Past Events
Math-to-Industry Boot Camp VI
Monday, June 21, 2021, 8 a.m. through Friday, July 30, 2021, 5 p.m.
Online
Advisory: Application deadline is March 7, 2021
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: Hydrologic 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.
Winter Math-to-Industry Boot Camp
Monday, Jan. 4, 2021, 8 a.m. through Friday, Jan. 15, 2021, 5 p.m.
Virtual
Advisory: Application deadline is Friday, December 4, 2020
2021 Winter Virtual Boot Camp poster
Organizers:
- Jasmine Foo, University of Minnesota, Twin Cities
- Thomas Hoft, University of St. Thomas
- Daniel Spirn, University of Minnesota, Twin Cities
The Winter Math-to-Industry Boot Camp is an intensive, two-week program that provides graduate students with training and experience that is valuable for employment outside of academia. The program is targeted at Ph.D. students in mathematics and statistics. The winter camp consists of pre-camp coursework in the basics of programming, data analysis, and optimization.
During the program, students work in small teams under the guidance of an industry mentor using a variety of streaming technology. The mentor and camp staff will help guide the students in the modeling process, analysis, and computational work associated with a real-world industrial problem. Additional time will be spent on developing professional and networking skills, meeting industry scientists, and participating in a career fair.
Each team will be expected to make a final presentation and submit a written report at the end of the workshop.
Recent industrial sponsors included Cargill, D-Wave Systems, the Mayo Clinic, Securian Financial, World Wide Technology.
Eligibility
Applicants must be current graduate students in a mathematical sciences 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 $500 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 December.
Participants
Name | Department | Affiliation |
---|---|---|
Daniel Alhassan | Department of Mathematics and Statistics | Missouri University of Science and Technology |
Mohamed Imad Bakhira | Department of Mathematics | The University of Iowa |
Yiqing Cai | Gro Intelligence | |
Frankie Chan | Department of Mathematics | Purdue University |
Jorge Cisneros Paz | Department of Applied Mathematics | University of Washington |
Paula Dassbach | Medtronic | |
Jerry Dogbey-Gakpetor | Statistics | North Dakota State University |
Henry Fender | Department of Data Science | ITM TwentyFirst LLC |
Shihang Feng | Applied Mathematics and Plasma Physics | Los Alamos National Laboratory |
Jasmine Foo | School of Mathematics | University of Minnesota, Twin Cities |
Jonathan Hill | ITM TwentyFirst LLC | |
Thomas Hoft | Department of Mathematics | University of St. Thomas |
Salomea Jankovic | Department of Mathematics | University of Minnesota, Twin Cities |
Henry Kvinge | Pacific Northwest National Laboratory | |
Axel La Salle | School of Mathematical and Statistical Sciences | Arizona State University |
Youzuo Lin | Earth and Environmental Sciences Division | Los Alamos National Laboratory |
Sander Mack-Crane | Department of Mathematics | University of California, Berkeley |
Maia Powell | Department of Applied Mathematics | University of California, Merced |
Lee Przybylski | Mathematics | Iowa State University |
Priyanka Rao | Department of Mathematics & Statistics | Washington State University |
Majerle Reeves | Department of Applied Mathematics | University of California, Merced |
Daniel Spirn | University of Minnesota | University of Minnesota, Twin Cities |
Anna Srapionyan | Merrill Lynch | |
Wencel Valega Mackenzie | Department of Mathematics | University of Tennessee |
Christine Vaughan | Department of Mathematics and Mechanical Engineering | Iowa State University |
Elise Walker | Department of Mathematics | Texas A & M University |
Max Wimberley | Department of Mathematics | University of California, Berkeley |
Harrison Wong | Department of Mathematics | Purdue University |
Cancan Zhang | Department of Mathematics | Northeastern University |
Projects and teams
Project 1: Record Linkage: Synthesizing Expert Systems and Machine Learning
- Mentor Jonathan Hill, ITM TwentyFirst LLC
- Mentor Henry Fender, ITM TwentyFirst LLC
- Jorge Cisneros Paz, University of Washington
- Jerry Dogbey-Gakpetor, North Dakota State University
- Majerle Reeves, University of California, Merced
- Elise Walker, Texas A & M University
- Max Wimberley, University of California, Berkeley
- Harrison Wong, Purdue University
Record linkage is a common big data process where shared records in two large datasets are linked based on common fields. Longevity Holdings designed an expert system to automate record linkage between client data and a corpus of death records. This system produces scores that sort record pairs into matches and non-matches. Currently, high and low scores separate cleanly, but mid-tier scores must be manually reviewed. This led us to ask: Can machine learning improve an expert system in record linkage and reduce the size of this review set?
We are working with a variant of the Expectation Maximization (EM) algorithm following the Fellegi-Sunter approach to record linkage. We implemented this algorithm but have not found an optimal configuration for our data. The algorithm is general so we can manipulate many aspects of the input. Our priority is to determine whether there is a configuration that can improve the expert system.
EM is not the only viable approach to this problem. There are a wide range of existing methods that can be applied to record linkage. Our priority is to figure out the pros and cons for each, while trying to exceed EM and expert system performance.
On this project, you will work with real-world data and learn to organize as a team. You will deliver a whitepaper summarizing your process and results. We are most interested in your clear thinking and structured approach to this problem. We will divide into two groups focusing on one of the priorities above. Both groups will receive two validated sets of record pairs, one deriving from obituaries and the other from state and federal records. Our toolset will include python, pandas, and scikit-learn.
Project 2: Data-Driven Computational Seismic Inversion
- Mentor Youzuo Lin, Los Alamos National Laboratory
- Mentor Shihang Feng, Los Alamos National Laboratory
- Frankie Chan, Purdue University
- Salomea Jankovic, University of Minnesota, Twin Cities
- Sander Mack-Crane, University of California, Berkeley
- Priyanka Rao, Washington State University
- Christine Vaughan, Iowa State University
- Cancan Zhang, Northeastern University
Computational seismic inversion turns geophysical data into actionable information. The technique has been widely used in geophysical exploration to characterize the subsurface structure. Such a clear and accurate map of the subsurface is crucial for determining the location and size of reservoirs and mineral features.
Seismic inversion usually presents itself as an inverse problem. However, solving those inverse problems has been notoriously challenging due to their ill-posed and computationally expensive nature. On the other hand, with advances in machine learning and computing, and the availability of more and better data, there has been notable progress in solving such problems. In our recent work [1, 2], we developed end-to-end data-driven subsurface imaging techniques and produced encouraging results when test data and training data share similar statistics characteristics. The high accuracy of the predictive model is built on the assumption that the training dataset captures the distribution of the target dataset. Therefore, it is critical to obtain a sufficient amount of high-quality training set.
In this project, students will work with LANL scientists to study the impact of the training data on the resulting predictive model. In particular, students will explore and develop different techniques to generate high-quality synthetic data that could be used to enhance the training data quality. Through the project, students will have the opportunity to learn deep learning and its applications in computational imaging and the fundamentals of ill-posed inverse problems.
Reference:
[1]. Yue Wu and Youzuo Lin, “InversionNet: An Efficient and Accurate Data-driven Full Waveform Inversion,” IEEE Transactions on Computational Imaging, 6(1):419-433, 2019.
[2]. Zhongping Zhang and Youzuo Lin, “Data-driven Seismic Waveform Inversion: A Study on the Robustness and Generalization,” in IEEE Transactions on Geoscience and Remote Sensing, 58(10):6900-6913, 2020.
Project 3: The Impact of Climate Change on Crop Yield
- Mentor Yiqing Cai, Gro Intelligence
- Daniel Alhassan, Missouri University of Science and Technology
- Mohamed Imad Bakhira, The University of Iowa
- Axel La Salle, Arizona State University
- Maia Powell, University of California, Merced
- Lee Przybylski, Iowa State University
- Wencel Valega Mackenzie, University of Tennessee
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. In this project, the students will use data from Gro to quantify the impact of climate change on crop yield, and create visualizations to demonstrate their findings. For example, they can use long-term climate data from Gro, to predict corn yield in Minnesota, 100 years from now. Based on the results, they might be able to conclude that Minnesota will no longer be suitable for growing corn in 100 years, or the areas suitable for corn will shift from the south to the north within Minnesota. Furthermore, they can scale the analysis to the whole globe, and create cool visualizations to show the results.
Data will be provided through Gro API (Python client). For data discovery and visualizations, the students can interact with the Gro web app directly. Once they decide what data to pull from Gro, they can export a code snippet and use the API client to download the data. Data pulled from Gro are in the format of time series, which are called data series. A data series is made up of data points, each with a start and end timestamp. Different data series can come from different sources, and have different frequencies. For example, there are projected monthly precipitation and air temperature from the GFDL B1 model all the way to year 2100, that are available across the whole world.
The deliverables of this project are two-fold: a Jupyter notebook (hosted on Infrastructure provided by Gro) and a visual presentation of the results. It can even be the combination of the two. The Jupyter notebook should be executable end-to-end, from fetching the data from Gro API, to export predictions as files, or as visualizations.
Concluding Remarks
Saturday, Nov. 7, 2020, 3:45 p.m. through Saturday, Nov. 7, 2020, 4 p.m.
Webcast
David Goldberg (Purdue University), Phil Kutzko (The University of Iowa), Oscar Vega (California State University)
Plenary Conversation II
Saturday, Nov. 7, 2020, 3 p.m. through Saturday, Nov. 7, 2020, 3:45 p.m.
Webcast
Donald Cole (University of Mississippi), David Goldberg (Purdue University), Fabrice Ulysse (University of Notre Dame), Oscar Vega (California State University)
Fields of Success - Stories from Math Alliance Alumni
Saturday, Nov. 7, 2020, 1:30 p.m. through Saturday, Nov. 7, 2020, 2:30 p.m.
Webcast
Julia Anderson-Lee (The Boeing Company), Alexander Diaz-Lopez (Villanova University), April Harry (Rover.com), Anarina Murillo (Brown University), Roberto Soto (California State University), Oscar Vega (California State University)
Report of the Math Alliance Leadership
Saturday, Nov. 7, 2020, 11:40 a.m. through Saturday, Nov. 7, 2020, 12:30 p.m.
Zoom
David Goldberg (Purdue University), Phil Kutzko (The University of Iowa), Kyndra Middleton (Howard University)
Plenary Conversation 1
Friday, Nov. 6, 2020, 1 p.m. through Friday, Nov. 6, 2020, 1:45 p.m.
Webcast
Ranthony Edmonds (The Ohio State University), Phil Kutzko (The University of Iowa), Victoria Uribe (Arizona State University)
Math-to-Industry Boot Camp V
Monday, June 22, 2020, 8 a.m. through Thursday, June 30, 2022, 5 p.m.
University of Minnesota
Advisory: Application deadline is February 28, 2020
Poster
Organizers: 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 Cargill, D-Wave Systems, Exxonmobil, Gro Intelligence, ITM TwentyFirst LLC, World Wide Technology.
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 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.
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 |
---|---|---|
Nawaf Alansari | Department of Mathematics | The Pennsylvania State University |
Gabrielle Angeloro | Department of Mathematics | Iowa State University |
Skye Binegar | School of Mathematics | Georgia Institute of Technology |
Nicole Bridgland | World Wide Technology | |
Cameron Cook | Department of Mathematics | University of Tennessee |
Ryan Coopergard | Department of Mathematics | University of Minnesota, Twin Cities |
Erica de la Canal | Department of Mathematics | The University of Texas at Austin |
Kari Eifler | Department of Mathematics | Texas A & M University |
Nazar Emirov | Department of Mathematics | University of Central Florida |
Alexander Estes | Institute for Mathematics and its Applications | University of Minnesota, Twin Cities |
Adeyemi Fagbade | Department of Mathematics and Statistics | University of Wyoming |
Jasmine Foo | School of Mathematics | University of Minnesota, Twin Cities |
Priyanga Ganesan | Department of Mathematics | Texas A & M University |
Alketa Henderson | University of North Carolina, Greensboro | |
Thomas Hoft | Department of Mathematics | University of St. Thomas |
Ruihao Huang | OCP/Division of Pharmacometrics | FDA |
Yu-Li Huang | Health Care Systems Engineering | Mayo Clinic |
Alicia Johnson | Department of Mathematics, Statistics, and Computer Science | Macalester College |
Marshall Lagani | Securian Financial | |
Kevin Leder | Department of Industrial System and Engineering | University of Minnesota, Twin Cities |
Chang Li | Department of Mathematics | University of Central Florida |
Sarah Miracle | Department of Computer and Information Sciences | University of St. Thomas |
Liban Mohamed | Department of Mathematics | University of Wisconsin, Madison |
Dhir Patel | Department of Mathematics | The Ohio State University |
Hansen Pei | Department of Mathematical Sciences | University of Delaware (Newark, DE, US) |
John Portin | Department of Mathematics | University of Kansas |
Nilay Shah | Kern Center for the Science of Health Care Delivery | Mayo Clinic |
David Shuman | Department of Mathematics, Statistics and Computer Science | Macalester College |
Daniel Spirn | University of Minnesota | University of Minnesota, Twin Cities |
Yanru Su | Department of Applied and Computational Mathematics | University of Kansas |
Radmir Sultamuratov | Department of Mathematics | Wayne State University |
Jidong Wang | Department of Mathematics | University of Oregon |
Katherine Weber | Department of Mathematics | University of Minnesota, Twin Cities |
Zhimin Wu | School of Mathematical and Statistical Sciences | Arizona State University |
Projects and teams
Project 1: Modeling equity-linked insurance benefits
- Mentor Marshall Lagani, Securian Financial
- Gabrielle Angeloro, Iowa State University
- Adeyemi Fagbade, University of Wyoming
- Priyanga Ganesan, Texas A & M University
- Chang Li, University of Central Florida
- Liban Mohamed, University of Wisconsin, Madison
- Radmir Sultamuratov, Wayne State University
- Jidong Wang, University of Oregon
It has become commonplace for insurance companies to offer products that link benefit guarantees to stock market indices, such as the S&P 500. Modeling the risks inherent in such a product requires a strong understanding of mathematical finance as well as significant computational resources. Derivatives instruments, primarily futures, options, and swaps, can be used to hedge the liability, providing an effective mitigation of product risks.
Participants will learn about variable annuities, a common equity-linked product, as well as some of the common derivatives instruments used to hedge the risks in these products. We will explore some of the techniques used to model the liabilities they generate and develop methods to create proxy models, allowing us to monitor risks and rebalance hedge positions intraday as the markets move in between model runs. This project assumes little to no background in mathematical finance and should be of interest to participants who are interested in computational statistics, quantitative finance, and Python.
Project 2: Optimizing warehouse operations
- Mentor Nicole Bridgland, World Wide Technology
- Cameron Cook, University of Tennessee
- Erica de la Canal, The University of Texas at Austin
- Kari Eifler, Texas A & M University
- Nazar Emirov, University of Central Florida
- Hansen Pei, University of Delaware (Newark, DE, US)
- John Portin, University of Kansas
- Katherine Weber, University of Minnesota, Twin Cities
Supply chain operations motivate many data science and optimization problems. From a demand and pricing perspective, one might ask: how much of item X do we anticipate selling? How much do we expect it to pay for it, depending on when we buy it? From a storage and operations perspective, one might ask how we best store it in warehouses to get it to where it's going. Do we have enough warehouse space for all the stuff we will need to store in the near future? What are the error bars on that space usage estimate? There's plenty of questions from a purely operational perspective as well. For example, in a busy warehouse, forklift traffic can cause significant slowdowns. A forklift at one load or drop-off location may block access to several locations in the warehouse. Forklifts waiting to enter one row could block the major paths through the warehouse. This project is directed at optimizing internal warehouse transit operations, through any of storage location choices, job scheduling, or pathing choices.
Project 3: Bone marrow transplant process modeling and optimization
- Mentor Yu-Li Huang, Mayo Clinic
- Nawaf Alansari, The Pennsylvania State University
- Skye Binegar, Georgia Institute of Technology
- Ryan Coopergard, University of Minnesota, Twin Cities
- Alketa Henderson, University of North Carolina, Greensboro
- Dhir Patel, The Ohio State University
- Yanru Su, University of Kansas
- Zhimin Wu, Arizona State University
Bone Marrow Transplant (BMT) is an effective treatment for many hematological malignancies. This modality has become integral to the management of many patients resulting in a dramatic increase in the volume of patients undergoing the procedure. The volume of patients coming for transplant (about 500 patients undergo this highly complex procedure annually at Mayo Clinic Rochester) has progressively increased over the past decade leading to many innovative solutions to adapt to this challenge. Over the past two decades the infrastructure has been developed to allow a majority of patients to undergo many components of the procedure as an outpatient visit despite the highly complex nature of the patients and associated risk of complications. Ultimately we have reached maximum safe capacity with our current workflow. This has posed major stresses on many areas including patient scheduling, stem cell collection, outpatient visit, human cellular therapy laboratory, hospital based outpatient facility, and inpatient facility. BMT practice has recently implemented a predictive model for stem cell collections. This model is expected to increase capacity by 20% with the same resources. The practice also adopted pre scheduling concept to plan for entire patient transplant itinerary starting from stem cell collections, pre-chemo visits, to chemo treatment and stem cell infusion. There are uncertainties in all three stages due to patient conditions, resource constraints, and process complexity. This short term project will focus on modeling and optimizing the stochastic nature of these three stages which could potentially provide recommendations for scheduling policy and resource planning.
Math-to-Industry Boot Camp IV
Monday, June 24, 2019, 8 a.m. through Friday, Aug. 2, 2019, 5 p.m.
University of Minnesota
Advisory: Extended application deadline is March 22, 2019
Organizers:
- Benjamin Brubaker, University of Minnesota, Twin Cities
- Fadil Santosa, University of Minnesota, Twin Cities
- 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. 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.
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 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.
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 |
---|---|---|
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.
Math-to-Industry Boot Camp III
Monday, June 18, 2018, 8 a.m. through Friday, July 27, 2018, 5 p.m.
University of Minnesota
Advisory: Application deadline is February 28, 2018
Organizers:
- Benjamin Brubaker, University of Minnesota, Twin Cities
- Fadil Santosa, University of Minnesota, Twin Cities
- 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.
Weekly seminars by industrial scientists 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 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.
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 |
---|---|---|
Muhammad Afridi | 3M | |
Nicholas Asendorf | 3M | |
Christopher Bemis | Whitebox Advisors | |
Nitsan Ben-Gal | Software, Electronics and Mechanical Systems Laboratory | 3M |
Jesse Berwald | D-Wave Systems | |
Ariel Bowman | Department of Mathematics | University of Texas at Arlington |
Chris Browne | Center for Applied Mathematics | Cornell University |
Benjamin Brubaker | School of Mathematics | University of Minnesota, Twin Cities |
Kate Brubaker | Department of Mathematics | Purdue University |
Irfan Bulu | Department of Math and Modeling | Schlumberger-Doll Research |
Shawn Burkett | Mathematics | University of Colorado |
Olivia Cannon | Department of Mathematics | University of Minnesota, Twin Cities |
Jared Catenacci | Diagnostic Research and Material Studies | National Security Technologies, LLC |
Chirasree Chatterjee | Department of Mathematics and Statistics | Saint Louis University |
Hua Chen | Department of Mathematical Sciences | University of Delaware |
Aaron Cohen | Department of Mathematics | Indiana University |
Paula Dassbach | Medtronic | |
Mingchang Ding | Department of Mathematical Sciences | University of Delaware |
Jasmine Foo | School of Mathematics | University of Minnesota, Twin Cities |
Zhen Gao | Department of Mathematics | Vanderbilt University |
Maria Gommel | Department of Mathematics | The University of Iowa |
Hayley Guy | School of Mathematics | North Carolina State University |
Qie He | Department of Industrial and Systems Engineering | University of Minnesota, Twin Cities |
Thomas Hoft | Department of Mathematics | University of St. Thomas |
Ruihao Huang | Department of Mathematical Sciences | Michigan Technological University |
Jeffrey Humpherys | UnitedHealth Group | |
Laura Iosip | Department of Mathematics | University of Maryland |
Melanie Jensen | Department of Mathematics | Tulane University |
Alicia Johnson | Macalester College | |
Ekaterina Kryuchkova | Center for Applied Mathematics | Cornell University |
Kevin Leder | Department of Industrial System and Engineering | University of Minnesota, Twin Cities |
Philku Lee | Department of Mathematics and Statistics | Mississippi State University |
SangJoon Lee | Department of Mathematics | University of Connecticut |
Hengguang Li | Department of Mathematics | Wayne State University |
Aaron Luttman | Diagnostic Research and Material Studies | National Security Technologies, LLC |
Christopher Miller | School of Mathematics | University of California, Berkeley |
Cristian Minoccheri | Department of Mathematics | State University of New York, Stony Brook (SUNY) |
Sarah Miracle | Department of Computer and Information Sciences | University of St. Thomas |
Shannon Negaard-Paper | University of Minnesota, Twin Cities | |
Elpiniki Nikolopoulou | Department of Applied Mathematics and Statistics | Arizona State University |
Michelle Pinharry | School of Mathematics | University of Minnesota, Twin Cities |
Iurii Posukhovskyi | Department of Mathematics | University of Kansas |
Mrinal Raghupathi | USAA Asset Management Company | USAA Asset Management Company |
Michael Ramsey | Department of Applied Mathematics | University of Colorado |
Eric Roberts | Department of Applied Mathematics | University of California, Merced |
Tanushree Roy | School of Mathematics | University of Central Florida |
Keith Rush | Department of Strategy and Analytics | Milwaukee Brewers |
Fadil Santosa | School of Mathematics | University of Minnesota, Twin Cities |
Chang Shu | Department of Applied Mathematics | University of California, Davis |
Dallas Smith | School of Mathematics | Brigham Young University |
Alberto Speranzon | Aerospace | Honeywell |
Daniel Spirn | University of Minnesota | University of Minnesota, Twin Cities |
Binh Tang | Department of Statistical Science | Cornell University |
Elizabeth Wicks | School of Mathematics | University of Washington |
Shiqiang Xia | University of Minnesota, Twin Cities | |
Di Ye | Zhennovate | |
Yufei Yu | Department of Mathematics | University of Kansas |
Sheng Zhang | Department of Mathematics | Purdue University |
Projects and teams
Team 1: Mathematical Models for Adaptive Multi-modal Sensing
- Mentor Aaron Luttman, National Security Technologies, LLC
- Mentor Jared Catenacci, National Security Technologies, LLC
- Ariel Bowman, University of Texas at Arlington
- Shawn Burkett, University of Colorado
- Hayley Guy, North Carolina State University
- Laura Iosip, University of Maryland
- Yufei Yu, University of Kansas
- Sheng Zhang, Purdue University
Scientific experiments are a natural source of data – which usually means diagnostic systems fielded to collect information within the experiments themselves – but there has been a recent trend towards collecting data around big science experiments to understand if we can detect and characterize the behaviors associated with the experiments. The question is whether it is possible to determine what experiments are being conducted by analyzing human patterns, so-call “patterns of life,” around and in the experimental facilities. In order to measure patterns of life, we analyze many different types of data, from power grid load profiles to internet activity to sound and pressure signals from cars.
There are two primary challenges that must be addressed:
Mathematical Models for Adaptive Sensing – When should a sensor system turn on its sensors and transmit its data, given that these two activities take a lot of power?
Physics-based Multi-modal Feature Selection and Detection – How can one incorporate physics models for sensing into machine learning approaches to data analysis?
Real multi-sensor data will be provided for testing and validation.
Team 2: Quantum Computation and QUBO Slicing
- Mentor Jesse Berwald, D-Wave Systems
- Olivia Cannon, University of Minnesota, Twin Cities
- Tanushree Roy, University of Central Florida
- Chang Shu, University of California, Davis
- Dallas Smith, Brigham Young University
- Elizabeth Wicks, University of Washington
Background
Quantum annealing computers have begun to enter the business and academic worlds. Over the past five years they have been used for a wide variety of (prototypical) applications, with evidence of differentiated performance in some cases.
A first step in utilizing these computers is to reformulate the problem in an energy minimization framework. This is typically cast as a Hamiltonian, or alternatively as a quadratic unconstrained binary optimization (QUBO), which can be represented as a matrix. These formulations are translated to the physical qubits on the quantum processing unit (QPU) through a process termed “embedding”. Embedding a given problem onto the QPU is handled through a number of different heuristics and is an active area of research in itself, one of which is described below.
Problem statement
In this project we will investigate one proposed solution to the embedding problem:
The goal is to make the most efficient use of the qubit hardware by developing a parameterized transformation from the space spanned by physical qubits, “qubit space”, to the space spanned by problem variables, the “problem search space”. Our goal will be to define a linear transformation from qubit space to problem search space that allows for a more efficient use of available hardware.
Since the problem space is (in general) much larger than the qubit space, a fixed parameterization will succeed in mapping the qubit space into an proper subspace of the problem space. We term these subspaces “slices”. This reduced problem can then be solved with an optimal use of the available hardware. Using different parameterizations, we can define a series of linear transformations onto orthogonal subspaces of the problem space.
There are many parameterizations to choose from, each of which raises a number of research questions. We will prioritize our investigation roughly as follows:
- Given a QUBO matrix defining the problem search space, is there an algorithm that produces the most efficient set of transformations (parameterizations) from qubit space to problem space?
- Is there a greedy algorithm that is best in practice — i.e. choose a slice that maximizes the use of the chip, and then choose successively smaller slices to query the entire search space.
- What is the role of sparsity in the choice of transformations?
- The QPU itself has a unique architecture. How does this architecture affect the choice of transformations?
References
- Traffic flow optimization using a quantum annealer: https://arxiv.org/pdf/1708.01625.pdf
- A NASA Perspective on Quantum Computing: Opportunities and Challenges: https://arxiv.org/pdf/1704.04836.pdf
Team 3: Time Series Analysis of Gas Mixture Data
- Mentor Nicholas Asendorf, 3M
- Kate Brubaker, Purdue University
- Ruihao Huang, Michigan Technological University
- Philku Lee, Mississippi State University
- Elpiniki Nikolopoulou, Arizona State University
- Michelle Pinharry, University of Minnesota, Twin Cities
Motivation
Sensor networks are ubiquitous in today’s Internet of Things, capable of collecting high frequency data in a cost efficient way. This results in mountains of time-series data that hopefully contain signals of interest buried in noise. As the number of deployed sensors grows, so does the dimensionality of the observed data, further increasing the complexity of the problem. 3M is interested in such large scale time series analyses because many of our datasets can be framed in this way: manufacturing, sales, and chemical experiments to name a few.
Dataset
This publicly available dataset contains time series sensor readings from chemical sensors over the duration of 12 hours. The input to these sensors are known concentrations of various gases. The dataset contains timestamped measurements from 16 gas sensors and the input concentrations of the gases. This is a labeled time series dataset. There are two different gas mixture measurement files, one for Ethylene and CO, and one for Ethylene and Methane. At 3M, we may have similar types of experimental data (perhaps using different sensors) where we would like to determine the interactions between materials or understand fundamental properties of materials. Being able to intelligently and efficiently mine these rich datasets for insights about material characteristics is critical.
The Challenge
Some interesting problems to consider:
- Develop an algorithm to estimate the concentration of each gas given sensor measurements. You might approach this problem using classical machine learning, splitting data into training, validation, and testing, while treating time series measurements as independent points.
- Develop algorithms to estimate the concentrations of each gas using time series based methods like windowing, tsfresh, or RNNs. In this approach, we don’t want to treat each measurement as independent. How do these algorithms compare to classical machine learning techniques?
- Can you use the fact that we have 4 replicates of each sensor at each time point to improve your algorithms? Can you use any clever data fusion techniques or outlier detection strategies?
- What can you tell about the importance or accuracy of the 4 types of sensors used?
- What happens when we purposely introduce missing data? Can we use the replicates of each sensor to overcome this? How robust are your algorithms to missing data?
- Since each dataset has measurements for Ethylene, can we use both datasets to develop a more robust estimation scheme for that gas?
Team 4: Structured Variational Auto Encoders
- Mentor Irfan Bulu, Schlumberger-Doll Research
- Hua Chen, University of Delaware
- Aaron Cohen, Indiana University
- Mingchang Ding, University of Delaware
- Melanie Jensen, Tulane University
- Christopher Miller, University of California, Berkeley
- Michael Ramsey, University of Colorado
Generative models such as Variational Auto Encoders (VAE), Generative Adversarial Networks(GAN) have been very successful in unsupervised learning settings. In a VAE setting, we would like to learn a set of latent variables that explain our data. Although, this has been very successful as a generative model, the interpretation of latent variables is still a challenge. Ideally, what we would like to do is unsupervised learning through which we identify a number of classes (not specified yet). Once a set of classes has been identified, we can then label once instead of having to label the entire data set. Imagine you have a sample of handwritten digits without labels. If we can structure VAE in a way that it can identify 10 classes, we can then go label these classes as the relevant digits. This would be very helpful as most of our data is unlabeled or poorly labeled.
Concepts that may be helpful to know: neural network, generative models, graphical models, stochastic variational inference.
Team 5: Tailored Discovery in Stock Portfolios
- Mentor Christopher Bemis, Whitebox Advisors
- Chirasree Chatterjee, Saint Louis University
- Zhen Gao, Vanderbilt University
- Cristian Minoccheri, State University of New York, Stony Brook (SUNY)
- Shannon Negaard-Paper, University of Minnesota, Twin Cities
- Shiqiang Xia, University of Minnesota, Twin Cities
Modern portfolio theory has provided tools to identify systemic and idiosyncratic risks via models like Markowitz' Mean-Variance Optimization. In addition, a taxonomy of equities has emerged through feature identification, with one of the earliest and most impactful being Fama and French's three factor model.
In this project, we will leverage technical and fundamental data like return series and earnings information along with well understood equity features like exposure to so-called size, value, and market portfolios to develop tools for suggesting supplements (e.g., technology stocks when looking at Apple) and complements (e.g., energy stocks when looking at Delta Airlines) for individual equities and portfolios. These tools may be used in tailored discovery and research by analysts looking to either construct a portfolio based on a theme or to diversify. The work will ideally evolve from point estimates using simple norms in a predetermined feature space to applying machine learning techniques.
Data will be supplied from Quandl, and the preferred language for development will be Python.
Team 6: Sequence-to-sequence modeling for the business of baseball
- Mentor Keith Rush, Milwaukee Brewers
- Maria Gommel, The University of Iowa
- Ekaterina Kryuchkova, Cornell University
- SangJoon Lee, University of Connecticut
- Iurii Posukhovskyi, University of Kansas
- Eric Roberts, University of California, Merced
Each fan has a unique relationship to his or her favorite sports teams, and each has a different ideal every time they step into the stadium. When a team makes a big free-agent signing in February, the fan who follows he competition closely will be ecstatic--the fan who primarily enjoys the communal aspects will only see this effect in the buzz generated in his or her social circles. In order to cherish their fans to the utmost, teams must have a global view of their business and be able to structure data from all sources and across all levels of granularity, creating one universe into which all inputs and from which all outputs feed.
This project is fundamentally a first step in that direction. The problem we are focusing on is roughly the following: conditioned on a vector representing a fan's history with the Club and the attributes of a particular game, how well can we ingest information in time and map it forward one time step. For this purpose, we will test the standard recurrent and convolutional network architectures, as well as experimenting with variants and discussing the reasons for applying each and their limitations. Data will be provided from the Brewers and the development will take place in Python, utilizing cloud infrastructure for the computing power.