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    Michigan Tech Team Among 17 Teams Selected for Marine Energy Competition


    Michigan Tech is among 17 top colleges and universities nationwide that have been selected to compete in the 2021-22 Marine Energy Collegiate Competition: Powering the Blue Economy The event is hosted by the U.S. Department of Energy’s (DOE) Office of Energy Efficiency and Renewable Energy (EERE).

    These student competitors are poised to be the next blue economy innovators as they gain real-world experience and make industry connections to prepare for future careers in marine energy, according to the Marine Energy Collegiate Competition.

    The team’s faculty advisors are Andrew Barnard (ME-EM, GLRC), Gordon Parker, and Timothy Havens (CC, ICC).


    Administered by the National Renewable Energy Laboratory, on behalf of EERE’s Water Power Technologies Office, the competition challenges interdisciplinary teams of undergraduate and graduate students to explore opportunities for marine energy technologies via real-world concept development experiences, and to propose unique solutions to the burgeoning marine energy industry.

    Submissions can run the gamut from concepts that aid in ocean observation and underwater vehicle charging to desalination and more, including—but not limited to—the markets identified in DOE’s Powering the Blue Economy™ report.


    Learn more about the competition and sign up for email alerts to keep up with the latest from the Marine Energy Collegiate Competition.


    The DOE is hosting the challenge to advance one of the most up-and-coming industries: marine energy. Marine energy has the potential to provide reliable power to the blue economy, but further work is needed to optimize designs and reduce costs, according to the competition website.

    The “blue economy” describes the sustainable use of ocean resources for economic growth, improved livelihoods and jobs, and ocean ecosystem health.


    Competition Elements

    • Develop a market-research-supported business plan, which will include key aspects of their design of a system that could be commercialized to address power needs for a chosen sector of the blue economy
    • Pitch their plan to a panel of judges and hypothetical investors
    • Have the option to build and test a device to achieve energy production
    • Engage with their community through outreach and educational activities.

    Competition Deliverables

    • A 20- to 30-page market research-supported business plan and technical design of a marketable device powering any marine energy sector of the blue economy
    • A 20-minute public pitch that will be presented to a panel of judges during the competition event at Water Power Week 2022 or virtual followed by a 15-minute Q&A session
      • 5 minutes of the public pitch will focus on community engagement and outreach activities the team conducted throughout the year
    • A poster summarizing the entire technical and business plan
    • Optional: An effective prototype that will be tested for power performance at model scale. Results of the test will be summarized in the written report.

    Inspiring Blue Economy Ingenuity

    “The MECC provides an opportunity for a diversity of experience, education, and perspectives in exploring the possibilities of the blue economy,” said Arielle Cardinal, the MECC operations manager at NREL. “We’re excited to support the 2022 competitors in bringing new ideas and innovations to the forefront of marine energy.”


    Michigan Tech Team Ranks #3 in Spring 2021 NCL Power Rankings


    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.


    See how the NCL competitions work.


    View the full list of NCL rankings.


    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.


    Fall 2021 Finishing Fellowship Nominations Open

    by Debra Charlesworth, Graduate School

    Applications for Fall 2021 Finishing Fellowships are being accepted and are due no later than 4 p.m. June 30 to the Graduate School. Please email applications to gradschool@mtu.edu.

    Instructions on the application and evaluation process are found online. Students are eligible if all of the following criteria are met:

    1. Must be a Ph.D. student.
    2. Must expect to finish during the semester supported as a finishing fellow.
    3. Must have submitted no more than one previous application for a Finishing Fellowship.
    4. Must be eligible for candidacy (tuition charged at Research Mode rate) at the time of application.
    5. Must not hold a final oral examination (“defense”) prior to the start of the award semester.

    Finishing Fellowships provide support to Ph.D. candidates who are close to completing their degrees. These fellowships are available through the generosity of alumni and friends of the University. They are intended to recognize outstanding Ph.D. candidates who are in need of financial support to finish their degrees and are also contributing to the attainment of goals outlined in The Michigan Tech Plan.

    The Graduate School anticipates funding up to 10 fellowships, with support ranging from $2,000 to full support (stipend plus tuition). Students who receive full support through a Finishing Fellowship may not accept any other employment. For example, students cannot be fully supported by a Finishing Fellowship and accept support as a GTA or GRA.


    Pursue a Cybersecurity Career with this Generous NSF CyberCorps Scholarship


    Apply now for Michigan Tech’s 2021-22 cohort of Cybersecurity Scholars and jumpstart your cybersecurity career!

    The deadline to apply is June 1, 2021.

    This generous scholarship opportunity provides up to three years of tuition and annual stipend.

    Then, following completion of your degree, you’ll work in a cybersecurity-related position for a federal, state, local, or tribal agency for up to three years– a period equal to the length of your scholarship.

    See full guidelines, requirements, and application information on the SFS website: mtu.edu/sfs.


    Eligible Degree Programs

    1. BS in Cybersecurity (CyS)
    2. BS in Computer Network and System Administration (CNSA)
    3. BS in Computer Science (CS)
    4. BS in Software Engineering (SE)
    5. BS in Computer Engineering (CpE)
    6. BS in Electrical Engineering (EE)
    7. BS in Management Information Systems (MIS)
    8. MS in Cybersecurity

    Ready to apply? Visit mtu.edu/sfs

    Questions? Email sfs@mtu.edu


    The Michigan Tech SFS Program

    The SFS program at Michigan Tech involves multiple programs and departments, including the College of Computing and its departments of Applied Computing and Computer Science; the College of Engineering’s Department of  Electrical and Computer Engineering; and the College of Business’s Management Information Systems B.S. program. 

    “The U.S. is facing a significant shortage of well-trained and well-prepared cybersecurity professionals,” said Dr. Yu Cai, professor of applied computing and the principal investigator of the grant. “This new scholarship will continue to develop Michigan Tech’s national and international reputation as a leader and innovator in cybersecurity education, research and outreach activities.”

    The five-year, $3.3 million NSF grant provides up to three years of full scholarship support for 20 Michigan Tech undergraduate and graduate students.


    About the NSF Scholarship

    Protecting worldwide digital infrastructure has become an urgent focus of industry and government. And employment in this sector is expected to grow exponentially in the coming years.

    In response, the National Science Foundation CyberCorps: Scholarship for Service (SFS) program was introduced as a nationwide program to recruit and train the next generation of information technology professionals, industrial control system security professionals, and security managers.


    Start Your Cybersecurity Career with this Generous CyberCorps Scholarship


    Apply now for Michigan Tech’s 2021-22 cohort of Cybersecurity Scholars and jumpstart your cybersecurity career!

    The deadline to apply is June 1, 2021.

    This generous scholarship opportunity provides up to three years of tuition and annual stipend.

    Then, following completion of your degree, you’ll work in a cybersecurity-related position for a federal, state, local, or tribal agency for up to three years– a period equal to the length of your scholarship.

    See full guidelines, requirements, and application information on the SFS website: mtu.edu/sfs.


    Eligible Degree Programs

    1. BS in Cybersecurity (CyS)
    2. BS in Computer Network and System Administration (CNSA)
    3. BS in Computer Science (CS)
    4. BS in Software Engineering (SE)
    5. BS in Computer Engineering (CpE)
    6. BS in Electrical Engineering (EE)
    7. BS in Management Information Systems (MIS)
    8. MS in Cybersecurity

    Ready to apply? Visit mtu.edu/sfs

    Questions? Email sfs@mtu.edu


    The Michigan Tech SFS Program

    The SFS program at Michigan Tech involves multiple programs and departments, including the College of Computing and its departments of Applied Computing and Computer Science; the College of Engineering’s Department of  Electrical and Computer Engineering; and the College of Business’s Management Information Systems B.S. program. 

    “The U.S. is facing a significant shortage of well-trained and well-prepared cybersecurity professionals,” said Dr. Yu Cai, professor of applied computing and the principal investigator of the grant. “This new scholarship will continue to develop Michigan Tech’s national and international reputation as a leader and innovator in cybersecurity education, research and outreach activities.”

    The five-year, $3.3 million NSF grant provides up to three years of full scholarship support for 20 Michigan Tech undergraduate and graduate students.


    About the NSF Scholarship

    Protecting worldwide digital infrastructure has become an urgent focus of industry and government. And employment in this sector is expected to grow exponentially in the coming years.

    In response, the National Science Foundation CyberCorps: Scholarship for Service (SFS) program was introduced as a nationwide program to recruit and train the next generation of information technology professionals, industrial control system security professionals, and security managers.


    Undergrad Summer Lab Positions: Autonomous Driving Research


    Dr. Xiaoyong (Brian) Yuan, Applied Computing and Computer Science, is seeking several hourly paid undergraduate students to work in the areas of autonomous driving.

    The project is funded by MTU Research Excellence Fund (REF) and expected to begin in summer 2021 (7/1/2021).

    Preferred Qualifications

    • Passion for research in autonomous driving and machine learning
    • Solid programming skills in C, Python, Java, or related programming languages
    • Familiar with Linux OS

    To Apply

    To apply, please send a resume and a transcript to Dr. Yuan (xyyuan@mtu.edu).

    Dr. Yuan is a member of the Data Sciences and Cybersecurity research groups of the Institute of Computing and Cybersystems (ICC).


    Chinmay Kondekar, MS in Electrical Engineering Graduate, 2021

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

    Graduate student Chinmay Kondekar heard about Michigan Tech during his undergraduate studies. Sometime later he read a social media post about work opportunities in the robotic and automation labs, and Michigan Tech again came to his attention.

    “At that time, I was working as a controls engineer in India,” he says. “Robotics and automation interest me, and when I saw who had written the post (a former graduate student of Sergeyev’s), I knew I had found the perfect degree program.”

    Kondekar’s final design project was to create an interconnected system that is flexible, reconfigurable, and controlled from a central control interface to emulate a production process.

    “We decided on machining as the process because it is tricky to program and one of the more challenging applications for an industrial robot,” he says.

    The system has a number of industrial applications. “Most of the robotic work cells in the industry have similar control and communication layout,” Kondekar confirms.

    “The data generated from the project has helped me to create lab manuals on interconnected systems,” Kondekar adds. “The system has potential applications in data acquisition and analytics, cybersecurity, and future projects requiring interconnected systems.”

    The system is a result of combining multiple components that are controlled from a central interface by a method called systems integration. Similar manufacturing system layouts can be commonly found in the automotive, pharma, and food industry.

    The system is used to machine different patterns on a block of foam using various robotic attachments. Correct dimensions are assured using machine vision, and by transporting the workpiece between different stations.

    What sparked Kondekar’s interest in creating the system was the challenge presented by the hardware and software interfacing required, which is accomplished through hands-on work and software programming, which he enjoys immensely.

    “I enjoy solving problems and coming up with a solution to make things work,” he shares. “When starting the project, I had a lot of unknown variables but I knew how to approach them and, eventually, I came up with solutions and made the system work. It’s highly rewarding to watch the finished system come together, and then to see it work automatically after pressing just three buttons.”

    Kondekar had some background knowledge going into the project, gained during his employment as a controls engineer. In that position, he worked on boiler, turbine, pharma, and automotive automation verticals, making “the PLC part of the project easy.”

    His background in electrical engineering also made the controls and wiring easy. “But I had to learn robotics and electro-pneumatics from scratch, as I had never worked on either of them,” he says.

    Kondekar’s project would not have been possible without generous support from Mark Gauthier and his team at Donald Engineering. “Mark has helped the department acquire the best industry-grade hardware, and his expertise in pneumatics helped the project concept become reality,” Kondekar says.

    Kondekar has worked as a teaching assistant, an instructor for high school students and engineering undergrads, and a student researcher for Professor Aleks Sergeyev, Applied Computing.

    “Aleks has been a wonderful mentor and a great advisor,” Kondekar says. “I love his vision and his approach towards automation and robotics. I will definitely miss working with him, and I look forward to opportunities to work with him again.”

    “Chinmay is a very knowledgeable student with a great work ethic,” says Sergeyev. “Through his study and research, he acquired all the needed skills to become a very successful contributor to the industry. I certainly enjoyed working with him”

    For the next few years, Kondekar sees himself working in the automation and controls industry for systems integrator companies. He’ll soon start a controls engineer position with Patti Engineering, Auburn Hills, Mich. Research work has been interesting for him, and he says he would consider a PhD opportunity in the future.

    Professor David Labyak (MMET) helped Kondekar with the machining aspect of his project. “He is one of the best teachers I have ever had,” he says. “I would look forward to working with him in the future, as well.”

    During his high school teaching experiences—for a local mechatronics program—he worked with Professor John Irwin (MMET), whom he also identifies as a mentor. “I like his approach towards mechanical and mechatronics education, and would like to work with him in the future,” Kondekar says of Irwin.

    Kondekar graduates this spring with his master of science in electrical engineering. He completed a bachelor’s in engineering in electrical engineering at University of Pune, India, in 2017. In 2019, he completed a Michigan Tech certificate in FANUC handling tool operations and material handling.

    He says he has enjoyed his learning and life experiences at Michigan Tech. Plus, he loves the outdoors. “I am an outdoors guy and I love the UP, especially the summers. It’s full of good people and great beer!”



    Husky Innovate Students Win Top Prizes in New Venture Online Competition

    by Husky Innovate

    For the 11th year running, Central Michigan University and Michigan Tech collaborated to offer Tech students a chance to compete at CMU’s New Venture Competition. 2021 marked the second year the pitch competition was held online as the New Venture Online Competition (NVOC).

    Despite the challenges of a pandemic and a virtual platform, our students persevered, honed their pitches and won top prizes. This year’s NVOC winners were also winners at the 2021 Bob Mark Business Model Pitch Competition held at Tech in January. All of their hard work and effort paid off!

    Congratulations to this year’s MTU winners:

    • In the 2020-track 10-minute pitch category, Team Focus with Ranit Karmakar won the Best Overall Venture Award for $25,000. Watch Karmakar’s pitch.
    • In the two-minute pitch category, Team The Fitting Room with Jordan Craven won third place for $1,000. Watch Craven’s pitch.
    • Team Recirculate with Hunter Malinowski won an honorable mention award for $750. Watch Malinowski’s pitch.

    Read more in the NVOC 2021 Booklet.


    Congratulations Class of 2021!

    It has been a challenging academic year, to say the least. As part of the Class of 2021, you are an exceptional group of graduates. Your final academic year presented you with unforeseen and unprecedented challenges, yet you persevered.

    We are all proud to have mentored, instructed, and supported you on your educational journey. We know you’ll do well. You are a Husky, after all!

    Please stay in touch!


    Grad Students Take 6th Place in Navy’s AI Tracks at Sea Challenge

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


    The Challenge

    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.

    The Problem

    “The 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 coordinates.”

    “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.

    Competitions like this one often connect directly or indirectly with a student’s academic and career goals.

    “It’s 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.”

    Dividing Responsibilities

    The 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?

    “Once 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.”

    AI Tracks at Sea Flowchart

    The 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.

    “Dr. 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.

    For 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.

    “We tried some different sub-projects independently and then worked together to combine the parts we thought worked best,” Evan Lucas says.

    The Solution

    To 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.

    AI Tracks at Sea Neural Net Summary

    “These 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.

    “Part 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.”

    Whitaker 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.”

    DeKraker 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.

    A 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.

    “Most 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 target. “

    “The 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.”

    AI Tracks at Sea Homography Transform

    Zach DeKraker

    DeKraker’s graduate studies focus heavily on various machine learning techniques, He says that this opportunity to integrate machine learning into our solution was a fantastic experience.

    “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.”

    DeKraker 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.

    “Before 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.”

    And 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.

    DeKraker 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.”

    And 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 good school.”

    And he says the second-best thing about Tech is the location. “The Keweenaw is one of the most beautiful places on earth.”

    DeKraker 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

    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.

    “It 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.”

    Following 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 PhD.


    Steven Whitaker

    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.

    Whitaker would love to be a university professor one day, but first he wants to work in industry.


    Background Info

    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.

    View more details about the Challenge competition here: https://www.challenge.gov/challenge/AI-tracks-at-sea/

    Watch a Navy webinar about the Challenge here: https://www.youtube.com/watch?v=MjZwvCX4Tx0.

    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.