Teaching AI and Engineering — Bridging Two Disciplines
Dr. Amin Riazi teaches graduate courses in artificial intelligence and machine learning, as well as undergraduate courses in civil and environmental engineering, at Seattle University. Ten courses across two disciplines with one pedagogical approach: connect theory to real-world applications, ensure deep understanding, and develop practical problem-solving skills.
Artificial Intelligence and Machine Learning — Graduate Level
Artificial Intelligence
Comprehensive coverage of AI fundamentals and contemporary applications — including search algorithms, knowledge representation, neural network architectures, natural language processing, and responsible AI frameworks. Students design and implement working AI systems while critically evaluating their capabilities and limitations.
Advanced Machine Learning
In-depth exploration of supervised and unsupervised learning, deep learning architectures, optimization algorithms, and model deployment methodologies. Curriculum covers convolutional neural networks, recurrent networks, transformer models, and generative AI — emphasizing both theoretical foundations and practical implementation decisions.
Water Quality Management
Computational modeling and data-driven approaches for water quality monitoring, pollution analysis, environmental impact assessment, and evidence-based decision-making in water resources management.
Computer Applications in Hydraulics
Programming and computational methods applied to hydraulic engineering challenges — integrating coding skills with physical modeling, numerical simulation, and engineering problem-solving.
Civil and Environmental Engineering Fundamentals
Fluid Mechanics
Applied Hydraulics
Water Resources Engineering
Water Supply and Wastewater Engineering
Surveying and Geomatics
Global Engineering Economics
Academic Teaching Expertise Applied to Workshops and Training
The pedagogical approach that makes Dr. Riazi effective in university instruction translates directly to professional development: making complex AI and machine learning concepts accessible and actionable for diverse audiences, from faculty colleagues to practicing engineers.
His academic workshops and professional training sessions are built on the same teaching principles refined at Seattle University — hands-on learning, clear explanations without unnecessary jargon, and structured content ensuring participants gain immediately applicable knowledge.
AI Literacy for Academic Leaders
Understanding AI capabilities and limitations — designed for academic administrators, department chairs, and research leaders evaluating AI initiatives and educational programs.
Hands-On Machine Learning Workshops
Interactive sessions where participants build, evaluate, and interpret machine learning models. Designed for researchers and professionals without extensive programming backgrounds.
AI Integration for Research Teams
Workshops helping research groups assess data readiness, identify high-impact AI applications in their domains, and develop evidence-based implementation strategies.
Academic Workshops and Professional Development
Dr. Riazi delivers AI workshops, professional training sessions, and invited talks for academic institutions, research organizations, and professional development programs — bringing the same clarity and hands-on pedagogical approach refined through university teaching.