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    Kelly Steelman Co-Author of Paper in Brain and Behavior Journal

    Kay Tislar and Kelly Steelman (CLS, ICC-HCC)) are the authors of a paper accepted for publication in Brain and Behavior.

    The paper is titled “Inconsistent seduction: Addressing confounds and methodological issues in the study of the seductive detail effect.”

    preprint version of the paper is available for download.


    Michigan Tech Submits Record Number of Concept Papers to Federal Railroad Administration

    by Pasi Lautala

    Thomas Oommen (GMES, ICC), Ricardo Eiris, (CEGE, ICC), and Beth Veinott (CLS, ICC) are among eight Michigan Tech researchers who have submitted a a record number of eight concept papers for proposed research projects with the Federal Railroad Administration.

    The Federal Railroad Administration (FRA) requested that Michigan Tech submit a record number of eight concept papers for proposed research projects as part of their 2021 Broad Agency Announcement.

    In addition, Tech is a subcontractor for two more concept paper proposals. The paper submittal was coordinated by the Rail Transportation Program and the range of topics speaks to the diversity of Michigan Tech’s expertise applicable to the rail transportation. The PIs are looking forward to FRA decisions on how many of these papers advance to full proposals.

    Each of the 10 projects had a different principal investigator (PI), representing six university departments/institutes and several more co-PIs.

    The project titles and their PIs include:

    • Hyper- and Multi-spectral Sensing and Deep Learning for Automated Identification of Roadbed Condition, (PI, Thomas Oommen, GMES).
    • Wire Arc Additive Manufacturing (WAAM) for Weld Enhanced Cast Steel Coupler Knuckles (PI, Paul Sanders, MSE).
    • IoT Assisted Data-analytics Framework Enables Assessment of Location Based Ride Quality (LBRQ) (PI, Sriram Malladi, MEEM).
    • RailStory: Using Web-based Immersive Storytelling to Attract the Next Generation of Young Women in Rail (PI, Ricardo Eiris, CEGE).
    • A Risk Informed Decision-Making Framework for Coastal Railroad System Subjected to Storm Hazards and Sea Level Rise (PI, Yousef Darestani, CEGE).
    • Rail Corridor Life-Cycle Assessment (LCA) Framework, Factors and Models to Support Project Evaluation and Multi-Modal Comparisons (PI, Pasi Lautala, CEGE).
    • Development of Infrared Thermography for Effective Rail Weld Inspection (PI, Qingli Dai, CEGE).
    • Enabling Longer-distance, AI-enabled Drone-based Grade Crossing Assessment in Potentially GPS Denied Environments (PI, Colin Brooks).
    • Multi-Site Simulation to Examine Driver Behavior Impact of Integrated Rail Crossing Violation Warning (RCVW) and In-Vehicle Auditory/Visual Alert (IVAA) System (PI, Elizabeth Veinott, subcontract with Virginia Tech).
    • Evaluation of Non-traditional Methods of Reducing Emissions in Short Line Railroad Operations (PI, Jeremy Worm, subcontract with ASLRRA).



    Human Factors Grad Student Wins Hackathon, Cites Pandemic for Opportunity

    One Michigan Tech graduate student found a silver lining of the pandemic-driven shift to remote study: the ability to gain experiences previously prevented by distance. And “gained experience” is an understatement, as Brooke Poyhonen recently was on the winning team in the Texas Health Care Challenge, an online hackathon that sought solutions to problems in health care.

    The winning project, from Team WatsonCares, focused on women’s postpartum health and proposed a suite of services for new mothers:

    • A natural-language chatbot, powered by IBM Watson’s AI, to answer patient questions about both mental and physical health
    • A community feature allowing postpartum women to support one another
    • Deep informational and support resources

    Poyhonen said the team came together because after hearing initial “problem pitches,” in which existing teams outline the projects they want to tackle, some were uninterested in the originally pitched ideas. So they created their own team. “Ideally, we want the chatbot to be personalized to the patient’s history,” she said. “And we wanted to create a safe space for women to talk to each other.”

    Poyhonen will complete her accelerated M.S. in applied cognitive science and human factors this spring. She earned a B.S. in psychology from Michigan Tech in 2020. Both degrees are offered by the Cognitive and Learning Sciences department in the University’s College of Sciences and Arts.

    The Texas challenge is normally on-site only, and she appreciated the chance to participate and urges other students to seek out similar opportunities. “It was great to meet people from around the country and work with a team on a real-world goal,” Poyhonen said. “It’s a great networking opportunity and gives me a concrete project to discuss in interviews. It was just so rewarding.”

    The team’s prize included $120,000 in credits toward IBM products and services, a smaller cash award, and temporary office space with a Dallas venture capital firm. Poyhonen is working with team members on the project as a start-up while also pursuing other opportunities.

    She got her first taste of hackathons over the winter in the Work Related Musculoskeletal Disorders Grand Challenge, run by the American Registry for Diagnostic Medical Sonography. The challenge was to help the up to 90% of sonographers who develop disorders such as occupational overuse syndrome. Her team, which included a sonography mentor, an engineering student and two sonography students, created the Air Buddy, a device to help sonographers apply pressure to a probe with reduced physical stress. Poyhonen’s team won first place after judges deliberated for an entire week after the month-long window for teams to work on the problem.

    Kelly Steelman, interim chair of the Cognitive and Learning Sciences Department, said hackathons are great supplements to classroom experiences. “I commend Brooke for taking the initiative to seek out design challenges as a way to build her portfolio of experiences and hone the skills she’s learned in our program,” Steelman said. “Brooke took advantage of opportunities through outside organizations, but we also offer hack-a-thons right here on campus.”

    She said Husky Innovate is currently planning their inaugural hack-a-thon as part of an initiative to grow the human-centered design community at Michigan Tech. For more information on this, contact Lisa Casper.

    Dr. Steelman is a member of the Human-Centered Computing research group of the Institute of Computing and Cybersystems (ICC).

    Michigan Tech’s graduate program in Applied Cognitive Science and Human Factors teaches students how to apply principles of psychology to the design and evaluation of human-technological systems. Steelman said Beth Veinott, director of the Center for Human-Centered Computing, frequently reinforces for students that, “If you get the psychology right first, you design the right system, it is easier to train, and people are more likely to adopt it.”


    CS Lecture: Kelly Steelman, CLS, March 19, 3 pm

    The Department of Computer Science will present a lecture by Dr. Kelly Steelman, Cognitive and Learning Sciences, on Friday, March 19, 2021, at 3:00 p.m.

    The title of the lecture is, “Keeping Up with Tech.”

    Join the virtual lecture here.

    Steelman is interim department chair and associate professor in the Department of Cognitive and Learning Sciences. Her research interests include basic and applied attention, models of attention, human performance in aviation, display design, tech adoption, and technology training.

    Lecture Title

    “Keeping Up with Tech”

    Lecture Abstract

    COVID has revealed much in the past year, including our dependence on technology and the challenges that many of us experience trying to keep up with it. Dr. Kelly Steelman has spent the past 15 years studying human attention and applying it to support the introduction of new technologies in contexts ranging from aviation to education.

    In her presentation, Steelman will provide an overview of her research, using examples from Next Gen Aviation and the BASIC Digital Literacy Training Program to illustrate how understanding human attention can help us predict the consequences of introducing new technology, improve the design of technology, and support training to help people keep up with the rapid pace of technological change.



    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.


    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.