Math-to-Industry Boot Camp II

• 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/)

• 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.

• 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.

• 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)

• 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.

• 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?