Category: Events

The College of Computing presented a Convocation Ceremony on May 1, 2020, to honor and recognize Spring and Summer 2020 graduates. At the virtual event, undergraduate student achievement awards were announced, graduates were congratulated, and faculty and staff congratulatory videos were viewed.

Michigan Tech Computer Science alumnus Brian VanVoorst ’93 presented the Convocation address. VanVoorst is a Lead Scientist at BBN Technologies, a member of BBN’s Distinguished Scientists, and a Raytheon Technologies Fellow.

The College’s inaugural class of 109 graduates comprises 5 doctor of philosophy, 14 master of science, and 90 bachelor of science degrees. The College of Computing Class of 2020 is nearly 20% women, 27% of the class graduated with honors, and the average undergraduate GPA is 3.28.

View the Convocation video below and on YouTube.

Play – College of Computing Convocation 2020

See a lists of all the graduates here. Two undergraduates completed dual majors: Lucas Catron, who majored in Computer Science and Humanities, and Mark Heinonen, Electrical Engineering Technology and Audio Productions and Technology.

View faculty and staff congratulatory videos, read student and faculty profiles, and discover all things Class of 2020, on the College of Computing webpage: mtu.edu/computing/class-of-2020.

The Department of Computer Science awarded Class of 2020 undergraduate awards to the following Computer Science (CS) and Software Engineering (SE) graduates:
Christina Anderson, CS: Award for Excellence in Teaching
Keith Atkinson, CS: Award for Exceptional Community Service and Leadership
Dean Bassett, CS: Award for Excellence in Teaching
Jack Bergman, CS: Award for Exceptional Leadership
Lucas Catron, CS: Award for Excellence in Teaching
Crystal Fletcher, CS: Award for Excellence in Teaching
Chris Holmes, CS: Award for Excellence in Teaching
Mads Howard, CS: Award for Excellence in Teaching
Jacob Jablonsky, SE: Award for Excellence in Teaching, Award for Excellence in Teaching
Maddie Le Clair, SE: Award for Exceptional Leadership
Amy Slabbekoorn, CS: Award for Excellence in Teaching
Emily Winkleman, CS: Award for Excellence in Teaching
Parker Young, SE: Award for Exceptional Leadership and Teaching, Award for Excellence in Teaching

Award for Exceptional Community Service and Leadership: Keith Atkinson
Keith has helped older adults in the Houghton community become comfortable with digital technology through one on one tutoring through the BASIC (Building Adult Skills in Computing) program. He taught several cohorts of middle school students about computer programming through the Copper Country Coders organization, and served as president of that organization. Keith developed and deployed a food inventory system for the Husky Food Access Network, which helps combat hunger issues on Tech’s campus.

Award for Exceptional Leadership: Jack Bergman
Jack has served as the president of MTU RedTeam, a student organization dedicated to promoting cybersecurity education among Tech students. Under his leadership, RedTeam organized students to participate in national cybersecurity competitions. In Fall 2019, the MTU Red Team was ranked 8th out of 689 in the NCL cyber competition. Jack led RedTeam to host a cybersecurity competition at MTU in Spring 2020, which attracted 35 students competing on 15 different teams.

Award for Exceptional Leadership: Maddie LeClair
Maddie has been a highly effective leader of the Women in Computing Sciences (WiCS) student organization.  Under her leadership, the group has increased its visibility, holding regular events on campus to highlight the opportunities for women in computing fields.  She led the effort for the WiCS group to become affiliated as an ACM-W chapter, and she has been active in supporting departmental efforts to diversify our undergraduate student body, both individually and as a leader of WiCS.

Award for Exceptional Leadership and Teaching: Parker Young
Parker served as president of not one, but two student organizations: Copper Country Coders and the Michigan Tech Pep Band.  Under his leadership, the Coders group made great strides in its organization and sustainability through revising its charter. Parker is passionate about teaching others, whether it is young students learning to mod Minecraft at Copper Country Coders or older adults learning to Zoom with their families in the BASIC program.  His leadership skills also facilitated his Senior Design team’s  successful completion of the Dragonfly app, an offline app developed for the North Carolina Natural History Museum’s after-school program to assist children monitoring the weather and counting dragonflies.

Award for Excellence In Teaching: Christina Anderson, Crystal Fletcher, Chris Holmes | Mads Howard, Jacob Jablonsky, Parker Young
Christina, Crystal, Chris, Mads, Jacob, and Parker have been mainstays at the College of Computing Learning Center, which provides peer assistance for Michigan Tech students in their computing studies. Learning Center coaches help students from a wide range of backgrounds in a wide array of topics, and must be able to quickly assess and deploy the right tutoring strategy for the situation.

Award for Excellence In Teaching: Dean Bassett, Lucas Catron, Jacob Jablonsky, Amy Slabbekoorn, Emily Winkleman
Dean, Lucas, Jacob, Amy, and Emily have served as lab assistants for our introductory courses. These programming labs are where some of the most important learning moments happen for our beginning students. Lab assistants play a crucial role in providing peer support and guidance. These four individuals have shown great commitment, compassion, and patience in this role.

The CMH Division presented Class of 2020 undergraduate awards to the following students:
Michael Dabish: Outstanding CNSA Graduate Award for exceptional performance as a research and laboratory assistant.
Bernard Kluskens: Outstanding CNSA Graduate Award for exceptional performance as a teaching assistant.
Gary Tropp: Outstanding CNSA Graduate Award, for excellent student academic mentoring in the College of Computing Learning Center.
Emma Davidson: Outstanding EET Graduate Award for exceptional service as a laboratory assistant and grader.
Mark Heinonen: Outstanding EET Graduate Award for an exceptional Senior Design project in audio system design.
Spencer Thompson: Outstanding EET Graduate Award for exceptional service as a teaching assistant in the transition to remote instruction.

Outstanding CNSA Graduate Award: Michael Dabish
For exceptional performance as a research and laboratory assistant. 
Michael’s work in the lab has been very helpful in fulfilling our needs to provide the best lab environment for students. He has shown that he is always willing to put in the work necessary to get the job done.
In 2018 Michael became a research/teaching assistant, working with the CNSA faculty on two NSA grants to create and update course content regarding cyber ethics and cybersecurity.
Michael is constantly collaborating with CNSA faculty and students to discover new ways to implement popular technologies in system administration and security.
He has even created a YouTube channel to document and share methods of implementing these technologies.
What Michael learned in these jobs has inspired him to pursue graduate school in the hope of becoming a teacher right here at Michigan Tech.

Outstanding CNSA Graduate Award: Bernard Kluskens
For exceptional performance as a teaching assistant.
Bernard was teaching assistant for four classes taught by Todd Arney, who nominated Bernard for this award.  Arney says Bernard took the lead on answering lab questions, and then even made calendar appointment slots for students to get one-on-one help using Zoom online. Arney says he would not have been able to manage his  classes with Bernard’s help with grading, fielding questions, and reviewing material before posting to Canvas.

Outstanding CNSA Graduate Award: Gary Tropp
For excellent student academic mentoring in the College of Computing Learning Center.
Gary is the first CNSA student to work as a “Student Academic Mentor” (SAM) in the new “College of Computing Learning Center” (CCLC), offering in person one-on-one help with two of the lab intensive classes in the CNSA program and then even continuing to offer online personalized help for students.

Outstanding EET Graduate Award: Emma Davidson
For exceptional service as a laboratory assistant and grader.
Emma has been helping faculty and students in the lab for over three years, and she also helped with “texting day” to reach out to prospective students.

Outstanding EET Graduate Award: Mark Heinonen
For an exceptional Senior Design project in audio system design.
Mark designed a 4-way passive electrical circuit specifically tuned for a pair of loudspeakers he created as part of his Audio Production and Technology degree.  He started out with a design based on the latest in digital signal processing, but in the end he discovered the value in “old school” analog electrical circuits built from resistors, capacitors, and inductors – what used to be considered mainstream electrical engineering but is now something of a lost art.

Outstanding EET Graduate Award: Spencer Thompson
For exceptional service as a teaching assistant in the transition to remote instruction.
Spencer has been lab assistant for most, if not all of the EET labs. He was nominated for this award by new faculty member Jungyun Bae, who pointed out his dedication to helping students with labs and homework in the EET data acquisition course. After mid-semester, Spencer actively helped the students during lab hours through emails and Zoom meetings. He also took videos of all the labs left within the semester when we transferred into remote instruction and, thanks to him, the course went smoothly even after the campus was locked down.

Honors Graduates: These Department of Computer Science students graduated with honors.
Christina Anderson, CS, Magna Cum Laude
Isaac Appleby, CS, Magna Cum Laude
Daniel Carrara, CS, Magna Cum Laude
Lucas Catron, CS, Magna Cum Laude
Zach Dill, CS, Cum Laude
Peter Dukes, CS, Magna Cum Laude
Trevor Good, CS, Magna Cum Laude
Ethan Hegg, CS, Cum Laude
Mads Howard, CS, Magna Cum Laude
Sophia Jensen, CS, Cum Laude
Derek Kamin, CS, Magna Cum Laude
Alex Larkin, CS, Cum Laude
Maddie LeClair, SE, Cum Laude
James Michniewicz, CS, Summa Cum Laude
Michael Munoz, CS, Summa Cum Laude
Dante Paglia, CS, Summa Cum Laude
Brandon Paupore, SE, Cum Laude
Elijah Potter, CS, Cum Laude
Emily Winkleman, CS, Cum Laude
Kieran Young, CS, Cum Laude
Parker Young, SE, Magna Cum Laude

Honors Graduates: These CMH Division students graduated with honors:
Dina Falzarano, CNSA, Cum Laude
Timothy Graham, CNSA, Cum Laude
Mark Heinonen, EET, Cum Laude
Andrew Hitchcock, CNSA, Magna Cum Laude
Chris Koch, CNSA, Summa Cum Laude
Zack Metiva, CNSA, Magna Cum Laude
Joshua Peter, CNSA, Magna Cum Laude
Spencer Thompson, EET, Cum Laude

This May, AVA Labs, a next-generation blockchain platform spun out of Cornell and led by Professor Emin Gün Sirer, is hosting a month-long virtual hackathon for the best and brightest computer science and engineering students in the world.

The event will focus on developing new applications for financial products and services, and infrastructure tools that could someday be used by millions of people on the AVA platform. In addition to opportunities for close mentorship with our engineering leadership and early exposure to the most anticipated blockchain launch of 2020, students will have the opportunity to compete for up to $50,000 in prizes.

More details on the event are in the note below, and linked here: https://www.avalabs.org/ava-x/hackathons/university-hackathon-may-2020

And here is a recent Bloomberg News article featuring AVA: New Startup Aims to Prove Blockchain Fast Enough for Finance

AVA is a leading blockchain platform spun out of Cornell and led by Professor Emin Gün Sirer. It is an open-source platform for launching highly decentralized applications, new financial primitives, and new interoperable blockchains.

All GenCyber 2020 summer programs will be deferred to 2021 due to the impact of the COVID-19 virus.

For a listing of those programs who are receiving funding to host a camp, please see gen-cyber.com.

For updates and questions regarding specific camps and/or outreach activities, please contact the host institution using the information found on gen-cyber.com.

Please direct questions about the Michigan Tech GenCyber program to gencyber@mtu.edu.

The College of Computing’s Department of Applied Computing invites the campus community to lecture by MERET faculty candidate Dr. Sidike Paheding, Friday, April 10, 2020, at 3:30 p.m., via an online Zoom meeting. The title of Paheding’s lecture is, “Machine Learning in Multiscale and Multimodal Remote Sensing: From Ground to UAV with a stop at Satellite through Different Sensors.”

Link to the Zoom meeting here.

Paheding is currently a visiting assistant professor in the ECE department at Purdue University Northwest. His research interests cover a variety of topics in image/video processing, machine learning, deep learning, computer vision, and remote sensing.

 Abstract: Remote sensing data provide timely, non-destructive, instantaneous estimates of the earth’s surface over a large area, and has been accepted as a valuable tool for agriculture, weather, forestry, defense, biodiversity, etc. In recent years, machine learning for remote sensing has gained significant momentum due to advances in algorithm development, computing power, sensor systems, and data availability.

In his talk, Paheding will discuss the potential applications of machine learning in remote sensing from the aspects of different scales and modalities. Research topics such as multimodal data fusion and machine learning for yield prediction, plant phenotyping, augmented reality and heterogeneous agricultural landscape mapping will be covered.

Paheding earned his M.S. and Ph.D. degrees in electrical engineering at the University of South Alabama, Mobile, and University of Dayton, Ohio, respectively. He was a postdoctoral research associate and and assistant research professor in the Remote Sensing Lab at Saint Louis University from 2017 to 2019, prior to joining Purdue University Northwest.

He has advised students at the undergraduate, master’s, and doctoral levels, and authored or co-authored close to 100 research articles, including in several top peer-review journal papers.

He is an associate editor of the Springer journal Signal, Image, and Video Processing, a guest editor/reviewer for a number of reputed journals, and he has served on international conference committees. He is an invited member of Tau Beta Pi (Engineering Honor Society). 

The College of Computing’s Department of Applied Computing invites the campus community to a lecture by MERET faculty candidate Muhammad Fahad on Thursday, April 9, 2020, at 3:30 p.m., via an online Zoom meeting. His talk is titled, “Motion Planning and Control of Autonomous Mobile using Model Free Method.”

Link to the Zoom meeting here.

Dr. Fahad currently works as a robotics engineer at National Oil Well Varco. He received his M.S. and Ph.D. in electrical engineering from Stevens Institute of Technology, Hoboken, NJ, and his B.S in EE at University of Engineering and Technology, Lahore, Pakistan.

Fahad has extensive experience designing control and automation systems for the process industry using traditional control methods and robots. His research interests include cooperative distributed localization, human robot interaction (HRI), deep reinforcement learning (DRL), deep inverse reinforcement learning (DIRL) and generative adversarial imitation learning (GAIL), simulation tools design, parallel simulation frameworks and multi-agent learning.

Lecture Abstract. Robots are playing an increasingly important part in our daily lives. This increasing involvement of robots in our everyday lives has highlighted the importance of human-robot interaction, specifically, robot navigation of environments occupied by humans, such as offices, malls and airports. Navigation in complex environments is an important research topic in robotics.

The human motion model consists of several complex behaviors that are difficult to capture using analytical models. Existing analytical models, such as the social force model, although commonly used, are unable to generate realistic human motion and do not fully capture behaviors exhibited by humans. These models are also dependent on various parameters that are required to be identified and customized for each new simulation environment. 

Artificial intelligence has received booming research interest in recent years. Solving problems that are easy for people to perform but difficult to describe formally is one of the main challenges for artificial intelligence. The human navigation problem falls directly in this category, where it is hard to define a universal set of rules to navigate in an environment with other humans and static obstacles.

Reinforcement learning has been used to learn model-free navigation, but it requires a reward function that captures the behaviors intended to be inculcated in the learned navigation policy. Designing such a reward function for human like navigation is not possible due to complex nature of human navigation behaviors. The speaker proposes to use measured human trajectories to learn both the reward function and navigation policy that drives the human behavior.

Using a database of real-world human trajectories–collected over a period of 90 days inside a mall–we have developed a deep inverse reinforcement learning approach that learns the reward function capturing human motion behaviors. Further, this dataset was visualized in a robot simulator to generate 3D sensor measurement using a simulated LIDAR sensor onboard the robot. A generative adversarial imitation learning based method is developed to learn the human navigation policy using these human trajectories as expert demonstration. The learned navigation policy is shown to be able to replicate human trajectories both quantitatively, for similarity in traversed trajectories, and qualitatively, in the ability to capture complex human navigation behaviors. These navigation behaviors include leader follower behavior, collision avoidance behavior, and group behavior. 

The College of Computing’s Department of Applied Computing invites the campus community to a lecture by faculty candidate Kahlid Miah on Friday, April 3, 2020, at 3:30 p.m., via an online Zoom meeting. The title of Miah’s lecture is, “Fiber-Optic Distributed Sensing Technology: Applications and Challenges.”

Link to the Zoom meeting here.

Dr. Miah is currently a visiting faculty member in the ECE department at Indiana University – Purdue University Indianapolis (IUPUI). He received his Ph.D. and M.S. in electrical engineering from University of Texas at Austin, and a B.S. in aerospace engineering, also from Indiana University. His research interests are in computational geophysics, signal and image processing, instrumentation, and fiber-optic distributed sensing system development.

Lecture Abstract: In distributed fiber-optic sensing systems, a fiber-optic cable itself acts as an array of sensors, allowing users to detect and monitor multiple physical parameters such as temperature, vibration and strain with fine spatial resolution over a long sensing distance. There are many applications, especially in geophysical, geotechnical, and mining engineering where simultaneous multiparameter measurements are essential. Data deluge, difficulty in multicomponent measurements, and poor sensor-medium coupling are key challenges, and thus provide opportunities for future research and development.  

Dr. Miah’s past teaching and research experience includes a faculty position in the Geophysical Engineering department at Montana Technological University. He has held a postdoctoral research position at University of Alberta and a visiting fellowship position at the Geological Survey of Canada. He has also worked as a process engineer for a semiconductor equipment manufacturer in Austin, Tex.

Note: The College of Computing Department of Applied Computing is a new administrative unit replacing the CMH Division; its official start date is July 1, 2020. Applied Computing academic programs include Computer Network and System Administration (CNSA), Cybersecurity, Electrical Engineering Technology (EET), Health Informatics, and Mechatronics.

Welcome prospective students and friends and families! The Michigan Tech College of Computing is pleased to welcome you to Spring 2020 Preview Day.

Since you’re at home instead of on campus, we’ve prepared a special video to share with you today. Well, actually our academic advisor Kay Oliver produced the video. Thanks, Kay! (Scroll down to play the video.)

In the video, Kay will tell you about our undergraduate and graduate degree programs, and show you lots of photos of Michigan Tech students, faculty, labs, and classrooms.

Kay, along with Denise Landsberg, our second academic advisor, are standing by to answer your questions. You can email Kay and Denise at csadvisor@mtu.edu.

Please read more below the video.

Play – College of Computing Preview Day: Spring 2020

On the virtual tour, you’ll also hear from Dr. Linda Ott, chair of the Computer Science department, who’ll fill you in on the Computer Science and Software Engineering degree programs, concentrations, and minors and go over some of the first-year Computing courses.

And you’ll learn a little bit about our Applied Computing degrees:

—Computer Network and System Administration (CNSA)
—Cybersecurity
—Electrical Engineering Technology (EET)
—Mechatronics

And if you’re still exploring which Computing degree is the right one for you, check out our General Computing major, which gives you a little time and space to make this important decision.

Finally, Computer Science prof Dr. Chuck Wallace will tell you about Michigan Tech’s unique student Enterprise program, where Computing students are working on real computing solutions for real clients. The Computing-focused student Enterprises are:

Husky Games
HIDE (Human Interface Development Enterprise)
IT Oxygen Enterprise.

Please enjoy the video. Contact us anytime with your questions, large or small, and be sure to visit our website (mtu.edu/computing), our news blog, and visit, share, connect, and like us on social media.

We hope to see you on campus this fall!

The Strategic Faculty Hiring Initiative (SFHI) candidates affected by this change are:

Briana Bettin, March 16-17, 2020 | View blog post
Zoom Meeting: https://michigantech.zoom.us/j/468935183

Leo Ureel, March 24-26 | View blog post
Zoom Meeting: https://michigantech.zoom.us/j/696407720

The Computer Science faculty candidates affected by this change are:

Junqiao Qiu, March 30-31, 2020 | View blog post
Zoom Meeting: https://michigantech.zoom.us/j/842795573

Teseo Schneider, March 23-24  | View blog post
Zoom Meeting: https://michigantech.zoom.us/j/519255087

Vidhyashree Nagaraju, March 20-21 | View blog post
Zoom Meeting: https://michigantech.zoom.us/j/636248962

Please note, two faculty candidates who requested that their time on campus not be publicized on this blog are not included here. Please contact Vicky Roy, director of administration, if you have questions about these candidates.

Instructions on how to use Zoom can be found here.

More information about Michigan Tech’s response to COVID-2019 can be found here.

Undergraduate researchers and scholars from all colleges—first-year students to soon-to-graduate seniors—will present a record 76 posters at the 2020 Undergraduate Research Symposium, Friday, March 27, 2020, in the lobby of the Rozsa Center. Two sessions will take place, from 11:00 a.m. to 1:00 p.m., and from 2:00 to 4:00 p.m.

The Symposium, hosted by the Pavlis Honors College, highlights the amazing cutting-edge research being conducted on Michigan Tech’s campus by some of our best and brightest undergraduate students.

All faculty, staff and students are encourage to attend and support our excellent undergraduate researchers. Faculty members who would like to serve as distinguished judges at this year’s symposium may complete this short form. 

Learn more about the Symposium here.

The College of Computing invites the campus community to a lecture by faculty candidate Teseo Schneider on Monday, March 23, 2020, at 3:00 p.m. The title of Schneider’s lecture is, “Robust Black-box Analysis.”

Link to the online Zoom meeting here.

Schneider is an assistant professor and faculty fellow in computer science at the Courant Institute of Mathematical Sciences at New York University. He holds a Ph.D. in computer science from the Universita della Svizzera Italiana (2017). His research interests are in finite element simulations, mathematics, discrete differential geometry, and geometry processing. 

Numerical solutions of partial differential equations (PDEs) are ubiquitous in many different applications, ranging from simulations of elastic deformations for manufacturing to flow simulations to reduce drag in airplanes, and to organs’ physiology simulations to anticipate and prevent diseases.

The finite element method (FEM) is the most commonly used discretization of PDEs due to its generality and rich selection of off-the-shelf commercial implementations. Ideally, a PDE solver should be a “black-box”: the user provides as input the domain’s boundary, the boundary conditions, and the governing equations, and the code returns an evaluator that can compute the value of the solution at any point of the input domain. This is surprisingly far from being the case for all existing open-source or commercial software, despite the many research efforts in this direction and the sustained interest from academia and industry.

To a large extent, this issues from treating meshing (and geometry more in general) and FEM basis construction as two disjoint problems. The FEM basis construction may make a seemingly innocuous assumption (e.g., on the geometry of elements), leading to exceedingly difficult requirements for meshing software.

This state of matters presents a fundamental problem for all applications, and is even more problematic in applications that require fully automatic, robust processing of large collections of meshes of varying sizes, which have become increasingly common as large collections of geometric data become available. Most importantly, this situation arises in the context of machine learning on geometric and physical data, where one needs to run large numbers of simulations to learn from, as well as solve problems of shape optimization, which require solving PDEs in the inner optimization loop on a constantly changing domain.

Schneider’s research presents recent advancements towards an integrated pipeline, considering meshing and element design as a unique challenge, leading thus to a black-box pipeline that can solve simulations on 10,000 in the wild meshes, without any parameter tuning.

Schneider earned a Postdoc.Mobility fellowship from the Swiss National Science Foundation (SNSF) to pursue his research aiming to bridge physical simulations and geometry.Teseo is also the main developer of Polyfem (https://polyfem.github.io/), a flexible and easy to use Finite Element Library. He is one of the maintainers of libigl (https://github.com/libigl/libigl), and a contributor to wild meshing (https://github.com/wildmeshing), a 2D and 3D robust meshing library.