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Future Blog Post

less than 1 minute read

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This post will show up by default. To disable scheduling of future posts, edit config.yml and set future: false.

Blog Post number 4

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 3

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 2

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

Blog Post number 1

less than 1 minute read

Published:

This is a sample blog post. Lorem ipsum I can’t remember the rest of lorem ipsum and don’t have an internet connection right now. Testing testing testing this blog post. Blog posts are cool.

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teaching

ESE 421: Control for Autonomous Robots

Undergraduate course, University of Pennsylvania, 2019

This course introduces the students to the hardware and software technology essential to autonomous ground vehicles. This fast paced course covers a breadth of robotics topics including kinematic modelling, feedback control, state estimation and sensing, and motion planning. Lectures are reinforced by hands-on laboratories where students apply the lessons from lecture to the implementation of their own self-driving cars.

ESE 605: Modern Convex Optimization

Graduate Course, University of Pennsylvania, 2020

This second year graduate course introduces students to the theory needed to recognize and solve convex optimization problems, particularly those that arise in engineering. The first half of this course covers essential theory including convex analysis, the identification of linear, quadratic, geometric, and semidefinite programs, and duality. The second half of the course emphasizes the practice of transforming a broad range of engineering objectives into convex programs such as statistical estimation, approximation, and control as well standard first and second order algorithms to solving both unconstrained and constrained convex optimization programs.