Month: April 2020

Online Book Buyback

The University Bookstore has arranged an Online Book Buyback event.

Two used book wholesalers, Nebraska Book Company and MBS Textbook Exchange, will buy your books.

Search for your books and compare buyback prices using the links above. The book wholesalers will provide you with a USPS label and free shipping.

If you have questions, please contact Jennifer Cowan, Textbook Manager, 906-487-2410 or campusstore@mtu.edu.


Register for Summer 2020 Classes by April 20

NEW CLASSES ADDED!

Students, for best course selection, please register by April 20.

Access the Summer 2020 full schedule of classes and start registration through the Registrar’s website: https://www.mtu.edu/registrar/.

View undergraduate class descriptions here.

Please visit with an academic advisor if you have questions about what classes to take.

NEW CLASSES ADDED!

CS 1121 | Intro to Programming I
3 Credits | 05/11-06/25 | Instructed by: TBA

CS 1122 | Intro to Programming II
0-3 Credits | 05/11-06/25 | Instructed by: Pomerville 

CS 1142 | Programming at HW/SW Interface
3 Credits | 05/11-06/25 | Instructed by: Vertanen 

CS 3331 | Concurrent Computing | NEW
3 Credits | 05/11-06/25 | Instructed by: Hiebel

CS 3411 | Systems Programming | NEW
3 Credits |Instructed by: Asilioglu

CS 3421 Computer Organization | NEW
3 Credits | Track A

CS4321 | Intro to Algorithms
05/11-06/25 | Instructed by: Zhenlin Wang (Online)

CS 4461 | Computer Networks
3 Credits | 05/11-06/25 | Instructed by: Jalooli 

CS 4710 | Model-Driven Software Development
3 Credits | 06/29-08/13 | Instructed by: Ebnenasir 

CS 4821 | Data Mining
3 Credits | 05/11-06/25 | Instructed by: Kakula 

EET 1120 | Circuits I
3 Credits | 05/11-06/25 | Instructed by: Hazaveh

EET 2233 | Electrical Machinery
4 Credits | 05/11-06/25 | Instructed by: Sergeyev 

EET 2241 | C++ and MATLAB Programming 
3 Credits | 05/11-06/25 | Instructed by: Hazaveh

EET 3373 | Intro to Prog Controllers
3 Credits | 05/11-06/25 | Instructed by: Sergeyev 

EET 4144 | Real-Time Robotics Systems
4 Credits | 05/04-05/15 | Instructed by: Sergeyev  

EET 4147 | Industrial Robotic Vision Syst
4 Credits | 06/29-08/13 | Instructed by: Sergeyev 

EET 4460 | Senior Project I
3 Credits | 05/11-08/13 | Instructed by: TBA

EET 4480 | Senior Project II
3 Credits | 05/11-08/13 | Instructed by: TBA

EET 5144 | Real-Time Robotics Systems
4 Credits | 05/04-05/15 | Instructed by: Sergeyev 

EET 5147 | Industrial Robotic Vision Syst
4 Credits | 06/29-08/13 | Instructed by: Sergeyev 

SAT 2343 | Network Administration I
4 Credits | 05/11-06/25 | Instructed by: TBA 

SAT 2511 | Microsoft System Administration
4 Credits | 05/11-06/25 | Instructed by: Arney 

SAT 2711 | Linux System Administration
4 Credits | 05/11-06/25 | Instructed by: Arney 

SAT 3310 | Scripting Administration & Automation
3 Credits | 05/11-06/25 | Instructed by: Arney 

SAT 3611 | Infrastructure Service Administration
3 Credits | 05/11-06/25 | Instructed by: Arney 

SAT 3812 | Cyber Security I
3 Credits | 05/11-06/25 | Instructed by: Cai 

SAT 3820 | Wireless System Administration
4 Credits | 05/11-06/25 | Instructed by: TBA

SAT 4480 | Senior Project I
3 Credits | 05/11-08/13 | Instructed by: TBA

SAT 4812 | Cyber Security II
3 Credits | 05/11-06/25 | Instructed by: Arney 

SAT 4816 | Digital Forensics
3 Credits | 05/11-06/25 | Instructed by: TBA

SAT 4880 | Senior Project II
3 Credits | 05/11-08/13 | Instructed by: TBA

SAT 4996 | Big Data: Tools & Techniques
3 Credits | 06/29-08/13 | Instructed by: Tang 

SAT 5990 | Big Data: Tools & Techniques
3 Credits | 06/29-08/13 | Instructed by: Tang 

SAT 5998 | Experience in Med Informatics
3 Credits | 06/29-08/13 | Instructed by: TBA


Register for Summer 2020 Classes by April 20

NEW CLASSES ADDED!

Students, for best course selection, please register as soon as possible.

Access the Summer 2020 full schedule of classes through the Registrar’s website: https://www.mtu.edu/registrar/.

View undergraduate class descriptions here.

Please visit with an academic advisor if you have questions about what classes to take.

NEW CLASSES ADDED!

CS 1121 | Intro to Programming I
3 Credits | 05/11-06/25 | Instructed by: TBA

CS 1122 | Intro to Programming II
0-3 Credits | 05/11-06/25 | Instructed by: Pomerville 

CS 1142 | Programming at HW/SW Interface
3 Credits | 05/11-06/25 | Instructed by: Vertanen 

CS 3331 | Concurrent Computing | NEW
3 Credits | 05/11-06/25 | Instructed by: Hiebel

CS 3411 | Systems Programming | NEW
3 Credits |Instructed by: Asilioglu

CS4321 | Intro to Algorithms
05/11-06/25 | Instructed by: Zhenlin Wang (Online)

CS 4461 | Computer Networks
3 Credits | 05/11-06/25 | Instructed by: Jalooli 

CS 4710 | Model-Driven Software Development
3 Credits | 06/29-08/13 | Instructed by: Ebnenasir 

CS 4821 | Data Mining
3 Credits | 05/11-06/25 | Instructed by: Kakula 

EET 1120 | Circuits I
3 Credits | 05/11-06/25 | Instructed by: Hazaveh

EET 2233 | Electrical Machinery
4 Credits | 05/11-06/25 | Instructed by: Sergeyev 

EET 2241 | C++ and MATLAB Programming 
3 Credits | 05/11-06/25 | Instructed by: Hazaveh

EET 3373 | Intro to Prog Controllers
3 Credits | 05/11-06/25 | Instructed by: Sergeyev 

EET 4144 | Real-Time Robotics Systems
4 Credits | 05/04-05/15 | Instructed by: Sergeyev  

EET 4147 | Industrial Robotic Vision Syst
4 Credits | 06/29-08/13 | Instructed by: Sergeyev 

EET 4460 | Senior Project I
3 Credits | 05/11-08/13 | Instructed by: TBA

EET 4480 | Senior Project II
3 Credits | 05/11-08/13 | Instructed by: TBA

EET 5144 | Real-Time Robotics Systems
4 Credits | 05/04-05/15 | Instructed by: Sergeyev 

EET 5147 | Industrial Robotic Vision Syst
4 Credits | 06/29-08/13 | Instructed by: Sergeyev 

SAT 2343 | Network Administration I
4 Credits | 05/11-06/25 | Instructed by: TBA 

SAT 2511 | Microsoft System Administration
4 Credits | 05/11-06/25 | Instructed by: Arney 

SAT 2711 | Linux System Administration
4 Credits | 05/11-06/25 | Instructed by: Arney 

SAT 3310 | Scripting Administration & Automation
3 Credits | 05/11-06/25 | Instructed by: Arney 

SAT 3611 | Infrastructure Service Administration
3 Credits | 05/11-06/25 | Instructed by: Arney 

SAT 3812 | Cyber Security I
3 Credits | 05/11-06/25 | Instructed by: Cai 

SAT 3820 | Wireless System Administration
4 Credits | 05/11-06/25 | Instructed by: TBA

SAT 4480 | Senior Project I
3 Credits | 05/11-08/13 | Instructed by: TBA

SAT 4812 | Cyber Security II
3 Credits | 05/11-06/25 | Instructed by: Arney 

SAT 4816 | Digital Forensics
3 Credits | 05/11-06/25 | Instructed by: TBA

SAT 4880 | Senior Project II
3 Credits | 05/11-08/13 | Instructed by: TBA

SAT 4996 | Big Data: Tools & Techniques
3 Credits | 06/29-08/13 | Instructed by: Tang 

SAT 5990 | Big Data: Tools & Techniques
3 Credits | 06/29-08/13 | Instructed by: Tang 

SAT 5998 | Experience in Med Informatics
3 Credits | 06/29-08/13 | Instructed by: TBA


Faculty Candidate Lecture: Sidike Paheding

Flyer announcing faculty candidate lecture

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). 


Faculty Candidate Saleem Ashraf

The College of Computing Department of Applied Computing invites the campus community to a lecture by faculty candidate Saleem Ashraf on, April 7, 2020, at p.m., via an online Zoom meeting.

Dr. Ashraf is currently an assistant professor of mechatronics engineering in the ECE department at Sultan Qaboos University, Oman. He received his Ph.D. and MSc. degrees in mechatronics engineering from DeMontfort University, UK, in 2006 and 2003, respectively, and his BSc. in electrical and computer engineering from Philadelphia University, Pa., in 2000.

Ashraf’s research interests are unified under the theme, “developing real-time smart controllers for different engineering systems,” and his research investigates electromechanical, electro-pneumatic, and piezoelectric based systems. 

Advancements in field of unmanned vehicle system, artificial intelligence, and computer vision have empowered the integration of solutions that would potentially automate many processes. 

Ashraf’s seminar presents his research experience in the field of smart and vision-based unmanned vehicle systems, and how this technology has been employed to solve real-life problems in Oman.

The talk will present a selection of Ashraf’s fundamental research work focused on the modeling and control of long-stroke piezoelectric actuators, which are being used widely in micro positioning systems. He will also share his experience in the establishment of the “Embedded & Interconnected Vision Systems” (EIVS) lab. 

The second part of Ashraf’s talk will cover his teaching experience, including philosophy, courses, new courses, extracurricular activities, and practical projects. He will present his methodology in supervising multi-disciplinary final year projects with some examples of completed projects. Finally, Ashraf will discuss his ideas about how he can contribute to the Michigan Tech curriculum at all levels, undergraduate and graduate.

Ashraf has been awarded external research grants totaling more than $450K, and three internal grants totaling $58K; he attributes his success in this regard to his development of excellent relations with local industry and the Omani research council (TRC). The common aim of these research projects is to develop vision-based unmanned vehicles to solve real life problems such as oil spill in seawater. 

He has published more than 45 peer-reviewed papers in reputable journals and at international conferences. He is one of the founders of the “Embedded & Interconnected Vision Systems” (EIVS) lab at Sultan Qaboos University, which was inaugurated this March and funded by BP Oman. The lab hosts equipment for Embedded Vision Systems, Artificial Intelligence (UVS / Robotics), and IoT.


Faculty Candidate Muhammad Fahad to Present Lecture April 9

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. 


Stay Home. Stay Safe. Stay in Touch.

Dear College of Computing Students, Families, and Friends:

In all our daily tasks and interactions, Michigan Tech and the College of Computing remain closely focused on delivering to you the best possible educational experience; we are also mindful of your over-all health and well being. We wish to do as much as we possibly can to help you successfully complete this semester, and guide and support you on your way to finishing your degree.

We’ve compiled some of the many University and community resources available to you below. All kinds of help, support, and kindness is out there, and everyone is eager to assist in this uncertain time.

You are invited to contact Dean Minerick, and any of us in Computing and across campus, with your questions and concerns, large or small.

Academic Leadership
Adrienne Minerick, Dean: minerick@mtu.edu
Dan Fuhrmann, Director, MERET/CMH/Applied Computing: fuhrmann@mtu.edu
Linda Ott, Chair, Computer Science: linda@mtu.edu

Undergraduate Academic Advisors
Denise and Kay, The College of Computing’s academic advisors, are on duty and available by email, phone, and Zoom.
Denise Landsberg, Computer Science, Software Engineering: dllandsb@mtu.edu
Kay Oliver, CNSA, Cybersecurity, EET, Mechatronics, Health Informatics: koliver@mtu.edu
Advising Website:

Faculty and Staff
We hope that you always feel welcome to contact your instructors and mentors with questions, concerns, and help with an assignment. We are all standing by to help you successfully complete this semester, prepare for summer and fall classes, and get ready for for spring graduation.
Find all the Computing faculty here. Find the Computing staff here.

Finally, Michigan Tech and the College of Computing are continually populating and updating our websites and blogs with the latest news.

A few more links:

Husky Emergency Fund Application

Get the latest information and updates regarding Michigan Tech’s response to COVID-19 at mtu.edu/covid-19. View updates to this alert.

Meal Packets are available 24 hours a day, 7 days a week at Public Safety and Police Services.

The Dean of Students Office has compiled a comprehensive list of emergency resources for students.

Students who are experiencing unforeseen financial emergencies can apply for assistance.

More Student Resources.

Study Abroad and COVID-19.

FAQs from Facilities Management.

Info for Michigan Tech employees.

Info for Michigan Tech faculty.


Leo Ureel Is this Week’s Deans’ Teaching Showcase Selection

Dean Adrienne Minerick and the College of Computing are pleased to announce that Leo Ureel, Computer Science Lecturer and Ph.D. student, is this week’s Deans’ Teaching Showcase. Leo is also coordinator of the College of Computing Learning Center (CCLC) in Rekhi Hall and faculty advisor to the Computer Science Learning Committee in McNair Hall. 

Most notable among his accomplishments, Ureel’s student-centric efforts are increasing retention and diversifying the cohort of first-year Computing students. Further, his work, in coordination with many other valuable members of the College of Computing, has increased the visibility of Michigan Tech and the College of Computing, both on campus and in the community, and contributed substantially to sustained enrollments in Computer Science and other College of Computing programs.

“What becomes apparent immediately when thinking about Leo’s contributions is how much Leo cares about and invests into his student’s learning,” says Dean Minerick. “Student success is at the heart of all that he does.”  

Ureel’s work has provided him the opportunity to develop rich collaborations with researchers across the U.S. and in the U.K., Europe, and Africa, and he recently led an ITICSE working group of international researchers examining first year student experiences in CS. 

Ureel teaches CS 1121 and CS 1122 courses, primarily to first year students, in which he works to broaden students’ views of computing, ground them in a programming language, and teach them problem solving skills. His research has been supported by NSF, Google, and NCWIT. 

Ureel’s nomination emphasizes in particular his innovative and effective teaching of the entry-level programming classes in Computer Science, for which Ureel has developed a WebTA tool that gives students near real-time feedback on their programming code. 

“My classrooms are hands-on learning environments where I combine small hands-on projects with blended learning techniques to engage students and provide individual feedback” Ureel explains. “I’ve developed a software system, WebTA, that provides students with individualized feedback on their code while they are working on it – even when I am personally unavailable. (For example, at 2:00 a.m. when students are working on their programming assignments!)” 

“This engages students in the following programming practice: design, code, receive feedback, reflect, and repeat. The more I can engage the students in these tight cycles of programming and reflection, the better they learn to program.” 

Ureel’s adds that his research efforts focus on a constructionist approach to introductory computer science that leverages code critiquers to motivate students to learn computer programming. The critiquer systems engage students in test-driven agile development methods through small cycles of teaching, coding integrated with testing, and immediate feedback. 

This interest in student success was one component of Ureel’s close collaboration with Linda Ott, chair of the Computer Science department, in a project funded by the National Center for Women and Information Technology (NCWIT). As part of the collaboration focused on first-year student retention, a structure was developed to more effectively place students in their first programming course. 

“By improving the placement of students based on their previous programming experience, both students new to programming and those with experience are more satisfied and more successful in their first programming course at Michigan Tech” according to Dr. Ott. “Leo is constantly thinking about ways to engage students in programming”. 

Ureel is also part of a student and faculty team that regularly hosts community outreach and workshops for middle and high school students like Code Ninjas, Copper Country Coders, and numerous other programs. 

“My work with K-12 outreach activities, such as Code Ninjas and Copper Country Coders, benefits both the K-12 students, who are learning to program, and Michigan Tech undergraduate students, who volunteer as K-12 mentors,” Ureel says. “The undergraduate students benefit from the teaching process; learning more about computer science as they 

strive to articulate basic computer principles in simple language and entertaining memes for the K-12 students.” 

Ureel’s success teaching students with no coding experience also sparked the pilot of a foundational computing course for non-majors at Michigan Tech. Ureel was the key thought leader driving course structure and content for CS 1090, Computational Thinking, a course for non-Computing majors that teaches computing fundamentals using the Python language. 

“I am teaching the course in the context of several problem domains, including Big Data, Machine Learning, Image Processing, Simulation, and Video Game Design,” Ureel says. “As students tackle problems in these domains, I introduce the Python language structures required to construct a solution. Teaching programming in the context of larger problem domains gives students a way to ground their learning in practical applications.” 

The course, which could help instill computational thinking across campus, is being piloted this semester with students from outside the College of Computing. Designed to be compatible with the College Board AP Computer Science Principles course, the CS 1090 pilot is expected to be expanded through IDEA Hub continuation efforts. 

Ureel also leads the College of Computing Learning Center (CCLC), which has pivoted in a couple of ways over the last year, in step with the College of Computing. A cadre of 20 outstanding student coaches from both the Computer Science and Computer Network and System Administration majors have transformed the CCLC into an inclusive learning hub for all CC majors and courses, with students from across campus seeking out the CCLC. The number of students utilizing CCLC services has increased steadily over the past few years. 

Ureel also worked closely with Dr. Nilufer Onder (CS) to incorporate into CCLC services an upper-level Student Academic Mentors (SAM) program that Dr. Onder developed and spearheaded in Computer Science courses. Their vision is to expand the SAM program under the umbrella of the CCLC, increasing access and courses supported. 

And finally, in response to the recent COVID-19 pandemic, Ureel and his coaches have creatively and effectively coordinated the transition of CCLC services to an online format. 


Faculty / Researcher Profile: Weihua Zhou

Faculty/Researcher Profile: Weihua Zhou, Multi-Disciplinary Digital Healthcare Solutions

By Karen Johnson, Communications Director, College of Computing and Institute of Computing and Cybersystems

How can the cost-effectiveness of healthcare be improved, especially for complicated chronic diseases? This is the overarching question Dr. Weihua Zhou is seeking to answer with his research. The multi-disciplinary solutions he is investigating merge the fields of medical imaging and informatics, computer vision, and machine learning. 

An assistant professor in Michigan Tech’s Health Informatics program, and an affiliated associate professor in the Biomedical Engineering department, Zhou is working with students on a number of research projects in Michigan Tech’s Medical Imaging and Informatics Lab, which he directs. He is a member of the Institute of Computing and Cybersystems’s Center for Data Science.

Zhou says his research is driven by clinical significance, and he is especially interested in developing practical solutions to improve the cost-effectiveness of treating complicated chronic diseases, such as coronary artery disease, heart failure and senile dementia. 

He is excited about his career, his international research, and his work at Michigan Tech. “We have a very productive team, including dedicated Ph.D. students, self-motivated graduate and undergraduate students, and a lot of experienced clinical and technical collaborators,” he says of his colleagues and collaborators at Michigan Tech and around the world.

Zhou feels that he can be dedicated to both his research and teaching at Michigan Tech. “I joined the Health Informatics program at Michigan Tech, both because health informatics is my research focus, and because Michigan Tech’s leading reputation among engineering schools opens opportunities to find new and respected technical collaborators. 

Zhou often calls himself a salesman. “I sell techniques to our clinical collaborators and ask them to design the projects with me, provide the patient data, and test our tools,” he explains. “I also sell my ideas about clinical problems to technical collaborators and ask them to work with us to solve the important clinical problems.”

And when he communicates with his Ph.D. students, “sometimes I also consider them as my buyers and let them appreciate my ideas so that they can be really inspired.”

Primary Research

Zhou identifies two of his research projects of as primary. 

“This first is exploring image-guided approaches to improving the treatment of heart failure, which has been supported by AHA grants, and is now being supported by a new faculty startup grant,” Zhou says. “The second main project is seeking to employ machine learning to improve the risk stratification for osteoporosis, which is supported by a National Institutes of Health (NIH) subcontract award from Tulane University.”

On the NIH grant, awarded in December 2019, Zhou is working with internationally renowned researcher and educator Dr. Hong-Wen Deng, an endowed chair and professor in the School of Public Health and Tropical Diseases at Tulane University, New Orleans, La. Zhou and Deng are studying trans-omics integration of multi-omics studies for male osteoporosis.

Zhou is also co-PI with Jinshan Tang, professor of Applied Computing (eff. 7/1/20) at Michigan Tech, on a Portage Health Foundation Infrastructure Enhancement Grants titled, “High Performance Graphics Processing Units.” The project is focused on building big data computing capabilities toward advancing research and education. Several additional proposals are under review and revision. Zhou’s past research support includes an American Heart Association award, which studied a new image-guided approach for cardiac resynchronization therapy.

Teaching and Mentoring

Zhou, who started at Michigan Tech in fall 2019, instructed Introduction to Health Informatics in the fall semester, and Applied Artificial Intelligence in Health this spring.  He says that in the Medical Informatics program, the subjects he teaches are very practical.

“I believe the following strategies are very important and I practice them in my classes every day: 1) Make the class interactive; 2) Make the assignments and projects practical; 3) Emphasize the learning process; and 4) Keep the teaching materials up to date,” Zhou says.

Zhou supervises two Ph.D. candidates in the Department of Applied Computing, and a Health Informatics master’s student.

Applied Computing Ph.D. candidate Zhuo He’s primary research project concerns information fusion between electrical signal propagation and mechanical motion to improve the treatment of heart failure. Ph.D. candidate Chen Zhao’s primary research concerns using image fusion and computer vision to improve interventional cardiology. And Zhou’s Health Informatics master’s student, Rukayat Adeosun, is studying nuclear image-guided approaches to improving cardiac resynchronization therapy.

Education and Post-Doc

Zhou was awarded his Ph.D. in computer engineering by the Department of Electrical and Computer Engineering at Southern Illinois University Carbondale in 2012; his dissertation is titled, “Image reconstruction and imaging configuration optimization with a novel nanotechnology enabled breast tomosynthesis multi-beam X-ray system.”

Following, Zhou was a post-doctoral researcher in the Department of Radiology and Imaging Sciences at Emory University, Atlanta, Georgia, then he was appointed a Nina Bell Suggs Endowed Professor at University of Southern Mississippi, where he was a tenure-track assistant professor. Zhou also completed an MSc.-Ph.D. in computer science (2007) and a B.E. in computer science and technology (2003), both at Wuhan University, China.

Achievement

Zhou received the USM College of Arts and Sciences Scholarly Research Award in March 2019, participated in the AHA Research Leaders Academy of the American Heart Association in September 2017 and August 2018, and received the USM Butch Oustalet Distinguished Professorship Research Award in April 2018.

University and Professional Service

Zhou serves on Michigan Tech’s Review Committee for Graduate Dean’s Awards Advisory Committee, and in October 2019 he served on the Review Committee for Research Excellence Fund (REF) – Research Seed Grants (RS).

He was an invited speaker at the Machine Learning in SPECT MPI Applications session at the Annual Scientific Session of the American Society of Nuclear Cardiology in Washington, D.C., in 2009.

Zhou is a member of the American Heart Association (AHA) and the American Society of Nuclear Cardiology (ASNC).

Peer-Review

Since Zhou joined Michigan Tech in August 2019, he has published five scholarly papers, in Journal of Nuclear Cardiology and the IEEE Journal of Translational Engineering in Health and Medicine. Two additional articles are under revision with Journal of Nuclear Cardiology and the journal Medical Physics, and one is under review by the Medical Image Computing and Computer Assisted Intervention (MICCAI) Conference 2020.

Since 2007, he has published more than 80 peer-reviewed journal and conference papers and book chapters in publications including JACC: Journal of The American College of Cardiology: Cardiovascular Imaging, Journal of Nuclear Cardiology, and IEEE Journal of Translational Engineering in Health and Medicine.

Zhou is a translator of featured papers and abstracts for the Journal of Nuclear Cardiology, and a paper reviewer for the Journal of Nuclear Cardiology, JACC: Journal of The American College of Cardiology, and JACC: Cardiovascular Imaging. He is a reviewer for American Heart Association data science grants. 

Commercial Success

Zhou holds a number of patents and invention disclosures, including new methods to 1) diagnose apical hypertrophic cardiomyopathy from gated single-photon emission computed tomography (SPECT), and 2) measure right-ventricular and interventricular mechanical dyssynchrony from gated single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI); and 3) the integration of fluoroscopy venogram and myocardial perfusion SPECT image with left-ventricular contraction sequence and scar distribution to guide the real-time surgery of cardiac resynchronization therapy. 

He and his colleagues have developed a number of software tools, some of which are being used in hospitals for research purposes, and he believes that the tools can be successfully validated and become commercially available. For example, Zhou’s nuclear image-guided software toolkit to improve cardiac resynchronization therapy is being validated by a large clinical trial. 

A personal note.

Zhou loves independent thinking, facts and exact numbers, and he values persistence, all of which express themselves in his teaching and research, and his life.

Follow Weihua Zhou on Twitter: @LabMiil

The College of Computing Department of Applied Computing will officially replace the CMH Division effective July 1, 2020.