Teaching Assistant Software Engineer

**** ********* **, *******, ******* LI *****@***.***

VA, 22031, USA +1-571*******

EDUCATION

Guangdong University of Technology Sept/2015-Jun/2019 B.S. in Telecommunication Engineering

• Model Student of Academic Records (Top 2%) Scholarship for Outstanding Students, Second Prize (Top 10%) Academic Progress Scholarship

George Mason University Aug/2019-May/2024

Ph.D. in Electrical and Computer Engineering GPA: 3.9/4.0

• Outstanding Graduate Teaching Assistant Award (2024) WORK EXPERIENCE

Graduate Teaching Assistant (GTA) Feb/2021-Dec/2023

• Instructed and evaluated students to build up hardware circuit in the Linear Electronics Lab, utilizing equipment such as power supplies, oscilloscopes, multimeter and function generator for circuit setup and debug. Graduate Research Assistant (GRA) Aug/2019-Sep/2021

• Conducted extensive research on hardware systems, including intelligent gas sensing system, intelligent electrochemical sensor and the object detection using Lidar and Image-based technique. PROJECT EXPERIENCE

Embedded Linux Software Engineer Intern Nov/2017-May/2019

• System Integration and Development: Developed an advanced embedded system for video conferencing. Led the integration of a Sony IMX219 8-megapixel camera with an ARM Cortex-A55 processor and interfacing with host systems for real-time video encoding and decoding.

Hardware-Software Co-design: Collaborated in a team to co-design the hardware and software components, ensuring seamless operation and high-performance data processing. Utilized Embedded C and Bash scripting on a Linux platform tailored for the ARM Cortex-A55 processor.

Testing and Debugging: Engaged in rigorous testing and debugging to validate the functionality and reliability of the system, employing tools like GDB and performance metrics to fine-tune the system. Scent Analysis and Safety Monitoring [Publication 1-2,7] Sep/2019-May/2023

• PCB Design & System Integration: Developed an E-nose device by designing the PCB layout and integrating microcontrollers, A/D converters, gas sensors, and USB modules. Implemented PID control to manage gas flow and used I2C protocol for sensor data transfer.

• Software Development: Developed a C/C++ system for the E-nose device, complemented by a Python UI for real-time data monitoring and recording. Established robust connectivity between systems using USB, WiFi, and Ethernet (TCP/IP).

• Battery Manage System (BMS) monitoring: Designed a system utilizing the E-nose to detect cracked lithium-ion batteries and monitor their temperature based on the gases they emit. Conducted extensive research on battery safety, including thermal runaway.

Electrochemical sensor enabled by neural networks [Publication 3-6] May/2021-May/2023

• Advanced Hardware Design & Integration: Developed a portable VOC detector, meticulously designing the PCB layout and integrating an ESP12-F microcontroller, A/D converters, and gas sensors. Incorporated a USB to Serial converter for enhanced data communication. Utilized MCP4728 for precise digital-to-analog conversion and LMP9100 for versatile electrochemical sensing, facilitating accurate cyclic voltammetry detection.

• Power Management: Implemented AP2112M regulator for efficient lithium battery power supply management, ensuring sustained and reliable operation.

• Software Development & Interface: Crafted a robust C/C++ system tailored for the electrochemical sensor, augmented by a Python-based user interface for real-time data monitoring and analysis. This setup allowed for immediate data interpretation and enhanced user interaction.

SKILLS

Hardware Design Tools: PCB design(Eagle, KiCad, Altium Designer); circuit simulation(PSPICE, LTSPICE, and Multisim.) Microcontrollers&Processors: Experience with ARM Cortex, AVR, PIC, and Arduino, including interfacing and programming. Programming Languages: Embedded C, C++, Python for hardware interfacing and automation. Assembly language for low- level hardware interaction.

Protocols & Technologies: Communication protocols including I2C, SPI, UART, and TCP/IP for device communication. CAD Tools: CAD tools for creating schematic designs and mechanical drawings of electronic components. Testing & Debugging Tools: Proficient in oscilloscopes, multimeters, and GDB and Wireshark for debugging. PUBLICATION

[1] Zhenyi Ye, Yaonian Li, Ruth Jin and Qiliang Li, "Towards Accurate Odor Identification and Effective Feature Learning With an AI-Empowered Electronic Nose," in IEEE Internet of Things Journal, doi: 10.1109/JIOT.2023.3299555.

[2] Yaonian Li, Zhenyi Ye, and Qiliang Li. 2023. "Precise Identification of Food Smells to Enable Human–Computer Interface for Digital Smells" Electronics 12, no. 2: 418. https://doi.org/10.3390/electronics12020418

[3] Xiaozhou Huang, Yaonian Li, Erin Witherspoon et al., “Species-Selective Detection of Volatile Organic Compounds by Ionic Liquid-Based Electrolyte Using Electrochemical Methods,” ACS Sensors, 2023/08/17, 2023.

[4] Xiaozhou Huang, Erin Witherspoon, Yaonian Li et al., “Sustainable generator and in-situ monitor for reactive oxygen species using photodynamic effect of single-walled carbon nanotubes in ionic liquids,” Materials Today Sustainability, vol. 19, pp. 100171, 2022/11/01/, 2022.

[5] Xiaozhou Huang, Erin Witherspoon, Rui He, Yaonian Li et al., “Superior photodynamic effect of single-walled carbon nanotubes in aprotic media: a kinetic study,” Materials Today Energy, vol. 32, pp. 101242, 2023/03/01/, 2023

[6] Yaonian Li, X. Huang, E. Witherspoon, Z. Wang, P. Dong and Q. Li, "Intelligent electrochemical sensors for precise identification of volatile organic compounds enabled by neural network analysis," in IEEE Sensors Journal, doi: 10.1109/JSEN.2024.3374354.

[7] Yaonian Li, Zhenyi Ye, Xiaozhou Huang, Pei Dong, Chunlai Li, Yiqun Wei, Xiumei Wang and Qiliang Li, "Temperature- Resilient Scent Classification through Integrated 1D-Convolutional Neural Networks and Transformer Encoders: An Empirical Study," in IEEE Transactions Neural Networks and Learning Systems (submitted) PATENT APPLICATION

• Title: Machine learning based voc detection

• Inventors: Dr. Qiliang Li, Dr. Pei Dong, Dr. Xiaozhou Huang, Yaonian Li, Dr. Zhe Wang

• United States Application Number: 18/480,429

• Filing Date: October 3, 2023

• Status: In Progress

• Link: https://patents.google.com/patent/US20240119367A1/en

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