This course is an introduction to the basics of computer engineering. We cover the basics of how to measure how 'good' a computer is in terms of performance, the instruction sets, the MIPS assembly language, computer arithmetic, Logic and CPU design, and memory. These are all useful materials for students wishing to take higher classes on computer architecture or systems or studetns interested in learning about the inner workings of a computer. This couse can be framed as a course on basic concepts that all electrical and computer engineers should know and understand. The typical enrollment of this course is up to 100 students. 

This course teaches the C programming language at an introductory level. Students learn about procedural programming in C along with variables, data types, functions, pointers, memory management, arrays, and dynamic memory allocation. The typical enrollment of this course is up to 200 students. 

This course introduces object-oriented programming and data structures in the C++ programming language. Students learn about fundamental programming principles such as control structures, functions, memory management, and object-oriented design and development as well as custom-built data structures including linked lists, stacks, queues, and binary trees. Throughout the course, students are also be exposed to standard searching and sorting algorithms as well as the C++ Standard Template Library (STL). The typical enrollment of this course is up to 80 students

This course teaches fundamental circuit theory concepts including Kirchhoff’s voltage and current laws, Thevenin’s and Norton’s theorems, loop and nodal analysis, time-varying signals, transient first order circuits, and steady-state sinusoidal response. This course is designed to develop a strong foundation in the students electrical engineering knowledge as well as a strong aptitude for problem-solving in a variety of settings. 

SPIS is a program for incoming freshman already admitted to UCSD in a computer science or engineering major. It aims to bring a small cohort of students (40-50 students total) together for a socially and intellectually stimulating environment the summer preceding their first quarter on campus as freshman. The program’s goals are to (i) teach a series of computer science topics (ranging from introductory programming to advanced topics such as robotics, machine learning, and web applications) to help students enter their respective majors with confidence, (ii) help students get acquainted with college-style living and learning, and (iii) form a sense of community among their peers prior to beginning their journey at UCSD. More information about SPIS can be found at https://spis.ucsd.edu. 

COSMOS brings together academically advanced and motivated high school students entering grade 9 through exiting grade 12 with UCSD faculty, staff, researchers, and graduate students for an academic summer residential experience. The goal of the program is to introduce cutting edge topics in science and engineering in a fashion that will inspire high school students to pursue higher education in these areas. The machine learning cluster specifically focused on teaching the Python programming language as well as fundamental data analysis methods for data clustering and data classification on large datasets. 

This course introduced several foundational data science concepts, ranging from mathematical analysis to implementation in Python, with a typical enrollment of approximately 300-450 students per semester. My responsibilities consisted of preparing and delivering lectures,  managing the teaching staff (consisting of approximately 40 teaching assistants), teaching TAs administrative tasks associated with a large class (homework grading, holding office hours, addressing student questions, etc.), writing exam questions, and holding office hours. 

This course introduced undergraduate students to fundamental circuit implementation concepts in electrical engineering. I instructed two sections of ECE 207 with 25 students in each section. My responsibilities consisted of preparing and delivering introductory lectures at the beginning of the lab,  grading and providing feedback on weekly lab reports, assisting students with their experiments, conducting and grading lab practicals, and holding office hours. I received a 4.8/5.0 teaching evaluation in both sections. 

This course served as a rigorous introduction to deep learning, covering both theory and Python implementation for a variety of deep learning models and tasks. I served as a co-developer as well as a teaching assistant for this course. As a co-developer, I prepared four out of five of the deep learning coding assignments on (i) Convolutional LSTM Classifiers, (ii) Autoencoders, (iii) Generative Adversarial Networks (GANs), and (iv) Adversarial Deep Learning. For each project, I prepared the project handout (describing the assignment and objectives), the template code (distributed to students with assignments), the grading rubrics, and the solutions to each assignment. All code (templates and solutions) was prepared in Python using TensorFlow.

As a teaching assistant, I was responsible for writing the homework assignments and solutions, holding office hours, proctoring exams, and helping students with their coding projects. The course enrollment was approximately 100 students.

This course introduced several foundational data science concepts, ranging from mathematical analysis to implementation in Python, with a typical enrollment of approximately 300-450 students per semester. My responsibilities consisted of updating homework assignments and solutions, creating automated grading scripts, writing exam questions, grading student's exams,  holding office hours, and responding to student questions through e-mail and Piazza. I also delivered several guest lectures on Python lambda functions, list comprehensions, confidence intervals, hypothesis testing, Gaussian mixture models, inheritance, and neural networks. 

I mentored five senior design groups throughout  the semester, with each group consisting of 3-4 students. My responsibilities included leading the designated lab section twice a week, assessing design schematics, conducting weekly check-in reviews, conducting oral midterm and final exams, and holding office hours. 

This course developed students' written and oral communication skills and prepared students to deliver technical content to a wide variety of audiences. The course enrollment was approximately 30 students. My responsibilities included editing and providing feedback on written assignments, proctoring exams, and grading homework.