Math-to-Industry Boot Camp II
Advisory: Application deadline is February 17, 2017
Organizers:
- 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 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
- 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.
- 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).
- 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?
