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Computing Awards COVID-19 Research Seed Grants

The College of Computing is pleased to announce that it has awarded five faculty seed grants, which will provide immediate funding in support of research projects addressing critical needs during the current global pandemic.

Tim Havens, College of Computing associate dean for research, said that the faculty seed grants will enable progress in new research that has the potential to make an impact on the current research. Additional details will be shared soon.


Congratulations to the winning teams!

Guy Hembroff (AC, HI): “Development of a Novel Hospital Use Resource Prediction Model to Improve Local Community Pandemic Disaster Planning”

Leo Ureel (CS) and Charles Wallace (CS): “Classroom Cyber-Physical Simulation of Disease Transmission”

Bo Chen (CS): “Mobile Devices Can Help Mitigate Spreading of Coronavirus”

Nathir Rawashdeh (AC, MERET): “A Tele-Operated Mobile Robot for Sterilizing Indoor Space Using UV Light” (A special thanks to Paul Williams, who’s generous gift to support AI and robotics research made this grant possible)

Weihua Zhou (AC, HI) and Jinshan Tang (AC, MERET): “KD4COVID19: An Open Research Platform Using Feature Engineering and Machine Learning for Knowledge Discovery and Risk Stratification of COVID-19″

Weihua Zhou

Nathir Rawashdeh

Jinshan Tang

Guy Hembroff

Leo Ureel

Charles Wallace

Bo Chen


Havens, Yazdanparast Publish Article in IEEE Transactions on Big Data

Timothy Havens

An article by Audrey Yazdanparast (2019, PhD, Electrical Engineering) and Dr. Timothy Havens, “Linear Time Community Detection by a Novel Modularity Gain Acceleration in Label Propagation,” has been accepted for publication in the journal, IEEE Transactions on Big Data.

The paper presents an efficient approach for detecting self-similar communities in weighted graphs, with applications in social network analysis, online commodity recommendation systems, user clustering, biology, communications network analysis, etc.

Paper Abstract: Community detection is an important problem in complex network analysis. Among numerous approaches for community detection, label propagation (LP) has attracted a lot of attention. LP selects the optimum community (i.e., label) of a network vertex by optimizing an objective function (e.g., Newman’s modularity) subject to the available labels in the vicinity of the vertex. In this paper, a novel analysis of Newman’s modularity gain with respect to label transitions in graphs is presented. Here, we propose a new form of Newman’s modularity gain calculation that quantifies available label transitions for any LP based community detection.

The proposed approach is called Modularity Gain Acceleration (MGA) and is simplified and divided into two components, the local and global sum-weights. The Local Sum-Weight (LSW) is the component with lower complexity and is calculated for each candidate label transition. The General Sum-Weight (GSW) is more computationally complex, and is calculated only once per each label. GSW is updated by leveraging a simple process for each node-label transition, instead of for all available labels. The MGA approach leads to significant efficiency improvements by reducing time consumption up to 85% relative to the original algorithms with the exact same quality in terms of modularity value which is highly valuable in analyses of big data sets.

Timothy Havens is director of Michigan Tech’s Institute of Computing and Cybersystems (ICC), the associate dean for research for the College of Computing , and the William and Gloria Jackson Associate Professor of Computer Systems.

View the article abstract here.


Paid Research Assistant Position for Computationally-keen Grad Students

Sangyoon Han, assistant professor, Biomedical Engineering, is seeking applications for a funded research assistant position from computationally-keen graduate students who can program. Dr. Han’s research is in Computational Mechanobiology.

“We are seeking candidates with outstanding programming capability who are knowledgeable in particle tracking, inverse problem, vector field operation, machine learning, and deep learning. Masters and Ph.D. students in Data Science, Computer Science and Engineering, Mechanical Engineering, Electrical Engineering, and related disciplines are encouraged to apply. This is a funded position.”

Interested candidates are encouraged to send an e-mail to Dr. Han at sjhan@mtu.edu. Please include a brief statement of interest and CV. For more details, visit http://hanlab.biomed.mtu.edu.

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Minakata, Students, Rouleau Publish Paper

The Process Safety and Environmental Protection special issue on Advanced Oxidation Process (Elsevier), has accepted for publication a paper by associate professor Daisuke Minakata (CEE), his students Robert Zupko, Divya Kamath, and Erica Coscarelli, and his collaborator and co-PI Mark Rouleau (SS), ICC Center for Data Sciences. pictured at left with Mary Raber. Photo by Daily Mining Gazette.

The paper concerns research supported by the National Science Foundation’s Chemical, Bioengineering, Environmental and Transport Systems (CBET) Division.

Grant Title: Coupling Experimental and Theoretical Molecular-Level Investigations to Visualize the Fate of Degradation of Organic Compounds in Aqueous Phase Advanced Oxidation Systems

Grant Abstract: The lack of an overarching management plan combined with uncertainty about the adverse human health and ecological impacts of trace amounts of known and emerging organic compounds have raised public concerns about water. These issues also present major challenges to next generation water treatment utilities dealing with de facto and planned wastewater reuse. Advanced oxidation processes that produce highly reactive hydroxyl radicals are promising technologies to control trace amounts of organic compounds. Although the initial fate of hydroxyl radical induced reactions with diverse organic compounds have been studied, the mechanisms that produce intermediate radicals and stable-byproducts are not well understood. Significant barriers remain in our understanding of complex multi-channel elementary reaction pathways embedded in peroxyl radical bimolecular decay that produce identical intermediate-radicals and stable-byproducts. The model developed in the course of this research will give researchers and policy makers the ability to predict the likely chemical by-products and alternative options to provide least adverse impact on the general public who will directly consume this water or other ecological organisms who will be exposed indirectly.

The proposed study will integrate three thrusts to discover the currently unknown fate of the three major degradation pathways. First, we will perform pulse-photolysis kinetic measurement to determine the temperature-dependent overall reaction rate constants for multi-channel peroxyl radical reactions. We will also measure the resulting byproducts using a mass spectrometry. Second, we will employ quantum mechanical theoretical calculations to determine the elementary reaction pathways and associated reaction rate constants. Third, we will then combine our kinetic measurements with our theoretical calculations to develop an agent-based model that will enable us to visualize and predict the fate of organic compounds. With explicitly assigned reaction rules and molecular behavior embedded within a simulated reaction network, the resulting agent-based model will use software agents to represent radical species and organic compounds and then simulate their interactions to predict corresponding consequences (i.e., byproducts) over time and space. Finally, experimental observations will validate the outcomes from the agent-based model.

The Chemical, Bioengineering, Environmental and Transport Systems (CBET) Division supports innovative research and education in the fields of chemical engineering, biotechnology, bioengineering, and environmental engineering, and in areas that involve the transformation and/or transport of matter and energy by chemical, thermal, or mechanical means.

View additional grant info on the NSF website.

Find more information about the Process Safety and Environmental Protection special issue on Advanced Oxidation Process here.


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


ECE’s Tony Pinar Joins ICC

The Institute of Computing and Cybersystems (ICC) is pleased to welcome Tony Pinar as a member. Pinar’s primary research interests are in applied machine learning and data fusion. A lecturer in Michigan Tech’s Electrical and Computer Engineering department, Pinar holds a Ph.D. and M.S. in Electrical Engineering from Michigan Tech. His previous positions include research engineer for Michigan Tech’s Advanced Power System Research Center and electrical design engineer for GE Aviation. He is a member of the Institute of Electrical and Electronics Engineers (IEEE) and the IEEE Computational Intelligence Society.

Pinar’s teaching interests include machine learning, signal processing, and electronic design. Included among the classes he teaches are Electronics, Electronic Applications, Probability—Signal Analysis, and Control Systems I.

“Teaching is like a puzzle where one may have to take a difficult concept, reduce it to digestible pieces, and deliver them to fresh minds in a way to maximize understanding and insight,” Pinar says. “That challenge is what drives me to be a better teacher.”

Pinar believes that to be a good teacher one must understand the topics very well and he strives for the most effective delivery. “This keeps me on my toes, forces me to constantly identify holes in my knowledge, and drives me to continuously strive to learn new things,” he explains.

On research, Pinar says it is rewarding to work on open-ended and novel problems that are in their infancy and at the cutting edge of today’s technology.

“It is also exciting to me to watch the cutting edge move forward, see what sticks and what doesn’t, and observe how the direction(s) of the field evolve,” he adds. “I’m very new to this domain so I haven’t been able to observe it for long, but I am looking forward to witnessing the future of the field.”

Pinar is looking forward to becoming more involved with research, and he is looking for new collaborations with other ICC and Michigan Tech researchers.

“The resources and network the ICC provides to new—and even established—researchers are set up in a way to cultivate its members’ talent and support career pathways. I am looking forward to being a part of this dynamic Michigan Tech research institute,” Pinar says.

Pinar’s recent publications include the following.

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”. IEEE Transactions on Fuzzy Systems(2019).

U. Agrawal, A.J. Pinar, C. Wagner, T.C. Havens, D. Soria, J.M. Garibaldi. “Comparison of Fuzzy Integral-Fuzzy Measure Based Ensemble Algorithms with the State-of-the-Art Ensemble Algorithms”. International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU) (2018).

B. Murray, M.A. Islam, A.J. Pinar, T.C. Havens, D.T. Anderson, G. Scott. “Explainable AI for Understanding Decisions and Data-Driven Optimization of the Choquet Integral”. IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (2018).

A.J. Pinar, D.T. Anderson, T.C. Havens, A. Zare, T. Adeyeba. “Measures of the Shapley Index for Learning Lower Complexity Fuzzy Integrals”. SpringerGranular Computing(2017).


“Artificial UnIntelligence,” A Keynote Lecture from Meredith Broussard

Meredith Broussard

Artificial UnIntelligence Book Cover

The Institute for Policy, Ethics, and Culture’s Algorithmic Culture series continues with “Artificial UnIntelligence,” a keynote lecture from Meredith Broussard, on Thursday, Dec. 5 at 7 p.m. in Memorial Union Building Ballroom B, followed by a Q&A.

Collective enthusiasm for applying computer technology to every aspect of life has resulted in a vast number of poorly designed systems. We are so eager to do everything digitally—hiring, driving, paying bills, even choosing romantic partners—that we have stopped demanding our technology actually work.

In this talk, author and professor Meredith Broussard looks at the inner workings and outer limits of technology, and explains why we should never assume that computers always get things right. Making a case against technochauvinism—the belief that technology is always the solution—Broussard looks at whether self-driving cars really work and why social problems persist in every digital Utopia. If we understand the limits of what we can do with technology, Broussard tells us, we can make better choices about what we should do with it to make the world better for everyone.

Meredith Broussard is an associate professor at the Arthur L. Carter Journalism Institute of New York University and the author of Artificial Unintelligence: How Computers Misunderstand the World. Her research focuses on artificial intelligence in investigative reporting, with a particular interest in using data analysis for social good. You can follow her on Twitter @merbroussard or contact her via meredithbroussard.com.


Benjamin Ong Is PI on NSF Grant that Supports June 2020 Parallel-in-Time Conference

Benjamin Ong

Benjamin Ong (Math/ICC-DataS) is the principal investigator on a one-year project that has received a $36,636 other sponsored activities grant from the Mathematical Sciences Infrastructure Program at National Science Foundation. The project is entitled, “CBMS Conference: Parallel Time Integration.”

The award provides support for the NSF-CBMS Conference on Parallel Time Integration, to be held June 1-5, 2020, at Michigan Tech. The focus of the conference is to educate and inspire researchers and students in new and innovative numerical techniques for the parallel-in-time solution of large-scale evolution problems on modern supercomputing architectures, and to stimulate further studies in their analysis and applications.

Co-organizing the conference with Ong is Jacob B. Schroder, assistant professor in the Dept. of Mathematics and Statistics at University of New Mexico.

The conference will feature ten lectures by Professor Martin Gander, an expert in parallel time integration and a professor at the University of Geneva, Switzerland. Using appropriate mathematical methodologies from the theory of partial differential equations in a functional analytic setting, numerical discretizations, integration techniques, and convergence analyses of these iterative methods, conference participants will be exposed to the numerical analysis of parallel-in-time methodologies and their implementations. The proposed topics include multiple shooting type methods, waveform relaxation methods, time-multigrid methods, and direct time-parallel methods. These lectures will be accessible to a wide audience from a broad range of disciplines, including mathematics, computer science and engineering.

Visit the conference website at http://conferences.math.mtu.edu/cbms2020/.


Benjamin Ong Awarded 2019-2020 Kliakhandler Fellowship

Benjamin Ong

Benjamin Ong (Math/ICC-DataS) has been awarded the Michigan Tech Department of Mathematics 2019-2020 Kliakhandler Fellowship, which will support two parallel-in-time conferences being held at Michigan Tech in June 2020.  As Kliakhandler Fellow, Ong will receive a $5,000 stipend, plus $10,000 to organize a workshop or conference at Michigan Tech.

The purpose of the Kliakhandler Fellowship is to stimulate research activity in the Michigan Tech Department of Mathematics. Awarded annually, the Kliakhandler Fellow is chosen based on a record of excellence in research and the potential of the proposed workshop to stimulate further research achievements and bring visibility to Michigan Tech and the Department of Mathematical Sciences.

Ong, along with Jacob B. Schroder, assistant professor in the Dept. of Mathematics and Statistics at University of New Mexico, is organizing two conferences to take place at Michigan Tech in June 2020. The first, “The CBMS Conference – Parallel Time Integration,” will take place June 1-5, 2020. The focus of this parallel-in-time workshop is to educate and inspire researchers and students in new and innovative numerical techniques for the parallel-in-time solution of large-scale evolution problems on modern supercomputing architectures, and to stimulate further studies in their analysis and applications.  The conference will feature ten lectures by Professor Martin Gander, an expert in parallel time integration and a professor at the University of Geneva, Switzerland.

The second conference, 9th Workshop on Parallel-in-Time Integration,” takes place June 8-12, 2020. The workshop the workshop will bring together an interdisciplinary group of experts to disseminate cutting-edge research and facilitate scientific discussions on the field of parallel time integration methods.
Igor Kliakhandler, a former Michigan Technological University mathematics faculty member, was born in Moscow, Russia in 1966. He graduated from the Moscow Oil and Gas Institute in 1983 and started his PhD studies there in 1988. He then emigrated with his family to Israel in 1991 and began his PhD at Tel-Aviv University under the guidance of Gregory Sivashinsky. He received his PhD in Applied Mathematics in 1997 from Tel-Aviv University. He held positions at Universidad Complutense de Madrid, Lawrence Berkeley National Laboratory and Northwestern University before joining Michigan Tech’s Department of Mathematical Sciences as an assistant professor in 2001. He was promoted to associate professor in 2005 and left the University in 2007 to work in the energy sector in Houston. He manages a group of companies that trade electric power across US, and is involved in a few start-up projects. Igor remained fond of Michigan Tech and its Math Department. Kliakhandler provides a generous gift to host the Kliakhandler Conference, an annual event at Michigan Tech to stimulate research activity in the mathematical sciences.

Link to more information about the two conferences at:

http://conferences.math.mtu.edu/cbms2020, June 1-5 2020

http://conferences.math.mtu.edu, June 8 – 12, 2020


Anna Little to Present Talk October 18, 1 p.m.

Anna Little

Dr. Anna Little, a postdoc in the Department of Computational Mathematics, Science, and Engineering at Michigan State University, will present her talk, “Robust Statistical Procedures for Clustering in High Dimensions,” on Friday, October 18, 2019, at 1:00 p.m., in Fisher Hall Room 327B.

Dr. Little completed a PhD in mathematics at Duke University in 2011. She has been at Michigan State since 2018.  Visit her website at www.anna-little.com.

Lecture Abstract: This talk addresses multiple topics related to robust statistical procedures for clustering in high dimensions, including path-based spectral clustering (a new method), classical multidimensional scaling (an old method), and clustering in signal processing. Path-based spectral clustering is a novel approach which combines a data driven metric with graph-based clustering. Using a data driven metric allows for fast algorithms and strong theoretical guarantees when clusters concentrate around low-dimensional sets.

Another approach to high-dimensional clustering is classical multidimensional scaling (CMDS), a dimension reduction technique widely popular across disciplines due to its simplicity and generality. CMDS followed by a simple clustering algorithm can exactly recover all cluster labels with high probability when the signal to noise ratio is high enough. However, scaling conditions become increasingly restrictive as the ambient dimension increases, illustrating the need for robust unbiasing procedures in high dimensions.  Clustering in signal
processing is the final topic; in this context each data point corresponds to a corrupted signal. The classic multireference alignment problem is generalized to include random dilation in addition to random translation and additive noise, and a wavelet based approach is used to define an unbiased representation of the target signal(s) which is robust to high frequency perturbations.

Download the event flyer.