Michigan Tech ranks number three (3) in the Spring 2021 National Cyber League’s Cyber Power Rankings, rising 12 points from a Fall 2020 ranking of 15. One hundred (100) teams were ranked.
In the NCL cyber-competitions, thousands of students from hundreds of colleges and universities nationwide are challenged to identify hackers from forensic data, pentest and audit vulnerable websites, recover from ransomware attacks, and more.
Three factors are considered in a school’s annual Cyber Power Ranking. In descending magnitude of weight, they are:
The school’s top performing team during the Team Game
The school’s top performing student during the Individual Game
The number of participating students from the school, with additional consideration given to better student performance during the Individual Game
Schools are ranked based on their top team performance, their top student’s individual performance, and the aggregate individual performance of their students. The rankings represent the ability of students from these schools to perform real-world cybersecurity tasks on the Cyber Skyline platform.
The Cyber Power Rankings were created by Cyber Skyline in partnership with the National Cyber League (NCL). Every year, over 10,000 students from more than 300 colleges and universities across the US participate in the NCL competitions.
Intelligent.com, a resource for ranking online degree rankings and higher education planning, has placed two Michigan Tech online graduate programs on its lists of the nation’s best.
Michigan Tech is on Intelligent.com’s list of Online Master’s in Civil Engineering Degree programs. The website analyzed 181 schools on a scale of 0 to 100, with 29 making the final list. Michigan Tech’s online Master’s in Civil Engineering ranked #21 on the list.
Michigan Tech was also on Intelligent.com’s list of the Best Online Master’s in Electrical Engineering Degree Programs. The website assessed 190 colleges and universities with 424 education programs compared. Once again, programs were scored on a scale of 0 to 100 with a total of 41 programs making the list. Michigan Tech’s online master’s in electrical engineering program was ranked #28 on the list.
The 23 members of the Michigan Tech RedTeam achieved a historic breakthrough in the Spring 2021 National Cyber League (NCL) competition.
The primary team finished the capture-the-flag (CTF) team competition 3rd Place in the overall ranking (tied for 1st Place in score). More than 900 teams from across the country participated in the CTF.
Students on the primary team are: Trevor Hornsby, Dakoda Patterson, Stu Kernstock, Matthew Chau, Ryan Klemm, Shane Hoppe, and Joshua Stiebel.
Further, of the 4,180 individual players competing in this spring’s NCL, four RedTeam players ranked in the Top 100: Trevor Hornsby (50th Place), Dakoda Patterson (59th), Stu Kernstock (75th), and Matthew Chau (100th).
“Amazing achievements!” said Dr. Bo Chen, Computer Science. “We are proud of you guys!” Chen, along with Dr. Yu Cai, Applied Computing, are advisors to the student organization.
The biannual NCL cybersecurity competition, for college and high school students, consists of a series of individual and team challenges, which present opportunities for students to prepare and test themselves against practical cybersecurity knowledge and skills, such as identifying hackers from forensic data, pentesting and auditing vulnerable websites, and recovering from ransomware attacks.
RedTeam is a registered Michigan Tech student organization. The team works to promote a security-driven mindset among students, and provide a community and resource for those wishing to learn more about information security.
Interested in cybersecurity? RedTeam meets every Thursday, 6:00-7:00 p.m., in Discord. Students with little or no background in cybersecurity are welcome. Contact the Red Team (email@example.com) for more information.
Two recent rankings place Michigan Tech among elite colleges and universities on both the state and national level.
Michigan Tech was rated #2 on the list of the Best Accredited Online Colleges in Michigan by EDsmart. The ranking service assesses online colleges in Michigan based on data that covers cost, academic quality, student satisfaction and salary after attending.
Michigan Tech was ranked #13 on the list of the 50 Best Value Public Colleges in America by Stacker. The ranking included only public, four-year colleges and weighed the cost of tuition with each school’s acceptance rate, quality of professors, diversity and the median earnings for alumni six years after graduation.
Michigan Tech is #2 on a list of 30 Michigan colleges and university top be ranked among the 2021 Best Accredited Online Colleges in Michigan by EDsmart.org, a nationally recognized publisher of college resources and independent rankings.
This press release was orginally distributed by SBWire
Draper, UT — (SBWIRE) — 04/05/2021 — EDsmart’s ranking of the Best Online Colleges in Michigan is the most comprehensive and well-rounded to date. The ranking includes only fully accredited schools. Rankings are based on affordability, academic quality, student satisfaction and student outcomes according to data from the U.S. Department of Education.
“It is important to recognize the colleges and universities that go above and beyond,” said Tyson Stevens, managing editor of EDsmart. “Our goal is to highlight these schools and their commitment to higher education.”
“The Best Online Colleges in Michigan ranking allows students to compare accredited schools and find those that best fit their education interests and career goals,” said EDsmart’s spokesperson. “Beyond providing affordable education, a college is not successful if it does not graduate its students, which is why EDsmart rankings place a high value on outcomes, including graduation and retention rates, and post-graduation earnings.”
The rankings and data were produced for EDsmart, a leading higher education research organization. All evaluated data was gathered from IPEDs, U.S. Department of Higher Education, College Scorecard, Payscale.com, school websites, and other reputable sources.
The rankings have been published at https://www.edsmart.org/accredited-online-colleges/michigan/
EDsmart reviews publicly available data to produce independent ranking assessments of various educational programs, in addition to student guides and resources. The site is regularly updated by a committed team of writers and researchers, who produce college rankings and resources that will help prospective and current college students get into, pay for, and thrive at the college of their choice.
by Karen S. Johnson, Communications Director, College of Computing
Four Michigan Tech graduate students recently took 6th place in the U.S. Navy’s Artificial Intelligence (AI) Tracks at Sea Challenge, receiving a $6,000 prize.
The Challenge solicited software
solutions to automatically generate georeferenced tracks of maritime vessel
traffic based on data recorded from a single electro-optical camera imaging the
traffic from a moving platform.
Each Challenge team was presented
with a dataset of recorded camera imagery of vessel traffic, along with the
recorded GPS track of a vessel of interest that is seen in the imagery.
Graduate students involved in the challenge were Zach DeKraker and Nicholas Hamilton, both Computer Science majors advised by Tim Havens; Evan Lucas, Electrical Engineering, advised by Zhaohui Wang; and Steven Whitaker, Electrical Engineering.
Submitted solutions were evaluated
against additional camera data not included in the competition testing set in
order to verify generalization of the solutions. Judging was based on track
accuracy (70%) and overall processing time (30%).
“We never got our final score, but we were the “first runner up” team,” says Lucas. “Based on our testing before sending it, we think it worked well most of the time and occasionally tracked a seagull or the wrong boat.”
The total $200,000 prize was distributed among five winning teams, which submitted full working solutions, and three runners-up, which submitted partial working solutions.
The Challenge was sponsored by the Naval
Information Warfare Center (NIWC) Pacific and the Naval Science, Technology,
Engineering, and Mathematics (STEM) Coordination Office, and managed by the Office
of Naval Research. Its goal was to engage with the workforce of tomorrow on
challenging and relevant naval problems, with the immediate need to augment
unmanned surface vehicles’ (USVs’) maritime contact tracking capability.
problem presented was to find a particular boat in a video taken of a harbor,
and track its GPS coordinates.,” says Zach DeKraker. “We were provided with
samples of other videos along with the target boat’s GPS coordinates for that
video, which we were able to use to come up with a mapping from pixels to GPS
“Basically, we wanted to track boats with a video camera,” adds ECE graduate student Steven Whitaker. “Our team used machine learning and computer vision to do this. At weekly meetings we brainstormed approaches to tackling the problem, and at regular work sessions, together we programmed it all and produced a white paper with the technical details.”
Whitaker says the competition tied in pretty closely to work the students have already done. “We had a good majority of the code already written. We just needed to fit everything together and add in a few more details and specialize it for the AI Tracks at Sea research,” he explains.
like this one often connect directly or indirectly with a student’s academic
and career goals.
good to not be pigeon-holed, and to use our knowledge in a different scenario,”
Steven Whitaker says of these opportunities. “This helps us remember that there
are other things in the world other than our small section of research.”
team knew that there were two primary issues at hand. First, how can the pixel
coordinates be translated into GPS coordinates? And second, how can the boat be
located so that GPS pixel coordinates can be determined?
we broke it down into these two subproblems, it became pretty clear how to
solve each half,” DeKraker says. “Steven had already done a significant amount
of work mapping pixel coordinates into GPS coordinates, so we had a pretty
quick answer to subproblem one.”
team met weekly to discuss their ideas for the project and compare and contrast
how effective they would be as solutions to the problem at hand. Then, they got
together on Fridays or during the weekends to work together on the project.
Havens would come in to our weekly meetings and nudge us in the right direction
or give tips on what we should do and what we should avoid,” Whitaker adds.
subproblem two, after some discussion the group decided it was probably best to
use a machine learning approach, as that promised the most significant gains
for the least amount of effort, which was important given the tight schedule.
tried some different sub-projects independently and then worked together to
combine the parts we thought worked best,” Evan Lucas says.
identify the boat and track its movement, the team used a simple neural network
and a computer vision technique called optical flow, which made the analysis
much faster and cleaner. They used a pre-built algorithm, adding a bit of
optical flow so that the boat’s position didn’t have to be verified every time.
two tools allowed us to find the pixel coordinates of the boat and turn them
into GPS coordinates,” DeKraker says, whose primary role in the project was
integrating the two tools and packaging it for testing.
of my PhD is to map out a snowmobile’s GPS coordinates with a camera,” Whitaker
says. “This is extremely similar to mapping out a boat’s GPS coordinates. I
could even say that it was exactly the same. I don’t believe I’ll add anything
new, but I’ve tweaked it to work for my research.”
sums up the team’s division of responsibilities like this: “Evan detects all
the boats in the picture; Nik detects which of those boats is our boat; Steven
takes our boat position and converts it to GPS coordinates, Zach glued all of
our pieces together.”
says, “One of the things the judges stressed was the ease of implementing the
solution. Since that falls under what I would consider user experience (UX) or
user interface (UI), it was pretty natural for me to take these tasks on,
having studied software engineering for my undergrad,” DeKraker says.
primary focus was speed. “Using machine learning for object detection tends to
be slow, so to mitigate that we used the boat detector only once every 5
seconds,” DeKraker explains.
of the tracking was done using a very fast technique called optical flow, which
looks at the difference between two consecutive frames of a video to track
motion,” DeKraker says. “It tended to drift from the target though, so we
decided on running the boat detector every 5 seconds to keep optical flow on
end result is that our solution could run nearly in real-time,” he says. “The
accuracy wasn’t the best, but given a little bit more time and more training
data, the neural network could be significantly improved.”
graduate studies focus heavily on various machine learning techniques, He says
that this opportunity to integrate machine learning into our solution was a
“First, it sounded like an interesting challenge. I don’t get to do a lot of software design these days, and this challenge sounded like a great opportunity to do just that,” he explains.
“Second, it looked like a great opportunity to build up my resume a little bit. Saying that you won thousands of dollars for your university in a nationwide competition sounds really good. And finally, I really wanted the chance to see a practical application of machine learning in action.”
completed a BS in Software Engineering at Michigan Tech in 2018. He returned to
Michigan Tech the next year to complete his master’s degree. He says the
biggest reason he did so was to learn more about machine learning.
embarking on this journey, I really didn’t know anything about it,” he says of
machine learning. “Having this chance to actually solve a problem, to integrate
a neural network into a fully realized boat tracker using nothing but a video
helped me see how machine learning can be used practically, rather than merely
understanding how it works.”
although it was a fascinating exploration into the practical side of machine
learning and computer vision, DeKraker says it’s rather tangential to his main research
focus right now, which is on comparing different network architectures to evaluate
which one performs best given particular data and the problem being solved.
believes that the culture is the most magnetizing thing about Tech. “Everybody
here is cut from the same cloth. We’re all nerds and proud of it,” he explains.
“You can have a half-hour conversation with a complete stranger about
singularities, the economics of fielding a fleet of star destroyers, or how
Sting was forged.”
the most appealing thing about Michigan Tech was its size. DeKraker says. “When
I looked at a ranking of the top universities in Michigan, Tech was number 3,
but still extremely small. It was a perfect blend of being a small but very
he says the second-best thing about Tech is the location. “The Keweenaw is one
of the most beautiful places on earth.”
has many ideas about where he’d like to take his career. For instance, he’d
love the chance to work for DARPA, Los Alamos National Laboratory, or NASIC. He
also intends to commission into the Air Force in the next couple of years, “if
they have a place for programmers like me.”
Evan Lucas is a PhD candidate in the Electrical Engineering department., advised by Zhaohui Wang. Lucas completed both a bachelor’s and master’s in Mechanical Engineering at Tech in 2012 and 2014,
Lucas, whose research interests are in applying machine learning methods to underwater acoustic communication systems, worked on developing a classifier to separate the boat of interest from the many other boats in the image. Although the subject of the competition is tangential to Lucas’s graduate studies, as computer vision isn’t his area, there was some overlap in general machine learning concepts. respectively.
sounded like a fun challenge to put together an entry and learn more about
computer vision,” Lucas says. “Working with the rest of the team was a really
good opportunity to learn from people who have experience making software that is
used by other people.”
completion of his doctoral degree, hopefully in spring 2023, Lucas plans to return
to industry in a research focused role that applies some of the work he did in his
Steven Whitaker’s research interests are in machine learning and acoustics. He tracks and locates the position of on-ice vehicles, like snowmobiles, based on acoustics. He says he has used some of the results from this competition project in his PhD research.
Whitaker’s machine learning research is experiment-based., and that’s why he chose Michigan Tech. “There aren’t many opportunities in academia to do experiment-based research,” he says. “Most machine learning is very software-focused using pre-made datasets. I love doing the experiments myself. Research is fun. I enjoy getting paid to do what I normally would do in my free time.”
In 2019, Whitaker completed his BS in Electrical Engineering at Michigan Tech. He expects to complete his master’s degree in Electrical Engineering at the end of the summer 2021 semester, and his PhD in summer 2022. His advisors are Tim Havens and Andrew Barnard.
would love to be a university professor one day, but first he wants to work in
Timothy Havens is associate dean for research, College of Computing; the William and Gloria Jackson Associate Professor of Computer Systems; and director of the Institute of Computing and Cybersystems (ICC). His research interests are in pattern recognition and machine learning, signal and image processing, sensor and data fusion, heterogeneous data mining, and explosive hazard detection.
Michael Roggeman is a professor in the Electrical and Computer Engineering department. His research interests include optics, image reconstruction and processing, pattern recognition, and adaptive and atmospheric optics.
Zhaohui Wang is an associate professor in the Electrical and Computer Engineering department. Her research interests are in communications, signal processing, communication networks, and network security, with an emphasis on underwater acoustic applications.
The Naval Information Warfare Center (NIWC) Pacific and the Naval Science, Technology, Engineering, and Mathematics (STEM) Coordination Office, managed by the Office of Naval Research are conducting the Artificial Intelligence (AI) Tracks at Sea challenge.
Challenge.gov is a web platform that assists federal agencies with inviting ideas and solutions directly from the public, or “crowd.” This is called crowdsourcing, and it’s a tenet of the Challenge.gov program. The website enables the U.S. government to engage citizen-solvers in prize competitions for top ideas and concepts as well as breakthrough software, scientific and technology solutions that help achieve their agency missions.
This site also provides a comprehensive
toolkit, a robust repository of considerations, best practices, and case
studies on running public-sector prize competitions as developed with insights
from prize experts across government.