Education & Outreach

Teaching

• ME501: Continuum Mechanics (Fall 2024)
• ME/MSE 241:  Engineering Computations (Spring 2025, Spring 2026)
• ME483: Machine Learning for Engineering Applications (Fall 2025)

Summer Program in Research, Education, and Mentoring (SUPREME) 2026

The Computational Intelligence for Materials and Process Innovation (CIMPI) Lab at Washington State University, Pullman, WA, is offering the Summer Program in Research, Education, and Mentoring (SUPREME)—a free 2-week summer course on AI in Manufacturing. Designed for undergraduate students, this program provides hands-on tutorials, project-based learning, small class sizes, and expert mentorship. This year, classes meet every weekdays from 9:00–11:00 AM (PDT), June 1-June 12, offering a unique opportunity to gain AI/ML skills for manufacturing and explore research opportunities. Apply by April15, 2026 by filling out the Google form: https://forms.gle/dk3DqCYm1LowTPfU6.

Tentative program outline

Day	Lecture Theme	Core Topics Covered	Hands On Tutorial
1, M	AI in manufacturing — the big picture	Manufacturing AI problem types: quality control, predictive maintenance, anomaly detection, process control. Industry 4.0 context.	Python review tutorial, Dataset cleaning, preparation, feature engineering, Sensor-to-Insight pipeline for predictive maintenance
2, T	Practice, TA hours, Q&A, mini project progress on dataset preparation
3, W	Supervised learning methods	Decision trees, random forests, support vector machines, regularization, model selection	Defect prediction and Remaining Useful Life (RUL)
4, Th	Practice, TA hours, Q&A, mini project progress on supervised learning
5, F	Unsupervised learning Methods	Clustering, dimension reduction using PCA, SVD	Anomaly Detection in Production Lines
6, M	Deep learning methods	Intro to feed-forward neural network, convolutional neural network	Visual quality inspection with CNNs
7, T	Practice, TA hours, Q&A, mini project progress on unsupervised learning & deep learning
8, W	Reinforcement learning	Markov decision processes, reward functions, Q-learning, policy gradient methods (PPO)	Process control optimization (Closed-Loop Control)
9, Th	Practice, TA hours, Q&A, mini project progress on reinforcement learning
10, F	Mini-project presentations

Summer Program in Research, Education, and Mentoring (SUPREME) 2025

The Computational Intelligence for Materials and Process Innovation (CIMPI) Lab at Washington State University, Pullman, WA, is offering the Summer Program in Research, Education, and Mentoring (SUPREME)—a free 2-week in-person summer course on Deep Learning for Scientific Computing. Designed for undergraduate students, this program provides hands-on tutorials, project-based learning, small class sizes, and expert mentorship. This year, classes meet Every Weekday from 9:00–11:30 AM (PDT), June 16-June 27, offering a unique opportunity to gain AI/ML skills and explore research opportunities. Apply now by filling out the Google form: https://forms.gle/dk3DqCYm1LowTPfU6. Apply by March 31, 2025.

 

SUPREME 2025

The SUPREME’25 program took place from June 16 to June 27. A total of 11 students from four departments—Mechanical Engineering (ME), Computer Science and Engineering (CSE), Electrical and Electronic Engineering (EEE), and Materials Science and Engineering (MSE)—participated in the program. Among the attendees were three undergraduate students and one master’s student, with two participants coming from outside of WSU. During the two-week program, the students engaged in lectures and hands-on sessions, gaining an overview of recent developments in deep learning and its applications in scientific computing.

Cohort of 2025:

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

 

Student feedback: Some comments from the course feedback:   “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.“, “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.“, “I already had a bit of a background from this last semester, but I learned a lot over the two-week course.” Another undergraduate student wrote: “knew nothing going in and feel like I could create a simple network, and a complex one with help.”

 

SUPREME’26  will return in 2026. Stay tuned to the CIMPI Lab Education and Outreach Page.