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    Lan Zhang, ECE, to Present Lecture Jan. 15, 3 pm

    Assistant Professor Lan “Emily” Zhang, Electrical and Computer Engineering, will present her lecture, “Augmenting Radio Environments for Better Wireless Ecosystems,” on Friday, January 15, 2021, at 3:00 p.m., via online meeting.

    The lecture is hosted by the Michigan Tech Department of Computer Science. Zhang is a member of the Cyber-Physical Systems (CPS) research group of the Institute of Computing and Cybersystems (ICC).

    Zhang’s research interest span the fields of cyber-physical systems, distributed machine learning, wireless communications, and cybersecurity. In her talk, she will discuss a series of studies leveraging smart-surfaces, e.g., meta-surfaces or reconfigurable intelligent surfaces (RISs), to augment radio environments for various purposes.

    Lecture Abstract

    In the last several decades, wireless technologies have become well-established to fight against propagation obstacles. Most conventional efforts are focused on optimizing end devices, such as transmitters and receivers, in order to adapt to the given transmission environment for better communications. However, the recent rapid convergence of the cyber and physical worlds (Cyber-Physical Systems or CPSs) presents unprecedented challenges to the wisdom of conventional design. Given ever-growing service demands, as well as the diverse wireless application scenarios, it is critical to adaptively augment the radio environments in a cost-effective way, while maintaining the aesthetic nature of living environments.

    In her talk, Zhang will discuss a series of studies leveraging smart-surfaces–e.g., meta-surfaces or reconfigurable intelligent surfaces (RISs)–to augment radio environments for various purposes. Specifically, she will focus on three promising areas for enhancing the throughput and reliability of wireless communications, mitigating the physical-layer security threats, and facilitating wireless sensing activities. Both model-based and learning-based methods will be used for theoretical and practical analysis.

    Biography

    Dr. Lan Zhang is an assistant professor in the Department of Electrical and Computer Engineering at Michigan Tech. She received a Ph.D. degree in computer engineering from the University of Florida in 2020, and M.S. and B.Eng. degrees in telecommunication engineering from the University of Electronic Science and Technology of China in 2016 and 2013, respectively.

    Zhang has served as a technical program committee member for several respected conferences, such as NeurIPS-SpicyFL 2020 and the 2020 IEEE IFOCOM poster/demo section. She has also served as reviewer for leading journals, such as IEEE Transactions on Communications, IEEE Transactions on Vehicular Technology, IEEE Transactions on Mobile Computing, and IEEE Transactions on Wireless Computing.

    Lan Zhang, ECE


    Master’s Defense: Taylor Morris, CS, Tues., Jan. 5

    Computer Science graduate student Taylor Morris will present a Master’s Defense on Tuesday, January 5, from 6:00 to 7:00 p.m.

    Presentation Title: “Using Text Mining and Machine Learning Classifiers to Analyze Stack Overflow.”

    Advisor: Associate Professor Laura Brown, Computer Science

    Link to the Michigan Tech Events Calendar entry here.


    SURF Applications Open

    Applications for 2021 Summer Undergraduate Research Fellowships (SURF) are now open. Fellowship recipients will spend the summer working on an individual research project under the guidance of a Michigan Tech faculty mentor.

    SURFs are open to all Tech undergraduates who have at least one semester remaining after the summer term. Awards are up to $4,000. Applications are due by 4 p.m. Feb. 12.

    For more information and access to the application materials and instructions, visit the SURF webpage or contact Rob Handler.


    Research Day is Thurs., Jan. 7

    by Research Development

    The eighth annual research day event will be held Thursday (Jan. 7). We welcome research faculty from all ranks, research staff, postdocs, and staff who support research to join, learn, and share. The theme for the day is: Research Efficiency; Knowing the right things to optimize your research strategy.

    All information and sessions happening on Research Day can be accessed through the Research Day site.

    Interested participants are encouraged to RSVP for sessions here.


    Panel Discussion Jan. 5: Mobility at Michigan Tech: “Where are we?”

    Mobility is an increasingly used word today in conjunction with the advent of automated vehicle technologies, but what else is covered under this term that is often defined as“the ability to move or be moved freely and easily“? Even more importantly, what is happening at Michigan Tech related to Mobility? Dr. Pasi Lautala (CEE) is working as a Faculty Fellow sponsored by the Vice President for Research Office toward building a collaborative environment for Mobility-related development and research and expanding Michigan Tech’s role as a leader in the field. 

    As a kickoff event for these efforts, Dr. Lautala will be hosting a virtual panel discussion on Tuesday, January 5th, from 3:00-4:30 p.m. (EST).  This virtual event will bring together leading Mobility experts from our Michigan Tech community to discuss the wide range of issues addressed under the umbrella of Mobility. The panelists will start the event by briefly introducing how they and their teams are involved in Mobility, followed by an hour-long open discussion on Mobility and related issues. We encourage all university and local community members interested in Mobility to tune in and participate in the discussion. 
    The panelists will include:

    • Bill Buller,  Senior Research Scientist, Michigan Tech Research Institute (MTRI) 
    • Timothy Havens, William and Gloria Jackson Associate Professor of Computer Systems
    • Don LaFreniere, Associate Professor of Geography and GIS
    • Jeff Naber,  Ron and Elaine Starr Professor in Energy Systems, Mechanical Engineering—Engineering Mechanics
    • Chelsea Schelly, Associate Professor of Sociology, Social Sciences
    • Roman Sidortsov,  Assistant Professor, Energy Policy, Social Sciences

    This panel discussion is the first in a series of events related to Mobility planned for the spring semester, and will largely focus on the current state of Mobility at Michigan Tech.  Following events will seek to bring in external experts to share their insights and begin to develop building blocks that will lay the foundation for specific Mobility-related collaborative research proposals.

    To participate in the event, use the Zoom link provided below. For more information, please contact Pasi Lautala at ptlautal@mtu.edu.


    Robert West of DePauw University to Present Lecture Feb. 8

    Dr. Robert West, the Elizabeth P. Allen Distinguished University Professor, Department of Psychology and Neuroscience, DePauw University, will present a lecture on Monday, February 8, 2021, at 2:00 p.m., via online meeting.

    The title of Dr. West’s lecture is, “Why Josh Stole the Password: A Decision Neuorscience Approach to Insider Threat in Information Security.”

    The lecture is hosted by the Human-Centered Computing (HCC) research group of the Institute of Computing and Cybersystems (ICC) and the Department of Cognitive and Learning Sciences (CLS).

    Download

    Robert West Bio

    Dr. Robert West received his Ph.D from the University of South Carolina in Cognitive Development, and completed postdoctoral work at the Rotman Research Institute in Toronto, studying cognitive aging and cognitive neuroscience.

    He has been on faculty at the University of Notre Dame, Iowa State University, and is currently the Elizabeth P. Allen Distinguished University Professor in the Department of Psychology and Neuroscience at DePauw University. He is a fellow of the Association for Psychological Science, the Psychonomic Society, and the Midwestern Psychological Association; and a founding member of the NeuroIS Society.

    West’s research interests and publications span the areas of decision neuroscience, cognitive neuroscience of aging, and cognitive control. He has served as the associate editor for the Journals of Gerontology: Psychological Science, and is currently associate editor for Psychology and Aging.

    Lecture Abstract

    Cybercrime has a significant impact on nations, corporations, and individuals. Violations of information security can reduce consumer confidence and valuation at the corporate level, and jeopardize social and financial well-being at the personal level. In this talk, I will explore the findings of some of my recent research in order to demonstrate the utility of a decision neuroscience approach to providing insight into the neural correlates of ethical decision making in the context of information security.


    Sun Named to Lou and Herbert Wacker Professorship in Mechanical Engineering

    by Office of the Provost & Senior VP for Academic Affairs

    Ye “Sarah” Sun (ME-EM) has accepted the Lou and Herbert Wacker Professorship in Mechanical Engineering, which was created to retain and attract high-quality faculty who are at the top of their profession, can excite students to think beyond the classroom material, and knows how to integrate their research into the classroom.

    Sun was chosen for this position as she is recognized as a rising star and outstanding researcher in the area of wearable sensors, systems, and robotics and a respected member of the smart health community.

    In recognition of her innovative research in wearable sensors, Sun’s NSF CAREER award was selected for presentation to congressional offices in April 2019.

    Sun is the director of the Institute of Computing and Cybersystems’s Center for Cyber-Physical Systems.

    Among her research honors is the prestigious National Science Foundation (NSF) CAREER Research Award on “System-on-Cloth: A Cloud Manufacturing Framework for Embroidered Wearable Electronics.”

    Sun will use this recognition and support to enhance her research in wearable and soft robotics. Her goal is to develop flexible textile robotics by leveraging the physical understanding and modeling of textile materials and dynamics and the recent advances of morphological computing.

    Textile robotics are not only able to enhance human capabilities via wearable design but also achieve autonomous locomotion. The controllable structures of textiles directly provide a unified platform that is capable of integrating sensing and actuating into textile robotics itself. The positioning support will be used to recruit graduate students and to set up the manufacturing platform.


    1010 with … Nathir Rawashdeh, Weds., Dec. 16

    Nathir Rawashdeh (right) and Dan Fuhrmann, Interim Dean, Dept. of Applied Computing

    You are invited to spend one-zero-one-zero—that is, ten—minutes with Dr. Nathir Rawashdeh on Wednesday, December 16, from 5:30 to 5:40 p.m.

    Rawashdeh is assistant professor of applied computing in the College of Computing at Michigan Tech.

    He will present his current research work, including the using artificial intelligence for autonomous driving on snow covered roads, and a mobile robot using ultraviolet light to disinfect indoor spaces. Following, Rawashdeh will field listener questions.

    We look forward to spending 1010 minutes with you!

    Did you miss last week’s 1010 with Chuck Wallace? Watch the video below.

    The 1010 with … series will continue on Wednesday afternoons in the new year on January 6, 13, 20, and 27 … with more to come!


    Celebrate Husky Innovation January 25-29

    Husky Innovate is organizing Innovation Week, a series of innovation themed events the week of January 25 to 29, 2020. Our goal is to provide opportunities for students, faculty and alumni to meet virtually to engage around the topic of innovation.

    We will host panel discussions, alumni office hours and the Bob Mark Business Model Pitch Competition from 5:30 to 7:30 p.m. on Thursday, January 28.

    We will celebrate entrepreneurship, innovative research and projects on campus and within our extended MTU community.

    If you are interested in hosting an innovation tour, participating in a panel discussion, leading a workshop or something else, sign-up here.

    Faculty and staff are invited to celebrate innovation week with an innovation themed learning module or student activity.


    Siva Kakula to Present PhD Defense Dec. 21, 3 pm

    Graduate student Siva Krishna Kakula, Computer Science, will present his PhD defense, “Explainable Feature- and Decision-Level Fusion,” on Monday, December 21, 2020, from 3:00 to 5:00 p.m. EST Kakula is advised by Dr. Timothy Havens, College of Computing.

    Siva Kakula earned his master of science in computer engineering at Michigan Tech in 2014, and completed a bachelor of technology in civil engineering at IIT Guwahati in 2011. His research interests include machine learning, pattern recognition, and information fusion.

    Download the informational flier below.

    Lecture Abstract

    Information fusion is the process of aggregating knowledge from multiple data sources to produce more consistent, accurate, and useful information than any one individual source can provide. In general, there are three primary sources of data/information: humans, algorithms, and sensors. Typically, objective data—e.g., measurements—arise from sensors. Using these data sources, applications such as computer vision and remote sensing have long been applying fusion at different “levels” (signal, feature, decision, etc.). Furthermore, the daily advancement in engineering technologies like smart cars, which operate in complex and dynamic environments using multiple sensors, are raising both the demand for and complexity of fusion. There is a great need to discover new theories to combine and analyze heterogeneous data arising from one or more sources.

    The work collected in this dissertation addresses the problem of feature- and decision-level fusion. Specifically, this work focuses on Fuzzy Choquet Integral (ChI)-based data fusion methods. Most mathematical approaches for data fusion have focused on combining inputs relative to the assumption of independence between them. However, often there are rich interactions (e.g., correlations) between inputs that should be exploited. The ChI is a powerful aggregation tool that is capable modeling these interactions. Consider the fusion of N sources, where there are 2N unique subsets (interactions); the ChI is capable of learning the worth of each of these possible source subsets. However, the complexity of fuzzy integral-based methods grows quickly, as the fusion of N sources requires training 2N-2 parameters; hence, we require a large amount of training data to avoid the problem of over-fitting. This work addresses the over-fitting problem of ChI-based data fusion with novel regularization strategies. These regularization strategies alleviate the issue of over-fitting while training with limited data and also enable the user to consciously push the learned methods to take a predefined, or perhaps known, structure. Also, the existing methods for training the ChI for decision- and feature-level data fusion involve quadratic programming (QP)-based learning approaches that are exorbitant with their space complexity. This has limited the practical application of ChI-based data fusion methods to six or fewer input sources. This work introduces an online training algorithm for learning ChI. The online method is an iterative gradient descent approach that processes one observation at a time, enabling the applicability of ChI-based data fusion on higher dimensional data sets.

    In many real-world data fusion applications, it is imperative to have an explanation or interpretation. This may include providing information on what was learned, what is the worth of individual sources, why a decision was reached, what evidence process(es) were used, and what confidence does the system have on its decision. However, most existing machine learning solutions for data fusion are “black boxes,” e.g., deep learning. In this work, we designed methods and metrics that help with answering these questions of interpretation, and we also developed visualization methods that help users better understand the machine learning solution and its behavior for different instances of data.