kansas
Lei Yang, Northeastern University
Shuxian Xu, University of Pittsburgh

During a pandemic the ability to predict risk for clients becomes paramount to manage risk effectively. The impact that a pandemic has may differ depending on the demographics and regional considerations for each client. This brings in additional complexity to the analysis and forecasting of future risk a client may pose. In this project, students will enrich a simulated client dataset with publicly available data before developing a machine-learning based approach to predict adverse risk of multiple clients.

[+] Team 3 - CH Robinson: Impact of Weather and Agricultural Events on Truckload Cost Per Mile

Mentor Kaisa Taipale, CH Robinson
Raymond Friend Jr, Pennsylvania State University
Ghodsieh Ghanbari, Mississippi State University
Tony Haines, Old Dominion University
Malick Kebe, Howard University
Elizabeth Sprangel, Iowa State University
Grace Zhang, University of Minnesota

Fresh fruits and vegetables are an important group of commodities in the US commonly transported by truck from fields in predominantly southern growing regions across the US (for instance, from California to the Northeast). While irrigation dampens the effect of rainfall crop yields, temperature and rainfall are still important factors in the timing of fresh fruit and vegetable harvest and thus transport. This work will examine the magnitude of impact of vegetable harvest timing on transportation costs, using external inputs like temperature and rainfall as well as variables intrinsic to the truckload market. Challenges include combining the geographic characteristics of the time series involved: univariate time series methods provide some benefit but stronger results come from exploiting geography and freight characteristics. Bayesian models and causal impact analysis are natural tools for this application.

[+] Team 4 - CH Robinson: CH Robinson Volume Simulation

Mentor Natalie Heer, CH Robinson
Mentor Bethany Stai, CH Robinson
Mentor Michael Chmutov, CH Robinson
Mentor Kaisa Taipale, CH Robinson
Muharrem Otus, University of Pittsburgh
Samanwita Samal, Indiana University
Lauren Snider, Texas A & M University
Sijie Tang, University of Wyoming
Miao Zhang, Louisiana State University
Ahmed Zytoon, University of Pittsburgh

In Economics there is classically an inverse relationship between the price of an item and the quantity of the item that customers will choose to purchase. If prices increase, customers will purchase fewer items, and if prices decrease customers will choose to purchase more items. If companies can predict the volume change associated with a change in price, they can optimize their pricing strategy for overall profitability max(Unit Price * Volume). The goal of this project is to help CHR be smarter in optimizing our business strategy.