Math-to-Industry Boot Camp VII
Advisory: Application deadline is March 21, 2022
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 Gargill, Securian Financial and CH Robinson.
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 online. Students will receive a $2,000 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.
Projects and teams
CH Robinson: Team 1 — Dynamic pricing in logistics
- Mentor Brady Thompson, CH Robinson
- Mentor Veronika Koubova, CH Robinson
- Mentor Matthew Smith, CH Robinson
- Mentor Orion Wynblatt, CH Robinson
- Mentor Daniel Prentice, CH Robinson
- Ibrahem Aljabea, Louisiana State University
- Abhinav Chand, Kansas State University
- Mengting Chao, University of Maryland
- Zhaobidan (Amy) Feng, Texas A & M University
- Vanny Khon, Boston University
- Christian McRoberts, Iowa State University
Pricing in logistics is a very fast paced environment. Pricing managers need to be aware of the constant changes in the market, and be able to adapt quickly. Depending on the business needs, whether that be to increase profit or increase volume, we need to be able to quickly explore the data, develop a model, and test to determine if the strategy is meeting the business goals. Our team of data scientists work in Less-Than-Truckload pricing, a fast growing market segment driven by recent trends in the supply chain. Given a business goal, we use historical data to build a pricing strategy that we believe will achieve the goal, and then run controlled experiments to determine the success of our strategy.
A boot camp student working on our project would become familiarized with dynamic pricing, as well as research surrounding multi-choice pricing dynamics. They will develop the python skills needed to implement machine learning models that use historical and streaming data, including reinforcement learning. Lastly, it is expected that they would be able to manage an online controlled experiment to test the quality of their pricing strategy.
ITM TwentyFirst: Team 2 — Identifying Longevity Risk with Machine Learning
- Mentor Jonathan Hill, ITM TwentyFirst LLC
- Mentor Tianze Li, ITM TwentyFirst LLC
- Shan Chen, University of Minnesota, Twin Cities
- Haridas Kumar Das, Oklahoma State University
- Kimberlyn Eversman, University of Tennessee
- Dumindu Sandakith Kasiwatte Kankanamge, Vanderbilt University
- Mark Roach, Michigan State University
- Matthew Wynne, University of Washington
Traditional approaches to predicting life outcomes are robust and interpretable but come with limitations. They are limited in the number of interactions between medical conditions (“comorbidities”) that can be considered, unable to handle missing values, and have a fixed base table shape. We developed a machine learning model (the Longevity Risk Model) to address these limitations. Its target is to identify insureds where our traditional model (“RevH”) is likely too long (low longevity risk) or too short (high longevity risk). Our goal is to expand the ideas behind the Longevity Risk Model and find creative ways to improve its predictions. These improvements might include, but are not limited to, identifying COVID deaths in our dataset and adding COVID as a feature to the model, using quantile objective functions to make prediction intervals, altering the structure of the stacked models, and predicting the conditional probability of outliving life expectancy.
We will use Python 3, along with a unique world-class dataset on senior life outcomes provided by ITM TwentyFirst, a Minneapolis-based life settlements servicing company.
US Bank: Team 3 — Forecasting Prepayments in High Interest Rate Environment
- Mentor Chistopher Jones, US Bank
- Caroline Bang, Iowa State University
- Katheryn Beck, University of Kansas
- Zanbing Dai, University of Minnesota, Twin Cities
- Leonardo Digiosia, Rice University
- Arpan Pal, Texas A & M University
- Bo Zhu, University of Minnesota, Twin Cities
U.S. agency 1 residential mortgage-backed securities (MBS) are the largest and most liquid securitized asset class in the world. Banks, insurance companies, and money managers invest in MBS because they provide an attractive yield relative to U.S. Treasury securities with comparable credit risk. However, unlike most fixed income securities, which have specified contractual coupon and principal payments, the timing and amount of MBS cash flows is uncertain. This is because MBS are pools of individual mortgages on which the borrower has the right to prepay the loan at any given time during the life of the loan. Prepayment risk, which impacts the yield and interest rate risk of an MBS comes from five sources:
Rate/term refinance which occur when borrowers lower interest payments or shorten the term of the current mortgage.
Cash-out refinance which involve extracting equity from a home.
Involuntary buyouts, which in the case of agency MBS result in an early return of principal. However, the timing is contingent on the GSEs or GNMA loan servicers.
Curtailments, which are partial prepayment or full payoff before maturity.
Turnover, which is caused by geographic migration and home upgrades. Turnover creates a baseline level of prepayments that are highly seasonal.
The COVID-19 crisis and response by the Federal Reserve resulted in a low-rate environment that elevated both levels of refinance and buyout activity. Since the initiation of quantitative tightening, sustained inflation, and rising interest rates, refinance activity has slowed considerably. At present, a major question affecting the risk profile of MBS is to what extent prepayment activity will decrease. With diminishing refinance incentives, the major sources of prepayment are now turnover, curtailment, and buyouts. As a baseline level of prepayment activity, understanding turnover is important to evaluating the risks of mortgage-backed securities. The goal of this project is to use loan-level mortgage data and macroeconomic data to quantify turnover prepayment speed.