Massimiliano de Sa

Massimiliano de Sa

PhD student in control & dynamical systems

Teaching

CDS 131: Linear System Theory

Graduate course, Caltech, Department of Control & Dynamical Systems, 2025

I developed and taught 16 lectures of the CDS core PhD course on linear system theory. This course focuses on the analysis and control of linear control systems (e.g. control systems of the form \(\dot x(t) = Ax(t) + Bu(t)\), etc.). We developed from scratch the fundamental concepts from dynamical systems, starting from existence and uniqueness of solutions to linear systems systems. We then studied state transition matrices, transfer functions, stability theory, convex controller synthesis from Lyapunov equations. Following this, we examined controllability & observability, and state & output feedback, concluding the classical linear systems material with a study of optimal control & the Youla parameterization. As opposed to emphasizing hand calculations, we focused primaily on developing deeper theoretical insights and leveraging tools from optimization in control design.

EECS C106/206AB: Robotics

Undergraduate course, UC Berkeley, Department of Electrical Engineering & Computer Science, 2023

I was a TA in the two-semester undergraduate & masters robotics course at UC Berkeley. In part A of the course (Fall 2022), I taught weekly discussion sections on classical topics in robotics, ranging from transformation groups & exponential maps to forward & inverse kinematics and computer vision. Here, I wrote a set of course notes on robot kinematics to supplement the course lectures. In part B of the course (Spring 2023), I taught weekly discussion sections on introductory nonlinear control, nonholonomic planning, and optimization-based control. Here, I wrote bi-weekly assignments consisting both of theoretical and computational questions. Additionally, I created a new hardware & software lab on safety-critical control for multi-agent systems. In simulation, students were tasked with designing control barrier function-based controllers for swarms of robots to avoid collisions with one another. Then, students were tasked with implementing their controllers on hardware on the Turtlebot platform, using lidar sensors to detect obstacles. In this course, I also wrote a set of notes, introducing the basic concepts of nonlinear control, planning, and estimation to students at the advanced undergraduate and beginning graduate level.

ME 100: Electronics for the Internet of Things

Undergraduate course, UC Berkeley, Department of Mechanical Engineering, 2021

I was a TA for the electronics course required for undergraduate mechanical engineers. Each week, I would hold a total of six hours of lab sections, one hour of discussion section, and one hour of office hours. In lab sections, we focused on the fundamentals of practical electronics for mechanical engineers. Labs ranged from topics such as embedded programming in Python (for the ESP32 microcontroller), circuit design and debugging (including an introduction to soldering and oscilloscope use), interfacing with sensors and power sources (e.g solar panels and current sensors), and the internet of things (e.g. design of web apps interfacing with microcontrollers). In discussion sections, I would review course content on material such as python programming, basic circuit analysis (e.g. KCL & KVL analysis, Thevenin & Norton equivalent circuits), transistors & diodes, amplifiers, and digital logic. To accompany these weekly discussion sections, I wrote a set of comprehensive course notes on introductory programming and circuit analysis.