Institute of Computing and Cybersystems (ICC) Blog

Category: News

MTU Creates Dave House Deanship in College of Computing

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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.

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ICC Distinguished Lecture: Alina Zare, Univ. of Florida

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The Institute of Computing and Cybersystems will present a Distinguished Lecture by Dr. Alina Zare on Friday, April 16, 2021, at 3:00 p.m.

Her talk is titled, “Multiple Instance Learning for Plant Root Phenotyping.”

Dr. Zare is a professor in the Electrical and Computer Engineering department at University of Florida. She teaches and conducts research in the areas of pattern recognition and machine learning.

Lecture Title

Multiple Instance Learning for Plant Root Phenotyping

Lecture Abstract

In order to understand how to increase crop yields, breed drought tolerant plants, investigate relationships between root architecture and soil organic matter, and explore how roots can play in a role in greenhouse gas mitigation, we need to be able to study plant root systems effectively. However, we are lacking high-throughput, high-quality sensors, instruments and techniques for plant root analysis. Techniques available for analyzing root systems in field conditions are generally very labor intensive, allow for the collection of only a limited amount of data and are often destructive to the plant. Once root data and imagery have been collected using current root imaging technology, analysis is often further hampered by the challenges associated with generating accurate training data.

Most supervised machine learning algorithms assume that each training data point is paired with an accurate training label. Obtaining accurate training label information is often time consuming and expensive, making it infeasible for large plant root image data sets. Furthermore, human annotators may be inconsistent when labeling a data set, providing inherently imprecise label information. Given this, often one has access only to inaccurately labeled training data. To overcome the lack of accurately labeled training, an approach that can learn from uncertain training labels, such as Multiple Instance Learning (MIL) methods, is required. In this talk, I will discuss our team’s approaches to characterizing and understanding plant roots using methods that focus on alleviating the labor intensive, expensive and time consuming aspects of algorithm training and testing.

Speaker Bio

Dr. Zare earned her Ph.D. in December 2008 from the University of Florida. Prior to joining the faculty at the University of Florida in 2016, she was a faculty member at the University of Missouri.

Zare’s research has focused primarily on developing machine learning and pattern recognition algorithms to autonomously understand and process non-visual imagery. Her research work has included automated plant root phenotyping using visual and X-ray imagery, 3D reconstruction and analysis of X-ray micro-CT imagery, sub-pixel hyperspectral image analysis, target detection and underwater scene understanding using synthetic aperture sonar, LIDAR data analysis, Ground Penetrating Radar analysis, and buried landmine and explosive hazard detection.

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2021 Summer Youth Programs Announced

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by Center for Educational Outreach

Since 1972, Summer Youth Programs (SYP) at Michigan Tech has offered students in grades 6-11 a variety of hands-on explorations in engineering, science, technology, computer science, business, design, and the humanities.

From college and career discovery to academic immersion, SYP is a fantastic mini college experience that packs a ton of learning, experimenting, and fun into each day.

Around 50 programs are offered, along with several scholarship opportunities, and run weekly from June 20-Aug. 7.

View the 2021 SYP course catalog here.

In addition, if any Michigan Tech staff or faculty have children in college (other universities welcome) that are interested in learning more about our summer staffing opportunities on campus please visit the employment page of our website.

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Beth Veinott to Present Lecture February 12, 3 pm

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The Department of Computer Science will present a lecture by Dr. Elizabeth Veinott on Friday, February 12, 2021, at 3:00 p.m.

Veinott is an associate professor in the Cognitive and Learning Sciences department. She will present, “Beyond the system interface: Using human-centered design to support better collaborative forecasting.”

Speaker Biography

Elizabeth Veinott is a cognitive psychologist working in technology-mediated environments to improve decision making, problem solving and collaboration. She directs Michigan Tech’s Games, Learning and Decision Lab and is the lead for the Human-Centered Computing group of Michigan Tech’s Institute of Computing and Cybersystems (ICC).

She has been active in the ACM’s SIGCHI and on the conference organizing committees for CHI Play and CSCW. Prior to joining Michigan Tech in 2016, she worked as a principal scientist in an industry research and development lab and as a contractor at NASA Ames Research Center. Her research has been funded by NIH, Army Research Institute, Army Research Lab, Air Force Research Laboratory, and IARPA.

Lecture Abstract

Teams use technology to help them make judgments in a variety of operational environments. Collaborative forecasting is one type of judgment performed by analyst teams in weather, business, epidemiology, and intelligence analysis. Research related to collaborative forecasting has produced mixed results.

In her talk, Veinott will describe a case of using cognitive task analysis to develop and evaluate a new forecast process and tool. The method captured analysts’ mental models of game-based forecasting problems, and allowed the process to co-evolve with the system design. The tool was tested in a simulation environment with expert teams conducting analyses over the course of hours and compared to a control group. Challenges and lessons learned will be discussed, including implications for human-centered design of collaborative tools.

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College of Computing Invites Applications for Two Faculty Positions

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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.

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Sidike Paheding Wins MDPI Electronics Best Paper Award

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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
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Susanta Ghosh Publishes Paper in APS Physical Review B Journal

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Assistant Professor Susanta Ghosh, ME-EM, has published the article, “Interpretable machine learning model for the deformation of multiwalled carbon nanotubes,” in the APS publication, Physical Review B.

Co-authors of the paper are Upendra Yadav and Shashank Pathrudkar. The article was published January 11, 2021.

Ghosh is a member of the Institute of Computing and Cybersystems’ Center for Data Sciences.

Article Abstract

In the paper, researchers present an interpretable machine learning model to predict accurately the complex rippling deformations of multiwalled carbon nanotubes made of millions of atoms. Atomistic-physics-based models are accurate but computationally prohibitive for such large systems. To overcome this bottleneck, we have developed a machine learning model. The proposed model accurately matches an atomistic-physics-based model whereas being orders of magnitude faster. It extracts universally dominant patterns of deformation in an unsupervised manner. These patterns are comprehensible and explain how the model predicts yielding interpretability. The proposed model can form a basis for an exploration of machine learning toward the mechanics of one- and two-dimensional materials.

APS Physics advances and diffuses the knowledge of physics for the benefit of humanity, promote physics, and serve the broader physics community.

Physical Review B (PRB) is the world’s largest dedicated physics journal, publishing approximately 100 new, high-quality papers each week. The most highly cited journal in condensed matter physics, PRB provides outstanding depth and breadth of coverage, combined with unrivaled context and background for ongoing research by scientists worldwide.

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Dean Livesay to Hold Open Office Hours Fridays, 3-4 pm

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New College of Computing Dean Dennis Livesay will hold open virtual office hours every Friday from 3:00 to 4:00 p.m., beginning February 5, 2021.

All faculty, staff, and students who wish to chat with Dr. Livesay are invited to “stop in” to this weekly Zoom meeting. Appointments are not needed.

Open office hours will not be held when classes are not in session.

Link to the meeting here: https://michigantech.zoom.us/j/83846079187.

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CsE PhD candidate Karen Colbert Named 2021 Diversity Scholar

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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.

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ETS-IMPRESS Scholarship for Transfer Students in Technology Majors

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Applying to MTU as a transfer student? Interested in engineering technology? Check out the ETS-IMPRESS scholarship program.

Open to community college transfer students, applicants must select as their major the College of Computing undergraduate degree programs in Computer Network and System Administration (CNSA) and Electrical Engineering Technology (EET), or the Mechanical Engineering Technology (MET) bachelor’s program.

The program requires participation in the Honors Pathway Program in the Pavlis Honors College, as well as mentoring activities. It fulfills unmet need of $4,500.

Other requirements are listed on the scholarship website, and the deadline for application is February 15.

When I had discovered the ETS-IMPRESS scholarship, it took very little time to understand how helpful it would be to my life, both in and out of college. Not only was I able to afford to go to college, but I was also getting more out of my college experience.

Brad Gipson, 3rd-year CNSA major

Check out ETS-IMPRESS scholar Caleb Devonta Rogers’ story, below, in which he describes his journey to MTU and his plans for his Honors Project, and remember to apply by February 15!

View the original blog article.

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