Category: Computer Science

Michigan Tech Team Places 4th Overall in TiM$10K Challenge

A team of five Michigan Tech students received Honorable Mention honors in the second annual SICK Inc. TiM$10K Challenge, a national innovation and design competition. University students from around the country participated in the event designed to support innovation and student achievement in automation and technology.

The Michigan Tech team members — Brian Parvin (ME), Paul Allen (EE), David Brushaber (CompEng), Kurtis Alessi (CompEng) and Alex Kirchner (CompEng) — earned Honorable Mention (fourth place overall) for their project, “Evaluating Road Markings (the Road Stripe Evaluator). Their project was sponsored by SICK Inc. Watch a video about the project below.

SICK’s TiM$10K Challenge 2020 – Evaluating Road Markings (The Road Stripe Evaluator)

For the competition, teams were supplied with a 270-foot SICK LiDAR sensor and accessories, and challenged to solve a problem, create a solution, or bring a new application to any industry that utilizes the SICK LiDAR.

Each team was asked to submit a video and paper for judging upon completion of its project. A panel of judges decided the winning submissions based on creativity and innovation, ability to solve a customer problem, commercial potential, entrepreneurship of the team, and reporting.

The Tech team developed an innovative product to help resolve issues caused by poor road markings, while reducing maintenance costs and improving motorist safety. Their new software uses reflectivity values obtained using a SICK LiDAR unit to identify deterioration of road stripes and recommend timely repainting, also aiding in the safety and reliability of self-driving vehicles on roadways.

The Michigan Tech Team

They constructed a prototype to demonstrate functionality, in the form of a pushable cart that evaluates road markings. An intuitive user interface displays the markings being evaluated, and indicates if they meet necessary levels of reflectivity.

Pinar said the team was well organized and demonstrated an excellent work ethic from day one. “It was exciting to watch them identify a salient problem and develop a functional proof-of-concept solution despite the setbacks that affected us all after spring break,” he said.

“This was a unique project in that the team was required to identify a problem and develop a solution to it that is based on SICK’s TiM LiDAR, while most teams are handed a problem and asked to create a solution,” Pinar noted. “I think this format allowed the team to exercise even more innovation than on a ‘typical’ project.”

The same team of students was awarded Honorable Mention honors at this spring’s Senior Design competition for their project, “Road Marking Reflectivity Evaluator.”

SICK, Inc. is one of the world’s leading manufacturers of sensors, safety systems, machine vision, encoders and automatic identification products for industrial applications.


Jason Hiebel, The College of Computing’s First Graduate

By Karen S. Johnson, Communications Director, College of Computing and ICC
This is the first part of a two-part article about Jason Hiebel, Ph.D., the college of Computings first graduate. Watch this blog and College of Computing social media channels for “Part II, A Supportive and Wise Network.”

PART I | THE FAST TRACK

A Profile of Dr. Jason Hiebel: The College of Computing’s First Graduate

In fall 2007, Jason Hiebel enrolled in his first semester at Michigan Tech. He’s been studying and teaching computer science and mathematics at Tech ever since, participating in December 2019 Commencement ceremonies. Shortly after, he successfully defended his dissertation and was awarded his Doctor of Philosophy in Computer Science, the very first from the College of Computing.

“Graduating when I did, and becoming the first Ph.D. for the College of Computing, was really a fluke of timing,” Hiebel says. “But after all my time here in Houghton and with the Computer Science department, I am pleased to have the honor of being the college’s first Ph.D. It’s something that can be mine and mine alone, and I’m okay with being a little bit greedy about it!”

Husky Tenacity

Hiebel grew up north of Green Bay, Wis., and attended Bay Port High School, where he was active in the chess club and the marching band.

He says that during high school, “I was also the kind of person to push for opportunities far beyond those normally available. For example, while our school did offer some computer science courses, they were mostly self-taught in a small computer lab. But I wanted to be a computer scientist, and I wanted to be a professor—even if I lacked an understanding of what that truly entailed at the time.”

So, he pushed himself to complete the entire computer science curriculum before the end of his sophomore year, then took the AP exam. Following, Hiebel continued with the curriculum thanks to the State of Wisconsin, which paid for him to complete several computer science courses at the University of Wisconsin Green Bay.

“These were not opportunities that were typically available to me or anyone else at Bay Port,” Hiebel notes. “I had to fight for these opportunities, with the school and the state. But that Tenacity is exactly what Huskies are all about, right?”

All in all, Hiebel says he started at Michigan Tech as a junior in the Computer Science department and as a sophomore overall.

The Fast Track to Teaching and Research

During his first few weeks at Michigan Tech, Hiebel’s sole major was Computer Science. But soon, he began to pursue a double major in another field he enjoys: mathematics.

Hiebel completed his B.S. in Computer Science in summer 2010, and while he was finishing his B.S. in Mathematics, he dual-enrolled as a graduate student in Computer Science. He completed the Mathematics B.S. a year later, and in spring 2012 he received his Master of Science in Computer Science.

In pursuit of his Ph.D., Hiebel was supported by graduate teaching assistantships (GTA), teaching assistantships, and graduate research stipends (GRA). He spent several summers interning at MIT Lincoln Labs, the Department of Defense, and the Michigan Tech Research Institute (MTRI).

“As a GTA, I did my fair share of grading and also led the lab sections for the introductory courses for three semesters,” Hiebel explains. For two semesters, as an instructor, he taught the accelerated introductory programming course (CS1131) and the undergraduate AI course (CS4811). Some semesters he was both a GTA and an instructor. Finally, with the support of his advisors, Hiebel was able to advance his research full time with a GRA stipend.

As a master’s student, Hiebel worked on developing tools for AI education with Laura Brown and another graduate student. As a Ph.D. student, his focus was on building his dissertation research under graduate advisors Laura Brown and Zhenlin Wang.

“Jason’s research applies machine learning to computer system optimization. He has become an expert in both fields,” says Professor Zhenlin Wang, Computer Science.

“The nature of this type of research makes it very challenging for a student to focus, as it requires continual effort and extended skills,” Wang adds. “Throughout his Ph.D. study, Jason consistently demonstrated diligence, perseverance, and creativity. For these reasons and others, Jason has always been a stand-out among our graduate students.”

In his dissertation, Hiebel investigates the application of online machine learning methods, particularly multi-armed bandit methods, to performance optimization problems in computer systems.

“Computers offer a myriad of configurations for customizing how the system performs. Depending on what you run on the system, different configurations can have a drastic effect on performance,” Hiebel explains. “Ideally, we would like to match the configuration to the workload, but doing so requires a broad expertise of how different components of an individual system interact.”

“My work focuses on modeling this type of configuration problem and uses artificial intelligence to automatically, without human intervention, select the best-performing configurations for a given workload.”

Looking Ahead

Hiebel signed on to instruct some spring 2020 semester courses for the Computer Science department while he waited on his paperwork to process for a job with the Department of Defense. “With the growing pains of the new college, there was a need for a few more people to teach and I was happy to lend my experience,” he says.

But like many in the wake of the global pandemic, Hiebel’s plans are in flux right now. He’s teaching a Summer Track A course at Michigan Tech, and advising an undergraduate research project, as well.

“Mainly, I’ll just be waiting for things to open back up so I can get processed for the job I’m waiting for,” Heibel says. “During that limbo, I hope to tackle some research problems and continue to keep myself busy.”
Hiebel says that in the short term, he is hoping to lend his expertise to government research. But in the long term, his aspirations are to return to academia.

“Only time will tell where I end up,” he muses. “But wherever I do end up, I think I will be happy if I’m working on interesting problems and using the skills and knowledge I gained as a Ph.D. student here at Tech.”

In the meantime, Hiebel enjoys living in the Houghton community. He’s a big fan of winter, and even Houghton summers are far too warm for his tastes. “Small town life suits my sensibilities better,” he confirms.


Elijah Cobb Awarded Undergraduate Research Fellowship

In November, Michigan Tech undergraduates begin submitting research proposals to the annual competition for Summer Undergraduate Research Fellowships—SURF—which are due the following February and awarded that spring.

Computer Science major Elijah Cobb was one of the many students to submit a SURF proposal this academic year. His was excellent, but so were many others, and the SURF funds were limited, so unfortunately Cobb missed out.

Enter Associate Professor Dr. Charles Wallace (Computer Science) and the College of Computing, which is providing additional funding to allow Cobb to join this summer’s cohort of SURF recipients.

Open to all Michigan Tech undergraduates who have at least one semester remaining after the summer, SURF recipients conduct a research project with a faculty mentor, prepare periodic progress reports, attend a series of professional development seminars over the summer, then present their research at Michigan Tech’s Undergraduate Research Symposium, or at a professional conference in their field. A modest stipend is also awarded.

Cobb will conduct his research project, “Designing Scaffolded Interactive Instruction in Discrete Mathematics,” with Dr. Charles Wallace, Associate Professor of Computer Science.

Watch the College of Computing blog, website, and social media channels for updates from Cobb and Dr. Wallace as their research moves forward.

Elijah Cobb is a fourth year Computer Science student at Michigan Tech. He describes himself as a passionate learner, and throughout his education he has challenged himself to explore learning opportunities outside of the classroom.

Cobb says he is thrilled to have the opportunity to participate in a research project with Professor Wallace. “Together, we will research and develop a new tool for undergraduate Computer Science students to use at Michigan Tech. Our research will focus on Alloy, a programming language used for modeling real-world systems.”

Because Alloy is so complex, Cobb explains that he and Wallace aim to produce an interactive drag and drop interface that will make this powerful programming language more accessible to undergraduate computer science students.

The Project: Designing Scaffolded Interactive Instruction

Discrete mathematics is a foundational Computer Science topic, and undergraduate computer science students enroll in a course at some point in their college career. The course focuses on topics including set theory, relational algebra, and predicate logic, extremely important concepts for beginning students as they form the basis of analysis in the field.

But Computer Science students typically do not get the kind of interactive practice with discrete math that they do with programming in languages like Java. As a result, misconceptions can persist, and students can develop an attitude that math has little relevance to their field.

To introduce the mathematical languages of relational algebra and predicate logic to undergraduate Computer Science students, Dr. Wallace and his colleagues have developed curriculum that uses the Alloy language and modeling tool. He notes that many progressive Computer Science departments across the nation have included programming-oriented exercises like these in their discrete math curricula.

“When I came to Michigan Tech and took discrete structures as a second-year student, the interactive element of the course with Alloy greatly benefited me in learning the course material,” confirms Cobb. “But it was also the most difficult part of the course for me. I knew what I wanted to do, but I could not replicate the thoughts in my head into the code required to run simulations.”

With his SURF project, Cobb aims to help other students with their learning of discrete math and eliminate some of the unnecessary confusion that may develop. He’ll develop a simple, easy to use graphical application that will allow undergraduate students to develop mathematical simulations without the need to fully understand the underlying programming language involved.

To introduce discrete mathematics–the mathematical languages of relational algebra and predicate logic–to undergraduate CS students, Dr. Wallace and his colleagues have developed curriculum that uses the interactive Alloy modeling tool. Wallace notes that many progressive CS departments across the nation have included interactive tools like these in their curriculum.

Alloy is both a programming language based on the mathematical languages of relational algebra and predicate logic, and an application for finding instances (situations) that follow (or break) the requirements set by an Alloy program. It is a powerful tool for “lightweight” modeling of complex systems, allowing designers to explore and gain insights early in the design process. In the classroom, Alloy can give students real-time feedback on their developing understanding of discrete math, and provide them with an authentic sense of how math is applied in the CS field.

“Alloy provides more feedback to students than traditional pencil-and-paper exercises, however the experience could be better for first-time students,” Cobb says. “When students work with Alloy, it can be daunting because as they are trying to develop models, they may encounter small, unfamiliar syntactical issues, and the error messages provided by Alloy can be confusing because they are written for experts, not first time users.”

So, first-time students need careful scaffolding to navigate the powerful Alloy tool. Cobb explains that scaffolding can be thought of as creating a base structure on which students can focus on the task at hand, without overloading them with distracting content.

Cobb’s SURF project will implement automated scaffolding to assist the student new to Alloy by:
(1) Providing a visual, block-based interface for the language, similar to introductory programming languages like Scratch and Snap! This has the potential to keep students focused on learning fundamental concepts of logic and relations, without distracting syntax problems.
(2) Developing automated detection and response for common problems in novice code. This follows from successful work in automated critique of Java programs using the WebTA tool, developed by Assistant Professor Dr. Leo Ureel for use in Michigan Tech’s introductory programming courses.

Cobb says his personal experiences have provided him with much expertise and motivation for a project like this. He notes, “After graduating high school I worked for start-up called Ampel Feedback. It was then that I learned Electron, ReactJS, Typescript, and many other technologies I aim to use for this research program.”

Cobb will begin his work in the second half of summer 2020, and continue its development in the 2020-2021 academic year, including pilot studies of the scaffolded tool. Feedback from the pilot studies will inform further development in summer 2021; Cobb and Wallace expect to introduce the new learning tool in the Michigan Tech Discrete Structures course in fall 2021.

Wallace is looking forward to the collaboration, noting that “it is gratifying to have a student like Elijah who is able to reflect on both what has been valuable and what has been challenging in his own learning experience, and act on those reflections to help future students.”


Department of Applied Computing Announced, Fuhrmann Named Interim Chair

Effective July 1, 2020, the Department of Applied Computing (AC) will open for business as Michigan Tech’s newest academic department, and the second department of the College of Computing. Daniel R. Fuhrmann, Dave House Professor of Computer Engineering, has been named the interim chair of the new department, for a period of one year.

The Applied Computing department administers undergraduate bachelor of science programs in Computer Network and System Administration (CNSA), Electrical Engineering Technology, and soon a new B.S. in Mechatronics (pending final approval by the Michigan Tech Board of Trustees and the state of Michigan). Applied Computing also shares responsibility, with the Department of Computer Science, for the B.S. in Cybersecurity, which began enrolling students in Fall 2019

On the graduate side, the Applied Computing Department hosts the Master of Science in Health Informatics and the Master of Science in Mechatronics, which also started in Fall 2019. In addition to teaching AC program courses, faculty in the new department will pursue research in a variety of areas where computers and computing play a major role, including cybersecurity, mechatronics, health informatics, and machine learning.

Fuhrmann notes, “I am delighted to be a part of the continued growth of the College of Computing, and to do what I can to get our new department up and running. I believe that the Department of Applied Computing makes the CC unique among similar colleges nationwide, and gives Michigan Tech a distinctive edge.”

Key goals for the interim chair position, identified during the nomination and selection process, include strategically increasing the visibility of and enrollment in in Department of Applied Computing degree and certificate programs, and supporting and strengthening collaborative, interdisciplinary, and interdepartmental relationships in curriculum and research.

“I think I speak for others when I express how extremely appreciative I am of Dan’s willingness to contribute to the growth and success of the College of Computing over the last year, and his further willingness to agree to serve as department chair during this particularly challenging time,” says Adrienne Minerick, dean of the College of Computing.

“Dan has repeatedly proven to be an excellent team member who is willing to do the work to support the greater good of our teams in the CC. By stepping forward into unfamiliar tasks as is needed and framing most things as opportunities, he helps bring out the best in our team.”

Fuhrmann says that the new department will continue to deliver strong programs in the AC department’s areas of primary responsibility, and he hopes to increase synergies among the various groups within the department, for instance, looking at how cybersecurity and mechatronics work together in industrial control systems.

Growth in the Health Informatics graduate program is also anticipated, and Fuhrmann notes that the need for trained, talented health informatics professionals has never been more apparent than it is right now.

“In addition to what we will be doing internally, I hope to build a culture of collaboration and cooperation with other Michigan Tech departments that have an interest in computing applications, both inside and outside the College of Computing. We have a lot to offer,” Fuhrmann says.

For more information about the Department of Applied Computing as it becomes available link here.


Computing Students Participate in DesignExpo 2020

College of Computing students participated widely at Michigan Tech’s Design Expo 2020, which was held virtually in April.

Participating Enterprise Teams included Humane Interface Design Enterprise (HIDE), IT Oxygen, and Husky Game Development.

College Senior Design Teams developed a cybersecurity “Penetration Testing Course,”a “Cloud Computing Cost Analysis,” and an “Automated Distributed Configuration Management Systems.”

See project details below. Learn more about Design Expo here.


Senior Design Team: Penetration Testing Course

Team Members: Chris Koch, Joe Bartkowiak, Kelson Rose, Austin Clark, Computer Network and System Administration
Advisor: Yu Cai, College of Computing

Project Overview: To meet the need for new courses in the new Cybersecurity degree program, our team was tasked with developing a Penetration Testing course, which includes the business how-to as well as technical skills necessary to succeed in the field as a professional ethical hacker. We delivered a completed course, including a chosen course textbook, slides, an online lab set with accompanying lab manuals, and exams. GenCyber is a Michigan Tech summer program for local younger students. We provided instructional material, utilized Google Interland activities for younger students, and created the GenCyber camp curriculum to further develop and improve this course—another step toward the future of cybersecurity.


Senior Design Team: Cloud Computing Cost Analysis

Team Members: Alex Kuhn, Austin Walhof, Ryan Jacobson, and Stephen Grobbel, Computer Network and System Administration
Advisor: Todd Arney, College of Computing

Project Overview: Our team compared the cost of running services in a cloud environment between the three largest service providers: Amazon Web Service, Google Cloud Platform, and Microsoft Azure.


Senior Design Team: Automated Distributed Configuration Management Systems

Team Members: Andrew Hitchcock, Tim Graham and Derek Laker, Computer Network and System Administration
Advisor: Tim Wagner, College of Computing
Sponsor: College of Computing

Project Overview: Systems administrators working in environments of all sizes are rapidly adopting configuration management systems to automate provisioning and deployment, enforce system configuration, and streamline their work. However, it can be difficult to figure out which product to choose. Our project consisted of deploying three of the most popular products on the market today— Puppet, Ansible, and Saltstack—and comparing the computing resources that they used, their ease of use, and the scenarios that they would be most fit for.


Enterprise Team: Husky Game Development (HGD)

Team Leaders: Colin Arkens and Xixi Tian, Computer Science
Advisor: Scott Kuhl, Computer Science
Sponsor: Michigan Technological University Pavlis Honors College’s Enterprise

Program Background: Husky Game Development (HGD) is a student-run Enterprise focused on developing video games. Each year, Husky Game Development breaks up into subteams of around six students who experience a full game development cycle, including ideation, design, and end product. HGD explores a wide variety of video game engines and platforms, including Windows, Android, Xbox, and an experimental Display Wall.
Overview: Do you know that old mansion down on the corner? Of course you do. Everyone does. No one who’s entered it was ever seen again. Will you be? Lost in Mazie Mansion is a 2D mystery-puzzle game. To reform the mansion and escape, you’ll need the help of Mazie, the only one to nearly solve the mystery. Play by the house’s rules, dodge monsters patrolling the halls, solve puzzles, and find the keys to get Mazie’s memory back.


Enterprise Team: IT Oxygen

Team Leaders: Calvin Voss, Computer Science; Zack Metiva, Computer Network and System Administration
Advisors: Nagesh Hatti, Electrical and Computer Engineering; James Walker, Computer Science
Sponsors: DENSO, Ford Motor Company, Little Brothers Friends of the Elderly, Mel and Gloria Visser, Northern Specialty Health, Michigan Technological University Pavlis Honors College’s Enterprise Program, Milan and Shailee Lathia

Background: IT Oxygen is a cross-disciplinary, student-run Enterprise that specializes in Information Technology (IT) for student organizations and businesses, with a focus on developing Information System and Information Technology solutions. Team members work on real-world projects that foster skill development and utilize business intelligence. Areas of interest include systems and information analysis, software development, database design, data sciences, cybersecurity, and web-based application development.

Overview: This year, the IT Oxygen Enterprise is working on projects sponsored by Ford, Little Brothers Friends of the Elderly, Northern Specialty Health, and DENSO. In the area of data analytics, IT Oxygen is building predictive models and applying statistical analyses to understand the relationship between technical obsolescence and purchasing strategy for automotive electronics—thanks to support from DENSO. For Ford, a team has been working with the Wireless Communication Enterprise (WCE) to provide data analysis and storage for a smart home energy management system. Finally, IT Oxygen is also collaborating with WCE on continued efforts to improve Little Brothers’ holiday resource management and medical transportation scheduling systems.



Enterprise Team: Human Interface Design Enterprise (HIDE)

Team Leaders: Christopher Ward and Justin Martin, Computer Science
Advisor: Robert Pastel, Computer Science
Sponsor: CCDC Ground Vehicle Systems Center (US Army)

Background: The members of Humane Interface Design Enterprise (HIDE) come together to design, develop, and evaluate user interfaces. The goal is to make daily work more efficient and easier to manage. As a whole, the team works together to design and test different applications for industry sponsors that can be used on Android, iPhone, and other devices. HIDE accomplishes these projects by combining knowledge from multiple disciplines, such as computer science, psychology, and human factors. HIDE team members can get involved in various stages of the design process, from developing an app by programming, to evaluation by designing usability tests and analyzing data.

Overview: Tempi.st is a project from the Ground Vehicle Systems Center, a research center for the US Army located in Warren, Michigan. Tempi.st is a program designed to provide students with the opportunity to work on a real-world project, and is aimed to connect the students to an industry where they can actively participate in research in order to expand their knowledge base and deliver new ideas to the industry in return.

Our objective is to utilize Raspberry Pis to collect weather data in real time for its given location, and to send the collected data to a user through a device such as a phone, computer, or tablet in the form of an alert or by the user opening a web page. How this will be implemented is purely up to our team. We will take these basic specifications and put our own twist to it.




Faculty Candidate Songtao Lu to Present Lecture March 2

The Colleges of Computing and Engineering invite the campus community to a lecture by faculty candidate Songtao Lu, Monday, March 2, 2020, at 3:00 p.m., in Chem Sci 102. Lu’s talk is titled, “Nonconvex Min-Max Optimization for Machine Learning.”

Songtao Lu is an AI resident at IBM Research AI, IBM Thomas J. Watson Research Center. His research interests include optimization, artificial intelligence, machine learning, and neural networks. Lu received his Ph.D. degree in electrical and computer engineering from Iowa State University in 2018, and he was a post-doctoral associate with the ECE department at the University of Minnesota Twin Cities from 2018 to 2019.

We live in an era of data explosion. Rapid advances in sensor, communication, and storage technologies have made data acquisition more ubiquitous than ever before. Making sense of data at such a scale is expected to bring ground-breaking advances across many industries and disciplines. 

However, to effectively handle data of such scale and complexity– and to better extract information from quintillion of bytes of data for inference, learning, and decision-making—increasingly complex mathematical models are needed. These models, often highly nonconvex, unstructured, and with millions or even billions of variables, render existing solution methods inapplicable.

Lu will present work that designs accurate, scalable, and robust algorithms for solving nonconvex machine learning problems. He will discuss the theoretical and practical properties of a class of gradient-based algorithms for solving a popular family of min-max non-convex problems.

Finally, Lu will showcase the practical performance of these algorithms in applications such as poisoning attacks to neural nets, decentralized neural nets training, and constrained Markov decision processes. He will briefly introduce ideas for the possible extension of his framework to other areas.

Lu is a recipient of the Iowa State University Graduate and Professional Student Senate Research Award (2015), the Research Excellence Award from the Graduate College of Iowa State (2017), and student travel awards from ICML and AISTATS.

Download

Faculty Candidate Tao Li to Present Lecture February 27

The Colleges of Computing and Engineering invite the campus community to a lecture by faculty candidate Tao Li on Thursday, February 27, 2020, at 3:00 p.m. in. Fisher 325. His talk is titled, “Security and Privacy in the Era of Artificial Intelligence of Things.”

Tao Li is a Ph.D. candidate in computer engineering in the School of Electrical, Computer and Energy Engineering at Arizona State University. He received an M.S. in somputer science and technology from Xi’an Jiaotong University in 2015, and a B.E. in software engineering from Hangzhou Dianzi University in 2012. His research focuses on cybersecurity and privacy, indoor navigation systems for visually impaired people, and mobile computing. 

AIoT—Artificial Intelligence of Things (AIoT)—combines artificial intelligence (AI) technologies with the Internet of Things (IoT) infrastructure. By 2025, the number of IoT devices in use is estimated to reach 75 billion.

And as AIoT plays an incrreasingly significant role in our everyday lives, the security and privacy of AIoT has become a critical concern for the research community and the public and private sectors. 

In his talk, Li will introduce his recent research focused on the protection of AIoT devices. A novel system that can automatically lock mobile devices against data theft will be introduced, and a touchscreen key stroke attack (based on a video capturing the victim’s eye movements) will be discussed. Li will briefly introduce additional projects of interest.

Li has served as a reviewer for journals and conferences including IEEE TMC, IEEE TWC, ACM MobiHoc, and IEEE INFOCOM.

Download

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: https://blogs.mtu.edu/computing/2020/02/12/faculty-candidate-brian-yaun-to-present-lecture/

Download

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 Intelligent.com, 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, Intelligent.com 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 OnlineSchoolsCenter.com 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. 


Soner Onder and Dave Whalley Investigate Instruction-level Parallelism

From Florida State University News

A Florida State University researcher is working to make computer processors execute applications in a more energy-efficient manner with the help of a new $1.2 million grant from the National Science Foundation.

Professor Dave Whalley, Florida State University

“The general goal is to increase performance but to do it in a manner that is more energy efficient than the dominant computer processors that are in use today,” Professor of Computer Science David Whalley said.

To do that, Whalley and his colleague Soner Onder, a professor at Michigan Technological University, hope to more efficiently exploit what’s called instruction-level parallelism, or the ability of a computer to simultaneously execute multiple machine instructions.

Professor Soner Onder, Michigan Tech Department of Computer Science
Professor Soner Onder, Michigan Tech Department of Computer Science

“In general, VLIW processors are more energy efficient but cannot approach the performance of OoO processors except in limited domains, such as digital signal processing,” Whalley said.

Whalley’s project, called SCALE for Statically Controlled Asynchronous Lane Execution, is designed to overcome these current limitations. SCALE supports separate execution lanes, so that instructions in separate lanes can execute in parallel and dependencies between instructions in different lanes are identified by the compiler to synchronize these lanes when necessary.

“Providing distinct lanes of instructions allows the compiler to generate code for different modes of execution to adapt to the type of parallelism that is available at each point within an application,” Whalley said.

The grant began this fall and will run through August 2023. Half of the funding will come to Florida State, with the other half supporting Onder’s part of the work at Michigan Technological University. The FSU portion will support two graduate students in computer science.