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    Beth Veinott to Present Lecture February 12, 3 pm

    The Department of Computer Science will present a lecture by Dr. Elizabeth Veinott on Friday, February 12, 2021, at 3:00 p.m.

    Veinott is an associate professor in the Cognitive and Learning Sciences department. She will present, “Beyond the system interface: Using human-centered design to support better collaborative forecasting.”


    Speaker Biography

    Elizabeth Veinott is a cognitive psychologist working in technology-mediated environments to improve decision making, problem solving and collaboration. She directs Michigan Tech’s Games, Learning and Decision Lab and is the lead for the Human-Centered Computing group of Michigan Tech’s Institute of Computing and Cybersystems (ICC).

    She has been active in the ACM’s SIGCHI and on the conference organizing committees for CHI Play and CSCW. Prior to joining Michigan Tech in 2016, she worked as a principal scientist in an industry research and development lab and as a contractor at NASA Ames Research Center. Her research has been funded by NIH, Army Research Institute, Army Research Lab, Air Force Research Laboratory, and IARPA.

    Lecture Abstract

    Teams use technology to help them make judgments in a variety of operational environments. Collaborative forecasting is one type of judgment performed by analyst teams in weather, business, epidemiology, and intelligence analysis. Research related to collaborative forecasting has produced mixed results.

    In her talk, Veinott will describe a case of using cognitive task analysis to develop and evaluate a new forecast process and tool. The method captured analysts’ mental models of game-based forecasting problems, and allowed the process to co-evolve with the system design. The tool was tested in a simulation environment with expert teams conducting analyses over the course of hours and compared to a control group. Challenges and lessons learned will be discussed, including implications for human-centered design of collaborative tools.


    Sidike Paheding Wins MDPI Electronics Best Paper Award

    A scholarly paper co-authored by Assistant Professor Sidike Paheding, Applied Computing, is one of two papers to receive the 2020 Best Paper Award from the open-access journal Electronics, published by MDPI.

    The paper presents a brief survey on the advances that have occurred in the area of Deep Learning.

    Paheding is a member of the Institute of Computing and Cybersystems’ (ICC) Center for Data Sciences (DataS).

    Co-authors of the article, “A State-of-the-Art Survey on Deep Learning Theory and Architectures,” are Md Zahangir Alom, Tarek M. Taha, Chris Yakopcic, Stefan Westberg, Mst Shamima Nasrin, Mahmudul Hasan, Brian C. Van Essen, Abdul A. S. Awwal, and Vijayan K. Asari. The paper was published March 5, 2019, appearing in volume 8, issue 3, page 292, of the journal.

    View and download the paper here.

    Papers were evaluated for originality and significance, citations, and downloads. The authors receive a monetary award , a certificate, and an opportunity to publish one paper free of charge before December 31, 2021, after the normal peer review procedure.

    Electronics is an international peer-reviewed open access journal on the science of electronics and its applications. It is published online semimonthly by MDPI.

    MDPI, a scholarly open access publishing venue founded in 1996, publishes 310 diverse, peer-reviewed, open access journals.

    Paper Abstract

    In recent years, deep learning has garnered tremendous success in a variety of application domains. This new field of machine learning has been growing rapidly and has been applied to most traditional application domains, as well as some new areas that present more opportunities. Different methods have been proposed based on different categories of learning, including supervised, semi-supervised, and un-supervised learning. Experimental results show state-of-the-art performance using deep learning when compared to traditional machine learning approaches in the fields of image processing, computer vision, speech recognition, machine translation, art, medical imaging, medical information processing, robotics and control, bioinformatics, natural language processing, cybersecurity, and many others.

    This survey presents a brief survey on the advances that have occurred in the area of Deep Learning (DL), starting with the Deep Neural Network (DNN). The survey goes on to cover Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), including Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), Auto-Encoder (AE), Deep Belief Network (DBN), Generative Adversarial Network (GAN), and Deep Reinforcement Learning (DRL). Additionally, we have discussed recent developments, such as advanced variant DL techniques based on these DL approaches. This work considers most of the papers published after 2012 from when the history of deep learning began.

    Furthermore, DL approaches that have been explored and evaluated in different application domains are also included in this survey. We also included recently developed frameworks, SDKs, and benchmark datasets that are used for implementing and evaluating deep learning approaches. There are some surveys that have been published on DL using neural networks and a survey on Reinforcement Learning (RL). However, those papers have not discussed individual advanced techniques for training large-scale deep learning models and the recently developed method of generative models.

    Sidike Paheding


    Susanta Ghosh Publishes Paper in APS Physical Review B Journal

    Assistant Professor Susanta Ghosh, ME-EM, has published the article, “Interpretable machine learning model for the deformation of multiwalled carbon nanotubes,” in the APS publication, Physical Review B.

    Co-authors of the paper are Upendra Yadav and Shashank Pathrudkar. The article was published January 11, 2021.

    Ghosh is a member of the Institute of Computing and Cybersystems’ Center for Data Sciences.

    Article Abstract

    In the paper, researchers present an interpretable machine learning model to predict accurately the complex rippling deformations of multiwalled carbon nanotubes made of millions of atoms. Atomistic-physics-based models are accurate but computationally prohibitive for such large systems. To overcome this bottleneck, we have developed a machine learning model. The proposed model accurately matches an atomistic-physics-based model whereas being orders of magnitude faster. It extracts universally dominant patterns of deformation in an unsupervised manner. These patterns are comprehensible and explain how the model predicts yielding interpretability. The proposed model can form a basis for an exploration of machine learning toward the mechanics of one- and two-dimensional materials.

    APS Physics advances and diffuses the knowledge of physics for the benefit of humanity, promote physics, and serve the broader physics community.

    Physical Review B (PRB) is the world’s largest dedicated physics journal, publishing approximately 100 new, high-quality papers each week. The most highly cited journal in condensed matter physics, PRB provides outstanding depth and breadth of coverage, combined with unrivaled context and background for ongoing research by scientists worldwide.


    New NSF Project to Improve Great Lakes Flood Hazard Modeling

    Thomas Oommen, Timothy C. Havens, Guy Meadows (GLRC), and Himanshu Grover (U. Washington) have been awarded funding in the NSF Civic Innovation Challenge for their project, “Helping Rural Counties to Enhance Flooding and Coastal Disaster Resilience and Adaptation.”

    The six-month project award is $49,999.

    Vision. The vision of the new project is to develop methods that use remote sensing data resources and citizen engagement (crowdsourcing) to address current data gaps for improved flood hazard modeling and visualization that is transferable to rural communities.

    Objective. The objective of the Phase-1 project is to bring together community-university partners to understand the data gaps in addressing flooding and coastal disaster in three Northern Michigan counties.  

    The Researchers

    Thomas Oommen is a professor in the Geological and Mining Engineering and Sciences department. His research efforts focus on developing improved susceptibility characterization and documentation of geo-hazards (e.g. earthquakes, landslides) and spatial modeling of georesource (e.g. mineral deposits) over a range of spatial scales and data types. Oommen is a member of the ICC’s Center for Data Sciences.

    Tim Havens is associate dean for research, College of Computing, the
    William and Gloria Jackson Associate Professor of Computer Systems, and director of the Institute of Computing and Cybersystems. His research interests include mobile robotics, explosive hazard detection, heterogeneous and big data, fuzzy sets, sensor networks, and data fusion. Havens is a member of the ICC’s Center for Data Sciences.

    Guy Meadows is director of the Marine Engineering Laboratory (Great Lakes Research Center), the Robbins Professor of Sustainable Marine Engineering, and a research professor in the Mechanical Engineering-Engineering Mechanics department. His research interests include large scale field experimentation in the Inland Seas of the Great Lakes and coastal oceans; nearshore hydrodynamics and prediction; autonomous and semi-autonomous environmental monitoring platforms (surface and sub-surface); underwater acoustic remote sensing; and marine engineering.

    Himanshu Grover is an asssistant professor at University of Washington. His research focus is at the intersection of land use planning, community resilience, and climate change.

    About the Civic Innovation Challenge

    The NSF Civic Innovation Challenge is a research and action competition that aims to fund ready-to-implement, research-based pilot projects that have the potential for scalable, sustainable, and transferable impact on community-identified priorities.


    Havens Appointed First IEEE CIS Conference Publication Editor

    Timothy C. Havens, College of Computing, has been appointed as the first Conference Publication Editor of the IEEE Computational Intelligence Society (IEEE CIS).

    Havens is associate dean for research, College of Computing, the William and Gloria Jackson Associate Professor of Computer Systems, director of the Institute of Computing and Cybersystems (ICC), and a member of the ICC’s Center for Data Sciences.

    In this position, Dr. Havens will serve as the editor-in-chief for all publications of IEEE CIS conferences, including the flagship conferences IEEE International Joint Conference on Neural Networks (IJCNN), IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), IEEE Congress Evolutionary Computation (IEEE CEC), IEEE World Congress Computational Intelligence (WCCI), and IEEE Symposium Series on Computational Intelligence (SSCI).


    Bob Mark Business Model Pitch Competition Is January 28

    The virtual Bob Mark Business Model Pitch Competition takes place Thursday, January 28, 2021, from 5:30 to 7:30 p.m.

    Graduate and undergraduate students from across campus disciplines are invited to compete. When registering, contestants can choose the competition category, as this year two pitch competition categories are available.

    A tribute to the late Professor of Practice Bob Mark, College of Business, the Bob Mark Business Model Pitch Competition recognizes student entrepreneurial spirit.

    Faculty, staff, students, alumni, and the community are invited to attend this energized virtual pitch competition.

    Register to attend the Bob Mark Business Model Competition

    Register to compete in the Bob Mark Business Model Competition

    Category 1: Idea Pitch

    A two-minute idea pitch that presents a creative solution to a problem. Pitches will be evaluated on their uniqueness and the potential impactfulness.

    Category 2: Business Model Pitch

    A four-minute business model pitch which touches on the innovation technology, emphasizes product-market fit and the potential value it brings to the market. Prizes will be awarded to the most scalable and actionable business model pitches.  Participants in the Business Model Pitch category are encouraged to sign up for the Business Model Boot Camp workshop on January 20, 2021 https://bit.ly/HuskyInnovateBootcamp

    This event is hosted by Husky Innovate, a collaboration between Pavlis Honors College, the College of Business and the Office of Innovation and Commercialization. Prizes will be awarded by the College of Business, the MTEC SmartZone, and Husky Innovate.  

    Prizes include:

    Idea Pitch Category

    • First Prize: $125
    • Second Prize: $75
    • Third Prize: $50
    • Social Impact Award: $100 (sponsored by Dr. Ellie Asgari – COB Gates Professor)

    Business Model Category

    • First Prize: $2,000 (sponsored by Rick and Jo Berquist)
    • Second Prize: $1,000 
    • Third Prize: $500
    • Honorable Mention (2 prizes): $250 each Audience Favorite: $250
    • MTEC SmartZone Breakthrough Innovation Award: $1,000
    • Social Impact Award: $1,000 (sponsored by Dr. Ellie Asgari – COB Gates Professor)

    Husky Innovate is Michigan Tech’s innovation and entrepreneurship resource hub. The unit hosts free workshops, competitions, NSF I-Corps lean startup workshops, innovation talks, internships, mentorship, and the Silicon Valley Experience.


    Health Research Institute Panel Is January 25, 12 pm

    Michigan Tech’s Health Research Institute (HRI) will host a panel discussion on Monday, January 25, 2021,, from 12:00 to 1:00 p.m.

    Health research at Michigan Tech has been steadily growing for over 10 years. This growth has led to many practical uses for the technology developed.  Three researchers, Dr. Megan Frost (Kinesiology and Integrative Physiology), Dr. Bruce Lee (Biomedical Engineering), and Assistant Professor Dr. Weihua Zhou (College of Computing) will discuss their experiences with start-ups and applying their research to relevant health problems.


    Nathir Rawashdeh Presents, Publishes Research at Mechatronics Conference

    A conference paper published in IEEE Xplore entitled, “Interfacing Computing Platforms for Dynamic Control and Identification of an Industrial KUKA Robot Arm” has been published by Assistant Professor Nathir Rawashdeh, Applied Computing.

    In this work, a KUKA robotic arm controller was interfaced with a PC using open source Java tools to record the robot axis movements and implement a 2D printing/drawing feature.

    The paper was presented at the 2020 21st International Conference on Research and Education in Mechatronics (REM). Details available at the IEEE Xplore database.


    Shane Mueller to Present Lecture Jan. 22, 3 pm

    The Department of Computer Science will present a lecture, by Dr. Shane Mueller on Friday, January 22, 2021, at 3:00 p.m.

    Mueller is an associate professor in the Applied Cognitive Science and Human Factors program of the Cognitive and Learning Science department. His lecture is titled, “Explainable AI, and principles for building human-centered XAI systems.”

    Mueller’s research focuses on human memory and the representational, perceptual, strategic, and decisional factors that support it. He employs applied and basic research methodologies, typically with a goal of implementing formal quantitative mathematical or computational models of cognition and behavior.

    He is also the primary developer of the Psychology Experiment Building Language (PEBL), a software platform for creating psychology experiments.

    Mueller has undergraduate degrees in mathematics and psychology from Drew University, and a Ph.D. in cognitive psychology from the University of Michigan. He was a senior scientist at Klein Associates Division of Applied Research Associates from 2006 to 2011. His research has been supported by NIH, DARPA, IARPA, the Air Force Research Laboratory, the Army Research Institute, the Defense Threat Reduction Agency, and others.

    Lecture Title:

    Explainable AI, and principles for building human-centered XAI systems

    Lecture Abstract

    In recent years, Explainable Artificial Intelligence (XAI) has re-emerged in response to the development of modern AI and ML systems. These systems are complex and sometimes biased, but they nevertheless make decisions that impact our lives. XAI systems are frequently algorithm-focused; starting and ending with an algorithm that implements a basic untested idea about explainability. These systems are often not tested to determine whether the algorithm helps users accomplish any goals, and so their explainability remains unproven. I will discuss some recent advances and approaches to developing XAI, and describe how many of these systems are likely to incorporate many of the lessons from past successes and failures to build explainable systems. I will then review some of the basic concepts that have been used for user-centered XAI systems over the past 40 years of research. Based on this, I will describe a set of empirically-grounded, human user-centered design principles that may guide developers to create successful explainable systems.


    ICC Distinguished Lecture: James Bezdek, Jan 29, 3 pm

    The Institute of Computing and Cybersystems will present a Distinguished Lecture by James C. Bezdek on Friday, January 29, 2021, at 3:00 p.m. via online meeting. Dr. Bezdek will present his lecture, “Streaming Data Analysis: Old Clothes Don’t Fit.”

    Bezdek is a visiting research fellow at The University of Melbourne, Australia. His interests include clustering in big data, woodworking, optimization, data visualization, cigars, fishing, anomaly detection, blues music, poker. He retired in 2007, and will be coming to a university near you soon.

    Bezdek received a Ph.D. in Applied Mathematics from Cornell University in 1973. He is past president of NAFIPS (North American Fuzzy Information Processing Society), IFSA (International Fuzzy Systems Association), and the IEEE CIS (Computational Intelligence Society). He is founding editor the international journals Approximate Reasoning and IEEE Transactions on Fuzzy Systems. He is life fellow of the IEEE and IFSA; and a recipient of the IEEE 3rd Millennium award, the IEEE CIS Fuzzy Systems Pioneer award, and the IEEE Rosenblatt and Kampe de Feriet award.

    Lecture Title

    Streaming Data Analysis: Old Clothes Don’t Fit

    Lecture Abstract

    This talk concerns models and algorithms that are generally described as “streaming clustering.” Some of the semantics and methods that are used in this field are co-opted from static clustering. But often, they don’t serve their purposes for streaming data very well. A review of “state of the art” methods such as sequential k-means, Birch, CluStream, DenStream, etc. shows that methods borrowed from classical batch techniques don’t transfer well to the streaming data case. Most of these models fail to acknowledge that the data are seen but once in real streaming analysis (e.g., intrusion detection, quality control). When the data are not saved, batch clustering ideas such as pre-clustering assessment, partitioning, and cluster validity are not relevant. I do not argue that current approaches to streaming clustering are wrong: but they are described wrong. This class of algorithms comprises transitional methods for an intermediate case that lies between static and (near real time) dynamic analysis which will eventually lead to a new and useful paradigm for this type of computation. I call these methods start and stop streaming data analysis.

    Five models are briefly reviewed and illustrated (albeit poorly, with small labeled data sets!). Then I will discuss four new incremental Stream Monitoring Functions and a new approach for visual assessment of streaming data. The conclusions? Useful analysis of real streaming data is in its infancy. We need to carefully define the objectives of streaming analysis, and then choose terminology and methods that suit this evolving paradigm.

    Bezdek says his views on this topic are a bit controversial. You can read them here:

    Bezdek, J. C. and Keller, J. M. (2021). Streaming data analysis: Clustering or Classification?, IEEE Trans. SMC, DOI: 10.1109/TSMC.2020.3035957