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Yu Cai is PI of 2-year NSA GenCyber Project

Professor Yu Cai, Applied Computing, a member of the ICC’s Center for Cybersecurity, is the principal investigator on a two-year project that has received a $99,942 grant from the National Security Agency (GenCyber). The project is titled, “GenCyber Teacher Camp at Michigan Tech. “

Lecturer Tim Van Wagner (AC) and Assistant Professor Bo Chen (CS, DataS) are Co-PIs. Cai will serve as the camp director, Tim Van Wagner as lead instructor.

This GenCyber project aims to host a week-long, residential summer camp for twenty K-12 STEM teachers in 2021 at Michigan Tech. Target educators are primarily from Michigan and surrounding states.

The objectives of the camp are to teach cybersecurity knowledge and safe online behavior, develop innovative teaching methods for delivering cybersecurity content, and provide professional development opportunities so participants will return to their home schools with contagious enthusiasm about teaching cybersecurity.

The GenCyber camp will be offered at no cost to camp participants. Room and board will be provided. Teacher participants will receive a stipend of $500 for attending and completing camp activities.

Read about the 2019 Michigan Tech GenCyber camps for teachers and students here.


Tim Havens Gives Talk at Los Alamos National Lab

Dr. Timothy Havens presented the lead talk at the Los Alamos National Laboratory’s ISR-2 Seminar Series on Advancing Toward Modern Detection and Estimation Techniques for Multi-Sensor Scenarios, presented online July 9, 2020.

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

The talk, “Explainable Deep Fusion,” described Havens’s sensor fusion systems research that seeks to combine cooperative and complementary sources to achieve optimal inference from pooled evidence.

Havens specifically discussed his innovations in non-linear aggregation learning with Choquet integrals and their applications in deep learning and Explainable AI.


Michigan Tech Produces Best Software Engineers in U.S.

Michigan Tech ranks 5th on a list of 13 non-ivy league schools that produce the best software engineers in the U.S., recently published by DesignRush.

The demand for software developers is steadily increasing, with 21% expected growth from 2018 to 2028. To help industry meet this need, DesignRush has published a list of non-ivy league schools that produce the best software engineers in the U.S.

  1. University of California, Irvine
  2. Stevens Institute of Technology
  3. California Polytechnic State University
  4. Iowa State University
  5. Michigan Technological University
  6. Milwaukee School of Engineering
  7. The University of Texas at Dallas
  8. Drexel University
  9. Auburn University
  10. Miami University
  11. Grantham University
  12. University of Louisiana Lafayette
  13. Robert Morris University

DesignRush.com is a B2B marketplace connecting brands with agencies. DesignRush features the top agencies around the world, including the best Digital Agencies, Software Developers, Logo Design, Branding, Digital Marketing, Website Design, eCommerce Web Design Companies and more.


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.