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  • Month: January 2020

    Guy Hembroff Awarded CCISD Contract for CTE Cybersecurity Course

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

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

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

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

    Learn more about the CCISD CTE program at:

    CNSA Major Vies for Winter Carnival Queen Honors

    Zack Metiva, a fourth-year Computer Network and System Administration (CNSA) major, is running for Winter Carnival Queen. Michigan Tech students can vote for Metiva on the Winter Carnival website at Voting closes on Friday, January 31.

    “I’d love to be your Winter Carnival Queen. I’m President of IT Oxygen Enterprise and the social chair of the drumline at Michigan Tech. I’m a fourth-year Computer Network and System Administration major and over the years I’ve grown to enjoy the snow. I love waking up in the morning and seeing a fresh dusting everywhere I look. My favorite winter activity is skiing and that constant supply of fresh powder makes Mont Ripley one of the best places to ski north of The Mighty Mac. Winter Carnival is a celebration of that snow as well as all of the great feats students at Michigan Tech can accomplish with it during some of the most brutal months of the year. I think that as long as they use their head and eat some bread, watch out for their friends, and stay hydrated — that means water — anyone should be able to feel like royalty during Winter Carnival. If you agree, vote for me. I’d like to thank the Huskies Pep Band, the most progressive drumline in the Keweenaw, for sponsoring me in this competition.”

    Metiva’s candidacy is sponsored by the Huskies Pep Band and Swift True Value Hardware, Houghton.

    Faculty Candidate Lan Zhang to Present Lecture February 5

    The Colleges of Computing and Engineering invite the campus community to a lecture by faculty candidate Lan Zhang on Wednesday, February 5, 2020, at 3:00 p.m., in Chem. Sci. 101. Zhang’s lecture is titled, “Machine Learning Enabled Better Cyber-Physical Systems: A Case Study on Better Networking for Connected Vehicles.”

    Bio: Lan Zhang is a Ph.D. candidate in the Department of Electrical and Computer Engineering at the University of Florida. She received the B.Eng. and M.S. degrees in telecommunication engineering from the University of Electronic Science and Technology of China, in 2013 and 2016, respectively. Zhang’s research interest spans across the fields of big data, cyber-physical systems, machine learning, wireless communications, and cybersecurity. She has published 15 technical papers in top-tier journals and conference venues, such as IEEE Transactions on Vehicular Technology, Proceedings of the IEEE, and IEEE Transactions on Wireless Communication.

    Zhang has served as a technical program committee (TPC) member for several high-quality conferences, such as the 2020 IEEE INFOCOM poster/demo section and the 2018 International Conference on Computing, Networking and Communications. She also serves as reviewer for several leading journals, such as IEEE Transactions on Communications, IEEE Transactions on Vehicular Technology, IEEE Transactions on Mobile Computing, and IEEE Transactions on Wireless Computing. Zhang was the speaker at several flagship celebrations and conferences, such as IEEE Global Communications Conference 19, Grace Hopper Celebration 19, and the IEEE International Conference on Communications 19.

    Lecture Abstract: With the recent success of big data analytics, machine learning is being used in various Cyber-Physical Systems (CPS) applications, such as smart transportation, smart healthcare, and industrial automation. As a highly interdisciplinary field, the CPS applications require the machine learning-enabled wireless communication strategies to facilitate information exchanges, and meanwhile call for secure and private learning pipelines to manage information exchanges.

    In her talk, Zhang focuses on connected vehicles, aiming at supporting the demand for multi-Gbps sensory data exchanges through millimeter-wave bands for enhancing (semi)-autonomous driving. Unlike most traditional networking analysis that manipulates end devices to adapt to the transmission environments, i.e., fight against any transmission obstacles, we propose an innovative idea to proactively manipulate, reconfigure, and augment the transmission environments for better communications.

    Without damaging the aesthetic nature of environments, we deploy multiple small-piece controllable reflecting surfaces, and adaptively manipulate the angle of the used reflecting surfaces to address the vulnerability of blockages in mmWave vehicular communications by creating alternative indirect line-of-sight connections. To autonomously and efficiently augment the highly dynamic vehicular environments in real-time, deep reinforcement learning techniques are implemented. Effectiveness of our proposal is showcased on the traffic at the City of Luxembourg using a traffic simulation toolkit, Simulation of Urban MObility (SUMO). 


    Computing Majors on GLIAC All-Academic Team

    Congratulations to College of Computing grad student Bernard Kluskens, Cybersecurity, and senior Robbie Watling, Computer Science, who are among 18 Michigan Tech students recognized on the 2019 GLIAC Men’s Cross Country All-Academic Excellence Team.

    Bernard Kluskens

    Robbie Watling

    Faculty Candidate Fan Chen to Present Lecture February 10

    The Colleges of Computing and Engineering invite the campus community to a lecture by faculty candidate Fan Chen, Monday, February 10, 2020, at 3:00 p.m., in Chem. Sci. 102. Chen’s talk is titled, “Efficient Hardware Acceleration of Unsupervised Deep Learning.”

    Chen is a Ph.D. candidate in the Department of Electrical and Computer Engineering, Duke University, where she is advised by Professor Yiran Chen and Professor Hai “Helen” Li. Her research interests include computer architecture, emerging nonvolatile memory technologies, and hardware accelerators for machine learning. Fan won the Best Paper Award and the Ph.D. forum Best Poster Award at ASP-DAC 2018. She is a recipient of the 2019 Cadence Women in Technology Scholarship.

    Abstract: Recent advances in deep learning are at the core of the latest revolution in various artificial intelligence (AI) applications including computer vision, autonomous systems, medicine, and other key aspects of human life. The current mainstream supervised learning relies heavily on the availability of labeled training data, which is often prohibitively expensive to collect and accessible to only a few industry giants. The unsupervised learning algorithm represented by Generative Adversarial Networks (GAN) is seen as an effective technique to obtain a learning representation from unlabeled data. However, the effective execution of GANs poses a major challenge to the underlying computing platform.

    In her talk, Chen will discuss her work that devises a comprehensive full-stack solution for enabling GAN training in emerging resistive memory based main memory. A zero-free dataflow and pipelined/parallel training method is proposed to improve resource utilization and computation efficiency. Hao will also introduce an inference accelerator that enables developed deep learning models to run on edge devices with limited resources. Finally, Hao’s lecture will discuss her vision of incorporating hardware acceleration for emerging compact deep learning models, large-scale decentralized training models, and other application areas.


    Faculty Candidate Cong “Callie” Hao to Present Lecture February 17

    The Colleges of Computing and Engineering invite the campus community to a lecture by faculty candidate Cong (Callie) Hao, Monday, February 17, 2020, at 3:00 p.m., in Chem Sci 102. Hao’s talk is titled, “NAIS: neural architecture and implementation search.”

    Dr. Hao is a postdoctoral researcher in the Department of Electrical and Computer Engineering (ECE) at University of Illinois at Urbana-Champaign (UIUC), under the supervision of Prof. Deming Chen. She holds a PhD (2017) degree in electrical engineering from Waseda University, and M.S. and B.S. degrees in computer science and engineering from Shanghai Jiao Tong University. Her research interests include high-performance reconfigurable computing, hardware-aware machine learning and acceleration, electronic design automation (EDA) tools, and autonomous driving.

    Abstract: In her talk, Hao introduces a neural network and hardware implementation co-search methodology, named NAIS, to pursue aggregated solutions of high accuracy DNN designs and efficient hardware deployments simultaneously. To enable a comprehensive co-search framework, there are three indispensable components: 1) efficient hardware accelerator design (e.g. FPGA); 2) hardware-aware neural architecture search (NAS); and 3) automatic design tools to quickly deploy DNNs to hardware platforms. I will discuss each component and their integrations to support an efficient and optimal NAIS implementation.