Teaching Experience
Teaching Experience
Robot Mobility (EE599), Fall 2022
Role: Teaching Assistant
Course Description: Applications involving mobile robots are becoming an increasingly important part of society and industry, including delivery, search and rescue, healthcare, and extraterrestrial explorations. How to achieve high mobility in the real world has been a key topic in robotics, and requires an integration of knowledge and skills from different fields including morphology design, kinematics control, dynamics modeling, sensing information analysis, and motion planning. This specialized course will combine lectures, student presentations, and hands-on lab projects to provide an overview of robotic locomotion control and analysis, and expose students to the latest challenges and progress associated with robotic mobility in complex environments. This course is primarily oriented towards 1st and 2nd year PhD students in ECE, AME, CS, and other relevant engineering programs.
A Computational Introduction to Deep Learning (EE541), Spring 2023, fall 2023 and spring 2024
Role: Teaching Assistant
Course Description: Machine learning using large datasets stands as one of the most transformative technologies of the 21st century. It enables reliable face and speech recognition, internet search and monetization, computer vision, and self‐ driving vehicles. Machine learning proficiency requires software skills as well as an understanding of the underlying mathematics and theoretical concepts. This class introduces important aspects of deep learning using a computation‐first approach. It emphasizes using frameworksto solve reasonably well‐defined machine learning problems. Two advanced courses provide a deeper study of mathematical concepts: EE 559 Machine Learning I: Supervised Methods and EE 641 Deep Learning Systems.
MOS VLSI Circuit Design (EE477L), fall 2024 and spring 2025
Role: Teaching Assistant
Course Objective: Learn fundamental techniques for the design of VLSI circuits, be able to design simple cells, and be able to configure chips containing the simple cells. Course Outline: Analysis and design of digital MOS VLSI circuits including area, delay and power minimization. Laboratory assignments including design, layout, extraction, simulation and automatic synthesis.
Services and Awards
USC Viterbi CURVE program Mentor Awards ($ 3, 000), 2022 - 2025
USC Viterbi Ph.D. student Fellowship Awards ($ 32, 000), 2021
NEFU Excellent Graduates Awards, 2019
Supervised Students: Luke Cortez, Jerry (Yue) Wu, Seojoon Kwon, Tian Xie, Brendon Lee.
Served as Ph.D. Mentor in USC CURVE program in fall 2022 and spring 2023. Mentee: Josheta Srinivasan
Served as Ph.D. Mentor in USC CURVE program in fall 2023 and spring 2024. Mentee: Elliott Meeks
Served as Ph.D. Mentor in USC CURVE program in fall 2024 and spring 2025. Mentee: John Peng