Math-to-Industry Boot Camp VIII
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 Cargill, 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.
Capstone Projects
Evaluating the real-world safety and robustness of deep learning models
Charles Godfrey, Pacific Northwest National Laboratory
Henry Kvinge, Pacific Northwest National Laboratory
Team: Kean Fallon, Iowa State University; Aidan Lorenz, Vanderbilt University; Jessie Loucks-Tavitas, University of Washington; Sandra Annie Tsiorintsoa, Clemson University; Benjamin Warren, Texas A&M University
Abstract: Deep learning has shown remarkable capabilities in a range of important tasks, but at the same time has also been shown to be brittle in many ways, especially in real-world deployed environments. This can range from a language model that can be triggered to insult users to a vision model that doesn’t recognize machine parts in certain lighting conditions. Understanding how a model will behave at deployment is a serious problem in real-world AI and requires a mixture of mathematical and out-of-the-box thinking. In this capstone project, participants will be asked to evaluate models delivered to a client by 3rd party vendors from the perspective of overall robustness. This will include (i) evaluating how well a model performs outside of its test set and (ii) particular failure modes of the model that should be avoided. The final project deliverable will include a short report recommending for or against proceeding with use of the model in the client’s application.
Interpolating the Implied Volatility Surface
Chris Bemis, X Cubed Capital Management
Team: Qinying Chen, University of Delaware; Nellie Garcia, University of Minnesota; Emily Gullerud, University of Minnesota; Shaoyu Huang, University of Pittsburgh; Pascal Kingsley (PK) Kataboh, University of Delaware; Matthew Williams, Colorado State University
Abstract: Financial markets price volatility in underlying securities primarily through what are called options. These options are defined by reference to their payoffs and the date at which they expire, along with other features such as prevailing rates, the underlying security price, and so on. The result is that markets reference a surface of implied volatilities based on market prices.
In this project, we will fit such surfaces in financially meaningful ways; especially focusing on the preclusion of arbitrage opportunities in the resulting interpolation. These methods are critical in creating assessments of constant expiry volatility time series amongst many other applications. They also sometimes suffer from a lack of stability in parameter estimation as new surfaces are fit.
We will use real (and noisy) data with the goal of efficiently creating stable volatility surface interpolations and time series of constant expiry volatility.
Multimodal Search in eCommerce
Christopher Miller, eBay
Team: Tanuj Gupta, Texas A&M University; Meagan Kenney, University of Minnesota; Pavel Kovalev, Indiana University; Chiara Mattamira, University of Tennessee; Jeremy Shahan, Louisiana State University; Hannah Solomon, Texas A&M University
Abstract: In classical search, most or all user interfaces are text-based. Users submit queries made up of strings, and possibly assert filters (also text-based) to limit the result set. When a user does not like their results, they can “requery” with slightly different terms to produce better results. This process continues until the user is satisfied or gives up.
In visual search, users submit images for their queries. The query images might come from the internet, other eCommerce sites, or from the users’ own library. They expect to see results that look similar to their query image. If the user does not like their results, now they’re stuck: they cannot simply tweak an image the way they can tweak a text-based query.
This is the problem we will resolve with a two-phase multimodal search. The first phase is regular visual search. In the second phase, users can add text to their query to augment their search results. For example, they submit a photo of a yellow dress they have at home, but add the text “green dress” to get results that are green, but otherwise similar to the dress they already have. This enables users to iteratively improve their search results just like they would in classical search.
An Excess Demand Model of Home Price Appreciation
Christopher Jones, US Bank
Matt Mansell, US Bank
Leo Digiosia, US Bank
Team: Ismail Aboumal, California Institute of Technology; Daniela Beckelhymer, University of Minnesota; Jarrad Botchway, Missouri University of Science and Technology; Jordan Pellett, University of Tennessee; Marshall Smith, University of Minnesota
Abstract: National home prices in the U.S. are tracked by one of a few indices: the Case-Shiller and FHFA home price indices being the most popular. Home price appreciation is an important metric tracked by commercial banks. Because bank originators have the ability to hold mortgages on their balance sheets, refinance activity such as cash-out and rate/term refinance contribute to the interest rate and macroeconomic risk of these assets. Traditionally, home prices are forecasted using a form of econometric regression where multiple correlated variables are used in a model. However, these models often lead decision-makers in a bank lacking in terms of interpretability or insights into the mortgage market. In this project, we will explore home prices from the point of view of a differential equation so that we can obtain forecasted values of home price appreciation on a variety of time scales. We will explore the conceptual soundness of a model of excess demand and quantify uncertainty around parameter estimation and shape optimization. We will create a story around this model to explain past events and potential future scenarios.
