Category: Achievements and Awards

In light of recent changes to Michigan’s COVID-19 epidemic orders that increase the size of allowable group gatherings, Michigan Technological University has modified its graduation celebration planned for April 30, 2021 to allow families and guests to participate alongside their graduate.

As announced in an earlier email to students, the University will host a graduation walk through campus to celebrate this significant milestone. Students may now invite up to six guests to walk with them. Details, including the start times, are still being worked out. Graduates who would like to participate will be asked to sign up prior to the celebration. A signup link will be emailed to all eligible graduates on March 22.

As a reminder, Michigan Tech remains committed to the health and safety of our campus community. All guests and graduates will be required to wear a face mask at all times and practice social distancing during the event. Please be sure to check www.mtu.edu/commencement for the latest information.

Congratulations, Huskies—you did it! The pride you feel now will only grow stronger with time. Your Michigan Tech family and our community joins you and your loved ones in celebrating your completion of this journey. 

Regalia Update

Regalia is encouraged, but not required at the outdoor event. Regalia can be ordered through Herff Jones with direct delivery to the graduate. If you have questions regarding your order, contact Michele Nash from Herff Jones at mnash@herffjones.com or 248-667-9018. 

Class of 2021, you’ve done an amazing job! If you have any questions, contact commencement@mtu.edu.

Please Note

Neither participation in the commencement ceremony nor inclusion in the program constitutes official completion of degree requirements or the attainment of honors or other recognitions.

Graduates do not receive their diploma at the commencement ceremony. Diplomas are mailed to the graduate approximately six weeks after degree requirements are met.

The 2020-2021 CTL Instructional Award for Innovative or Out of Class Teaching is being presented to two instructors, and Assistant Professor Leo Ureel, Computer Science, and Libby Meyer, senior lecturer, Visual and Performing Arts.

Ureel was nominated in recognition of his “student-centric efforts which have increased retention and diversified the cohort of first-year computing students.”

Ureel’s presentation, “Three course innovations to support communication,” will be presented at 3:30 p.m. on Thursday, February 18, 2021, as part of the CTL Instructional Award Presentation Series.

Link here to register for the event.

Ureel is a member of the Institute of Computing and Cybersystems’ (ICC) Computing Education Center.

Meyer’s presentation, “Beyond Carrots and Sticks: Mastery Based Grading and Narrative Assessment” will also be presented on February 18.

During spring 2017, academic deans were asked to begin recognizing instructors making contributions in these areas as part of the Deans’ Teaching Showcase, effectively nominating them for instructional awards.

CTL and Provost’s office members along with previous awardees then select one individual in each category from a pool composed of the Showcase and those nominated to the Academy of Teaching Excellence.

Ureel Lecture Abstract

Three course innovations to support communication Introductory courses present many communication challenges between faculty and first year students. In this context, we discuss three innovations used in our introductory computer science courses.

The first is the use of Snap, a high-level, visual programming language, as a form of pseudocode during the first five weeks of the course to build student vocabulary and problem solving skills before tackling programming in Java.

The second is a Code Critiquer developed as a Canvas plugin to provide immediate guidance and feedback to students when they submit their programming assignments.

The third is a grade visualization tool that helps students understand their current performance in the course and project a range that will contain their final grade. While not everyone teaches introductory computer science, we discuss how these or similar innovations and tools might apply to your course.

by Husky Innovate

Congratulations to Husky Innovate’s Bob Mark Business Model Competition contestants and winners!

Michigan Tech Computer Engineering Ph.D. candidate Ranit Karmakar is among the awardees, winning first place and a $2,000 prize in the Business Model category.

The Bob Mark Business Model Competition was the main event during Innovation Week, a celebration of innovation by and with Michigan Tech students, alumni, faculty and staff.

On Jan. 28, thirteen student teams pitched their ideas and business models on a virtual platform. A panel of nine judges evaluated teams in four different categories and provided teams with feedback. Thousands of dollars worth of prizes were awarded.

This is the first virtual Bob Mark Pitch Competition and Tech student participation was a testament to Husky resilience during Covid-19. Tech student contestants showed that despite a different platform they were agile and enthusiastically pitched their ideas.

Congratulations to our prize winners:

Idea Pitch Category

• First prize, $125 – Jordan Craven
• Second prize, $75 – Ella Faulk
• Third prize, $50- Rachel May

Business Model Category

• First prize, $2,000 Awarded by Rick and Jo Berquist – Ranit Karmaker
• Second prize, $1,000- Kyra Pratley
• Third prize, $500 – Hunter Malinowski
• Honorable Mention, $250 – Tanner Sheahan & Marcus Lamarucciola
• Audience Favorite, $250 – Kyra Pratley
• Social Impact Awards — Awarded by Dr. Elham Asgari, Gates Professor, College of Business

Other Awards

In the Idea Category, $100 – Rachel May
In the Business Model Category, $1,000 – Ranit Karmakar
MTEC SmartZone’s Breakout Innovation Award – Awarded by the MTEC SmartZone
In the Business Model Category Hunter Malinowski, $1,000

Special Thanks

Special thanks to those who gave their time and resources to make the evening a success:

Our sponsors — Dean Johnson (COB), Rick and Jo Berquist, Elham Asgari (COB), Mary Raber (PHC), Patrick Visser (MTEC SmartZone).

Our judges — Lorelle Meadows (PHC), Leonard Bohmann (CoE), Marika Siegel (HU), Josue Reynoso (COB), Emanuel Xavier-Oliveira (COB), Elham Asgari (COB), Lexi Steve student (ME/PHC), Hajj Flemings ( MTU alumni) and Patrick Visser, (chief commercial officer, MTEC SmartZone).

Thanks to our emcee Nate Yenor, MTRAC Commercial Program Director; Our marketing team including Vienna Leonarduzzi and Rebecca Barnard, (PHC) and Megan Cole, (Husky Innovate Intern).

And last, but definitely not least, our graphic designer Laura Vidal Chiesa, Graduate Student (CAS/Husky Innovate Intern).

Thanks to all who attended. We look forward to next year’s event.

A scholarly paper co-authored by Assistant Professor Sidike Paheding, Applied Computing, is one of two papers to receive the 2020 Best Paper Award from the open-access journal Electronics, published by MDPI.

The paper presents a brief survey on the advances that have occurred in the area of Deep Learning.

Paheding is a member of the Institute of Computing and Cybersystems’ (ICC) Center for Data Sciences (DataS).

Co-authors of the article, “A State-of-the-Art Survey on Deep Learning Theory and Architectures,” are Md Zahangir Alom, Tarek M. Taha, Chris Yakopcic, Stefan Westberg, Mst Shamima Nasrin, Mahmudul Hasan, Brian C. Van Essen, Abdul A. S. Awwal, and Vijayan K. Asari. The paper was published March 5, 2019, appearing in volume 8, issue 3, page 292, of the journal.

View and download the paper here.

Papers were evaluated for originality and significance, citations, and downloads. The authors receive a monetary award , a certificate, and an opportunity to publish one paper free of charge before December 31, 2021, after the normal peer review procedure.

Electronics is an international peer-reviewed open access journal on the science of electronics and its applications. It is published online semimonthly by MDPI.

MDPI, a scholarly open access publishing venue founded in 1996, publishes 310 diverse, peer-reviewed, open access journals.

Paper Abstract

In recent years, deep learning has garnered tremendous success in a variety of application domains. This new field of machine learning has been growing rapidly and has been applied to most traditional application domains, as well as some new areas that present more opportunities. Different methods have been proposed based on different categories of learning, including supervised, semi-supervised, and un-supervised learning. Experimental results show state-of-the-art performance using deep learning when compared to traditional machine learning approaches in the fields of image processing, computer vision, speech recognition, machine translation, art, medical imaging, medical information processing, robotics and control, bioinformatics, natural language processing, cybersecurity, and many others.

This survey presents a brief survey on the advances that have occurred in the area of Deep Learning (DL), starting with the Deep Neural Network (DNN). The survey goes on to cover Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), including Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), Auto-Encoder (AE), Deep Belief Network (DBN), Generative Adversarial Network (GAN), and Deep Reinforcement Learning (DRL). Additionally, we have discussed recent developments, such as advanced variant DL techniques based on these DL approaches. This work considers most of the papers published after 2012 from when the history of deep learning began.

Furthermore, DL approaches that have been explored and evaluated in different application domains are also included in this survey. We also included recently developed frameworks, SDKs, and benchmark datasets that are used for implementing and evaluating deep learning approaches. There are some surveys that have been published on DL using neural networks and a survey on Reinforcement Learning (RL). However, those papers have not discussed individual advanced techniques for training large-scale deep learning models and the recently developed method of generative models.

Ms. Karen Colbert , a PhD student in Computational Sciences and Engineering and a graduate research assistant for ADVANCE at Michigan Tech, has been selected as a Diversity Scholar for the 2021 RStudio Virtual Conference.

Ms. Colbert is one of 70 Diversity Scholars selected from around the globe, all of them focused on building skills for teaching and sharing. Ms. Colbert notes that her role as a Diversity Scholar will focus on ways she can use RStudio to help “bridge equity for Native faculty and faculty who serve Tribal communities.”

A plethora of teaching and user workshops and resources are available through the RStudio network. Following completion of the Virtual Conference, Ms. Colbert will participate in two online workshops and become part of an enhanced network of scholars and resources, available both before and after the conference.

Ms. Colbert says that a large barrier facing tribal colleges is accessibility and sustainability with regard to costly technology, such as licenses, equipment, and support. Since RStudio is open source and has vast capabilities to perform tasks ranging from web design to reporting to statistical analyses and assessments, Ms. Colbert hopes that learning how to “teach” R will enable her to host workshops for faculty. She says it may also help her design an interactive course to help those who may be intimidated by programming, and ultimately create a platform to introduce tribal colleges to the data visualization, supercomputing, and cloud computing communities.

In addition to the equity gaps facing Native faculty, Ms. Colbert also acknowledged that there are many equity gaps for faculty at all ranks and across institutions, including MIchigan Tech.

This is where Ms. Colbert’s connection to ADVANCE at Michigan Tech–and its mission to enhance equity in STEM faculty–comes into play. She hopes that her research, her experiences as a Diversity Scholar, and her position as a graduate research assistant with ADVANCE, will allow her to pursue opportunities to bring resources to all faculty members.

Further, she will endeavor to assist faculty in demonstrating “their best work to the world in the most professional way, whether it’s for teaching undergraduates or within our own research.”

Ms. Colbert believes this goal starts with making tools and resources accessible to everyone. Her ultimate aim is to develop unique R packages as a part of the solution.

Ms. Colbert holds a bachelor of science in electrical engineering and a master of science in data science, both from Michigan Tech. She also serves as lead math faculty at Keweenaw Bay Ojibwa Community College, Baraga. Mich., in addition to pursuing her PhD and conducting research.

ADVANCE is an NSF-funded initiative dedicated to improving faculty career success, retention, diversity, equity, and inclusion. To learn more about our mission, programming efforts, and to check out our growing collection of resources, contact us at advance-mtu@mtu.edu and visit our website at mtu.edu/advance.

Read the original ADVANCE blog post here.

Michigan Tech’s BS in Software Engineering is in the top 10 nationwide according to College Rank. The website ranked the 35 Best Bachelor’s in Software Engineering.

Michigan Tech, which appears at number nine on the list, was one of only two Michigan colleges to make the ranking. The University of Michigan – Dearborn was ranked 15th.

“It’s great to see our program get this well-deserved recognition,” says Professor and Chair Linda Ott, Computer Science. “We consistently hear from industries that hire our graduates that our alumni are well-prepared and quickly become productive developers in their organizations.”

“Our students gain a solid theoretical framework, which provides the foundation for life-long career growth and success, as well as extensive practical, hands-on experience through class projects, internships and the Michigan Tech Enterprise program,” Ott explains.

College Rank uses a ranking methodology based on three aspects — Potential Salary After Graduation (40%), Individual Program Accreditation (30%) and Overall Affordability (30%).

“This program will help you to secure your position in a well-regarded profession,” says the College Rank website about Michigan Tech’s Software Engineering program. “You’ll be able to work with teams in your classes as well as labs and in the Senior Enterprise or Design programs. The Enterprise Program is a unique opportunity that brings together students of all majors to work on real projects with real clients in a business-like environment. You’ll receive guidance and coaching from faculty mentors throughout every step of your journey here.”