Category: Havens

From the ICC Director: Reflections and Goals

Dear ICC Members and Friends,

Happy New Year! As we begin the new year and the Spring 2020 semester, I wanted to offer some reflections about the 2019 and share some goals for 2020.

For the ICC, the past six months have been thrilling, to say the least. The number of new awards is far above last year, with over $2 million in new projects to-date. And ICC research expenditures are on track for a record year. Thank you to everyone for all your hard work in developing collaborations, writing proposals, winning awards, executing your exciting research, mentoring, advising, and so much more.

The launch of Michigan Tech’s new College of Computing is such a fantastic opportunity. With this shift, we boldly announce that computing is a major field of study and not just an underpinning to other disciplines. I see the new College as a place of opportunity to experiment, collaborate, develop new pedagogies, and become a model for other institutions of higher learning. Our team is strong and creative, and it’s fun working on this puzzle with them.”

As the ICC is the research arm of the College of Computing, we are very much a part of the strategic vision for research in the College. This integration allows us to best utilize the finite resources of both the College of Computing and the ICC to realize the greatest return on key investments in people and resources.

To further support our members, the ICC has secured donor funding  that will make it possible to hire two key personnel in 2020. First, a search for a full-time assistant director for research development is underway. This new position will support ICC researchers as they collectively work to create and implement activities aimed at the growth and development of ICC-affiliated research and graduate programs, including pre- and post-award support, assisting with the financial processes of the institute, and helping to lead the daily administrative functions of the institute. We will also be starting a search soon for the first full-time Research Scientist in the ICC. More details on these hires will made public soon.

I’m very much looking forward to working with all of you in the new year.

Timothy C. Havens
Director, Institute for Computing and Cybersystems

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Tim Havens Named Associate Dean for Research

Timothy C.  Havens, the William and Gloria Jackson Associate Professor of Computer Systems and the director of the Institute of Computing and Cybersystems, has been appointed the associate dean for research for the College of Computing, effective immediately. 

In his new role, Havens will encourage and enable research success in the College and promote collaborative, cross-disciplinary research and learning experiences through research support and development, communication and marketing, advancement, and College strategy and planning.

“Tim is highly passionate about supporting research creativity and pushing the boundaries of computational knowledge.  He also has a strong history of supporting student degree completion and growing Michigan Tech’s reputation,” said Dean Adrienne Minerick, College of Computing. “For these reasons and more, he is an outstanding individual to cultivate and grow the College of Computing via independent research, collaborative research, and large team endeavors.  I am thrilled he has agreed to lead in this exciting new era of computing at Michigan Tech.”

In his new role, Havens will collaborate with faculty and staff in identifying and pursuing research opportunities, lead and assist with College efforts to support and secure large, externally funded research awards, and work closely with the Dean, College leadership, and other constituent groups to advance the College of Computing’s reputation, research capabilities, and impact. Havens will also work to enhance communication and relationships between other units on campus surrounding computing and related research areas and contribute to College teaching needs, among other duties.

Havens’s overarching goals for his new position encompass developing key, sustainable resources to enable research success in the College and Michigan Tech as a whole. This includes recruitment of technical research support, mentoring for new faculty and research staff, continued development of a seminar series for distinguished visitors and rising stars, and growing donor engagement in research activity.

“My long-term goal is to develop a flourishing, sustainable culture of creativity, innovation, and education, where research is the thread of daily eagerness to move the boundaries of knowledge and to solve hard puzzles,” Havens explained. “The product of this culture will be productive, rewarded researchers who exemplify their passion for pushing the envelope to our students, our alumni, and the greater research community.”

Havens knows that this sounds lofty and utopic, but his hope is that someday “we will all turn to each other and say, ahhhhh, this is it! This is inspiring!”

“During his time at Michigan Tech, Tim has proven to be a dedicated and productive researcher and—most importantly—a great collaborator,” said Peter Larson, director of research development at Michigan Tech. “It has been a pleasure to work alongside Tim this academic year in the ICC. I am confident that his leadership will be a great asset to both ICC and the College of Computing in the coming years. Tim’s collaborative nature will be instrumental in bringing teams together as we seek to expand the portfolio of computing research at Michigan Tech across new programs, new areas of research, new sponsors, and larger projects.”

Havens has a passion both for academic research and innovation, and also for mentoring. This is why he loves being a professor, where he can do both. “I really look forward to working with all the College researchers—it’s a unique opportunity to both act as a mentor to our researchers, and also to continue my own learning experience. I’m especially eager to learn more about all the great research going on in the College and at Tech, and to help our researchers accomplish their research goals,” Havens said.

“Those who know me well, know that I also like to put on a show. I view part of being an Associate Dean as exactly that—I really enjoy telling the stories of the College and our researchers, and cultivating the visibility of our new College. It’s an exciting time to be in computing at Michigan Tech.”

Havens considers himself fortunate to have to have worked with several talented research mentors in his career path, starting with his experience as a master’s student at Michigan Tech, where he investigated the optical properties of the atmosphere with his M.S. advisor, Michigan Tech professor Dr. Mike Roggemann.

Havens first job following completion of his M.S. was at MIT Lincoln Laboratory, where he investigated adaptive optics systems in support of the Airborne Laser program. Following that experience, he knew he wanted to be an academic researcher and a professor, so he returned to school to complete his Ph.D. at University of Missouri with advisor Dr. Jim Keller.

“Dr. Keller is a consummate researcher and one can’t help but to catch the research bug working with him. He was and continues to be a great mentor; he made sure that I received lots of practice writing papers and proposals, talking to program managers, strategizing research projects, collaborating outside my field, all important aspects of running a research program,” Havens said.

Havens notes that the duties of his latest gig, as director of the Institute of Computing and Cybersystems (ICC), are very similar to those of the Associate Dean for Research. 

“The ICC is very much a part of the strategic vision for research in the College of Computing, as the institute acts as the research arm of the College. This integration allows us to best utilize the finite resources of both the College of Computing and the ICC to get the greatest return on key investments in people and resources,” Havens explained.

“Launching the new College has been a wild experience so far and such a fantastic opportunity,” Havens said. “With this shift, we boldly announce that computing is a major field of study and not just an underpinning to other disciplines. I see the new College as a place of opportunity to experiment, collaborate, develop new pedagogies, and become a model for other institutions of higher learning. Our team is strong and creative, and it’s fun working on this puzzle with them.”

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Tim Havens Receives $120K Award from Signature Research, Inc.

Timothy Havens

Tim Havens, College of Computing associate dean for research, has been awarded an 18-month, $120,000 grant by Signature Research, Inc. The project, “Machine Learning for Human-Based Visual Detection Metrics,” contributes to an effort to develop a methodology that predicts the impact to human vision due to the existence of atmospheric particles. Havens is also the director of the Institute of Computing and Cybersystems and the William and Gloria Jackson Associate Professor of Computer Systems.

Abstract: This project contributes to an effort to develop a methodology that predicts the impact to human vision due to the existence of atmospheric particles. Due to the variability of atmospheric conditions and particulate matter (dust, ice, etc.) extensive field test campaigns to characterize the impacts to human vision are impractical. As a result, a model-based approach must be developed in order to evaluate all possible conditions in a virtual environment. It is envisioned that this approach will incorporate both human in-the-loop evaluations as well as generation of machine learning algorithms to serve as an in-situ human observer.

Signature Research, Inc. provides solutions to DoD and the Intelligence Community, specializing in Signature Phenomenology, Analysis, and Modeling of items of military interest covering the breadth of the electromagnetic spectrum. Signature Research, Inc. engineers and scientists have developed methodologies, tools and products to help visualize and interpret electromagnetic signatures, and Signature Research, Inc. staff are recognized experts within the various communities in which they work. SGR’s corporate headquarters is located in Calumet, Michigan, with a second operating location in Navarre, Florida near Eglin Air Force Base and Hurlburt Field. http://signatureresearchinc.com

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Tim Havens Quoted in The Enterprise Project Blog

Timothy Havens

Timothy Havens (CC/ICC), the William and Gloria Jackson Associate Professor of Computer Systems and director of the Institute of Computing and Cybersystems (ICC), was quoted extensively in the article “How to make a career switch into AI: 8 tips,” which was published September 5, 2019, on The Enterprisers Project blog.

https://enterprisersproject.com/article/2019/9/ai-career-path-how-make-switch

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Michigan Ag News Headlines: Found in Translation at Michigan Tech

James Bialas does an aerial drone demonstration for students attending the Surveying Summer Youth Program exploration at Michigan Technological University. Drones are one tool in the remote sensing arsenal. Image Credit: Peter Zhu

Research conducted by Michigan Tech doctoral candidate James Bialas and faculty members Thomas Oommen (DataS/GMES/CEE) and Timothy Havens (DataS/CS) made news in the Michigan Ag Connection, August 7, 2019. The item is a re-posting of the Michigan Tech Unscripted article, “Found in Translation, which was posted on the Michigan Tech website July 12, 2019.

http://michiganagconnection.com/story-state.php?Id=856&yr=2019

https://www.mtu.edu/news/stories/2019/july/found-in-translation.html

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Remotely Sensed Image Classification Refined by Michigan Tech Researchers

Thomas Oommen (left) and James Bialas

By Karen S. Johnson

View the press release.

With close to 2,000 working satellites currently orbiting the Earth, and about a third of them engaged in observing and imaging o

ur planet,* the sheer volume of remote sensing imagery being collected and transmitted to the surface is astounding. Add to this images collected by drones, and the estimation grows quite possibly beyond the imagination.

How on earth are science and industry making sense of it all? All of this remote sensing imagery needs to be converted into tangible information so it can be utilized by government and industry to respond to disasters and address other questions of global importance.

James Bialas demonstrates the use of a drone that records aerial images.

In the old days, say around the 1970s, a simpler pixel-by-pixel approach was used to decipher satellite imagery data; a single pixel in those low resolution images contained just one or two buildings. Since then, increasingly higher resolution has become the norm and a single building may now occupy several pixels in an image.

A new approach was needed. Enter GEOBIA– Geographic Object-Based Image Analysis— a processing framework of machine-learning computer algorithms that automate much of the process of translating all that data into a map useful for, say, identifying damage to urban areas following an earthquake.

In use since the 1990s, GEOBIA is an object-based, machine-learning method that results in more accurate classification of remotely sensed images. The method’s algorithms group adjacent pixels that share similar, user-defined characteristics, such as color or shape, in a process called segmentation. It’s similar to what our eyes (and brains) do to make sense of what we’re seeing when we look at a large image or scene.

In turn, these segmented groups of pixels are investigated by additional algorithms that determine if the group of pixels is, say, a damaged building or an undamaged stretch of pavement, in a process known as classification.

The refinement of GEOBIA methods have engaged geoscientists, data scientists, geographic information systems (GIS) professionals and others for several decades. Among them are Michigan Tech doctoral candidate James Bialas, along with his faculty advisors, Thomas Oommen(GMERS/DataS) and Timothy Havens (ECE/DataS). The interdisciplinary team’s successful research to improve the speed and accuracy of GEOBIA’s classification phase is the topic of the article “Optimal segmentation of high spatial resolution images for the classification of buildings using random forests” recently published in the International Journal of Applied Earth Observation and Geoinformation.

A classified scene.

A classified scene using a smaller segmentation level.

The team’s research started with aerial imagery of Christchurch, New Zealand, following the 2011 earthquake there.

“The specific question we looked at was, how do we translate the information we get from the crowd into labels that are coherent for an object-based image analysis?” Bialas said, adding that they specifically looked at the classification of city center buildings, which typically makes up about fifty percent of an image of any city center area.

After independently hand-classifying three sets of the same image data with which to verify their results (see images below), Bialas and his team started looking at how the image segmentation size affects the accuracy of the results.

A fully classified scene after the machine learning algorithm has been trained on all the classes the researchers used, and the remaining data has been classified.

“At an extremely small segmentation level, you’ll see individual things on building roofs, like HVAC equipment and other small features, and these will each become a separate image segment,” Bialas explained, but as the image segmentation parameter expands, it begins to encompass whole buildings or even whole city blocks.

“The big finding of this research is that, completely independent of the labeled data sets we used, our classification results stayed consistent across the different image segmentation levels,” Bialas said. “And more importantly, within a fairly large range of segmentation values, there was pretty much no impact on results. In the past several decades a lot of work has done trying to figure out this optimum segmentation level of exactly how big to make the image objects.”

“This research is important because as the GEOBIA problem becomes bigger and bigger—there are companies that are looking to image the entire planet earth per day—a massive amount of data is being collected,” Bialas noted, and in the case of natural disasters where response time is critical, for example, “there may not be enough time to calculate the most perfect segmentation level, and you’ll just have to pick a segmentation level and hope it works.”

This research is part of a larger project that is investigating how crowdsourcing can improve the outcome of geographic object-based image analysis, and also how GEOBIA methods can be used to improve the crowdsourced classification of any project, not just earthquake damage, such as massive oil spills and airplane crashes.

One vital use of of crowdsourced remotely sensed imagery is creating maps for first responders and disaster relief organizations. This faster, more accurate GEOBIA processing method can result in more timely disaster relief.

*Union of Concerned Scientists (UCS) Satellite Database

Illustrations of portions of the three different data sets used in the research.

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Havens Is Co-Chair of Fuzzy Systems Conference

Timothy HavensTimothy Havens (CC/ICC) was General Co-Chair of the 2019 IEEE International Conference on Fuzzy Systems in New Orleans, LA, June 23 to 26. At the conference, Havens presented his paper, “Machine Learning of Choquet Integral Regression with Respect to a Bounded Capacity (or Non-monotonic Fuzzy Measure),” and served on the panel, “Publishing in IEEE Transactions on Fuzzy Systems.”

Three additional papers authored by Havens were published in the conference’s proceedings: “Transfer Learning for the Choquet Integral,” “The Choquet Integral Neuron, Its PyTorch Implementation and Application to Decision Fusion,” and “Measuring Similarity Between Discontinuous Intervals – Challenges and Solutions.”

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Tim Havens Presents Talk at Technological University of Eindhoven

Timothy HavensICC Director Tim Havens (DataS) presented an invited talk, “Explainable Deep Fusion,” at the Technological University of Eindhoven, The Netherlands, on May 7, 2019.

Like a winning trivia team, sensor fusion systems seek to combine cooperative and complementary sources to achieve an optimal inference from pooled evidence. In his talk, Havens introduced data-, feature-, and decision-level fusions and discussed in detail two innovations he has made in his research: non-linear aggregation learning with Choquet integrals and their applications in deep learning and Explainable AI (XAI).

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Tim Havens Is Co-author of Article Published in IEEE Transactions on Fuzzy Systems

Timothy HavensTim Havens (CS/ICC) coauthored the article, “Enabling Explainable Fusion in Deep Learning with Fuzzy Integral Neural Networks,” which was accepted for publication in the journal IEEE Transactions on Fuzzy Systems.

Citation: M.A. Islam, D.T. Anderson, A. Pinar, T.C. Havens, G. Scott, and J.M. Keller. Enabling explainable fusion in deep learning with fuzzy integral neural networks. Accepted, IEEE Trans. Fuzzy Systems.

Abstract: Information fusion is an essential part of numerous engineering systems and biological functions, e.g., human cognition. Fusion occurs at many levels, ranging from the low-level combination of signals to the high-level aggregation of heterogeneous decision-making processes. While the last decade has witnessed an explosion of research in deep learning, fusion in neural networks has not observed the same revolution. Specifically, most neural fusion approaches are ad hoc, are not understood, are distributed versus localized, and/or explainability is low (if present at all). Herein, we prove that the fuzzy Choquet integral (ChI), a powerful nonlinear aggregation function, can be represented as a multi-layer network, referred to hereafter as ChIMP. We also put forth an improved ChIMP (iChIMP) that leads to a stochastic gradient descent-based optimization in light of the exponential number of ChI inequality constraints. An additional benefit of ChIMP/iChIMP is that it enables eXplainable AI (XAI). Synthetic validation experiments are provided and iChIMP is applied to the fusion of a set of heterogeneous architecture deep models in remote sensing. We show an improvement in model accuracy and our previously established XAI indices shed light on the quality of our data, model, and its decisions.

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