Fan Yang

Fan Yang

Duke alumnus, Master of Science in ECE

Posts by Collection

educations

Duke University

Master of Science, Electrical and Computer Engineering, 2022.09 - 2024.05

  • GPA 3.91/4.0

University of Liverpool

Bachelor of Engineering, Electrical and Electronics Engineering, 2018.08 - 2022.07

  • GPA: 3.91/4.0
  • Bachelor’s degree with First Class Honours

Xi’an Jiaotong-Liverpool University

Bachelor of Engineering, Electrical Engineering, 2018.08 - 2022.06

  • GPA: 3.91/4.0
  • Bachelor’s degree with First Class Honours
  • 2018/19 University Academic Achievement Award (10%)
  • 2019/20 University Academic Achievement Award (10%)
  • 2020/21 University Academic Excellence Award (5%)
  • 2021/2022 Best Overall Academic Performance Undergraduate Award of School of Advanced Technology (Top 1 in Major)

experiences

Data Analyst & Software Development Intern

Intership, Inspur Digital Enterprise Technology Ltd - Allin Big Data, 2023.05 - 2023.08

  • Skill Sets: Front-end/ETL/OracleSQL/OracleDB
  • Optimized a web application project by developing SQL queries for Oracle Database extraction and designing intuitive BA dashboards on GSCloud ERP, effectively consolidating 131 key metrics into 7 decision-making sections.
  • Automated retrieval processes from Oracle Database through ETL workflows within pipeline middleware, resulting in a 95% improvement in query speed and enhanced interface responsiveness
  • Assisted the management team in identifying production instabilities through detailed trend analyses and comparisons with competitors, informing the design and optimization of the upcoming quarter’s strategy.

Reasearch Assistant

Research, Duke University I3T Lab, 2023.11 - 2024.07

  • Skill Sets: C++/Python/Linux/SLAM/ROS/Swift/C#/Unity
  • Designed and implemented a modular testbed that integrates a Vicon motion capture system with external camera support to evaluate the spatial tracking accuracy of XR headsets, such as Apple Vision Pro and Meta Quest 3, using open-source VI-SLAM algorithms as benchmarks.
  • Developed robust Meta Quest 3 and Apple Vision Pro applications to enable synchronized sensor data collection and real-time tracking analysis. The system facilitated detailed performance evaluation under varied environmental conditions and user movements.
  • Led comprehensive experiments leveraging the Vicon system to assess the impact of movement patterns, environmental features, and lighting conditions on XR tracking performance. These experiments revealed a 33.9% reduction in relative pose error and a 14.6% reduction in absolute pose error for the Apple Vision Pro compared to Meta Quest 3

portfolio

HTTP_Proxy_Server

Used C++ to create a robust HTTP/HTTPS proxy that processes simultaneous GET, POST, and CONNECT requests following the RFC7231 Protocol, forwarding to the origin server and facilitating correct reception of chunked data.

Mini_Uber_Ride_Share_Web_App

Developed a web application using Django and PostgreSQL to simulate key functionalities of a ride-sharing platform, akin to Uber..

Stock_Exchange_Scalable

This is a program that achieved the functionality of Stock exchange, and also could be scalable with multiple process to improve the performance

Mini-UPS-Amazon

This a project that simulate the real world UPS and Amazon coopration, communicating with an agreed protocol to provide a purchasing and delivering functionalities.

publications

A Computer Game-based Tangible Upper Limb Rehabilitation Device

Published in HAI '22: Proceedings of the 10th International Conference on Human-Agent Interaction, 2022

In this paper, we presented a computer game-based tangible upper limb rehab device for stroke people to stimulate them to exercise.

Recommended citation: Qinglei Bu, Xiaoyi Cheng, Fan Yang, Jie Sun, Limin Yu, and Ying Hou. 2022. A Computer Game-based Tangible Upper Limb Rehabilitation Device. In Proceedings of the 10th International Conference on Human-Agent Interaction (HAI '22). Association for Computing Machinery, New York, NY, USA, 309–313. https://doi.org/10.1145/3527188.3563919 https://doi.org/10.1145/3527188.3563919

SEESys: Online Pose Error Estimation System for Visual SLAM.

Published in SENSYS '24: Proceedings of the 22nd ACM Conference on Embedded Networked Sensor Systems, 2024

In this paper, we develop a SLAM system run-time status monitor (RTS monitor) that performs feature collection with minimal overhead, along with a multi-modality attention-based Deep SLAM Error Estimator (DeepSEE) for error estimation.

Recommended citation: Tianyi Hu, Tim Scargill, Fan Yang, Ying Chen, Guohao Lan, and Maria Gorlatova. 2024. SEESys: Online Pose Error Estimation System for Visual SLAM. In Proceedings of the 22nd ACM Conference on Embedded Networked Sensor Systems (SenSys '24). Association for Computing Machinery, New York, NY, USA, 322–335. https://doi.org/10.1145/3666025.3699341 https://doi.org/10.1145/3666025.3699341

Apple vs. Meta: A Comparative Study on Spatial Tracking in SOTA XR Headsets

Published in Proc. ACM ImmerCom (co-located with ACM MobiCom), Washington, D.C., USA, Nov. 18, 2024, 2024

In this paper, we develop a modular testbed to evaluate the tracking performance of commercial XR headsets, incorporating system calibration, tracking data acquisition, and result analysis, and allowing the integration of external cameras and IMU sensors for comparison with opensource VI-SLAM algorithms.

Recommended citation: Hu, Tianyi & Yang, Fan & Scargill, Tim & Gorlatova, Maria. (2024). Apple vs. Meta: A Comparative Study on Spatial Tracking in SOTA XR Headsets. 10.1145/3636534.3696215. https://www.researchgate.net/publication/384267835_Apple_vs_Meta_A_Comparative_Study_on_Spatial_Tracking_in_SOTA_XR_Headsets

teaching

Fundamentals of Computer Systems and Engineering

Graduate Course, Duke University, Pratt school of Engineering, 190000 - 190000

  • Enriched course content and online resources as an assistant for “Computer Systems” and “Reliable Software” courses, enhancing accessibility and engagement for over 100 students
  • Utilized expertise in Quartus II, Rstudio and C, Verilog, and R coding to guide recitations, providing clarity on complex concepts and facilitating practical applications for better understanding

Towards More Reliable Software

Graduate Course, Duke University, Pratt school of Engineering, 190000 - 190000

  • Developed and maintained extensive online resources for the “Towards More Reliable Software” course, enhancing accessibility and enriching the student learning experience.
  • Assessed and graded assignments, providing constructive feedback to support student understanding and academic growth.
  • Conducted regular online office hours, offering personalized academic assistance and guidance to ensure student success.