Course Topics: State-space Modeling; Matrix Algebra; System Response; Coordinate Transformation; Stability; Controllability; Observability; Realization; State-Feedback Design and Observers; Nonlinear Systems; Lyapunov Functions; Optimal Control.
Instructor, Control Systems Lab II, ECE 5115 (undergraduate), University of Houston, Fall 2017, Fall 2018 (Course Link)
Course Topics: Computer-Aided State-space Modeling; Computer-Aided Stability, Controllability and Observability Analysis; Computer-Aided State-Feedback and Observer Design; Computer-Aided Optimal Control Design.
Instructor, Physiological Signal Processing, ECE6397 (graduate), University of Houston, Spring 2018 (Course Link)
Course Topics: Review of Probability; Likelihood Methods; Bayesian Inference; State-Space Models; State-Space Estimation; Point Processes; Estimation of Point Processes; Point Process Filtering and Smoothing; Sparse Signal Processing
Project Advisor, Special Projects, ECE 6393 (graduate), University of Houston, Spring 2018
Non-thesis masters students can perform research in our laboratory as a part of a special project course after obtaining approval from the Director of Graduate Studies to enroll in ECE 6393: Special Projects. If desired, and warranted by the progress made, the student may request to enroll in ECE 7393 for another semester to continue work on the project. Such requests must be made at least three weeks before the start of the semester. Please review departmental policy on Special Projects. Please use the special project request form.
Project Advisor, Independent Study, ECE 4398 (undergraduate), University of Houston, Spring 2018
Undergraduate students can perform research in our laboratory as a part of an independent study course after obtaining approval from the Director of Undergraduate Studies to enroll in ECE 4398: Independent Study. Please review Independent Study Proposal Guidelines.
Co-Instructor, Statistics for Brain and Cognitive Science, 9.07 (undergraduate), Massachusetts Institute of Technology, Fall 2016 (Course Link)
Course Topics: Axioms of Probability; Discrete and Continuous Probability Models; Law of Large Numbers; Central Limit Theorem; Estimation; Likelihood Theory; Bayesian Methods; Monte Carlo Methods; Hypothesis Testing; Confidence Intervals, Regression; Analysis of Variance.
Head Teaching Assistant, Probabilistic Systems Analysis and Applied Probability, 6.041(undergraduate)/6.431(graduate), Massachusetts Institute of Technology, Fall 2013 (Course Link)
Course Topics: Formulation and Solution in Sample Space; Random Variables; Transform Techniques; Simple Random Processes and Probability Distributions; Markov Processes; Limit Theorems; Elements of Statistical Inference.