Chris Xiaoxuan Lu

Assistant Professor @ School of Informatics, University of Edinburgh

I am an Assistant Professor in the School of Informatics at the University of Edinburgh. My research interests broadly lie in cyber-physical systems (e.g., mobile robots and mixed reality devices) with the goal to drastically improve their reliability, intelligence and security in the wild. These works are motivated by the urgent need of robust localization and spatial perception solutions for mobile robotics and wearables to deal with i. sensing degradation (e.g., bad weather & poor illumination), ii. resource constraints (e.g., payloads, compute & energy budgets) and iii. malicious faults (e.g., adversarial attacks). Representative lines of research I have been recently focusing on are 4D Automotive Radar-enabled Mobile Autonomy and Sense Augmentation for First Responders.

Before coming to Edinburgh, I did both my PhD study and post-doctoral in the Department of Computer Science, University of Oxford. Even earlier, I received my M.Eng degree from Nanyang Technological University (NTU).


Nov 21, 2023 My group has a 5-year PostDoc vacancy in the area of AI-enhanced workflows to join a recently launched Virtual Production R&D Lab funded by the UKRI. Please find the job description and apply [here] if you are interested. Application deadline 18th Dec. 2024.
Oct 1, 2022 Our embedded AI enabled firefighting helmet was covered by more than 20 top-tier hits across broadcast TV, radio, international newswires, including: BBC News, BBC Good Morning Scotland, Reuters, STV, Planet Radio, Sky News, Evening Standard Tech & Science Daily, Yahoo!, Scottish Daily Express, The Independent, Scottish Field, Scottish Daily Mail, Italy 24 News, Irish News, Engineering & Technology, Digit News, UoE News (FrontPage).

selected publications

  1. IoT-J
    CubeLearn: End-to-end Learning for Human Motion Recognition from Raw mmWave Radar Signals
    Zhao, Peijun, Lu, Chris Xiaoxuan, Wang, Bing, Trigoni, Niki, and Markham, Andrew
    IEEE Internet of Things Journal 2023
  2. CVPR’23
    Hidden Gems: 4D Radar Scene Flow Learning Using Cross-Modal Supervision
    Ding, Fangqiang, Palffy, Andras, Gavrila, Dariu M., and Lu, Chris Xiaoxuan
    In IEEE Conference on Computer Vision and Pattern Recognition 2023
    CVPR’23 Highlight
  3. T-RO
    Graph-Based Thermal–Inertial SLAM With Probabilistic Neural Networks
    Saputra, Muhamad Risqi U., Lu, Chris Xiaoxuan, Gusmao, Pedro Porto B., Wang, Bing, Markham, Andrew, and Trigoni, Niki
    IEEE Transactions on Robotics 2022
  4. ICRA’22
    AutoPlace: Robust Place Recognition with Single-chip Automotive Radar
    Cai, Kaiwen, Wang, Bing, and Lu, Chris Xiaoxuan
    In International Conference on Robotics and Automation (ICRA) 2022
  5. MobiSys’20
    See through smoke: robust indoor mapping with low-cost mmWave radar
    Lu, Chris Xiaoxuan, Rosa, Stefano, Zhao, Peijun, Wang, Bing, Chen, Changhao, Stankovic, John A., Trigoni, Niki, and Markham, Andrew
    In The 18th Annual International Conference on Mobile Systems, Applications, and Services (MobiSys) 2020
  6. SenSys’20
    milliEgo: single-chip mmWave radar aided egomotion estimation via deep sensor fusion
    Lu, Chris Xiaoxuan, Saputra, Muhamad Risqi U., Zhao, Peijun, Almalioglu, Yasin, Gusmao, Pedro P. B., Chen, Changhao, Sun, Ke, Trigoni, Niki, and Markham, Andrew
    In The 18th ACM Conference on Embedded Networked Sensor Systems (SenSys) 2020
  7. MobiCom’18
    Simultaneous Localization and Mapping with Power Network Electromagnetic Field
    Lu, Chris Xiaoxuan, Li, Yang, Zhao, Peijun, Chen, Changhao, Xie, Linhai, Wen, Hongkai, Tan, Rui, and Trigoni, Niki
    In The 24th Annual International Conference on Mobile Computing and Networking (MobiCom) 2018
  8. AAAI’18
    IONet: Learning to Cure the Curse of Drift in Inertial Odometry
    Chen, Changhao, Lu, Chris Xiaoxuan, Markham, Andrew, and Trigoni, Niki
    In The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI) 2018