Announcements Archives | Page 13 of 15 | Institute of Computing and Cybersystems (ICC) Blog

Flex Fall Faculty Workshops, Q-A Sessions from IDEA Hub, CTL

Published June 16, 2020

To help faculty prepare for Flex Fall, IDEA Hub and the William G. Jackson Center for Teaching and Learning have organized a series of events, each including Flex Fall Q&A sessions and discussions about teaching.

Click the below links to register and receive a Google calendar invitation and the Zoom link. Questions? Email margaret@mtu.edu.

Session #2: Wednesday, June 17, 3:00 – 4:30 pm. Online Teaching Showcase
Teaching, Q&A: 3 – 3:30 pm; Teaching Showcase, Discussion: 3:30-4:30 pm)

Session #3, Wednesday, June 24, 3-5 pm: Develop Innovative Solutions
(Teaching, Q&A: 3 – 3:30 pm, Design Thinking Workshop: Develop Innovative Solutions: 3:30 – 5 pm)

Session #4: Wednesday, July 1, 3 to 5 pm: Prototype Your Innovative Solutions
(Teaching, Q&A: 3 to 3:30 pm; Design Thinking Workshop–Prototype Your Innovative Solutions: 3:30 – 5 pm)

Read the full story in Monday’s Tech Today.

GSG to Present Webinar Series in Computer Programming

Published June 16, 2020

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The Graduate Student Government (GSG) Professional Development Committee has organized a free webinar series in Computer Programming, which begins Tuesday, July 14, 2020.

July 14: “Introduction to Machine Learning with Python,” by Timothy Havens (CC)

July 15: “Managing Data” (Data Mining)” by MS Data Science candidate Sneha Nimmagadda

July 16: “Introduction to Deep Learning,” by Timothy Havens (CC)

Seats are not limited, but participants are asked to register so webinar organizers know how many attendees to expect.

Find more information, including links to register and join Zoom meetings, visit the GSG website.

More Achievements for MTU RedTeam

Published June 10, 2020

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The MTU RedTeam ranked 13th out of 162 teams in a recent 24-hour Cybar OSINT Capture The Flag (CTF) cybersecurity competition. The team finished tied for 5th place, having completed all the challenges presented by the competition.

Students on the team were Trevor Hornsby (Software Engineering), Shane Hoppe (Computer Science), Matthew Chau (Cybersecurity), Steven Whitaker (Electrical Engineering), and Sankalp Shastry (Electrical Engineering).

Professor Yu Cai, Applied Computing, and Assistant Professor Bo Chen, Computer Science, are advisor and co-advisor of RedTeam, respectively. Both are members of the ICC’s Center for Cybersecurity.

RedTeam promotes a security-driven mindset among Michigan Tech students and provides a community and resource for those wishing to learn more about information security. The RedTeam competes in National Cyber League (NCL) competitions, a great way for students to gain competency in cybersecurity tools and boost their resumes.

RedTeam is on Slack at mturedteam.slack.com. Interested students can sign up with a Michigan Tech email. View past RedTeam presentations here.

This OSINT CTF is non-theoretical and contestants work in teams of up to four members to crowdsource the collection of OSINT to assist law enforcement in generating new leads on missing persons.

The contest runs as a Capture the Flag (CTF) format where contestants must collect various “flags” which equate to points. Since the each flag submitted is treated as potential “net new intelligence”, Trace Labs has a team of volunteers known as “Judges” who validate each submission and award points if the flag meets the category requirements. At the end of each CTF, the team with the most points on the scoreboard wins.

ICC, ME-EM’s Bo Chen Named ASME Fellow

Published June 3, 2020

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Bo Chen, the Michigan Tech Dave House Professor of Mechanical Engineering and Electrical Engineering, has received the designation of Fellow from the American Society of Mechanical Engineers (ASME).

The Fellow level of membership is conferred to worthy candidates by the ASME Committee of Past Presidents to recognize their outstanding engineering achievements.

Nominated by ASME Members and Fellows, an ASME Member nominee must have 10 or more years of active practice, and at least 10 years of active corporate membership in ASME.

Chen is the director of Michigan Tech’s Intelligent Mechatronics and Embedded Systems (IMES) Laboratory. She has a dual faculty appointment in the Department of Electrical and Computer Engineering. Visit Chen’s faculty webpage here.

A member of the Institute of Computing and Cybersystems (ICC)’s Center for Cyber-Physical Systems (CPS), Bo Chen conducts interdisciplinary research in the areas of mechatronics and embedded systems, agent technology, connected and autonomous vehicles, electric vehicle-smart grid integration, cyber-physical systems and automation.

William Predebon, chair of the the Department of Mechanical Engineering-Engineering Mechanics said, “Dr. Chen has made major contributions in her field of embedded systems with application to hybrid-electric and electric autonomous systems. Her course in Model-based Embedded Control System Design is regularly in high demand by not only ME students but also EE students. This is a testament to the importance of the topic and her teaching ability.”

ASME helps the global engineering community develop solutions to real world challenges. Founded in 1880 as the American Society of Mechanical Engineers, ASME is a not-for-profit professional organization that enables collaboration, knowledge sharing and skill development across all engineering disciplines, while promoting the vital role of the engineer in society. ASME codes and standards, publications, conferences, continuing education and professional development programs provide a foundation for advancing technical knowledge and a safer world.

Michigan Tech Team Places 4th Overall in TiM$10K Challenge

Published June 2, 2020

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A team of five Michigan Tech students received Honorable Mention honors in the second annual SICK Inc. TiM$10K Challenge, a national innovation and design competition. University students from around the country participated in the event designed to support innovation and student achievement in automation and technology.

The Michigan Tech team members — Brian Parvin (ME), Paul Allen (EE), David Brushaber (CompEng), Kurtis Alessi (CompEng) and Alex Kirchner (CompEng) — earned Honorable Mention (fourth place overall) for their project, “Evaluating Road Markings (the Road Stripe Evaluator). Their project was sponsored by SICK Inc. Watch a video about the project below.

SICK's TiM$10K Challenge 2020 – Evaluating Road Markings (The Road Stripe Evaluator)

Adrienne Minerick, dean of Michigan Tech’s College of Computing, said in a June 1, 20920, Tech Today article that the accomplishments of these outstanding students illustrates Michigan Tech’s creativity and tenacity when faced with a challenge. “Our rapidly growing presence in cybersecurity is built upon our students deep knowledge of the fundamentals combined with the learning environment that promotes agility to meet (and exceed) any challenge. These hardworking and bright students deserve this recognition of their competitiveness. All of us in the College of Computing are proud.”

For the competition, teams were supplied with a 270-degree SICK LiDAR sensor and accessories, and challenged to solve a problem, create a solution, or bring a new application to any industry that utilizes the SICK LiDAR.

Each team submitted a video and paper for judging upon completion of its project. A panel of judges decided the winning submissions based on creativity and innovation, ability to solve a customer problem, commercial potential, entrepreneurship of the team, and reporting.

The Tech team developed an innovative product to help resolve issues caused by poor road markings, while reducing maintenance costs and improving motorist safety. Their new software uses reflectivity values obtained using a SICK LiDAR unit to identify deterioration of road stripes and recommend timely repainting, also aiding in the safety and reliability of self-driving vehicles on roadways.

They constructed a prototype to demonstrate functionality, in the form of a pushable cart that evaluates road markings. An intuitive user interface displays the markings being evaluated, and indicates if they meet necessary levels of reflectivity.

Pinar said the team was well organized and demonstrated an excellent work ethic from day one. “It was exciting to watch them identify a salient problem and develop a functional proof-of-concept solution despite the setbacks that affected us all after spring break,” he said.

“This was a unique project in that the team was required to identify a problem and develop a solution to it that is based on SICK’s TiM LiDAR, while most teams are handed a problem and asked to create a solution,” Pinar noted. “I think this format allowed the team to exercise even more innovation than on a ‘typical’ project.”

The same team of students was awarded Honorable Mention honors at this spring’s Design Expo Senior Design competition for their project, “Road Marking Reflectivity Evaluator.”

SICK, Inc. is one of the world’s leading manufacturers of sensors, safety systems, machine vision, encoders and automatic identification products for industrial applications.

Computing Awards COVID-19 Research Seed Grants

Published May 27, 2020

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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

Leo Ureel

Charles Wallace

Bo Chen

Meet Bonnie Henderson, Data Science Master’s Student and CCLC Coach

Published May 21, 2020

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By Karen S. Johnson, Communications Director, College of Computing

Data Science graduate student Bonnie Henderson began her master’s degree at Michigan Tech in fall 2019. From Jarrell, Texas, Henderson earned a B.A. in mathematics and French at Southwestern University, Georgetown, Texas.

Henderson is a recipient of Michigan Tech’s David House Family Fellowship, which she describes as a great honor. Her research interests are in artificial intelligence and machine learning.

“The fellowship has made an incredible difference in my life,” Henderson says. “As the first person in my family to go to college, it is an amazing opportunity to pursue my graduate studies fully funded.”

This January, Henderson began managing the College of Computing Learning Center (CCLC), an undergraduate learning lab staffed entirely by student coaches and available to all Michigan Tech students in Computing classes.

“I mostly work to help manage the CCLC,” Henderson says. “I help tutor students in undergraduate computer science courses during CCLC walk-in hours, help run CCLC staff meetings, and when the time comes, I’ll help manage the interviewing and hiring process for new tutors.”

Many Opportunities for Learning

Henderson says her work with the CCLC often presents computer science issues and computing problems that are not always common in data science, providing her with many opportunities for learning.

“Studying data science, I work a lot with programming,” Henderson says. “However, I often work with problems related to mathematics in programming and not always the typical undergraduate programming issues.”

What Henderson likes best about tutoring is what she learns along the way. “Since I did not complete my undergraduate degree at MTU, I’m not always familiar with the problems that students are facing when they come in for tutoring. Everyone looks at a problem a little differently, and I get the opportunity to be exposed to many different thought processes and unique solutions.”

New Methods of Virtual Support

Not surprisingly, the plans for the CCLC have changed a lot since the COVID-19 pandemic began earlier this year. “Before this news, we were planning on hosting workshops and other events for students in the College of Computing and other departments, such as guided study groups, exam review sessions, and specialized support for individual classes,” Henderson says.

But since the CCLC cannot offer conventional face-to-face tutoring right now, these plans are changing and Henderson and the CCLC are responding with new methods of virtual support. “We have started sending out a weekly CCLC email to students, which shares coding tips and tricks, quizzes, and news, and we are working to encourage more student involvement, especially with the current difficulties we are all facing,” Henderson explains.

Balancing Life, Work and School

So, instead of hosting face-to-face events, CCLC walk-in hours are now being hosted through Zoom, and the Learning Center is maintaining a Canvas page where students can find help and find information on their own. They also hope to host some virtual workshops soon. Students can sign up for the CCLC Canvas page here: https://mtu.instructure.com/enroll/KWFTJ9.

Henderson says the most challenging thing about balancing life, work, and school is finding a separate time and place for each one.

“I’ll often be looking at one thing, and something in it reminds me of a problem from something else. I have a tendency to hop around a lot, and sometimes things may get lost,” she says. “It has become increasingly difficult working and studying from home, as everything is now sharing the same physical space.”

To help with that, Henderson says it’s helpful to try to have different spaces for the things she has to do. “Like one chair for working and another chair for schoolwork, even if they are in the same room. Some sort of distancing between everything is definitely needed.”

Learn More About the CCLC

Visit the CCLC website here. Visit the CCLC’s Infinite Loop: Resources to Explore, Learn, Code, Repeat.

The Dave House graduate student assistantships provide $30,000 annually for three years to each of three graduate assistants in Michigan Tech’s Master of Science in Data Science program.

About Dave House ’65, University Friend and Donor

Dave House ’65 (EE) is a longtime friend and generous donor to Michigan Tech.

“I support Michigan Tech because I believe in the critical importance of higher education, not only for the state and the nation, but most importantly for our graduates, House says in an EE department alumni profile.

“The Fourth Industrial Revolution changes everything, and Michigan Tech is perfectly positioned to prepare our students for these changes. I support fellowships in data science because of the role that sensing, networking, big data, artificial intelligence and human/machine interfacing has in the Fourth Industrial Revolution. Supporting graduate and research activities is critical to keeping Michigan Tech agile and at the cutting edge of this revolution.”

The Data Science Master of Science

The Data Science Master of Science degree is offered jointly by the College of Engineering and the Department of Computer Science. Associate Professor Laura Brown, Computer Science, is director of the program.

Havens, Yazdanparast Publish Article in IEEE Transactions on Big Data

Published May 20, 2020

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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.

Ford Mobility Funds AI, Acoustics Research

Published May 15, 2020

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Imagine if your car could tell you when you are passing by an area occupied by rare migratory birds, or it could listen to roads and bridges to determine when infrastructure repairs need to be made.

A recent gift from Mobility Research at Ford Motor Company recently provided a $149,518 gift to fund research that could make this possible.

Dr. Timothy Havens, Institute of Computing and Cybersystems, and Dr. Andrew Barnard, Great Lakes Research Center, will lead an exploration of how future connected vehicles could use AI and acoustics to detect, classify, and localize external sound events, and evaluate and monitor transportation infrastructure.

The gift will fund a Ph.D. student fellowship, a team of undergraduate students in the SENSE Enterprise, and build and develop a mobile acoustics test bed that will allow students, Havens, and Barnard to conduct cutting-edge research in AI and acoustics.

Michigan Tech would like to thank Chad Esselink (’94, Computer Science) and Tavan Eftekhar at Ford Mobility Research for making this possible.

The Institute of Computing and Cybersystems (ICC) is the research arm of the College of Computing at Michigan Tech. The ICC provides faculty and students the opportunity to work across organizational boundaries to create an environment that is a reflection of contemporary technological innovation. This collaboration allows for a convergence in communication, control and computing that mirrors today’s industry and society.

The Great Lakes Research Center (GLRC) provides state-of-the-art laboratories to support research on a broad array of topics. Faculty members from many departments across Michigan Technological University’s campus collaborate on interdisciplinary research, ranging from air–water interactions to biogeochemistry to food web relationships.

Article by Tim Havens in IEEE Transactions on Fuzzy Systems

Published March 20, 2020

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An article co-authored by Tim Havens, associate dean for research, College off Computing, “Soft Overlapping Community Detection in Large-Scale Networks via Fast Fuzzy Modularity Maximization,” was published in the March 2020 issue of IEEE Transactions on Fuzzy Systems.

Havens’s co-authors are Audrey Yazdanparast (ECE) and Mohsen Jamalabdollahi of Cisco Systems.

Article Abstract: Soft overlapping clustering is one of the notable problems of community detection. Extensive research has been conducted to develop efficient methods for non-overlapping and crisp-overlapping community detection in large-scale networks. In this paper, Fast Fuzzy Modularity Maximization (FFMM) for soft overlapping community detection is proposed.

FFMM exploits novel iterative equations to calculate the modularity gain associated with changing the fuzzy membership values of network vertices. The simplicity of the proposed scheme enables efficient modifications, reducing computational complexity to a linear function of the network size and the number of communities. Moreover, to further reduce the complexity of FFMM for very large networks, Multi-cycle FFMM (McFFMM) is proposed.

The proposed McFFMM reduces complexity by breaking networks into multiple sub-networks and applying FFMM to detect their communities. Performance of the proposed techniques are demonstrated with real-world data and the Lancichinetti-Fortunato-Radicchi (LFR) benchmark networks. Moreover, the performance of the proposed techniques is eval- uated versus some state-of-the-art soft overlapping community detection approaches. Results show that the McFFMM produces a remarkable performance in terms of overlapping modularity with fuzzy memberships, computational time, number of detected overlapping nodes, and Overlapping Normalized Mutual Informa- tion (ONMI).

View more info here.

Category: Announcements

SICK&#039;s TiM$10K Challenge 2020 – Evaluating Road Markings (The Road Stripe Evaluator)

SICK's TiM$10K Challenge 2020 – Evaluating Road Markings (The Road Stripe Evaluator)