{"id":117,"date":"2025-01-17T10:48:35","date_gmt":"2025-01-17T18:48:35","guid":{"rendered":"https:\/\/labs.wsu.edu\/cimpi\/?page_id=117"},"modified":"2026-08-18T00:15:52","modified_gmt":"2026-08-18T07:15:52","slug":"education-outreach","status":"publish","type":"page","link":"https:\/\/labs.wsu.edu\/cimpi\/education-outreach\/","title":{"rendered":"Education & Outreach"},"content":{"rendered":"
\n
\n

Education & Outreach<\/p>\n

Education and Outreach<\/h1>\n

Coursework, mentorship, and the CIMPI Lab’s summer research program for students new to AI and scientific computing.<\/p>\n<\/div>\n

\n

Teaching<\/h2>\n
    \n
  • ME501: Continuum Mechanics (Fall 2024)<\/li>\n
  • ME\/MSE 241: Engineering Computations (Spring 2025, Spring 2026)<\/li>\n
  • ME483: Machine Learning for Engineering Applications (Fall 2025)<\/li>\n
  • ME579: AI in Manufacturing (Fall 2026)<\/li>\n<\/ul>\n<\/div>\n
    \n

    Summer Program in Research, Education, and Mentoring (SUPREME)<\/h2>\n

    SUPREME is a free, two-week summer course run by the CIMPI Lab, designed primarily for undergraduate students though open to and regularly attended by graduate students as well. Each cohort works through hands-on tutorials and a project-based curriculum in small classes with direct mentorship from the lab. SUPREME will return in 2027 \u2014 updates will be posted on this page.<\/p>\n

    \n

    2026 \u00b7 AI in Manufacturing<\/p>\n

    SUPREME 2026<\/h3>\n

    The 2026 cohort met every weekday from 9:00\u201311:00 AM (PDT), June 1\u2013June 12, for a course on AI in manufacturing. Applications closed April 15, 2026.<\/p>\n

    Program outline<\/h4>\n
    \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
    Day<\/th>\nLecture theme<\/th>\nCore topics covered<\/th>\nHands-on tutorial<\/th>\n<\/tr>\n<\/thead>\n
    1, M<\/td>\nAI in manufacturing \u2014 the big picture<\/td>\nManufacturing AI problem types: quality control, predictive maintenance, anomaly detection, process control. Industry 4.0 context.<\/td>\nPython review tutorial, dataset cleaning and preparation, feature engineering, sensor-to-insight pipeline for predictive maintenance<\/td>\n<\/tr>\n
    2, T<\/td>\nPractice, TA hours, Q&A, mini-project progress on dataset preparation<\/td>\n<\/tr>\n
    3, W<\/td>\nSupervised learning methods<\/td>\nDecision trees, random forests, support vector machines, regularization, model selection<\/td>\nDefect prediction and remaining useful life (RUL)<\/td>\n<\/tr>\n
    4, Th<\/td>\nPractice, TA hours, Q&A, mini-project progress on supervised learning<\/td>\n<\/tr>\n
    5, F<\/td>\nUnsupervised learning methods<\/td>\nClustering, dimension reduction using PCA, SVD<\/td>\nAnomaly detection in production lines<\/td>\n<\/tr>\n
    6, M<\/td>\nDeep learning methods<\/td>\nIntro to feed-forward neural networks, convolutional neural networks<\/td>\nVisual quality inspection with CNNs<\/td>\n<\/tr>\n
    7, T<\/td>\nPractice, TA hours, Q&A, mini-project progress on unsupervised learning & deep learning<\/td>\n<\/tr>\n
    8, W<\/td>\nReinforcement learning<\/td>\nMarkov decision processes, reward functions, Q-learning, policy gradient methods (PPO)<\/td>\nProcess control optimization (closed-loop control)<\/td>\n<\/tr>\n
    9, Th<\/td>\nPractice, TA hours, Q&A, mini-project progress on reinforcement learning<\/td>\n<\/tr>\n
    10, F<\/td>\nMini-project presentations<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<\/div>\n

    Cohort of 2026<\/h4>\n
      \n
    1. Parsa Akbari (PhD, MME, WSU)<\/li>\n
    2. Stan Teagho (MME, WSU)<\/li>\n
    3. Erroll Aaron (UG, ChemE, WSU)<\/li>\n
    4. Christopher Munson (UG, CSE, Wash U St Louis)<\/li>\n
    5. Ted Charles Norton (UG, Honors College, WSU)<\/li>\n
    6. Michael B. Myers (PhD, MME, WSU)<\/li>\n
    7. Nathan W. Zuckschwerdt (PhD, MME, WSU)<\/li>\n
    8. Bryon Nicholas White (PhD, MME, WSU)<\/li>\n
    9. Wyatt Ballweber (UG, MME, WSU)<\/li>\n
    10. Matthew Schlichting (UG, MME, WSU)<\/li>\n
    11. Katana Mehtabel (UG, MME, WSU)<\/li>\n
    12. Benjamin Polovnikoff (UG, MME, WSU)<\/li>\n
    13. Colten A. Ladd (UG, MME, WSU)<\/li>\n
    14. Rayman Angulo Gutierrez<\/li>\n
    15. Akash Kanji (UG, MME, Jadavpur University)<\/li>\n
    16. Ratanjali Pandey (PhD, MME, WSU)<\/li>\n
    17. Joseph Patrick Drapal (MS, MSE, WSU)<\/li>\n
    18. Dr. Nandita Biswas (MME, WSU)<\/li>\n
    19. Dr. Emily A. Larsen (MME, WSU)<\/li>\n
    20. Pallock Halder (PhD, MME, WSU)<\/li>\n
    21. Tiana Tonge (PhD, MME, WSU)<\/li>\n<\/ol>\n
      \n
      \"SUPREME
      SUPREME 2026<\/figcaption><\/figure>\n
      \"SUPREME
      SUPREME 2026<\/figcaption><\/figure>\n<\/div>\n<\/div>\n
      \n

      2025 \u00b7 Deep Learning for Scientific Computing<\/p>\n

      SUPREME 2025<\/h3>\n

      The 2025 cohort met every weekday from 9:00\u201311:30 AM (PDT), June 16\u2013June 27, for an in-person course on deep learning for scientific computing. Eleven students from four departments \u2014
      \nMechanical Engineering (ME), Computer Science and Engineering (CSE), Electrical and Electronic Engineering (EEE), and Materials Science and Engineering (MSE) \u2014 took part, including three undergraduates and one master’s student, with two participants coming from outside WSU. Over the two weeks, students worked through lectures and hands-on sessions covering recent developments in deep learning and its applications in scientific computing.<\/p>\n

      Cohort of 2025<\/h4>\n
        \n
      1. Aruntapan Dash (PhD, MME, WSU)<\/li>\n
      2. Akash Kanji (UG, MME, Jadavpur University)<\/li>\n
      3. Lochan Upadhayay (PhD, MME, WSU)<\/li>\n
      4. Jeremy Colon-Castro (UG, CS, WSU)<\/li>\n
      5. Nolan Howard (UG, MME, WSU)<\/li>\n
      6. Priya Kushram (PhD, MME, WSU)<\/li>\n
      7. Dipan Kar (PhD, EECS, WSU)<\/li>\n
      8. Shohom Bandyopadhyay (PhD, ME, CMU)<\/li>\n
      9. William Hogg (MS, MME, WSU)<\/li>\n
      10. Pallock Halder (PhD, MME, WSU)<\/li>\n
      11. Tiana Tonge (PhD, MME, WSU)<\/li>\n<\/ol>\n

        Student feedback<\/h4>\n
        \n

        The structured workflow and real problem-solving approach made it easier to conceptualize how to apply deep learning to physics-based models in my research.<\/p><\/blockquote>\n

        The workshop provided clear explanations and practical engineering examples, which helped strengthen my understanding. I feel more prepared and confident to use deep learning methods in engineering problems.<\/p><\/blockquote>\n

        I already had a bit of a background from this last semester, but I learned a lot over the two-week course.<\/p><\/blockquote>\n

        Knew nothing going in and feel like I could create a simple network, and a complex one with help.<\/p><\/blockquote>\n<\/div>\n

        \n
        \"SUPREME
        SUPREME 2025 flyer<\/figcaption><\/figure>\n
        \"SUPREME
        SUPREME 2025<\/figcaption><\/figure>\n
        \"SUPREME
        SUPREME 2025<\/figcaption><\/figure>\n<\/div>\n<\/div>\n<\/div>\n<\/div>\n","protected":false},"excerpt":{"rendered":"

        Education & Outreach Education and Outreach Coursework, mentorship, and the CIMPI Lab’s summer research program for students new to AI and scientific computing. Teaching ME501: Continuum Mechanics (Fall 2024) ME\/MSE 241: Engineering Computations (Spring 2025, Spring 2026) ME483: Machine Learning for Engineering Applications (Fall 2025) ME579: AI in Manufacturing (Fall 2026) Summer Program in Research, […]<\/p>\n","protected":false},"author":[],"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_wsuwp_accessibility_report":[]},"categories":[],"tags":[],"wsuwp_university_location":[],"wsuwp_university_org":[],"_links":{"self":[{"href":"https:\/\/labs.wsu.edu\/cimpi\/wp-json\/wp\/v2\/pages\/117"}],"collection":[{"href":"https:\/\/labs.wsu.edu\/cimpi\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/labs.wsu.edu\/cimpi\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/labs.wsu.edu\/cimpi\/wp-json\/wp\/v2\/users\/43121"}],"replies":[{"embeddable":true,"href":"https:\/\/labs.wsu.edu\/cimpi\/wp-json\/wp\/v2\/comments?post=117"}],"version-history":[{"count":18,"href":"https:\/\/labs.wsu.edu\/cimpi\/wp-json\/wp\/v2\/pages\/117\/revisions"}],"predecessor-version":[{"id":376,"href":"https:\/\/labs.wsu.edu\/cimpi\/wp-json\/wp\/v2\/pages\/117\/revisions\/376"}],"wp:attachment":[{"href":"https:\/\/labs.wsu.edu\/cimpi\/wp-json\/wp\/v2\/media?parent=117"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/labs.wsu.edu\/cimpi\/wp-json\/wp\/v2\/categories?post=117"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/labs.wsu.edu\/cimpi\/wp-json\/wp\/v2\/tags?post=117"},{"taxonomy":"author","embeddable":true,"href":"https:\/\/labs.wsu.edu\/cimpi\/wp-json\/wp\/v2\/author?post=117"},{"taxonomy":"wsuwp_university_location","embeddable":true,"href":"https:\/\/labs.wsu.edu\/cimpi\/wp-json\/wp\/v2\/wsuwp_university_location?post=117"},{"taxonomy":"wsuwp_university_org","embeddable":true,"href":"https:\/\/labs.wsu.edu\/cimpi\/wp-json\/wp\/v2\/wsuwp_university_org?post=117"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}