This page highlights selected academic activities, invited lectures, workshops, outreach efforts, and professional engagements connected to teaching, research, and the development of RISE Lab.
Workshops and Short Courses#
Machine Learning for Scientific Computing
This workshop introduces machine learning methods for scientific computing and engineering applications, with emphasis on traditional numerical methods, physics-informed learning, surrogate modelling, and data-driven solution of differential equations.
The workshop will include hands-on training for structural problems, post-processing, visualization, open-source tools, and cloud-based computing workflows.
Machine Learning for Structural Health Monitoring
This workshop focuses on machine learning, computer vision, and physics-informed methods for structural health monitoring and bridge condition assessment.
The workshop will discuss how data-driven and physics-guided approaches can support reliable infrastructure monitoring, full-field response interpretation, and rapid condition assessment.
Invited Lectures and Talks#
Data Fusion Using Kalman-Filter Methods for Real-Time Structural Health Monitoring
This invited lecture presented Kalman-filter-based data fusion methods for real-time structural health monitoring, with emphasis on combining acceleration and displacement measurements for improved structural response estimation.
The talk discussed online estimation of displacement, velocity, and acceleration-bias effects, along with numerical and experimental validation for structural dynamics applications.
From Equations to Solutions: Physics-Informed Neural Networks for Mechanics Problems
This invited lecture introduces physics-informed neural networks for solving and discovering differential-equation-based models in mechanics and scientific computing.
The lecture will discuss data-driven solution and discovery of partial differential equations, continuous and discrete-time formulations, and connections between machine learning, numerical methods, and nonlinear dynamical systems.
Academic and Professional Engagement#
Research, Teaching, and Student Engagement
In addition to research and teaching, activities include student mentoring, academic discussions, workshop planning, seminar participation, and engagement with emerging topics in smart and resilient infrastructure systems.