Prof. Kene Mbanisi
Description: Studying basic robotic movement using a 5-DOF arm with a python interface.
Topics Covered: Frame translations, forward kinematics, inverse kinematics, trajectory generation, computer vision.
Course Projects: Calculating/implementing forward kinematics for 5-DOF arm; calculating/implementing numerical and analytical inverse kinematics for 5-DOF arm; using computer vision and inverse kinematics for autonomous sorting of objects.
Prof. Orion Taylor, Prof. Xuan Kong
Description: Exploring how linear systems process signals through mathematical modeling and simulation in MATLAB.
Topics Covered: Linearization, equilibrium, frequency response, convolution, impulse/step response, sampling and aliasing, laplace transform, fourier transform, z-transform, modulation, discrete/continuous time.
Course Projects: Parameter identification and control of an unstable mechatronic system; design and implemention of the receiver for an acoustic modem.
Prof. Brad Minch
Description: Exploring analog circuits through weekly labs focused on analysis, design, and modeling of linear and transistor-based systems via breadboarding and LTSpice.
Topics Covered: BJTs, MOSFETS, differential pairs, translinearity, differential/operation amplifiers, current mirrors.
Course Projects: Completed a total of eight hands-on labs covering all course material.
Prof. Kene Mbanisi, Prof. Amon Millner
Description: Analysis, design, construction, testing, and debugging of custom electromechanical systems.
Topics Covered: Arduino, basic C, basic CAD (Solidworks), electrical integration, wiring/soldering, web development.
Course Projects: Ultrasonic 3D scanner; line-following robot; robotic dog that responds to voice commands.
Prof. Paul Ruvolo, Prof. Sam Michalka
Description: Learning the multi-faceted and interdisciplinary skill set to understand, implement, and critically evaluate machine learning systems using TensorFlow.
Topics Covered: Linear/ridge regression, classification, neural networks, bag of words, GPTs, CNNs, GANs, reinforcement learning, text as data, images as data.
Course Projects: Identifying poets given poetry samples using bag of words; training an algorithmically ideal pong-bot using reinforcement learning.
Prof. Sarah Spence Adams
Description: An introduction to advanced counting techniques, critical thinking in different ways to partition a problem, and a variety of useful discrete structures such as graphs, trees, codes, and designs.
Topics Covered: Counting techniques (including permutations, combinations, the pigeonhole principle, and inclusion/exclusion), sets and functions, graphs (including theory-based properties and real-world situations), recurrence relations, induction, error-control codes.
Course Projects: Algorithmic determination if a graph contained a hamiltonian circuit, and if so, if the graph was planar; building a chess-bot using the minimax algorithm and alpha-beta pruning.
Prof. Zach del Rosario
Description: An introduction to differential equations and analytical tools for modeling and understanding dynamic systems in engineering contexts using MATLAB.
Topics Covered: Linear first and second order differential equations, nonlinear differential equations and linearization, discrete fourier transform.
Course Projects: Building and characterizing a harmonic oscillator; using the FFT to identify different sound signals; calculating the resonant frequency of a Tibetan singing bowl.
Prof. Kene Mbanisi, Prof. Xaun Kong
Description: Studying experimental design and data collection using modern sensors, supporting electronics, and computer-based measurement systems.
Topics Covered: Ohm's law (voltage, resistance, current), capacitors, diodes, high/low pass filters, generating data from sensors, osciloscopes.
Course Projects: Completed nine labs covering material from circuit basics to building custom EKG readers and ultrasonic sensors.
Prof. Erhardt Graeff
Description: Introductory course in computing (CS101 equivalent) that teaches how to design, write, and maintain software in Python.
Topics Covered: Python basics, unit tests, classes, APIs, software arcitecture, MVC pattern.
Course Projects: Comparative analysis of housing markets in America's cities using scraped data; a fully playable "tower defense" application built using pygame.
Prof. Jessic Townsend, Prof. Chris Lee, Prof. Paul Ruvulo
Description: Project-based math and physics course applying vector and multivariable calculus to engineering problems, with a focus on mobile robotics in MATLAB.
Topics Covered: Vector calculus, multivariable calculus, gradient descent, robotic systems, LiDAR integration, open loop control.
Course Projects: Cpmpleted three projects in MATLAB controling a Neato robot involving 1) following a parametric curve, 2) following gradient descent, and 3) using LiDAR scans to track a ball.
Prof. Zach del Rosario
Description: An introduction to mathematical modeling and computer simulation of physical systems in MATLAB.
Topics Covered: Model abstraction (states, parameters, actions, metrics), stock and flow/SIR models, assumptions and simplifications, mathematical representation, modeling epidemiology.
Course Projects: Completed three computational essays studying the spread of infectious disease in differing scenarios, including 1) mask efficiency, 2) community type (city vs town vs campus), and 3) testing policy.
Prof. David Shuman, Prof. Sam Michalka
Description: An introduction to linear algebra and computational tools for modeling, analyzing, and solving engineering problems through data-driven approaches.
Topics Covered: Matrix operations, matrix transformations, modeling systems, matrix decomposition, orthogonality, least squares solutions, correlation, eigenvalues/eigenvectors, eigendecomposition, principal component analysis (PCA).
Course Projects: Build a facial recognition algorithm using principle component analysis; designed an NBA champion predictor using PCA and a support vector machine.