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    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.

    College of Computing Convocation is December 18, 3:30 pm

    Congratulations, Class of 2020!

    We are looking forward to celebrating the accomplishments of our graduates at a Class of 2020 virtual Convocation program on Friday, December 18, 2020, at 3:30 p.m. EST.

    The celebration will include special well-wishes from CC faculty and staff, and many will be sporting their graduation regalia. It is our privilege to welcome Ms. Dianne Marsh, 86, ’92, as our Convocation speaker. Dianne is Director of Device and Content Security for Netflix, and a member of the new College of Computing External Advisory Board.

    We may be spread across the country and world this December, but we can still celebrate with some style. We look forward to sharing our best wishes with the Class of 2020 and wishing them continued success as they embark on the next phase of their lives!

    This December, 40 students are expected to graduate with College of Computing degrees, joining 92 additional Class of 2020 PhD, MS, and BS alumni.

    Dianne Marsh ’86, ’92 is Director of Device and Content Security for Netflix. Her team is responsible for securing the Netflix streaming client ecosystem and advancing the platform security of Netflix-enabled devices. Dianne has a BS (’86) and MS (’92) in Computer Science from Michigan Tech.

    Visit the Class of 2020 Webpage

    Congratulations Graduates. We’re proud of you.

    1010 Minutes with … Chuck Wallace

    Chuck Wallace, center, at a BASIC computer tutoring session at Portage Lake District Library, Houghton.

    You are invited to spend one-zero-one-zero—that is, 10 minutes—with Dr. Charles Wallace on Wednesday, December 9, from 5:30 to 5:40 p.m.

    Wallace is associate dean for curriculum and instruction and associate professor of computer science in the College of Computing at Michigan Tech. Wallace is a researcher with the ICC’s Human-Centered Computing and Computing Education research groups.

    In his informal discussion, Dr. Wallace will talk about computing at Michigan Tech, his research on how humans can better understand, build, and use software, and answer your questions.

    We look forward to spending 1010 minutes with you!

    Join 1010 with Chuck Wallace here.

    Next week, on Wednesday, December 15, at 5:30 p.m., Assistant Professor Dr. Nathir Rawashdeh, Applied Computing, will present his current research work, including his use of artificial intelligence for autonomous driving on snow covered roads, and a mobile robot using ultraviolet light to disinfect indoor spaces.

    Did you miss 1010 with Chuck Wallace on December 9? Watch the video below.

    1010 with … Chuck Wallace, Assoc. Prof, Computer Science, December 9, 2020.

    Master’s Defense: Ann Ciesla, Computer Science

    Computer Science graduate student Ann Ciesla will present her master’s defense on Tuesday, December 1, 2020 at 5:00 p.m. The presentation is titled, “Digital Skills Assessment: A Tool for Assessing the Digital Literacy of Older Adults.”

    Ciesla is advised by Associate Professor Charles Wallace, Computer Science.

    Sidike Paheding Lecture is Dec. 11, 3 pm

    Assistant Professor Sidike Paheding, Applied Computing, will present his lecture, “Deep Neural Networks for UAV and Satellite Remote Sensing Image Analysis,” on Dec. 11, 2020, at 3:00 p.m. via online meeting.

    Paheding’s research focuses on the areas of computer vision, machine learning, deep learning, image/video processing, and remote sensing.

    The lecture is presented by the Department of Computer Science.

    Lecture Abstract

    Remote sensing data can provide non-destructive and instantaneous estimates of the earth’s surface over a large area, and has been accepted as a valuable tool for agriculture, weather, forestry, defense, biodiversity, etc. In recent years, deep neural networks (DNN), as a subset of machine learning. for remote sensing has gained significant interest due to advances in algorithm development, computing power, and sensor systems.

    This talk will start with remote sensing image enhancement framework, and then primarily focuses on DNN architectures for crop yield prediction and heterogeneous agricultural landscape mapping using UAV and satellite imagery.

    Speaker Biography

    Paheding is an associate editor of the Springer journal Signal, Image, and Video Processing, ASPRS Journal Photogrammetric Engineering & Remote Sensing, and serves as a guest editor/reviewer for a number of reputed journals. He has advised students at undergraduate, M.S., and Ph.D. levels, and authored/coauthored close to 100 research articles.

    Bo Chen, CS, Wins REF Grant for Decentralized Cloud Storage Project

    Bo Chen, Computer Science, has been awarded a Fall 2020 REF Research Seed Grant (REF-RS) for his project, “Towards Secure and Reliable Decentralized Cloud Storage.” Funding for the 12-month, $25,800 award begins on January 1, 2021.

    “This grant will provide significant help to advance my current research,” says Chen. “This is really exciting news for me.”

    Bo Chen is a researcher with the ICC’s Cybersecurity and Computing Education research groups.

    As a recipient of the REF seed grant, which is awarded by the Michigan Tech Office of the Vice President for Research, Chen will participate in review and feedback for the next round of REF proposals. View the full list of Fall 2020 REF award recipients here.


    A decentralized cloud storage system eliminates the need of dedicated computing infrastructures by allowing peers which have spare storage space to join the network and to provide storage service. Compared to the conventional centralized cloud storage system, it can bring significant benefits including cheaper storage cost, better fault tolerance, greater scalability, as well as more efficient data storing and retrieval, making it well fit the emerging Internet of things (IoT) applications.

    While bringing immense benefits, the decentralized cloud storage system also raises significant security concerns, since the storage peers are much less reputable than the traditional data centers and may more likely misbehave.

    This project thus aims to build a secure and reliable decentralized cloud storage system which can serve as the cloud infrastructure for future IoT applications. The project will actively investigate two fundamental security issues faced by the decentralized cloud storage system: 1) How can we prevent the malicious storage peers from stealing the data? 2) How can we ensure that once the data are stored into the system, they are always retrievable even if the storage peers misbehave?

    To address the aforementioned issues in an untrusted p2p environment, the PI will integrate efficient integrity checking with the blockchain, as well as the broadly equipped secure hardware like Intel SGX. The PI will also broaden the educational impact of the proposed project by actively involving both graduate and undergraduate students from the MTU cybersecurity programs.

    Research Excellence Fund Awards Announced

    by Vice President for Research Office

    The Vice President for Research Office announces the Fall 2020 REF awards. Thanks to the individual REF reviewers and the REF review panelists, as well as the deans and department chairs, for their time spent on this important internal research award process.

    Research Seed Grants:

    • Sajjad Bigham, Mechanical Engineering-Engineering Mechanics
    • Bo Chen, Computer Science
    • Daniel Dowden, Civil and Environmental Engineering
    • Ana Dyreson, Mechanical Engineering-Engineering Mechanics
    • Hassan Masoud, Mechanical Engineering-Engineering Mechanics
    • Xinyu Ye, Civil and Environmental Engineering

    MTRI Research Scientist Joel LeBlanc to Present Lecture Dec. 4, 3 pm

    Senior Research Scientist Joel LeBlanc of Michigan Tech Research Institute (MTRI) will present his lecture, “Testing the Validity of Physical (Software) Models in Inverse Problems,” on Friday, December 4, 2020, at 3:00 p.m. via online meeting.

    The lecture is presented by the Michigan Tech Department of Computer Science.

    Lecturer Bio

    LeBlanc has a Ph.D. in Statistical Signal Processing. His areas of expertise include statistical signal processing, applied nonconvex optimization, EO/IR imaging, and Synthetic Aperture Radar (SAR) imaging. His research interests are in information theoretic approaches to inverse-imaging, computational techniques for solving large inverse problems, and fundamental limits of sensing.

    Lecture Abstract

    Numerical simulations are the modern analog of the “physical system” referenced by Rosenblueth and Wiener in their 1945 paper “The Role of Models in Science.” This talk will introduce the inverse-problem approach for making inferences about the physical world and discuss how the Maximum Likelihood (ML) principle leads to both performant estimators and algorithm agnostic bounds on performance. The resulting estimators and associated bounds are only valid when global convergence is achieved, so I will present new results on global convergence testing that I believe are widely applicable. Finally, I will discuss some of my ongoing research interests: optimal resource allocation and testing for adversarial behavior through model relaxation.

    Michigan Tech Research Institute focuses on technology development and research to sense and understand natural and human-made environments. Through innovation, education, and collaboration, the Institute supports meaningful solutions to critical global issues, from infrastructure to invasive species, national security to public health.

    Briana Bettin, Part II: Research, Mentors, Creative Energy

    Briana Bettin, front, far right, with fall 2019 Computer Science dept. teaching assistants

    Michigan Tech 2020 Ph.D. graduate Briana Bettin, Computer Science, is among six new faculty members the College of Computing welcomed this fall. Bettin is an assistant professor for the Department of Computer Science and the Cognitive and Learning Sciences department.

    This semester, she is teaching courses including CS1121 Introduction to Programming in C/C++, and pursuing research and other projects with faculty and students.

    In this, Part II of this profile of Briana Bettin, Bettin and her faculty mentors talk research, education, and novel ideas.

    Read the first installment of this article, ‘Briana Bettin, Asst. Prof., Part I: Neopets, HTML, Early Success Part I”, published Oct. 28, 2020, here.

    Mental models, constructing knowledge, programming analogies.

    Briana Bettin’s research interests are many. They include user experience, human factors, human-computer interactions, mental models, information representation, rural digital literacy, education, engagement, retention, and digital anthropology. Her Ph.D. dissertation aims to better understand how novice programmers approach learning programming, and how their construction of programming ideas might be better facilitated.

    “I delve into mental models research and explore theories for how students might construct knowledge,” she explains. “Specifically, the major studies in my dissertation explore how prior applicable knowledge might allow for transfer to programming concepts, which can feel very novel to students who have never programmed before.”

    Bettin is also exploring methods for designing programming analogies, testing their application in the classroom, and observing how their use may impact student understanding of specific topics. “I take a very user experience-oriented approach, and work to apply methods and ideas from user-experience research in the CS classroom space,” she says.

    Creative energy, insight, and humanity.

    With Computer Science department faculty members Associate Professor Charles Wallace and Assistant Professor Leo Ureel, Bettin has worked on projects studying how novice programmers communicate. She and Ureel also worked on several ideas in the introductory CS classrooms, including exploring pair programming obstacles in the classroom and in research.

    “I got to know Dr. Wallace during my Ph.D., and I love getting his perspective on research ideas,” Bettin says. “He has so many interesting ideas, and he’s so fun to talk to!”

    “Briana brings loads of creative energy, insight, and humanity to everything she does,” says Wallace. “We are very fortunate to have her with us.”

    Passionate about Computing Education.

    Other research collaborators include Lecturer Nathan Manser, Geological and Mining Engineering and Sciences, and Senior Lecturer Michelle Jarvie-Eggart, Engineering Fundamentals, College of Engineering, with whom Bettin has explored topics in technology acceptance across engineering and computer science.

    “Briana has been an enthusiastic addition to our research group,” Jarvie-Eggart says, who is working with Steelman and Wallace on improving engineering students’ acceptance of programming. “She really is amazing!”

    Jarvie-Eggart sat in on Bettin’s Intro to Programming class in fall 2019, and noted that Bettin’s. approach of teaching algorithmic thinking and logic—before students begin programming—helps make programming more accessible to all.

    “It builds foundational knowledge from the ground up,” she says. “Our research team is very excited about using her progressive CS education approaches to teach engineers programming.”

    Stefka Hristova, in Michigan Tech Humanities, has always been supportive, helping me cultivate an interdisciplinary research vision and voice,” Bettin says. “Dr. Robert Pastel has also been so valuable in helping me approach my research with strong design. He has given me a lot of insight and I am so appreciative!”

    “Briana is passionate about Computing Education, and she is invested in infusing equity and diversity into the STEM field,” Hristova says.

    In Part III of this article, to be published soon, Briana Bettin talks about peer mentors and friends … and they say a few words, too.

    Read the first installment of this article, ‘Briana Bettin, Asst. Prof., Part I: Neopets, HTML, Early Success Part I”, here.

    Junqiao Qiu to Present Lecture November 6

    Assistant Professor Junqiao Qiu, Computer Science, will present his lecture, “Speculative Parallelization for FSM-centric Computations,” this Friday, Nov. 6, 2020, at 3:00 p.m., via online meeting.

    Lecture Abstract

    As a fundamental computation model, finite-state machine (FSM) has been used in a wide range of data-intensive applications, including malware detection, bioinformatics, semi-structured data analytics, natural language processing and even machine learning. However, FSM execution is known to be “embarrassingly sequential” due to the state dependences among transitions. Current studies find that speculation is a promising solution to address the inherent dependencies in FSM computations and thus enables scalable FSM parallelization.
    This talk will firstly introduce the fundamental scalability bottleneck in the current FSM parallelization, and then an aggressive speculation, a generalized speculation model that allows a speculated state to be validated against the result from another speculation, is proposed to address the scalability limitations. Finally, this talk will discuss the possibility to enlarge the applicability of the proposed approach and go beyond the FSM-based computations.

    Juneiao Qiu is a member of the Institute of Computing and Cybersystems’ (ICC) Center for Scalable Architectures and Systems (SAS).