IMA/MCIM Industrial Problems Seminar

In collaboration with the University of Minnesota’s School of Mathematics, the Industrial Problems Seminars are a forum for industrial researchers to present their work to an audience of IMA postdocs, visitors, and graduate students, offering a first-hand glimpse into industrial research. The seminar series is often useful for initiating contact with industrial scientists. The IMA’s seminar series is the oldest and longest running seminar series in industrial mathematics.

This year's seminars are organized by Daniel Spirn, School of Mathematics, University of Minnesota.

All seminars are from 1:25pm - 2:25pm unless otherwise noted.
  • Automating the Crash Test Planning Decision-Making Process

    Daniel Reich, Ford Motor Company
    February 28, 2014
    Lind 305 [Map]


    Abstract: Ford's Safety organization performs crash tests on prototype vehicles at multiple planning phases of each new vehicle program. These tests ensure the vehicles meet all government and company requirements by the time the vehicles reach the production phase. However, crash tests are quite expensive to perform due to the high cost of prototype vehicles compared with that of production vehicles. Accordingly, improvements in scheduling that reduce the number of prototypes crashed yield a significant cost savings. This scheduling problem has many sources of complexity: varying deadlines, precedence relationships between tests, incompatibilities in vehicle specs required, etc. Currently, engineers spend weeks of time manually planning the crash schedule for each new vehicle program and coordinating with all the other prototype vehicle users. We are developing an automated system for crash test planning that both minimizes the resources needed and significantly reduces the time engineers spend planning. Co-Authors: University of Michigan - Yuhui Shi, Amy Cohn, Marina Epelman Ford Motor Company - Erica Klampfl Bio: Daniel Reich joined Ford Motor Company, Research & Advanced Engineering, as an Operations Research Analyst in 2011. He received his Bachelor of Science from Columbia University's School of Engineering and Applied Science in 2004 and his PhD in Applied Mathematics from the University of Arizona in 2009. Before joining Ford, Daniel spent two years as a Postdoctoral Research Fellow at the Universidad Adolfo Ibanez School of Business in Santiago, Chile. During his graduate studies and postdoctoral work, Daniel authored several papers on computationally tractable heuristic approaches for optimization under uncertainty. Daniel continues to retain strong ties to the academic community through the Ford-University of Michigan Alliance and Ford-MIT Alliance programs. Early work from his collaboration with MIT on electric vehicle routing was selected as a semi-finalist for the 2013 INFORMS Innovative Applications in Analytics Award. Daniel's current projects at Ford include developing optimization models for scheduling, sales and other applications. He co-developed Ford's Fleet Purchase Planner, which has been featured in Wards Auto, Green Car Reports, Automotive Fleet Magazine and other publications. This work also received the Best Paper Award at the 2013 International Conference on Operations Research and Enterprise Systems and is a finalist for this year's INFORMS Innovative Applications in Analytics Award.
  • Extracting information from ECG using robust, computationally efficient methods

    Marina Brockway, VivaQuant
    April 4, 2014
    Lind 305 [Map]


    The availability of ultra-low-power computing devices, low-power radio communications, and the internet of things is bringing about a revolution in body worn devices for monitoring and diagnosing health and fitness. Accurate extraction of information from the signals measured by these devices can be challenging, especially for signals such as electrocardiogram (ECG) that become quite noisy during patient movement. ECGs are commonly monitored to obtain heart rate (HR), arrhythmia incidence, and interval measurements that can indicate whether electrical currents are being conducted normally through the heart. For example, it’s common for people to monitor their HR during exercise. Advances in fabrics and materials is bringing about new, more comfortable, garments with embedded ECG electrodes that are easy to wear but result in extremely noisy ECGs that make extraction of even simple information such as HR quite difficult. Another example is monitoring ECG in drug safety studies in ambulatory subjects to measure PR, QRS, and QT intervals. These studies are required on all drugs to obtain marketing approval from the FDA and foreign regulatory bodies. The challenge in this application is to provide accurate measurements with low variability, despite the presence of noise.

    Yet another example is the monitoring and diagnosis of patients with transient ventricular and atrial arrhythmia using small body worn devices such as event recorders and mobile cardiac telemetry. This latter example represents a very large societal need and market opportunity, as roughly 2.6 million patients undergo this procedure annually in the US. This number will grow significantly as the population ages. A significant issue with current technology is that subpar sensitivity and specificity of ECG arrhythmia detection algorithms results in a longer than necessary time to diagnosis and a high number of false positive events. This translates to a much higher than necessary cost of care for these patients.

    To address these issues, VivaQuant, with support from the National Institute of Health, has developed a novel embedded algorithm for processing ECGs in body worn devices. This algorithm provides 95% reduction in in-band noise amplitude without distorting ECG morphology and provides arrhythmia event detection accuracy that is far superior to existing algorithms. The algorithm is also very computationally efficient, requiring only about 60 micro-watts to process a single lead ECG in real time. This algorithm will enable the development of very small battery powered body worn devices for monitoring patients undergoing monitoring and diagnosis of cardiac arrhythmias.

    Marina Brockway is founder and Chief Technology Officer at VivaQuant, a company focused on developing technology for accurate and efficient extraction of information from ECGs and other physiological signals. Prior to that, she worked at the Cardiac Rhythm Management Division of Guidant/ Boston Scientific. She was an Industrial Postdoctoral Fellow at the Institute for Mathematics and its Applications in 1997-1999. Dr. Brockway has a PhD in Applied Mathematics and an MBA. She is named as an inventor on 68 issued and pending patents.

Previous Industrial Seminars