Category: College of Computing

Leo Ureel Receives 2020-21 CTL Award for Innovative Teaching

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’s (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.

Leo Ureel, Computer Science

Michigan Tech Announces NSF CyberCorps: Scholarship for Service Program

Michigan Technological University is one of six universities to join the National Science Foundation CyberCorps: Scholarship for Service (SFS) program, a nationwide program to recruit and train the next generation of information technology professionals, industrial control system security professionals and security managers.

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

In return, following graduation, recipients must work in a cybersecurity-related job for federal, state, local or tribal government for a period equal to the length of the scholarship, among other requirements.

“The U.S. is facing a significant shortage of well-trained and well-prepared cybersecurity professionals,” said Yu Cai, professor of applied computing at Michigan Tech and the principal investigator of the grant. “Michigan Tech has developed a national and international reputation in cybersecurity education, research and outreach activities. We are thrilled to be part of the solution to the nation’s cybersecurity workforce challenge.”

Applications for Michigan Tech’s 2021-2022 cohort are now being accepted. Application guidelines and requirements can be found on the SFS website. The deadline to apply is June 1, 2021. Student informational sessions will be announced shortly. 

The degree programs included in the CyberCorps scholarship opportunity are listed below.

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

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

The SFS program also partners with the Pavlis Honors College to engage SFS scholars in a blend of faculty mentoring, peer mentoring and customized pathways.

Michigan Tech joins 78 current CyberCorps: Scholarship for Service universities across the country. In its announcement, NSF noted that Michigan Tech has a long history of K-12 outreach, which it expects to leverage as part of its project.

The project PI is Professor Yu Cai, Applied Computing. Co-PIs and other important personnel include Professor Jean MayoProfessor Todd O. ArneyProfessor Bo ChenProfessor Chee-Wooi TenProfessor Kedmon N. Hungwe, and Dr. Laura Kasson Fiss.


Michigan Technological University is a public research university, home to more than 7,000 students from 54 countries. Founded in 1885, the University offers more than 120 undergraduate and graduate degree programs in science and technology, engineering, forestry, business and economics, health professions, humanities, mathematics, and social sciences. Our campus in Michigan’s Upper Peninsula overlooks the Keweenaw Waterway and is just a few miles from Lake Superior.

Student Town Hall Meetings Scheduled for Feb. 23 and Feb. 25

College of Computing Dean Dennis Livesay will host two 60-minute virtual Town Hall meetings for College undergraduate and graduate students on February 23 and February 25, 2021.

At the Town Hall meetings, student are invited to share with Dean Livesay their thoughts and input about the College, curriculum, degree programs, departments, and other topics of interest.

The Undergraduate Town Hall is February 23, 2021, from 4:00 to 5:00 p.m. Join that meeting here: https://michigantech.zoom.us/j/87889920742.

The Graduate Town Hall is February 25, 2021, from 4:00 to 5:00 p.m. Join here: https://michigantech.zoom.us/j/82512917783.

Vijay Garg, UT Austin, to Present Lecture Feb. 19, 3 pm


This lecture has been canceled.


Dr. Vijay Garg, University of Texas Austin, will present a lecture on February 19, 2021, at 3:00 p.m. The lecture is hosted by the Department of Computer Science.

Vijay Garg Bio

Vijay Garg is a Cullen Trust Endowed Professor in the Department of Electrical & Computer Engineering at The University of Texas at Austin. He received his Ph.D. in computer science at the University of California at Berkeley and B. Tech. in computer science at IIT, Kanpur.

His research interests are in distributed computing, discrete event systems and lattice theory. He is the author of “Elements of Distributed Computing” (Wiley, 2002), “Introduction to Lattice Theory with Computer Science Applications” (Wiley, 2015), and “Modeling and Control of Logical Discrete Event Systems” (Springer, 2012). He is an IEEE Fellow.

Lecture Title

Applying Predicate Detection to Discrete Optimization Problems

Lecture Abstract

We present a method to design parallel algorithms for the constrained combinatorial optimization problems. Our method solves and generalizes many classical combinatorial optimization problems including the stable marriage problem, the shortest path problem and the market clearing price problem.

These three problems are solved in the literature using Gale-Shapley algorithm, Dijkstra’s algorithm, and Demange, Gale, Sotomayor algorithm. Our method solves all these problems by casting them as searching for an element that satisfies an appropriate predicate in a distributive lattice. Moreover, it solves generalizations of all these problems — namely finding the optimal solution satisfying additional constraints called lattice-linear predicates.

For stable marriage problems, an example of such a constraint is that Peter’s regret is less than that of Paul. Our algorithm, called Lattice-Linear Predicate Detection (LLP) can be implemented in parallel with without any locks or compare-and-set instructions. It just assumes atomicity of reads and writes.

In addition to finding the optimal solution, our method is useful in enumerating all constrained stable matchings, and all constrained market clearing price vectors. The talk is an extended version of a paper that appeared in ACM SPAA’20.

Dean Livesay Asks Students to Share Diversity and Inclusion Experiences

“My goal — nay our goal — is to make the College of Computing a place where everyone feels welcome and can thrive. And admittedly, I don’t know how to do that, which is why I am asking for your help.”

Dean Livesay

Dean Dennis Livesay wants to hear your story. What has your experience been with regard to diversity and inclusion at Michigan Tech?


What does the Dean need to be aware of as he starts his new position? What is working? What needs to change? How can we improve?

“My commitment to you, in this request and as dean, is that you will always have a forum to speak and be heard on topics of concern to you and our educational community,” Livesay says. “I will ask questions, listen to your responses, seek to understand your experiences, and proactively address your concerns.

Please reach out to Dean Livesay via email (dlivesay@mtu.edu) if you’d like to schedule a time to talk.

“I know that speaking truth to power can be uncomfortable, so please feel free to bring a friend. Our conversation will be completely confidential,” Livesay stresses.

Learn more about Dean Livesay’s commitment to diversity and inclusion here.

Yakov Nekrich Paper Accepted for Top Computing Conference

A publication by Associate Professor Yakov NekrichComputer Science, has been accepted to the 53rd Annual ACM Symposium on Theory of Computing (STOC).

The paper, “Optimal-Time Dynamic Planar Point Location in Connected Subdivisions,” describes an optimal-time solution for the dynamic point location problem and answers an open problem in computational geometry. 

The data structure described in the paper supports queries and updates in logarithmic time. This result is optimal in some models of computation.  Nekrich is the sole author of the publication.

The annual ACM Symposium on Theory of Computing (STOC), is the flagship
conference of SIGACT, the Special Interest Group on Algorithms and
Computation Theory, a special interest group of the Association for
Computing Machinery (ACM).

MTU Creates Dave House Deanship in College of Computing

by University Marketing and Communications
Read the Michigan Tech press release here. (Published Feb. 8, 2021)

Michigan Technological University has appointed Dennis Livesay to hold the inaugural Dave House Deanship in the College of Computing effective February 1, 2021. 

View a video of the announcement from the Feb. 5 Michigan Tech Alumni Board meeting.

Michigan Tech launched the College in 2019 to meet the technological, economic and social needs of the 21st century, and answer industry demand for talent in artificial intelligence (AI), software engineering, data science and cybersecurity. In doing so, Tech became the first University in the state with a college of computing.

The gift from Dave House ’65 to endow the dean position reinforces the University’s commitment to computing.

“The College of Computing is central to the future of Michigan Tech. Thanks, in part, to Dave’s visionary gift and Dennis’s leadership, the college is poised for tremendous success on both the national and international stage,” said Rick Koubek, President. 

House, whose many career accolades include growing Intel’s microprocessor product business from $40 million to $4 billion per year, has championed Michigan Tech’s efforts in computing.

“Computing is centric to all disciplines, and Michigan Tech has been wise to move forward with a focus on computing,” said House. “This endowed position will allow the new college to attract the best faculty and the brightest students and the University to continue to be the leader in computing education.”

Livesay, who most recently served as dean of the College of Engineering at Wichita State University, brings 20 years of experience in higher education to Michigan Tech. With a diverse background spanning the biomedical sciences, computing and engineering, he plans to work with partners across campus to address the digital transformation happening in every discipline.

Provost Jackie Huntoon stated she is very happy that Livesay is joining Michigan Tech. “His deep understanding of computing and its impact on all aspects of modern life make him well suited for the deanship of the College of Computing,” she said. “He brings an entrepreneurial perspective to the dean’s role that will enhance efforts currently underway in the College of Computing and across campus.” 

Livesay shares House’s conviction that computing is fundamental to all disciplines.

“Every discipline is a computing discipline,” said Livesay. “When I first started saying this a decade ago, it was more of a tagline, but it is absolutely true today. The modern economy is defined by our ability to create data, transmit it in a secure way and then translate it into action. This is particularly true in science, engineering and business, but also in the social sciences, humanities and the arts. Going forward, we want to be a critical partner in all of those areas.”

The Dave House Dean of Computing is Michigan Tech’s first endowed deanship. The University has nine endowed department chairs and dozens of endowed faculty positions, allowing it to maintain a world-class faculty.

“We thank Dave again for his vision and commitment to Michigan Tech’s future. We are indeed fortunate to have alumni like him who care so deeply for our students,” said Bill Roberts, Vice President for Advancement and Alumni Engagement.

View the announcement below about the new deanship from a recent meeting of the Michigan Tech Alumni Board.

College of Computing Invites Applications for Two Faculty Positions

Are you interested in a faculty position with the new Michigan Tech College of Computing? Do you know someone who is?

Michigan Technological University’s College of Computing invites applications for two (2) assistant, associate, or full professor positions to start in August 2021.

Areas of particular interest include cybersecurity, artificial intelligence/machine learning, and data science; exceptional candidates in other areas of computing will also be considered.

Successful candidates will demonstrate a passion for their research, an enthusiasm for undergraduate and graduate education, and a strong commitment to cultivating diverse and inclusive learning environments.

View the positions description and apply here: https://www.employment.mtu.edu/cw/en-us/job/492473

Review of applications will begin immediately and continue until the position is filled. To learn more about this opportunity, please visit https://www.mtu.edu/computing/about/employment/ or contact the search chair, Dr. Timothy Havens, at thavens@mtu.edu. Applications received by March 1, 2020 will receive full consideration.

Michigan Tech is building a culturally diverse faculty committed to teaching and working in a multicultural environment and strongly encourages applications from all individuals. We are an ADVANCE Institution having received three National Science Foundation grants in support of efforts to increase diversity, inclusion, and the participation and advancement of women and underrepresented individuals in STEM.

Michigan Tech actively supports dual-career partners to retain a quality workforce; we offer career exploration advice and assistance finding positions at the University and in the local community. Please visit https://www.mtu.edu/provost/programs/partner-engagement for more information.

An applicant must have earned a Ph.D. degree in Computer Science, Computer Engineering, Computing, or a closely related area. Michigan Tech places a strong emphasis on balancing cutting-edge research with effective teaching, outreach, and service. Candidates for these positions are expected to demonstrate potential for excellence in independent research, excellence in teaching, and the ability to contribute service to their department and profession. Salary is negotiable depending upon qualifications.

Michigan Tech is an internationally renowned doctoral research university with 7,100 students and 400 faculty located in Houghton, Michigan, in the scenic Upper Peninsula on the south shore of Lake Superior. The area provides a unique setting where natural beauty, culture, education, and a diversity of residents from around the world come together to share superb living and learning experiences.

The College of Computing has 36 faculty members, 650 undergraduate students in five degree programs (Computer Science, Computer Network and System Administration, Cybersecurity, Electrical Engineering Technology, Mechatronics, and Software Engineering) and 90 graduate students in four MS degree programs (Computer Science, Cybersecurity, Data Science, Health Informatics, and Mechatronics) and Ph.D. degree programs in Computer Science and Computational Science and Engineering.

Sidike Paheding Wins MDPI Electronics Best Paper Award

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.

Sidike Paheding