Also In This Section
  • Categories

  • Recent News

  • Category: Lectures

    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.


    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!


    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

    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.


    Sarah Sun to Present ME-EM Graduate Seminar Dec. 3, 4 pm

    by Mechanical Engineering – Engineering Mechanics

    The next virtual Graduate Seminar Speaker will be held at 4 p.m. tomorrow (Dec. 3) via Zoom. Sarah Sun (ME-EM) will present “E-Logo: Embroidered Wearable Electronics.”

    Sun is an associate professor in the Department of Mechanical Engineering-Engineering Mechanics and an affiliated associate professor in the Department of Biomedical Engineering at Michigan Tech since 2014.


    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.


    ECE Doctoral Defense – Yongyu Wang

    by Electrical and Computer Engineering

    Computer Engineering doctoral candidate Yongyu Wang will defend at 10 a.m. Tuesday (Dec 1) via Zoom.

    The title of his presentation is “High-Performance Spectral Methods for Graph-Based Machine Learning.” Co-advisors are Chee-Wooi Ten (ECE) and Zhuo Feng (ECE).

    Chee-wooi Ten is a researcher with the ICC’s Cyber-Physical Systems group.


    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.