Category: NSF

Chee-Wooi Ten’s Research Is Subject of Advisor News Article

Associate Professor Chee-Wooi Ten, Electrical and Computer Engineering, was cited in the article, “Reports Summarize Engineering Study Results from Electrical & Computer Engineering Department (Premium Calculation for Insurance Businesses Based On Cyber Risks In IP-based Power Substations),” published August 11, 2020 in Advisor News.

Ten is a member of the Institute of Computing and Cybersystems (ICC) at Michigan Tech and the ICC’s Center for Cyber-Physical Systems.

The paper emphasizes a framework of premium calculation for cyber insurance businesses by modeling potential electronic intrusion with steady-state simulation results and its direct hypothesized impacts, according to the article, citing a NewsRx press release.

The article discussed Ten’s National Science Foundation (NSF) Cyber-Physical Systems grant, “CPS: Medium: Collaborative Research: An Actuarial Framework of Cyber Risk Management for Power Grids.” Assistant Professor Yeonwoo Rho, Mathematical Sciences, is co-PI on the award. The three-year $349K project was awarded in August 2017. Read the abstract and view additional CPS and ICC research projects here, . View the award at

The Institute of Computing and Cybersystems, founded in 2015, promotes collaborative, cross-disciplinary research and learning experiences in the areas of computing education, cyber-physical systems, cybersecurity, data sciences, human-centered computing, and scalable architectures and systems for the benefit of Michigan Technological University and society at large.

It works to provide faculty and students the opportunity to work across organizational boundaries to create an environment that mirrors contemporary technological innovation.

Advisor News is published by InsuranceNewsNet, which describes itself as on the forefront of communicating breaking news and original insights to the industry. With thousands of news sources and hundreds of original articles, the site provides premium content typically only available through proprietary news outlets.

Article by Alex Sergeyev Published in Journal of Engineering Technology (JET)

Alex Sergeyev

An article co-authored by Aleksandr Sergeyev, College of Computing professor, director of the Mechatronics graduate program, and member of the ICC’s Center for Data Sciences, has been published in the Journal of Engineering Technology (JET).

The conclusive article, titled “A University, Community College, and Industry Partnership: Revamping Robotics Education to Meet 21st century Needs – NSF Sponsored Project Final Report,” summarizes the work funded by a $750K NSF grant received by Servgeyev in 2015 to to promote robotics education.  The paper details the grant-funded achievements in curriculum and educational tools development, dissemination, and implementation at Michigan Tech and beyond.

Co-PIs on the project are  Scott A. Kuhl (Michigan Technological University), Prince Mehandiratta (Michigan Technological University), Mark Highum (Bay de Noc Community College), Mark Bradley Kinney (West Shore Community College), and Nasser Alaraje (The University of Toledo).

A related paper was presented at the 2019 ASEE Annual Conference & Exposition, June 21-24, 2019, in Tampa, FL, as part of the panel “Academe/Industry Collaboration” presented by the Technical Engineering Technology Division, where it was awarded the Best Paper Award in the Engineering Technology Division. Download the conference paper here:

Conference Paper Abstract: Recently, educators have worked to improve STEM education at all levels, but challenges remain. Capitalizing on the appeal of robotics is one strategy proposed to increase STEM interest. The interdisciplinary nature of robots, which involve motors, sensors, and programs, make robotics a useful STEM pedagogical tool. There is also a significant need for industrial certification programs in robotics. Robots are increasingly used across industry sectors to improve production throughputs while maintaining product quality. The benefits of robotics, however, depend on workers with up-to-date knowledge and skills to maintain and use existing robots, enhance future technologies, and educate users. It is critical that education efforts respond to the demand for robotics specialists by offering courses and professional certification in robotics and automation. This NSF sponsored project introduces a new approach for Industrial Robotics in electrical engineering technology (EET) programs at University and Community College. The curriculum and software developed by this collaboration of two- and four-year institutions match industry needs and provide a replicable model for programs around the US. The project also addresses the need for certified robotic training centers (CRTCs) and provides curriculum and training opportunities for students from other institutions, industry representatives, and displaced workers. Resources developed via this project were extensively disseminated through a variety of means, including workshops, conferences, and publications. In this article, authors provide final report on project outcomes, including various curriculum models and industry certification development, final stage of the “RobotRun” robotic simulation software, benefits of professional development opportunities for the faculty members from the other institutions, training workshops for K-12 teachers, and robotic one-day camps for high school students.

The Journal of Engineering Technology® (JET) is a refereed journal published semi-annually, in spring and fall, by the Engineering Technology Division (ETD) of the American Society for Engineering Education (ASEE). The aim of JET is to provide a forum for the dissemination of original scholarly articles as well as review articles in all areas related to engineering technology education.

Keith Vertanen Is PI on $225K NSF Grant, “Improving Mobile Device Input for Users Who are Blind or Low Vision”

Keith Virtanen
Keith Vertanen

Keith Vertanen (CS/ICC-HCC) is the principal investigator on a three-year project that has received a $225,663 research and development grant from the National Science Foundation. The project is entitled, “CHS: Small: Collaborative Research: Improving Mobile Device Input for Users Who are Blind or Low Vision.”

Abstract: Smartphones are an essential part of our everyday lives. But for people with visual impairments, basic tasks like composing text messages or browsing the web can be prohibitively slow and difficult. The goal of this project is to develop accessible text entry methods that will enable people with visual impairments to enter text at rates comparable to sighted people. This project will design new algorithms and feedback methods for today’s standard text entry approaches of tapping on individual keys, gesturing across keys, or dictating via speech. The project aims to:  1) help users avoid errors by enabling more accurate input via audio and tactile feedback, 2) help users find errors by providing audio and visual annotation of uncertain portions of the text, and 3) help users correct errors by combining the probabilistic information from the original input, the correction, and approximate information about an error’s location. Improving text entry methods for people who are blind or have low vision will enable them to use their mobile devices more effectively for work and leisure. Thus, this project represents an important step to achieving equity for people with visual impairments.

This project will contribute novel interface designs to the accessibility and human-computer interaction literature. It will advance the state-of-the-art in mobile device accessibility by: 1) studying text entry accessibility for low vision in addition to blind people, 2) studying and developing accessible gesture typing input methods, and 3) studying and developing accessible speech input methods.  This project will produce design guidelines, feedback methods, input techniques, recognition algorithms, user study results, and software prototypes that will guide improvements to research and commercial input systems for users who are blind or low-vision. Further, the project’s work on the error correction and revision process will improve the usability and performance of touchscreen and speech input methods for everyone.

Alex Sergeyev Wins ASEE Best Paper Award

Alex Sergeyev

College of Computing Professor Alex Sergeyev (DataS) presented his research article, “University, Community College and Industry Partnership: Revamping Robotics Education to Meet 21st Century Workforce Needs – NSF Sponsored Project Final Report,” at the 2019 American Society of Engineering Education (ASEE) annual conference, receiving the Best Paper Award in the Engineering Technology Division.

The conference took place June 16-19 in Tampa, Florida.

Co-authors of the publication are S. Kuhl, N. Alaraje, M. Kinney, M. HIghum, and P. Mehandiratta. The paper will be published in the fall issue of the prestigious Journal of Engineering Technology (JET).

Bo Chen Receives $250K NSF Award for Mobile PDE Systems Research

Bo Chen, CS

Bo Chen, assistant professor of computer science and member of the Institute of Computing and Cybersystems Center for  Cybersecurity, is the principal investigator on a project that has received a $249,918 research and development grant from the National Science Foundation. The project is entitled, “SaTC: CORE: Small: Collaborative: Hardware-Assisted Plausibly Deniable System for Mobile Devices.” This is a potential three-year project.

Abstract: Mobile computing devices typically use encryption to protect sensitive information. However, traditional encryption systems used in mobile devices cannot defend against an active attacker who can force the mobile device owner to disclose the key used for decrypting the sensitive information. This is particularly of concern to dissident users who are targets of nation states. An example of this would be a human rights worker collecting evidence of untoward activities in a region of oppression or conflict and storing the same in an encrypted form on the mobile device, and then being coerced to disclose the decryption key by an official. Plausibly Deniable Encryption (PDE) has been proposed to defend against such adversaries who can coerce users into revealing the encrypted sensitive content. However, existing techniques suffer from several problems when used in flash-memory-based mobile devices, such as weak deniability because of the way read/write/erase operations are handled at the operating systems level and at the flash translation layer, various types of side channel attacks, and computation and power limitations of mobile devices. This project investigates a unique opportunity to develop an efficient (low-overhead) and effective (high-deniability) hardware-assisted PDE scheme on mainstream mobile devices that is robust against a multi snapshot adversary. The project includes significant curriculum development activities and outreach activities to K-12 students.

This project fundamentally advances the mobile PDE systems by leveraging existing hardware features such as flash translation layer (FTL) firmware and TrustZone to achieve a high deniability with a low overhead. Specifically, this project develops a PDE system with capabilities to: 1) defend against snapshot attacks using raw flash memory on mobile devices; and 2) eliminate side-channel attacks that compromise deniability; 3) be scalable to deploy on mainstream mobile devices; and 4) efficiently provide usable functions like fast mode switching. This project also develops novel teaching material on PDE and cybersecurity for K-12 students and the Regional Cybersecurity Education Collaboration (RCEC), a new educational partnership on cybersecurity in Michigan.

Publications related to this research:

[DSN ’18] Bing Chang, Fengwei Zhang, Bo Chen, Yingjiu Li, Wen Tao Zhu, Yangguang Tian, Zhan Wang, and Albert Ching. MobiCeal: Towards Secure and Practical Plausibly Deniable Encryption on Mobile Devices. The 48th IEEE/IFIP International Conference on Dependable Systems and Networks (DSN ’18), June 2018 (Acceptance rate: 28%)
[Cybersecurity ’18] Qionglu Zhang, Shijie Jia, Bing Chang, Bo Chen. Ensuring Data Confidentiality via Plausibly Deniable Encryption and Secure Deletion – A Survey. Cybersecurity (2018) 1: 1.
[ComSec ’18 ] Bing Chang, Yao Cheng, Bo Chen, Fengwei Zhang, Wen Tao Zhu, Yingjiu Li, and Zhan Wang. User-Friendly Deniable Storage for Mobile Devices. Elsevier Computers & Security, vol. 72, pp. 163-174, January 2018
[CCS ’17] Shijie Jia, Luning Xia, Bo Chen, and Peng Liu. DEFTL: Implementing Plausibly Deniable Encryption in Flash Translation Layer. 2017 ACM Conference on Computer and Communications Security (CCS ’17), Dallas, Texas, USA, Oct 30 – Nov 3, 2017 (Acceptance rate: 18%)
[ACSAC ’15] Bing Chang, Zhan Wang, Bo Chen, and Fengwei Zhang. MobiPluto: File System Friendly Deniable Storage for Mobile Devices. 2015 Annual Computer Security Applications Conference (ACSAC ’15), Los Angeles, California, USA, December 2015 (Acceptance rate: 24.4%)
[ISC ’14] Xingjie Yu, Bo Chen, Zhan Wang, Bing Chang, Wen Tao Zhu, and Jiwu Jing. MobiHydra: Pragmatic and Multi-Level Plausibly Deniable Encryption Storage for Mobile Devices. The 17th Information Security Conference (ISC ’14), Hong Kong, China, Oct. 2014

Link to more information about this project:

Soner Onder Receives Year One Funding for $1.2M NSF SCALE Project

Soner Onder

Dave Whalley

Soner Onder, professor of computer science, was recently awarded $246,329 for the first year of a four-year NSF grant for his project, “SHF: Medium: Collaborative Research: Statically Controlled Asynchronous Lane Execution (SCALE).” The project is in collaboration with Prof. David Whalley of Florida State University. Michigan Tech is the lead institution in the project, it is expected to total $1.2 million, with Michigan Tech receiving $600,000.

Abstract: Enabling better performing systems benefits applications that span those running on mobile devices to large data applications running on data centers. The efficiency of most applications is still primarily affected by single thread performance. Instruction-level parallelism (ILP) speeds up programs by executing instructions of the program in parallel, with ‘superscalar’ processors achieving maximum performance. At the same time, energy efficiency is a key criteria to keep in mind as such speedup happens, with these two being conflicting criteria in system design. This project develops a Statically Controlled Asynchronous Lane Execution (SCALE) approach that has the potential to meet or exceed the performance of a traditional superscalar processor while approaching the energy efficiency of a very long instruction word (VLIW) processor. As implied by its name, the SCALE approach has the ability to scale to different types and levels of parallelism. The toolset and designs developed in this project will be available as open-source and will also have an impact on both education and research. The SCALE architectural and compiler techniques will be included in undergraduate and graduate curricula.

The SCALE approach supports separate asynchronous execution lanes where dependencies between instructions in different lanes are statically identified by the compiler to provide inter-lane synchronization. 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. These execution modes include explicit packaging of parallel instructions, parallel and pipelined execution of loop iterations, single program multiple data (SPMD) execution, and independent multi-threading.

This award reflects NSF’s statutory mission and has been deemed worthy of support through evaluation using the Foundation’s intellectual merit and broader impacts review criteria.

Keith Vertanen and Scott Kuhl Awarded $500K NSF Grant

Scott Kuhl
Scott Kuhl

Keith Vertanen
Keith Vertanen

Keith Vertanen, assistant professor of computer science (HCC), and Scott Kuhl (HCC), associate professor of computer science, are principal investigators of a recently funded three-year National Science Foundation grant for their project, “CHS: Small: Rich Surface Interaction for Augmented Environments.” The expected funding over three years is $499,552.00.

Vertanen and Kuhl are members of Michigan Tech’s Institute of Computing and Cybersystems (ICC) Center for Human-Centered Computing. A 2018 ICC research seed grant funded by ECE Alumnus Paul Williams was used to produce some of the preliminary results in the successful proposal. More info about the Williams Seed Grant can be found here:

A related video can be found here:

Abstract: Virtual Reality (VR) and Augmented Reality (AR) head-mounted displays are increasingly being used in different computing related activities such as data visualization, education, and training. Currently, VR and AR devices lack efficient and ergonomic ways to perform common desktop interactions such as pointing-and-clicking and entering text. The goal of this project is to transform flat, everyday surfaces into a rich interactive surface. For example, a desk or a wall could be transformed into a virtual keyboard. Flat surfaces afford not only haptic feedback, but also provide ergonomic advantages by providing a place to rest your arms. This project will develop a system where microphones are placed on surfaces to enable the sensing of when and where a tap has occurred. Further, the system aims to differentiate different types of touch interactions such as tapping with a fingernail, tapping with a finger pad, or making short swipe gestures.

This project will investigate different machine learning algorithms for producing a continuous coordinate for taps on a surface along with associated error bars. Using the confidence of sensed taps, the project will investigate ways to intelligently inform aspects of the user interface, e.g. guiding the autocorrection algorithm of a virtual keyboard decoder. Initially, the project will investigate sensing via an array of surface-mounted microphones and design “surface algorithms” to determine and compare the location accuracy of the finger taps on the virtual keyboard. These algorithms will experiment with different models including existing time-of-flight model, a new model based on Gaussian Process Regression, and a baseline of classification using support vector machines. For all models, the project will investigate the impact of the amount of training data from other users, and varying the amount of adaptation data from the target user. The project will compare surface microphones with approaches utilizing cameras and wrist-based inertial sensors. The project will generate human-factors results on the accuracy, user preference, and ergonomics of interacting midair versus on a rigid surface. By examining different sensors, input surfaces, and interface designs, the project will map the design space for future AR and VR interactive systems. The project will disseminate software and data allowing others to outfit tables or walls with microphones to enable rich interactive experiences.

GenCyber Camp for Teachers Garners Local Media Coverage

Michigan Tech hosted two week-long GenCyber camps this summer. The first, held June 17–21, 2019, hosted 30 local middle/high school students. The second camp, August 12–16, 2019, hosted 21 local K-12 teachers. Camp participants gained cybersecurity knowledge, understood correct and safe online behavior, and explored ways to deliver cybersecurity content in K-12 curricula.

A story about the GenCyber teacher camp was reported on August 16, 2019, by TV6: “GenCyber cyber security training camp comes to Michigan Tech” and on August 13, 2019, by the Keweenaw Report: “Teachers Learn How To Include Cybersecurity In Their Lessons.”

Learn more about the camps on the Institute of Computing and Cybersystems blog:

Susanta Ghosh is PI on $170K NSF Grant

Susanta Ghosh

Susanta Ghosh (ICC-DataS/MEEM/MuSTI) is Principal Investigator on a project that has received a $170,604 research and development grant from the National Science Foundation. The project is titled “EAGER: An Atomistic-Continuum Formulation for the Mechanics of Monolayer Transition Metal Dichalcogenides.” This is a potential 19-month project.

Dr. Ghosh is an assistant professor of Mechanical Engineering-Engineering Mechanics at Michigan Tech. Before joining the Michigan Tech College pof Engineering, Dr. Ghosh was an associate in research in the Pratt School of Engineering at Duke University; a postdoctoral scholar in the departments of Aerospace Engineering and Materials Science & Engineering at the University of Michigan, Ann Arbor; and a research fellow at the Technical University of Catalunya, Barcelona, Spain. His M.S. and Ph.D. degrees are from the Indian Institute of Science (IISc), Bangalore. His research interests include multi-scale solid mechanics, atomistic modeling, ultrasound elastography, and inverse problem and computational science.

Abstract: Two-dimensional materials are made of chemical elements or compounds of elements while maintaining a single atomic layer crystalline structure. Two-dimensional materials, especially Transition Metal Dichalcogenides (TMDs), have shown tremendous promise to be transformed into advanced material systems and devices, e.g., field-effect transistors, solar cells, photodetectors, fuel cells, sensors, and transparent flexible displays. To achieve broader use of TMDs across cutting-edge applications, complex deformations for large-area TMDs must be better understood. Large-area TMDs can be simulated and analyzed through predictive modeling, a capability that is currently lacking. This EArly-concept Grant for Exploratory Research (EAGER) award supports fundamental research that overcomes current challenges in large-scale atomistic modeling to obtain an efficient but reliable continuum model for single-layer TMDs containing billions of atoms. The model will be translational and will contribute towards the development of a wide range of applications in the nanotechnology, electronics, and alternative energy industries. The award will further support development of an advanced graduate-level course on multiscale modeling and organization of symposia in two international conferences on mechanics of two-dimensional materials. Experimental samples of TMDs contain billions of atoms and hence are inaccessible to the state-of-the-art molecular dynamics simulations. Moreover, existing crystal elastic models for surfaces cannot be applied to multi-atom thick 2D TMDs due to the presence of interatomic bonds across the atomic surfaces. The crystal elastic model aims to solve this problem by projecting all interatomic bonds onto the mid-surface to track their deformations. The actual deformed bonds will, therefore, be computed using the deformations of the mid-surface. Additionally, a technique will be derived to incorporate the effects of curvature and stretching of TMDs on their interactions with substrates. The model will be exercised to generate insights into the mechanical instabilities and the role of substrate interactions on them. The coarse-grained model will overcome the computational bottleneck of molecular dynamics models to simulate TMDs samples comprising billions of atoms. This award reflects NSF’s statutory mission and has been deemed worthy of support through evaluation using the Foundation’s intellectual merit and broader impacts review criteria.

Scalable Spectral Sparsification of Graph Laplacians and Integrated Circuits

Circuit board

Researcher: Zhuo Feng, Associate Professor, Electrical and Computer Engineering

Sponsor: National Science Foundation: SHF: Small

Amount of Support: $450,000

Duration of Support: 3 years

Abstract: This research is motivated by investigations on scalable methods for design simplifications of nanoscale integrated circuits (ICs). This is to be achieved by extending the associated spectral graph sparsification framework to handle Laplacian-like matrices derived from general nonlinear IC modeling and simulation problems. The results from this research may prove to be key to the development of highly scalable computer-aided design algorithms for modeling, simulation, design, optimization, as well as verification of future nanoscale ICs that can easily involve multi-billions of circuit components. The algorithms and methodologies developed will be disseminated to leading technology companies that may include semiconductor and Electronic Design Automation companies as well as social and network companies, for potential industrial deployments.

Spectral graph sparsification aims to find an ultra-sparse subgraph (a.k.a. sparsifier) such that its Laplacian can well approximate the original one in terms of its eigenvalues and eigenvectors. Since spectrally similar subgraphs can approximately preserve the distances, much faster numerical and graph-based algorithms can be developed based on these “spectrally” sparsified networks. A nearly-linear complexity spectral graph sparsification algorithm is to be developed based on a spectral perturbation approach. The proposed method is highly scalable and thus can be immediately leveraged for the development of nearly-linear time sparse matrix solvers and spectral graph (data) partitioning (clustering) algorithms for large real-world graph problems in general. The results of the research may also influence a broad range of computer science and engineering problems related to complex system/network modeling, numerical linear algebra, optimization, machine learning, computational fluid dynamics, transportation and social networks, etc.

More details.