Past Events

Winter Math-to-Industry Boot Camp

2021 Winter Math-to-Industry Boot Camp poster

Advisory: Application deadline is Friday, December 4, 2020 

2021 Winter Virtual Boot Camp poster

Organizers: 

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

David Goldberg (Purdue University), Phil Kutzko (The University of Iowa), Oscar Vega (California State University)

Plenary Conversation II

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

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

David Goldberg (Purdue University), Phil Kutzko (The University of Iowa), Kyndra Middleton (Howard University)

Plenary Conversation 1

Ranthony Edmonds (The Ohio State University), Phil Kutzko (The University of Iowa), Victoria Uribe (Arizona State University)

Math-to-Industry Boot Camp V

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

Advisory: Extended application deadline is March 22, 2019

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 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

Advisory: Application deadline is February 28, 2018

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.

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:

  1. 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?
  2. 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.
  3. What is the role of sparsity in the choice of transformations?
  4. The QPU itself has a unique architecture. How does this architecture affect the choice of transformations?
References

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.

Math-to-Industry Boot Camp II

Advisory: Application deadline is February 17, 2017

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 will work in teams on projects and will be provided with soft skills training.

There will be 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 will be posed by industry scientists. The students will be able to interact with industry participants at various points in the program. 

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 by March 17.

Participants

Name Department Affiliation
Sameed Ahmed Department of Mathematics University of South Carolina
Christopher Bemis   Whitebox Advisors
Amanda Bernstein Department of Mathematics North Carolina State University
Jesse Berwald Enterprise Data Analytics & Business Intelligence Target Corporation
Neha Bora Department of Mathematics Iowa State University
Jeremy Brandman Computational Physics ExxonMobil
Phillip Bressie Mathematics Kansas State University
Nicole Bridgland School of Mathematics University of Minnesota, Twin Cities
Yiying Cheng Department of Mathematics University of Kansas
Michael Dairyko Department of Mathematics Iowa State University
Miandra Ellis School of Mathematical and Statistical Sciences Arizona State University
Wen Feng Department of Applied Mathematics University of Kansas
Jasmine Foo School of Mathematics University of Minnesota, Twin Cities
Melissa Gaddy Department of Mathematics North Carolina State University
Thomas Grandine   The Boeing Company
Ngartelbaye Guerngar Department of Mathematics and Statistics Auburn University
Jamie Haddock Department of Applied Mathematics University of California, Davis
Madeline Handschy   University of Minnesota, Twin Cities
Qie He Department of Industrial and Systems Engineering University of Minnesota, Twin Cities
Thomas Hoft Department of Mathematics University of St. Thomas
Tahir Bachar Issa Department of Mathematics and Statistics Auburn University (Auburn, AL, US)
Alicia Johnson   Macalester College
Cassidy Krause Department of Mathematics University of Kansas
Kevin Leder Department of Industrial System and Engineering University of Minnesota, Twin Cities
Gilad Lerman School of Mathematics University of Minnesota, Twin Cities
Hongshan Li Department of Mathematics Purdue University
Wenbo Li Applied Mathematics & Statistics, and Scientific Computation University of Maryland
Youzuo Lin   Los Alamos National Laboratory
John Lynch Department of Mathematics University of Wisconsin, Madison
Eric Malitz Department of Mathematics, Statistics and Computer Science University of Illinois, Chicago
Tianyi Mao Department of Mathematics City University of New York
Emily McMillon Department of Mathematics University of Nebraska
Christine Mennicke Department of Applied Mathematics North Carolina State University
Kacy Messerschmidt Department of Mathematics Iowa State University
Sarah Miracle Department of Computer and Information Sciences University of St. Thomas
Ngai Fung Ng   Purdue University
Hieu Nguyen Institute for Computational Engineering and Sciences The University of Texas at Austin
Kelly O'Connell Department of Mathematics Vanderbilt University
Luca Pallucchini   Temple University
Karoline Pershell Strategy and Evaluation Division Service Robotics & Technologies
Fesobi Saliu Department of Mathematical Sciences University of Memphis
Fadil Santosa Institute for Mathematics and its Applications University of Minnesota, Twin Cities
Richard Sharp   Starbucks
Samantha Shumacher   Target Corporation
Sudip Sinha Department of Mathematics Louisiana State University
Ryan Siskind   Target Corporation
Daniel Spirn University of Minnesota University of Minnesota, Twin Cities
Anna Srapionyan Center for Applied Mathematics Cornell University
Trevor Steil School of Mathematics University of Minnesota, Twin Cities
Andrew Stein Department of Modeling and Simulation Novartis Institute for Biomedical Research
Aditya Vaidyanathan Center for Applied Mathematics Cornell University
Zachary Voller   Target Corporation
Zhaoxia Wang   Louisiana State University
Dara Zirlin Mathematics Department University of Illinois at Urbana-Champaign

 

Projects and teams

Team 1: A Dictionary-Based Remote Sensing Imagery Classification/Clustering Techniques: Features Selection, Optimization Methods

  • Mentor Youzuo Lin, Los Alamos National Laboratory

Remotely sensed imagery classification/clustering seek grouped pixels to represent land cover features. It has broad applications across engineering and sciences domains. However, because of the large volume of imagery data and limited features available, it is challenging to correctly understand the contents within the imagery. This project team will develop efficient and accurate machine-learning methods for remotely sensed imagery classification/clustering. To achieve this goal, we will explore various image classification/clustering methods. In particular, we are interested dictionary-learning based image analysis methods. Being one of the most successful machine-learning methods, dictionary learning has shown promising performances in various machine learning applications. In this project, the team will focus on the following tasks:

  •  look into a couple of state-of-the-art dictionary learning methods including K-SVD [1] and SPORCO [2]
  •  apply dictionary-learning technique to remotely sensed imagery classification/clustering
  •  compare performances of employing different dictionary-learning methods
  •  analyze computational costs, and further improve the computational efficiency

Out of this project, the team will be able to learn the fundamentals of machine learning with applications to image analysis, understand the specific computational tools for solving large-scale applications, and be capable of solving real problems with those aforementioned techniques.

References:

[1] K-SVD: M. Aharon, M. Elad and A. Bruckstein, "K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation," IEEE Transactions on Signal Processing, vol. 54, no. 11, pp. 4311-4322, 2006. (Sources Available at http://www.cs.technion.ac.il/~elad/software/)

[2] SPORCO: B. Wohlberg, "Efficient Algorithms for Convolutional Sparse Representations," IEEE Transactions on Image Processing, vol. 25, no. 1, pp. 301-315, 2016. (Sources Available at http://brendt.wohlberg.net/software/SPORCO/)

Team 2: Optimizing Well Placement in an Oil Reservoir

  • Mentor Jeremy Brandman, ExxonMobil

Oil and gas – also known as hydrocarbons – are typically found thousands of meters below the earth’s surface in the void space of sedimentary rocks. The extraction of these hydrocarbons relies on the operation of injection and production wells.

Injection wells are used to displace hydrocarbons through the injection of other fluids (e.g. water and CO_2) and maintain overall reservoir pressure. Production wells are responsible for extracting reservoir fluids from the rocks and transporting them to the surface.

Drilling a well is expensive – the cost can be in the hundreds of millions of dollars – and time-consuming. Therefore, it is imperative that wells are placed and operated in a manner that optimizes reservoir profitability. The goal of this project is to develop a well placement strategy that addresses this business need.

The project’s focus will be non-invasive (i.e., black-box or derivative-free) optimization strategies for well placement. Non-invasive approaches are appealing because they do not require access to the computer code used to simulate the flow of hydrocarbons and other fluids. This is an important consideration as industrial flow simulators are complex and constantly in flux, making gradient information potentially difficult to acquire.

In order to test ideas and verify algorithms, the project will begin by considering well placement optimization in the context of a homogeneous two-dimensional reservoir. Following this, students will consider problems in heterogeneous reservoirs inspired by real-world examples.

Students will be provided with a flow simulator written in C that can be coupled to optimization algorithms written in C or Python. An introduction to modeling fluid flow in porous media will also be given.

Team 3: Machine Tool and Robot Calibration through Kinematic Analysis: A Least Squares Approach

  • Mentor Thomas Grandine, The Boeing Company

Modern machine tools and robots are constructed by assembling sequences of joints and linkages. An end effector, typically a cutter, tool, probe, or other device is attached to the end of the last linkage. Control of these devices is accomplished through a controller through which the location of the various components are programmed. In the usual cases, programming these joint and linkage locations leads to a programmed nominal position for the end effector. Because of mechanical variation and other sources of error, the nominal programmed location of the end effector and the actual location of the end effector are not exactly the same. Most controllers are equipped with compensation functions to account for this, so that the actual location of the linkages is set to the nominal position plus a correction term with the intent that the final position of the actual end effector should be much closer to the intended nominal position. One way of constructing the compensation functions is to program the machines to move the end effector to a collection of different locations. The actual location of the end effector is then measured using some independent means, often a laser scanner or other device, and the difference between the actual end effector location and the nominal end effector location can be measured. Given these discrepancies, a nonlinear least squares problem can be formulated from which accurate error functions can be constructed. In this workshop, we will review the standard methods for solving these problems and then explore some potential new ways of modeling the error functions with a view toward taking this good procedure and making it even better.

Team 4: Personalized Marketing

  • Mentor Richard Sharp, Starbucks

The goal of personalized marketing is to send the right message to the right person at the right time. Rules-based, targeted marketing suffers from a measurement problem: it works on average, being useful for some but irrelevant for others, and you can’t tell one group from the other. Online retailers are generally better able to track individual customer behavior than their brick and mortar counterparts, but still suffer from an inability to put that behavior in context. A common result is that a shed (or book or shoes or tent or whatever) chases you around the internet. Yes, you searched for it, but then you went down to the store and bought it in person. The next time that add pops up it’s gotten the behavior right, but completely missed the context: right message, right person, wrong time.

Personalized marketing attempts to reduce the inefficiency of targeted marketing by making algorithmic, rather than rules-based decisions, that treat the recipient as an individual rather than a representative of a general class. Challenges include discovering useful behavioral and contextual clues in a mountain of transactional and other data, determining an optimal decision strategy for making use of that information towards some objective, and selecting the objective itself. Unsurprisingly, increasing revenue is a common objective, but so is increasing engagement (or similarly decreasing churn) and objectives can range as widely as supporting health related decisions like smoking cessation or helping individuals make better financial decisions.

We will develop a mathematical model that is part of a working system for making offer decisions. Some of the significant topics we will work to address are:

  • measuring incremental impact
  • behavioral and contextual feature engineering
  • decision strategies and objectives
  • continual operation in a real-world setting (including feedback for system operators)

Team 5: Supporting oncology drug development by deriving a lumped parameter for characterizing target inhibition in standard math

  • Mentor Andrew Stein, Novartis Institute for Biomedical Research

During the development of biotherapeutic drugs, modelers are often asked to predict the dosing regimen needed to achieve sufficient target inhibition for efficacy in a solid tumor [1, 2]. Previous work showed that under many relevant clinical scenarios, target inhibition in blood can be characterized by a single lumped parameter: Kd*Tacc/Cavg, where Kd is the binding affinity of the drug, Tacc is the fold-accumulation of the target during therapy, and Cavg is the average drug concentration under the dosing regimen of interest [3]. This project will focus on extending these results to characterizing target inhibition in a tumor, to assist in development of targeted therapies and immunotherapies in oncology.

References
  1. Deng, Rong, et al. "Preclinical pharmacokinetics, pharmacodynamics, tissue distribution, and tumor penetration of anti-PD-L1 monoclonal antibody, an immune checkpoint inhibitor." MAbs. Vol. 8. No. 3. Taylor & Francis (2016) Suppl Fig 5.
  2. Lindauer, A., et al. "Translational Pharmacokinetic/Pharmacodynamic Modeling of Tumor Growth Inhibition Supports Dose‐Range Selection of the Anti–PD‐1 Antibody Pembrolizumab." CPT: Pharmacometrics & Systems Pharmacology (2017).
  3. Stein AM, Ramakrishna R. "AFIR: A dimensionless potency metric for characterizing the activity of monoclonal antibodies." Clin. Pharmacol. Ther: Pharmacometrics and Systems Pharmacol, doi 10.1002/psp4.12169, 2017.

Team 6: How do robots find their way home? Optimizing RFID beacon placement for robot localization and navigation in indoor spaces

  • Mentor Karoline Pershell, Service Robotics & Technologies

While map apps on mobile devices are excellent for getting around town, they are not precise enough to use within buildings. We are currently working on deploying service robots (vacuuming, security, mail delivery) throughout a facility, and the robotic systems will navigate the space based on a pre-made facility map and built-in obstacle avoidance technology. However, a robot still needs to localize itself within the map (i.e., determine where it is on the map) at regular intervals. Using RFID beaconing technology to triangulate position is a promising option for localization. Given a map and RFID readings along a path, can we extrapolate the signal strength to any point in the map. That is, can we develop a model that will allow a robot to localize on a map? How do we optimize the placement (and other variable settings) of beacons to reduce cost but ensure localization? How can we model reduced signals (e.g., beacons in neighboring rooms who signal is coming through a wall), and differentiate between reduced signals and beacons that are far away, acknowledging that signal strength is often variable?