Registration is open through May 21 for the workshop, The Practical Core of AI: From Fundamentals to Hands-On Application, set for May 28-29 on City Campus and led by M.R. Hasan.
This workshop is designed for faculty who have beginning to moderate experience in applying artificial intelligence (AI) in their research, covering fundamentals, practical tools, and hands-on implementation. Day 1 will focus on understanding AI’s potential and selecting appropriate tools; Day 2 will provide hands-on implementation with guided support for applying AI solutions. Participants completing both sessions will be eligible for a mini-grant to support their further implementation of AI over the 2025 summer.
DAY 1
- Morning Session (9-11 a.m.): Part 1 – Foundations
- Mid-Day Session (11:30 a.m.–12:30 p.m.): Part 2 – Applications
DAY 2
- Morning Session (9-11:30 a.m.): Part 3 – Hands-on Implementation
Part 1: Foundations – AI Landscape Survey
Objective: Introduce core AI concepts, technologies, and their relevance to engineering.
Topics:
- Data types: Structured, unstructured, time-series
- Modern AI: Machine learning and deep learning
- Focus: LLMs and VLMs for engineering applications
- Societal implications and limitations of AI
Part 2: Applications – Decision Framework for AI Implementation
Objective: Equip faculty with a methodology to apply AI to their research problems.
Topics:
- Problem assessment: When is AI appropriate for a given problem?
- Essential skills for faculty and graduate students (e.g., Python, fine-tuning, model evaluation)
- Ethical considerations and responsible AI use in engineering
Part 3: Hands-on Implementation
Objective: Enable faculty to implement AI solutions with guided support.
Led by: PhD student from the Human-First Artificial Intelligence Lab (HAL 2.0)
Activities:
- Guided implementation of AI solutions with increasing complexity (e.g., image classification, text analysis)
- Using pre-configured tools like Google Colab for accessibility
- Techniques for adapting and fine-tuning pre-trained models for engineering applications
- Evaluating and optimizing AI model performance
Participants who complete Parts 1 to 3, can submit an application for a mini-grant for use over summer 2025 to provide access to computing resources, consulting from grad students to facilitate preparation of data sets, and related support specifically to boost their use of AI tools in their research. COE will cover reasonable costs and with the expectation of a brief report on outcomes by the end of the summer.
Register at the link below.
More details at: https://go.unl.edu/kit3