Dr. Sidike Paheding, Applied Computing, is seeking a fall 2021 graduate student to assist with a research project. Details are below.
- Pay rate: $15/hour, 10 hours/week
- Employment dates: September 2021 through December 2021. An extension is possible.
- Basic requirements: Experience in Unity development platform and Virtual/Augmented Reality.
- Desired: Knowledge in machine learning/AI is a plus.
Sidike Paheding, Ph.D.
Assistant Professor, Dept. of Applied Computing
Affiliated Assistant Professor, Dept. of Computer Science
Affiliated Faculty, Center for Data Sciences, and Computational Science & Engineering
College of Computing, Michigan Technological University
Graduate student Chen Zhao will defend his dissertation proposal for the PhD in Computational Science and Engineering Monday, July 26, 2021, at 2:00 p.m. via online meeting.
Zhao’s dissertation advisor is Dr. Weihua Zhao, Applied Computing; his research topic is Deep Learning for Medical Image Segmentation using Prior Knowledge and Topology.
Join the Zoom meeting here: https://michigantech.zoom.us/j/85266979905
by Pasi Lautala
Thomas Oommen (GMES, ICC), Ricardo Eiris, (CEGE, ICC), and Beth Veinott (CLS, ICC) are among eight Michigan Tech researchers who have submitted a a record number of eight concept papers for proposed research projects with the Federal Railroad Administration.
The Federal Railroad Administration (FRA) requested that Michigan Tech submit a record number of eight concept papers for proposed research projects as part of their 2021 Broad Agency Announcement.
In addition, Tech is a subcontractor for two more concept paper proposals. The paper submittal was coordinated by the Rail Transportation Program and the range of topics speaks to the diversity of Michigan Tech’s expertise applicable to the rail transportation. The PIs are looking forward to FRA decisions on how many of these papers advance to full proposals.
Each of the 10 projects had a different principal investigator (PI), representing six university departments/institutes and several more co-PIs.
The project titles and their PIs include:
- Hyper- and Multi-spectral Sensing and Deep Learning for Automated Identification of Roadbed Condition, (PI, Thomas Oommen, GMES).
- Wire Arc Additive Manufacturing (WAAM) for Weld Enhanced Cast Steel Coupler Knuckles (PI, Paul Sanders, MSE).
- IoT Assisted Data-analytics Framework Enables Assessment of Location Based Ride Quality (LBRQ) (PI, Sriram Malladi, MEEM).
- RailStory: Using Web-based Immersive Storytelling to Attract the Next Generation of Young Women in Rail (PI, Ricardo Eiris, CEGE).
- A Risk Informed Decision-Making Framework for Coastal Railroad System Subjected to Storm Hazards and Sea Level Rise (PI, Yousef Darestani, CEGE).
- Rail Corridor Life-Cycle Assessment (LCA) Framework, Factors and Models to Support Project Evaluation and Multi-Modal Comparisons (PI, Pasi Lautala, CEGE).
- Development of Infrared Thermography for Effective Rail Weld Inspection (PI, Qingli Dai, CEGE).
- Enabling Longer-distance, AI-enabled Drone-based Grade Crossing Assessment in Potentially GPS Denied Environments (PI, Colin Brooks).
- Multi-Site Simulation to Examine Driver Behavior Impact of Integrated Rail Crossing Violation Warning (RCVW) and In-Vehicle Auditory/Visual Alert (IVAA) System (PI, Elizabeth Veinott, subcontract with Virginia Tech).
- Evaluation of Non-traditional Methods of Reducing Emissions in Short Line Railroad Operations (PI, Jeremy Worm, subcontract with ASLRRA).
On May 18, 2021, Dr. Guy Hembroff, Applied Computing, presented an invited talk at a meeting of Michigan’s Health Information Management Systems Society (HIMSS). Dr. Hembroff discussed his work developing a trusted framework architecture designed to improve population health management and patient engagement.
The talk demonstrated his team’s work in the development of accurate geo-tagged pandemic prediction algorithms, which are used to help coordinate medical supply chains to care for patients most vulnerable to COVID-19, an innovation that can be extended to help improve general population health management.
The framework of the pandemic prediction architecture, which aggregates longitudinal patient health data, including patient generated health data and social determinants of health, is a holistic and secure mHealth community model. The model can help Michigan residents overcome significant barriers in healthcare, while providing healthcare agencies with improved and coordinated population management and pandemic prediction.
The architecture’s machine learning algorithms strategically connect residents to community resources, providing customized health education aimed to increase the health literacy, empowerment and self-management of patients. The security of the architecture includes development of unique health identifiers and touch-less biometrics capable of large-scale identity management.
Dr. Guy Hembroff is an associate professor in the Applied Computing department of the Michigan Tech College of Computing, and director of the Health Informatics graduate program. His areas of expertise are network engineering, medical/health informatics, biometric development, intelligent medical devices, data analytics, and cybersecurity.
The event was sponsored by HIMSS and Blue Cross Blue Shield of Michigan (BCBSM).
A mission-driven non-profit, the Healthcare Information and Management Systems Society, Inc. (HIMSS) is a global advisor and thought leader supporting the transformation of the health ecosystem through information and technology, according to the organization’s website.
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.”
by Mark Wilcox, University Marketing and Communications
Michigan Tech is celebrating the accomplishments of more than 1,000 undergraduate and graduate students who completed their degrees by the end of the spring 2021 semester. Despite the lack of a formal ceremony due to COVID-19 restrictions, more than 600 undergraduate and graduate students are expected to participate in a physically distanced celebration tomorrow (April 30) throughout the afternoon.
The Graduation Celebration website shines a spotlight on the graduating class and provides links for graduates, their families and friends, and the community. Graduates and alumni can share their favorite memories and view profiles created by the Class of 2021.
Among the undergraduates, 99 will graduate cum laude, 116 magna cum laude and 68 summa cum laude. In the Graduate School, there will be 31 doctorates awarded, along with 203 Master of Science degrees, 46 graduate certificates, 27 Master of Business Administration degrees, two Master of Forestry degrees, five Master of Geographic Information Science degrees, and 46 graduate certificates.
The Air Force ROTC and Army ROTC will conduct a joint commissioning ceremony at 7:30 a.m. Saturday (May 1) at the Rozsa Center for the Performing Arts. Eleven cadets will be commissioned as officers in the Air Force and five cadets will be commissioned as officers in the Army. Due to limited seating, a livestream ceremony will be available via Zoom.
One of the highlights of Michigan Tech’s traditional commencement ceremony is an address by a member of the graduate class. Even without a formal ceremony, remarks by this year’s student speaker, Tanner Sheahan, can be viewed on the Celebration Messages page of the Graduation Celebration website.
Sheahan, a chemical engineering major from Bay City, Mich., is enthusiastic about the message he’ll share with his class. “I love writing, and I think I have a knack for telling stories. I had fun — this has been a great experience,” he said.
Because of COVID-19 restrictions, Sheahan’s internship last summer was conducted virtually, but it had a very positive outcome. “The situation really opened up extreme doors and led to a job as a sales operations manager at Nalco Water, an Ecolab Company in Naperville, Illinois,” he said.
University President Rick Koubek congratulated MTU’s newest alumni. “On behalf of Michigan Tech’s entire faculty and staff, we wish you the very best in your future endeavors and trust you will do great things with your degree from Michigan Tech,” said Koubek. “Congratulations again on this tremendous accomplishment.”
Read more about tomorrow’s celebration in yesterday’s Tech Today.
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.
“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.”
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.”
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.
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.
“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.”
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 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’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.
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.
The Department of Computer Science will present a lecture by Dr. Qun Li on Friday, April 23, 2021, at 3:00 p.m. Dr. Li is a professor in the computer science department at William and Mary university. The title of his lecture is, “Byzantine Fault Tolerant Distributed Machine Learning.”
Byzantine Fault Tolerant Distributed Machine Learning
Training a deep learning network requires a large amount of data and a lot of computational resources. As a result, more and more deep neural network training implementations in industry have been distributed on many machines. They can also preserve the privacy of the data collected and stored locally, as in Federated Deep Learning.
It is possible for an adversary to launch Byzantine attacks to a distributed or federated deep neural network training. That is, some participating machines may behave arbitrarily or maliciously to deflect the training process. In this talk, I will discuss our recent results on how to make distributed and federated neural network training resilient to Byzantine attacks. I will first show how to defend against Byzantine attacks in a distributed stochastic gradient descent (SGD) algorithm, which is the core of distributed neural network training. Then I will show how we can defend against Byzantine attacks in Federated Learning, which is quite different from distributed training.