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My Teaching Philosophy
My Teaching Philosophy
Advanced personalized learning is listed at the top of the fourteen Grand Challenges in the 21st century by the National Academy of Engineering. The scientific reason behind it is that learning styles, speeds, and interests all vary from individual to individual. This challenge is particularly significant for engineering – our students are trained to define a problem from a real-world observation, identify the underlying physics, simplify and solve the problem, and explain the observation using the solution – each of these actions can be performed in numerous correct ways which will depend on student preference and personality. Therefore, the core of my teaching philosophy is student-oriented instruction to realize personalized learning.
Courses
Courses
ME 3455 Dynamics and Vibrations, undergraduate level, Northeastern University, 2025 Spring
ME 5374 Scientific Machine Learning for Mechanical Engineers, graduate level, Northeastern University, 2024 Fall
ME 3455 Dynamics and Vibrations, undergraduate level, Northeastern University, 2023 Spring
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Structure of our course
List of featured projects for SciML course (2024 Fall)
List of featured projects for SciML course (2024 Fall)
Deep Learning for Cleavage Energy Prediction. Students: Ardavan Mehdizadeh and Jawad Ahmed
Deep Generative Design of Experiment-Specific Colloidal Gel Simulations. Rob Campbell
Maximizing Exit Conditions of a Particle in a Supersonic Nozzle Using Deep Learning. Ege Cura
Determining the Viscoelastic Properties of Murine Placenta through Micro-Indentation. Sean Harrington
Predict the Drug Absorption Using Physics-Informed Neural Networks. Ali Peyghambari Gharebagh
Learning Rheological Constitutive Models Using Transformers for PDEs’ Operator Learning. Maedeh Saberi
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