Semester: Spring 

Offering: 2025

This course covers theoretical and computational principles, tools, and techniques essential for bioengineers. The course is taught as three integrated modules (approx. 8 lectures per module), each led by the instructors listed above. The modules include:

1. Mathematical Frameworks in Bioengineering: Covers biomolecular kinetics, ordinary differential equation models, and reaction-diffusion.
2. Molecular Simulation Techniques: Includes stochastic and deterministic methods.
3. Fundamentals of Data Science & AI/ML: Addresses an introduction to data science, supervised and unsupervised learning, neural networks, and Poisson distributions.

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Semester: Fall 

Offered: 2023, 2024

This course offers an introduction to computational chemistry and molecular simulation, which are essential components to modern-day science and engineering, as they can provide both mechanistic insights underlying observed phenomena and predictions on thermodynamic/kinetic properties. Through pedagogical treatment of essential background, basic algorithmic implementation, and applications, students will develop knowledge necessary to follow, appreciate, and devise computational 'experiments'. Topics of emphasis include quantum chemical solution methods, Monte Carlo & molecular dynamics, and free energy/enhanced sampling.

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Semester: Spring 

Offered: 2024

This course examines methods for simulating matter at the atomistic scale with emphasis on the concepts that underline modern computational methodologies for classical many-body systems at or near statistical equilibrium. The course covers Monte Carlo and Molecular Dynamics (from basics to advanced techniques), and includes an introduction to molecular coarse graining and the use of Machine Learning techniques in molecular simulations.
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