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    Snehamoy Chatterjee Awarded R and D Contract from US DHHS

    Dr. Snehamoy Chatterjee (GMES/ICC-DataS) is the principal investigator (PI) on a project that has received a $288,343 research and development contract from the Department of Health and Human Services, Centers for Disease Control and Prevention, National Institute for Occupational Safety and Health (NIOSH).

    The project is titled “Mine Health and Safety Big Data Analysis and Text Mining by Machine Learning Algorithms.”

    Aref Majdara (ECE/ICC) is a co-PI on this potential two-year project.


    Mechatronics Machining System Topic of New Video


    Michigan Tech Professor Alex Sergeyev and Chinmay Kondekar, ’21 (M.S. in ECE/Mechatronics) discuss the Mechatronics degree programs and Kondekar’s final system design project, in a new video produced by the Applied Computing department. Watch the video below.

    The system machines patterns on blocks of foam using various robotic attachments, a tricky manufacturing process to program and one of the more challenging applications for an industrial robot.

    The interconnected system is flexible, reconfigurable, and controlled from a central control interface to emulate a production process. Correct dimensions are assured using machine vision, and by transporting the workpiece between different stations.

    A number of industrial applications are employed by the system, and most industrial robotic work cells have similar control and communication layouts. Manufacturing system layouts like this one are commonly found in the automotive, pharmaceutical, and food industries. Other potential applications include use in data acquisition and analytics, cybersecurity, and future projects requiring interconnected systems.

    Read a blog article about Kondekar’s final master’s program project.

    Watch the video.

    Mechatronics at Michigan Tech

    Learn more about Mechatronics.


    Michigan Tech Joins PSERC

    By Kimberly Geiger, College of Engineering, June 8, 2021

    Michigan Technological University has joined the Power Systems Engineering Research Center (PSERC) — a collaboration of university and industry members.

    “We are very pleased to be members of PSERC, where our researchers can combine efforts with other members to creatively address key challenges in creating a modern electric energy infrastructure,” stated Janet Callahan, dean of Michigan Tech’s College of Engineering. “Michigan Tech will be the 13th university in the partnership, and will bring three new industry partners into PSERC,” she added.

    Those partners are DTE, Consumers Energy and Hubbell. The full list of member universities is available on the PSERC website.

    “The overall goal of joining PSERC is to catalyze transdisciplinary research by teaming up with other institutions and relevant industry partners for national grant competition,” said Chee-Wooi Ten, associate professor of electrical and computer engineering at Michigan Tech. Ten will serve as Michigan Tech’s PSERC site director.

    Started as a National Science Foundation (NSF) Industry-University Cooperative Research Center (IUCRC), PSERC began in 1996 and was first led by Cornell professor Robert J. Thomas, and then Vijay Vittal of Arizona State University. Today PSERC is directed by Kory W. Hedman, professor of electrical and computer engineering at Arizona State University. 

    PSERC member expertise includes power systems, applied mathematics, complex systems, computing, control theory, power electronics, operations research, nonlinear systems, economics, industrial organization and public policy. 

    Michigan Tech brings much to the research collaborative, said Callahan, particularly in key areas of power systems engineering, social sciences and, most importantly, computing involved heavily in data science and cybersecurity. Cross-disciplinary interaction will be encouraged and expected, for example, with the University’s Department of Applied Computing where Ten holds an affiliated faculty position and where Hubbell is a member of the departmental industrial advisory board.

    Membership in PSERC will enable Michigan Tech to apply for seed grants together with other PSERC universities. Ten envisions Michigan Tech faculty members submitting seed grant proposals annually. “PSERC membership will enable Michigan Tech to go beyond its traditional research boundaries,” he said. “Historically, power area research at Michigan Tech focuses on the metering of electrical loads met by generation. We’ll see more opportunities that involve the intersection of new cross-disciplinary areas.”

    PSERC grants can also fund graduate student research, noted Callahan. “Any faculty member at Michigan Tech can submit proposals, but this is especially good news for assistant professors and other new faculty members seeking to establish a research program,” she said. “This aligns with our institutional Tech Forward initiatives and University vision to grow to 10,000 students, especially our graduate student population.”

    Members of PSERC typically meet in person three times per year with the PSERC  Industrial Advisory Board (IAB). This meeting provides a regular opportunity to build new and productive partnerships among faculty and students from other PSERC universities as well as with industrial partners.

    “These meetings are unparalleled, a regular opportunity to meet and mingle with energy researchers from other PSERC institutions. We’ll be able to brainstorm and discuss possible collaborations,” said Ten. “I am also very pleased to work with Kory Hedman, the new director of PSERC.”

    “While we are now part of the PSERC ecosystem that allows us to submit proposals, the work has only just begun,” Ten concluded. “I am looking forward to working with our PSERC members and creating value with Michigan Tech’s research strengths.”

    View the original article here.

    Michigan Technological University is a public research university, home to more than 7,000 students from 54 countries. Founded in 1885, the University offers more than 120 undergraduate and graduate degree programs in science and technology, engineering, forestry, business and economics, health professions, humanities, mathematics, and social sciences. Our campus in Michigan’s Upper Peninsula overlooks the Keweenaw Waterway and is just a few miles from Lake Superior.


    Mechatronics Master of Science, Class of ‘21


    The Michigan Tech Master of Science in Mechatronics, launched in 2019, has congratulated its first graduates this spring: Chinmay Kondekar (EE), Chukwuemeka George Ochieze, and Ahmat Oumar. Read their stories below.


    Ahmat Oumar


    Ahmat Oumar was very interested in finding an engineering discipline combining the new engineering principles of the age of automation.

    “I was looking for a discipline that will combine principles in electrical engineering, mechanical engineering and computer science,” he explains. “And the College of Computing Mechatronics program has been the right answer.”

    “Michigan Tech Mechatronics has been a great learning experience for me,” Oumar says. “The frequent lab practices to apply the principles learned in class especially enhanced my learning. This will make it easier to make a smooth transition into industry.”

    Oumar also credits his professors as instrumental in his success. “They make themselves available to students, not only in teaching and guiding, but also through mentoring.”


    Chukwuemeka George Ochieze


    Chukwuemeka George Ochieze—now enrolled in a Ph.D. program at Michigan Tech—checked out the College of Computing Mechatronics master of science program a few years ago.

    He was intrigued by the equipment available to students and the many research projects that could be accomplished within the program. Both aligned perfectly with his interests. And he regards the location of Michigan Tech and the region’s weather conditions as a good atmosphere for study.

    “Mechatronics is important in this century because every system consists of different subsystems that require a particular mechatronics application,” says Ochieze. “For example, people who work on fluid power systems should understand automation and controls.”

    Ochieze says that working with faculty and researchers in the various fields of mechatronics helped him to think differently with respect to the subject of application.

    “I pursued so many projects while here on campus,” he adds. “My work with wearable devices shaped my interests and allowed me to apply what I’ve learned so far in the Mechatronics program. His current focus is on the robotics field, which Ochieze says stems from his exposure to robotics and programming in the Mechatronics M.S. program.

    Ochieze was a mechatronics instructor in the 2020-21 academic year for the Career Technical Education (CTE) program in Mechatronics, recently launched by Michigan Tech and the Copper Country Intermediate School District (CCISD). The 12-month Career CTE program is for high school juniors or seniors. Read the story.

    And Ochieze tried many indoor and outdoor events that includes, “skiing, indoor and outdoor soccer, skating, tubing, winter carnivals, career fairs, late nights in the library trying to figure out projects, passport to the world, to mention but a few.”

    “Personally, I think the best memories I have was the career fairs events, having the opportunity to exchange information with people who have similar interests and also sharing your thoughts to people who have worked for a long time in the industry,” Ochieze says.

    Chinmay Kondekar


    Read Chinmay Kondekar’s story.

    Graduate student Chinmay Kondekar heard about Michigan Tech during his undergraduate studies. Sometime later he read a social media post about work opportunities in the robotic and automation labs, and Michigan Tech again came to his attention.

    “At that time, I was working as a controls engineer in India,” he says. “Robotics and automation interest me, and when I saw who had written the post (a former graduate student of Sergeyev’s), I knew I had found the perfect degree program.”

    Kondekar’s final design project was to create an interconnected system that is flexible, reconfigurable, and controlled from a central control interface to emulate a production process. The system is used to machine different patterns on a block of foam.

    “I enjoy solving problems and coming up with a solution to make things work,” he shares. “When starting the [final] project, I had a lot of unknown variables but I knew how to approach them and, eventually, I came up with solutions and made the system work. It’s highly rewarding to watch the finished system come together, and then to see it work automatically after pressing just three buttons.”

    Kondekar’s project would not have been possible without generous support from Mr. Mark Gauthier and his team at Donald Engineering. “Mark has helped the department acquire the best industry-grade hardware, and his expertise in pneumatics helped the project concept become reality,” Kondekar says.

    Kondekar says he has enjoyed his learning and life experiences at Michigan Tech. Plus, he loves the outdoors. “I am an outdoors guy and I love the UP, especially the summers. It’s full of good people and great beer!”


    Driving in the Snow is a Team Effort for AI Sensors

    by Allison Mills, University Marketing and Communications

    A major challenge for fully autonomous vehicles is navigating bad weather. Snow especially confounds crucial sensor data that helps a vehicle gauge depth, find obstacles and keep on the correct side of the yellow line, assuming it is visible. Averaging more than 200 inches of snow every winter, Michigan’s Keweenaw Peninsula is the perfect place to push autonomous vehicle tech to its limits.

    In two papers presented at SPIE Defense + Commercial Sensing 2021, researchers from Michigan Technological University discuss solutions for snowy driving scenarios that could help bring self-driving options to snowy cities like Chicago, Detroit, Minneapolis and Toronto.

    The team includes Nathir Rawashdeh and doctoral student Abu-Alrub (CC) as well as Jeremy Bos and student researchers Akhil Kurup, Derek Chopp and Zach Jeffries (ECE).

    Read more about their collaborative mobility research on mtu.edu/news.

    This MTU news story was published by Science DailyTechXploreKnowridge Science Report and other research news aggregators.


    Dr. Ali Yekkehkhany to Present Talk May 6


    Dr. Ali Yekkehkhany, a postdoctoral scholar at the University of California, Berkeley, will present a talk on Thursday, May 6, 2021, at 3:00 p.m.

    He will discuss adversarial attacks on the computation of reinforcement learning and risk-aversion in games and online learning.

    Dr. Yekkehkhany’s research interests include machine/reinforcement learning, queueing theory, applied probability theory and stochastic processes.

    Join the virtual talk here.

    Talk Title

    Adversarial Reinforcement Learning, Risk-Averse Game Theory and Online Learning with Applications to Autonomous Vehicles and Financial Investments

    Talk Abstract

    In this talk, we discuss:

    • a) Adversarial attacks on the computation of reinforcement learning: The emergence of cloud, edge, and fog computing has incentivized agents to offload the large-scale computation of reinforcement learning models to distributed servers, giving rise to edge reinforcement learning (RL). By the inherently distributed nature of edge RL, the swift shift to this technology brings a host of new adversarial attack challenges that can be catastrophic in safety-critical applications. A natural malevolent attack could be to contaminate the RL computation such that the contraction property of the Bellman operator is undermined in the value/policy iteration methods. This can result in luring the agent to search among suboptimal policies without improving the true values of policies. We prove that under certain conditions, the attacked value/policy iteration methods converge to the vicinity of the optimal policy with high probability if the number of value/policy evaluation iterations is larger than a threshold that is logarithmic in the inverse of a desired precision.
    • b) Risk-aversion in games and online learning: The fast-growing market of autonomous vehicles, unmanned aerial vehicles, and fleets in general necessitates the design of smart and automatic navigation systems considering the stochastic latency along different paths in a traffic network. To our knowledge, the existing navigation systems including Google Maps, Waze, MapQuest, Scout GPS, Apple Maps, and others are based on minimizing the expected travel time, ignoring the path delay uncertainty. To put the travel time uncertainty into perspective, we model the decision making of risk-averse travelers in a traffic network by an atomic stochastic congestion game and propose three classes of risk-averse equilibria. We show that the Braess paradox may not occur to the extent presented originally and the price of anarchy can be improved, benefiting the society, when players travel according to risk-averse equilibria rather than the Wardrop/Nash equilibrium. Furthermore, we extend the idea of risk-aversion to online learning; in particular, risk-averse explore-then-commit multi-armed-bandits. We use data from the New York Stock Exchange (NYSE) to show that the classical mean-variance and conditional value at risk approaches can come short in addressing risk-aversion for financial investments. We introduce new venues to study risk-aversion by taking the probability distributions into account rather than the summarized statistics of distributions.

    Biography

    Ali Yekkehkhany is a postdoctoral scholar with the Department of Industrial Engineering and Operations Research, University of California, Berkeley. He received his PhD and MSc degrees in Electrical and Computer Engineering from the University of Illinois, Urbana-Champaign (UIUC) in 2020 and 2017, respectively, and BSc degree in Electrical Engineering from Sharif University of Technology in 2014.

    He is the recipient of the “best poster award in recognition of high-quality research, professional poster, and outstanding presentation” in the 15th CSL Student Conference, 2020, and the “Harold L. Olesen award for excellence in undergraduate teaching by graduate students” in the 2019-2020 academic year at UIUC. He was chosen as “teachers ranked as excellent” twice and “teachers ranked as excellent and outstanding” twice at UIUC.

    His research interests include machine/reinforcement learning, queueing theory, applied probability theory and stochastic processes.


    Dr. Dukka KC, Wichita State, to Present Talk May 5


    Dr. Dukka KC, Electrical Engineering and Computer Science, Wichita State University, will present a talk on Wednesday, May 5, 2021, at 3:00 p.m.

    Dr. KC will discuss some past and ongoing projects in his lab related to machine learning/deep learning-based approaches for an important problem in Bioinformatics: protein post-translational modification.

    Join the virtual talk here.

    Talk Title

    Bioinformatics as an emerging field of Data Science: Protein post-translation modification prediction using Deep Learning

    Talk Abstract

    In this talk, I will be presenting about some of the past and ongoing projects in my lab especially related to Machine Learning/Deep Learning based approaches for one of the important problems in Bioinformatics – protein post-translational modification.

    Especially, I will focus on our endeavors to get away from manual feature extraction (hand-crafted feature extraction) from protein sequence, use of notion of transfer learning to solve problems where there is scarcity of labeled data in the field, and stacking/ensemble-based approaches.

    I will also summarize our future plans for using multi-label, multi-task and multi-modal learning for the problem. I will highlight some of the ongoing preliminary works in disaster resiliency. Finally, I will provide my vision for strengthening data science related research, teaching, and service for MTU’s college of computing.

    Biography

    Dr. Dukka KC is the Director of Data Science Lab, Director of Data Science Efforts, Director of Disaster Resilience Analytics Center and Associate Professor of Electrical Engineering and Computer Science (EECS) in the Department of EECS at Wichita State University. His current efforts are focused on application of various computing/data science concepts including but not limited to Machine Learning, Deep Learning, HPC, etc. for elucidation of protein sequence, structure, function and evolution relationship among others.

    He has received grant funds totaling $4.25M as PIs or Co-PIs, spanning 17 funded grants. He was the PI on the $499K NSF Excellence in Research project focused on developing Deep Learning based approaches for Protein Post-translational modification sites.

    He received his B.E. in computer science in 2001, his M.Inf. in 2003 and his Ph.D. in Informatics (Bioinformatics) in 2006 from Kyoto University, Japan. Subsequently he did a postdoc at Georgia Institute of Technology working on refinement algorithms for protein structure prediction. He then moved to UNC-Charlotte and did another postdoc working on functional site predictions in proteins. He was a CRTA Fellow in National Cancer Institute at National Institutes of Health where he was working on intrinsically symmetric domains.

    Prior to his arrival at WSU, he was associate professor and graduate program director in the Department of Computational Science and Engineering at North Carolina A&T State University.

    Dr. KC has published more than 30 journal and 20 conference papers in the field and is associate editor for two leading journals (BMC Bioinformatics and Frontiers in Bioinformatics) in the field. He also dedicates much of his efforts to K-12 education, STEM workforce development, and increasing diversity in engineering and science.


    Sidike Paheding, Applied Computing, Publishes Paper in IEEE Access

    A paper co-authored by Sidike Paheding, Applied Computing, has been published in the journal, IEEE Access. “Trends in Deep Learning for Medical Hyperspectral Image Analysis,” was available for early access on March 24, 2021.

    The paper discusses the implementation of deep learning for medical hyperspectral imaging.

    Co-authors of the paper are Uzair Khan, Colin Elkin, and Vijay Devabhaktuni, all with the Department of Electrical and Computer Engineering, Purdue University Northwest.

    Abstract

    Deep learning algorithms have seen acute growth of interest in their applications throughout several fields of interest in the last decade, with medical hyperspectral imaging being a particularly promising domain. So far, to the best of our knowledge, there is no review paper that discusses the implementation of deep learning for medical hyperspectral imaging, which is what this work aims to accomplish by examining publications that currently utilize deep learning to perform effective analysis of medical hyperspectral imagery.

    This paper discusses deep learning concepts that are relevant and applicable to medical hyperspectral imaging analysis, several of which have been implemented since the boom in deep learning. This will comprise of reviewing the use of deep learning for classification, segmentation, and detection in order to investigate the analysis of medical hyperspectral imaging. Lastly, we discuss the current and future challenges pertaining to this discipline and the possible efforts to overcome such trials.

    DOI: 10.1109/ACCESS.2021.3068392

    IEEE Access is a multidisciplinary, applications-oriented, all-electronic archival journal that continuously presents the results of original research or development across all of IEEE’s fields of interest. Supported by article processing charges, its hallmarks are a rapid peer review and publication process with open access to all readers.


    Emily Zhang Is ME-EM Graduate Seminar Speaker

    by Mechanical Engineering – Engineering Mechanics

    The next virtual Graduate Seminar Speaker will be held at 4 p.m. tomorrow (Feb. 25) via Zoom.

    Lan (Emily) Zhang (ECE) will present “Augmenting Radio Environments for better Wireless Ecosystems.”

    Zhang is a member of the Institute of Computing and Cybersystems’s (ICC) Center for Cyber-Physical Systems.


    Computing Programs Ranked Among Best in Nation

    Several Michigan Tech College of Computing degree programs have been ranked among the best in the nation by Intelligent.com. In addition, the research guide ranked the University number three among all colleges in Michigan.

    Intelligent.com looked at nearly 2,300 accredited colleges and universities nationwide making evaluations based on curriculum quality, graduation rate, reputation and post-graduate employment. Programs were evaluated on a scale of 0 to 100 with Michigan Tech making it to the final list for 12 separate degree programs.

    The four College of Computing programs and their national ranking as rated by Intelligent.com are:

    Additional Michigan Tech degree programs included in the ranking are: