Category: Feature Article

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.

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!”



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!


Dean’s Teaching Showcase, Todd Arney, Applied Computing


by Michael R. Meyer – Director, William G. Jackson CTL

Dennis Livesay , Dean of the College of Computing, has selected Todd Arney, Senior Lecturer in Applied Computing, as our twelfth-week Deans’ Teaching Showcase member.

Arney, an inaugural winner of the Provost’s Award for Sustained Teaching Excellence in 2020, has a long record of outstanding teaching. But, this time, Applied Computing Chair Dan Fuhrmann, while acknowledging that Todd continues to teach a “substantial load” at an “exceptionally high level of quality,” recommended Arney for his behind-the-scenes “efforts to modernize the curricula in the Department of Applied Computing, and to enhance the use of state-of-the-art computing resources across campus, through the use of our new Virtual Cluster.”

Fuhrmann notes the changes in instruction required by the pandemic made Arney’s work a particular “godsend” because it enabled remote teaching. But he emphasizes that “it facilitated a vast improvement in student experience, in comparison to the aging educational computing hardware in the Computer Network and Systems Administration program that preceded it.”

Fuhrmann calls Arney an “evangelist” for the Virtual Cluster and notes that in addition to its implementation within the CNSA and Cybersecurity programs, Arney has made special efforts to reach out to the Department of Civil and Environmental Engineering, bringing a modern computing framework to one of their senior/graduate courses, CEE 4610/5610 (Water Resources System Modeling and Design).

He also worked with AC Academic Advisor Kay Oliver, the instructor for SAT 1090 (Introduction to Applied Computing), to provide introductions on cybersecurity and privacy frameworks for the students to use as a common language for their group work discussions on project design using micro:bit hardware to solve real-world problems.

Currently, Arney is working on additional collaborations with Mechatronics faculty, two senior design projects, and two new faculty members in the College of Computing to help support their courses using the cluster. Fuhrmann emphasizes that “Bringing new resources into our educational programs does not happen overnight, and it does not happen without knowledgeable, dedicated faculty members who see the potential and who make the necessary effort to upgrade the curriculum to take advantage of those resources. Todd Arney is that person in the Department of Applied Computing.”

In choosing Arney, Dean Livesay heartily agrees, noting, “Ensuring that our students have access to the latest technology is time-consuming and represents work that isn’t acknowledged as regularly as it should be. As such, we’re especially proud to recognize Todd’s accomplishments in deploying virtual machines broadly in our classes, and helping others do the same in theirs.”

Arney will be recognized at an end-of-term event with other showcase members, and is also a candidate for the CTL Instructional Award Series (to be determined this summer) recognizing introductory or large-class teaching, innovative or outside the classroom teaching methods, or work in curriculum and assessment.


PhD Student Daniel Byrne, CS, Awarded Finishing Fellowship


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

The Graduate Dean Awards Advisory Panel and dean have awarded a Summer 2021 Finishing Fellowship to PhD student Daniel Byrne, Computer Science. Byrne will receive full support for the semester, which includes three research credit hours and a stipend.

“The panel was impressed with your research, publication record, and contribution to the mission of Michigan Tech,” says the award letter. “The intent of this fellowship is to allow you to focus your time on your dissertation so that you can complete your degree requirements during the fellowship period.”

Byrne’s research centers around the modeling and optimization of memory systems, which are found in today’s datacenters. He explains that data caching helps improve the speed and efficiency of front-end cloud applications, such as websites and video streaming.


In collaboration with researchers at the University of Rochester, Byrne has developed a new data caching system. “Our system uses intelligent data replication and allocation across multiple memory devices to maximize performance while reducing overall operating costs,” Byrne says.

“Specifically, we focus on utilizing new memory technologies to lower operational costs while meeting performance targets,” Byrne adds. “Even small increases in performance and energy savings have significant impact over an entire deployment of servers.”

His improvements to caching systems have already been adopted outside the lab, into a widely-used open-source caching system called “memcached.”

“Daniel’s research focuses on modeling and designing a hybrid memory system where the conventional DRAM (faster, but more expensive) and the emerging non-volatile memory (NVM, cheaper but slower) are combined to host a key-value store,” says Dr. Zhenlin Wang, Computer Science, Byrne’s faculty advisor, along with Dr. Nilufer Onder, associate professor in the CS department.

Wang expects that Byrne’s research will have a long term impact on design and implementation of a hybrid key-value store. “His work explores the theoretical properties of and interactions between inclusive and exclusive caches, a design space which has never been investigated before,” Wang says.

Byrne began his Michigan Tech PhD studies in computer science in Fall 2016. “I am grateful for the amount of support from my advisors, the Computer Science department, and the Graduate School during my PhD program,” he says.

“I am also incredibly grateful for my PhD committee’s support as I finish my dissertation over the summer. It has been a wonderful journey, and I have greatly enjoyed my time as a graduate student, especially my tenure as GSG vice president.”

“I extend my sincere gratitude to the Graduate School for this support during the final period of completing and defending my dissertation,” he adds.

“I also would like to thank the College of Computing for its efforts in creating a strong research environment and a supportive community of graduate students and faculty.”

Recipients of the fellowship are expected to finish during the semester for which funding is provided, maintain good academic and conduct standing, publish their work in internationally recognized peer review journals, among other requirements.

Byrne served as vice president of the Michigan Tech Graduate Student Government from Summer 2019 to Spring 2020. He says he is happy to have had the opportunity to advocate for graduate students and achieve increased support for health care, an initiative he championed during his tenure.

In Spring 2019 he received a Graduate Student Service Award, which is awarded by the Graduate Student Government Executive Board. The Service Award recognizes outstanding contributions to the graduate community at Michigan Tech. See the April 5, 2019, announcement in Tech Today here.

View Byrne’s Github page here.


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.”

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.

“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.”


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.


Our Stories: Dr. Nathir Rawashdeh

This is part of a series of short introductions about College students, faculty, and staff. Would you like to be featured? Send a photo and some background info about yourself to computing@mtu.edu.

Dr. Nathir Rawashdeh, Assistant Professor, Applied Computing

  • Affiliated Assistant Professor, Dept. of Electrical and Computer Engineering
  • Years teaching at Michigan Tech: 2
  • Years teaching overall: 12
  • Member, Data Sciences research group, Institute of Computing and Cybersystems (ICC)
  • Ph.D., Electrical Engineering, University of Kentucky, 2007
  • MS, Electrical and Computer Engineering, University of Massachusetts, Amherst, 2003
  • Faculty Profile

Classes Dr. Rawashdeh Teaches

  • Programmable Logic Control (PLC)
  • Digital Electronics
  • Analog Electronics
  • Image Processing
  • Automatic Control Systems
  • Instrumentation and Measurement

The “coolest” class you teach, and why:

Programmable Logic Controllers (PLCs), because every factory in the world is controlled by PLCs.

The importance of your class topics to the overall understanding of Computing and your discipline: 

Computing is the way of the future. And in all disciplines we rely more and more on sophisticated design, modeling, and control software. The Digital Electronics course is key to the overall understanding of computer systems. We discuss the building blocks of computers, and programmable logic controllers apply computing solutions for automation programming and industrial communication.

Your teaching philosophy: 

  • I believe in the social connection between teacher and student because it enables them to learn from each other, and more than just technical material and information.
  • In today’s changing world, courses and delivery methods must be constantly updated to maximize learning in a wide sense. When teaching online, I always turn on my camera and teach from the classroom.
  • I interact actively with students, and when I see that they need a break I tell them a story from my professional or personal experience. In the labs, I am almost always engaged with students, helping them solve problems.

Labs you direct and their general focus:

  • In the Programmable Logic Controllers labs (for introductory and advanced level courses), students learn how to program industrial controllers and interface with sensors and actuators.
  • In the Digital Electrics lab, students learn the building blocks of computers and program FPGA boards, which is the fastest programmable hardware possible.

Research projects in which students are assisting: 

  • An ECE PhD student is working on sensor fusion for autonomous driving in the snow.
  • I plan to hire a graduate student this summer to implement indoor simultaneous location and mapping of a mobile robot.
  • Recently, an undergraduate EET student helped me build a virus sterilizing mobile robot that uses ultraviolet light. Read a news article, view photos and a YouTube video here.
  • In personal research, I also work on image analysis and industrial inspection research.

Other cool things your students are doing:

  • Recent senior design projects include a gesture controlled robotic arm and a PID control system based on a levitating ball.
  • See more projects on my lab website: https://www.morolab.mtu.edu/students.

Interests beyond teaching and research:

  • I am married and have four children. The eldest is studying Environmental Engineering at Tech.
  • I like cars and ground robots, painting, swimming, and playing soccer.
  • I speak three languages and have lived in four countries, in each for over a decade.