Category: News

Computing Students Featured in Two New Videos

The College of Computing is pleased to share two new My Michigan Tech videos from Software Engineering major Parker Young and dual Audio Production and Technology/Computer Science major Drew Stockero.

My Michigan Tech: Parker Young

My Michigan Tech: Drew Stockero

RedTeam to Host Capture the Flag Competition, Feb. 21-23

In conjunction with the 36-hour Winter WonderHack, February 21-23, 2020, on Michigan Tech’s campus, the Michigan Tech RedTeam is running a Capture the Flag cybersecurity competition. The competition is designed to appeal to both beginners and the more experienced competitors. Everyone is welcome, especially undergraduates. Free swag and prizes will be awarded. Register at Email with questions.

About the Capture the Flag competition:
What: Jeopardy-style cybersecurity competition with questions broken down by category and difficulty.
When: All weekend, February Compete at your convenience.
Who: Students from any major in teams up to 5. No prior experience is necessary.
Win: Hak5 prizes including a WiFi Pineapple, Packet Squirrel, USB Rubber Ducky, and Sticker Packs.

Faculty Candidate Brian Yuan to Present Lecture February 26

The Colleges of Computing and Engineering invite the campus community to a lecture by faculty candidate Xiaoyong (Brian) Yuan on Wednesday, February 26, 2020, at 3:00 p.m. in Chem Sci 101. Yuan’s talk is titled, “Secure and Privacy: Preserving Machine Learning, A Case Study on Model Stealing Attacks Against Deep Learning.”

Brian Yuan is a computer science Ph.D. candidate at the University of Florida. He received an M.E. degree in computer engineering from Peking University in 2015, and a B.S. degree in mathematics from Fudan University in 2012. His research interests span the fields of deep learning, machine learning, security and privacy, and cloud computing.

In his talk, Yuan will provide an overview of security and privacy issues in deep learning, then focus on his recent research on a data-agnostic model stealing attack against deep learning.  He will conclude with a discussion of some future research directions to address security and privacy concerns in deep learning and potential countermeasures.  

Due to recent breakthroughs, machine learning, especially deep learning, is pervasively serving areas such as autonomous driving, game playing, and virtual assistants. Recently however, significant security and privacy concerns have been raised in deploying deep learning algorithms. 

On one hand, deep learning algorithms are fragile and easily fooled by attacks. For example, an imperceptible perturbation on a traffic sign can mislead the autonomous driving systems. On the other hand, with the increasing use of deep learning in personalization, virtual assistants, and healthcare, deep learning models may expose users’ sensitive and confidential information. 

With important business value, deep learning models have become essential components in various commercialized machine learning services, such as Machine Learning as a Service (MLaaS). Model stealing attacks aim to extract a functionally equivalent copy of deep learning models and cause a breach of confidentiality and integrity of deep learning algorithms. Most existing model stealing attacks require private training data or auxiliary data from service providers, which significantly limits the attacking impact and practicality. Yuan proposes a much more practical attack without the hurdle of training data, and its effectiveness will be showcased in several widely used datasets. 

Yuan has published 17 papers in top-tier journals and conferences, such as IEEE Transactions on Neural Networks and Learning Systems (TNNLS) and the AAAI Conference on Artificial Intelligence (AAAI). He has served as reviewer for several leading journals and conferences, such as IEEE Transactions on Neural Networks and Learning Systems (TNNLS), International Conference on Learning Representations (ICLR), IEEE Transactions on Dependable and Secure Computing (TDSC), and IEEE Transactions on Parallel and Distributed Systems (TPDS).

Read the blog post here:


Health Informatics Online Graduate Program Ranked Best in the Midwest, 11th in Nation

The Michigan Tech online Master’s in Health Informatics has been ranked best in the midwest and 11th nationally by, ahead of universities such as Stanford, Northwestern, and Boston University. Michigan Tech’s 2020 ranking rose from 17th nationally in 2019.

See the full rankings here.

According to their website, is a free, editorially independent, privately-supported website. It aims to “connect students to the best schools that meet their needs” through “unbiased, accurate, and fact-based information on a wide range of issues.” Their rankings are based on aggregated publicly available data about colleges and programs across the country.

In November 2019, the website ranked Michigan Tech’s online Health Informatics M.S. program among the 20 finest online colleges and universities. Michigan Tech was the only school from Michigan to make the list. 

Computing’s CMH Division Adds Academic Advisor

The College of Computing is pleased to welcome Kathryn (Kay) Oliver as our newest academic advisor effective February 10, 2020. Oliver will have primary responsibility for advising undergraduate students in the CNSA, EET, and Cybersecurity programs. She’ll also assist in managing the graduate programs in Mechatronics and Health Informatics, and the advising of other undergraduate students in the College of Computing, as needed.

Oliver has an M.A. in educational technology from Michigan State University and a B.S. in physics from Western Michigan University. For more than 20 years she worked with the Department of Defense Education Activity, a government agency responsible for K-12 education of children of American citizens working internationally for the DoD. For most of that time she was responsible for the professional development of teachers with education technology; the past two years she taught AP Computer Science to American high school students in South Korea.

“The search committee was very impressed with Kay’s background and her communication skills,” said Dan Fuhrmann, director of the CMH Division. “She is going to do an outstanding job, connecting with our students and providing information and support. It’s great to have her on board.

Two Papers by Yakov Nekrich Accepted by SoCG 2020 Conference

Yakov Nekrich, associate professor, Department of Computer Science, has been notified that two scholarly papers he has authored were accepted by the 36th International Symposium on Computational Geometry (SoCG 2020), which takes place June 23-26, 2020, in Zurich, Switzerland.

The two papers are “Further Results on Colored Range Searching,” by Timothy M. Chan, Qizheng He, and Nekrich, and “Four-Dimensional Dominance Range Reporting in Linear Space” by Nekrich alone.

The Annual Symposium on Computational Geometry (SoCG) is an academic conference in computational geometry. Founded in 1985, it was originally sponsored by the SIGACT and SIGGRAPH Special Interest Groups of the Association for Computing Machinery (ACM). It dissociated from the ACM in 2014. Since 2015 the conference proceedings have been published by the Leibniz International Proceedings in Informatics Since 2019 the conference has been organized by the Society for Computational Geometry. (Wikipedia)

Visit the SoCG 2020 website.

Faculty Candidate Chensheng Wu to Present Lecture March 4

The Colleges of Computing and Engineering invite the campus community to a lecture by faculty candidate Chensheng Wu on Wednesday, March 4, 2020, at 3:00 p.m. in Chem Sci 101. (In the original announcement, the date of the talk was incorrect.) Wu’s talk is titled, “Design and implementation of computational optics: perception, control, and processing of light-field information and future challenges.”

Dr. Wu is an assistant research scientist in the Department of Electrical and Computer Engineering at the University of Maryland College Park, where he received a Ph.D. degree in ECE.  His doctoral thesis, “the plenoptic sensor,” was was awarded distinguished dissertation honors. Wu also has a B.E. degree in micro-electronics and B.S. in economy, both from Tsinghua University, Beijing, China. 

The emerging field of computational optics is growing rapidly, and it constantly requires newer sensors and computational architectures to satisfy the exploding needs in data collection and processing. Many other research disciplines, such as machine learning, the internet of things, data privacy, and security have also added great challenges to the means of collecting, processing and transmitting data.

The concept of using special optical structures or coded lenses to perform the computation along with data collection, encryption or transmission is becoming a favorable solution in countless applications.

In his talk, Wu will discuss his recent work on the use of new computational optics hardware in solving difficult problems in wavefront sensing, adaptive laser beam formation and correction, imaging through turbulence, detecting hidden objects through scattering media, and space optics. He will discuss how these recent discoveries reveal the potential of specially designed optical structures for computing, and share examples of how future computational optics will take part in sensing, communication, and computation. Wu will conclude his talk with a monologue on predicting the future of computational optics. 

Wu is an advocator for computational sensing using optical and photonics approaches. He is a leading scientist on multiple projects funded by the Office of Naval Research (ONR) and the Directed Energy Joint Technology Office (DE-JTO) Wu’s innovations of the plenoptic sensor, multi-aperture laser transmissometer, computational beam shaping with two deformable mirrors, and lossy sensing-based adaptive optics correction have become well-known. 

Wu has also worked with the Naval Air Warfare Center Aircraft Division (NAWCAD) to configure a new approach to identify and profile hidden objects in murky water environments. He is recognized as a key contributor to NASA’s next generation lunar reflector (NGLR) task to put three new retro-reflectors on the Moon for lunar laser ranging experiments in the 21st Century. Wu is also a team member in the joint collaboration of the Lunar Geophysical Network.

Faculty Candidate Hongyu An to Present Lecture February 12

The Colleges of Computing and Engineering invite the campus community to a lecture by faculty candidate Hongyu An on Wednesday, February 12, at 3:00 p.m. in Chem Sci 101. Hongyu’s talk is titled, “Brain on a Chip: Designing Self-learning and Low-power Neuromorphic Systems.”

Hongyu is a doctoral candidate in the Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University (Virginia Tech). He received B.S. and M.S. degrees in electrical engineering at Shenyang University of Technology, Shenyang, China, and the Missouri University of Science and Technology, Rolla, Mo., respectively.

In 2019, Hongyu was awarded the Paul E. Torgersen Research Excellence Award and a fellowship from the Advanced Short-Term Research Opportunity Program at Oak Ridge National Laboratory. In 2017, he was awarded an NSF Student Travel Fellowship Award, and a paper authored by Hongyu was nominated as best paper in the IEEE International Symposium on Quality Electronic Design (ISQED).

Hongyu’s research interests include neuromorphic and brain-inspired computing, energy-efficient neuromorphic electronic circuit design for Artificial Intelligence, three-dimensional integrated circuit (3D-IC) design, and emerging nanoscale device design. 

His research aims to build a self-learning, low-power neuromorphic system. Inspired by the learning mechanism of the human brain, Hongyu proposed and realized an Associative Memory Learning through neuromorphic circuits and memristors. The proposed learning method correlates two concurrent visual and auditory information together through Artificial Neural Networks. 

Lecture Abstract: How can a silicon brain in a chip be built with self-learning capability? What are the challenges for neural network-based artificial intelligence in the next decade, and how can those challenges be solved? 

In order to answer these questions, Hongyu introduces a cutting-edge research topic: Brain-inspired Computing. Also called neuromorphic computing, Brain-inspired Computing aims to physically reproduce the brain’s structure in a silicon chip to resolve critical challenges in deep learning deployment.

In his talk, Hongyu will explore the underlying biological mechanism of associative memory learning, novel non-von Neumann computer architectures, and circuit implementations with transistors and memristors. 

A widespread self-learning method in animals, associative memory enables the nervous system to remember the relationship between two concurrent events. Rebuilding associative memory is significant, both to reveal a way of designing a brain-like self-learning neuromorphic system, and to explore a method of comprehending the function of the human brain.

Hingyu is a reviewer for several top-tier conferences and journals, including IEEE Transactions on Neural Networks and Learning Systems (TNNLS), IEEE Transactions on Circuits and System I: Regular Papers (TCAS-1),Design Automation Conference (DAC). Design, Automation and Test in Europe Conference and Exhibition (DATE), International Symposium on Circuits and Systems (ISCAS).

Visit Hongyu An’s personal website.


Technical Paper by Nathir Rawashdeh Accepted for SAE World Congress

An SAE technical paper, co-authored by Nathir Rawashdeh, assistant professor, CMH Division, College of Computing, has been accepted for publication at the WCX SAE World Congress Experience, April 21-23, 2020, in Detroit, MI.  The title of the paper is “Mobile Robot Localization Evaluations with Visual Odometry in Varying Environments using Festo-Robotino.” 

Abstract: Autonomous ground vehicles can use a variety of techniques to navigate the environment and deduce their motion and location from sensory inputs. Visual Odometry can provide a means for an autonomous vehicle to gain orientation and position information from camera images recording frames as the vehicle moves. This is especially useful when global positioning system (GPS) information is unavailable, or wheel encoder measurements are unreliable. Feature-based visual odometry algorithms extract corner points from image frames, thus detecting patterns of feature point movement over time. From this information, it is possible to estimate the camera, i.e. the vehicle’s motion. Visual odometry has its own set of challenges, such as detecting an insufficient number of points, poor camera setup, and fast passing objects interrupting the scene. This paper investigates the effects of various disturbances on visual odometry. Moreover, it discusses the outcomes of several experiments performed utilizing the Festo-Robotino robotic platform. The experiments are designed to evaluate how changing the system’s setup will affect the overall quality and performance of an autonomous driving system. Environmental effects such as ambient light, shadows, and terrain are also investigated. Finally, possible improvements including varying camera options and programming methods are discussed.

Learn more.

Guy Hembroff Awarded CCISD Contract for CTE Cybersecurity Course

Guy Hembroff, associate professor, CMH Division, and director of the Health Informatics graduate program and the Institute of Computing and Cybersystem’s Center for Cybersecurity, is the principal investigator on a one-year project that has been awarded a $40,000 contract from the Copper Country Intermediate School District (CCISD). The project is titled “Cybersecurity Course for Career and Technical Education (CTE) Program.”

The CCISD CTE program provides courses and labs to high school-age students from Baraga, Houghton, and Keweenaw counties. It is intended to provide the academic background, technical ability, and work experience that today’s youth will need to succeed in today’s changing job market.

The contract funds instructor time, use of facilities, labs, and equipment, and materials and supplies. Student enrolled in the program meet on Michigan Tech’s campus for two hours per day, Monday through Friday, from September to May.

The CTE Cybersecurity course covers topics including security architecture, cryptographic systems, security protocols, and security management tools. Students also learn about virus and worm propagation, malicious software scanning, cryptographic tools, intrusion detection, DoS, firewalls, best practices, and policy management.

Learn more about the CCISD CTE program at: