About Me
I specialize in autonomous surgical robotics integrating computer vision, robot learning, and control, with extensive real-robot experience in medical applications.
I'm a final-year PhD candidate (expected October 2026) at Imperial College London's Hamlyn Centre for Robotic Surgery, developing autonomous robotic systems that push the boundaries of surgical precision and safety. My research bridges computer vision, imitation learning, deep reinforcement learning, and robotics control to enable robots to perform complex medical instrument manipulation tasks.
With a track record that includes first-author publications at ICRA, experience integrating cutting-edge Vision-Language-Action models at Kinisi Robotics, and hands-on work with platforms from da Vinci surgical robots to KUKA systems, I specialise in translating AI advances into practical robotic solutions. My work on sim-to-real learning and visual servoing has achieved human-level performance in medical sampling tasks.
I'm actively seeking opportunities for full-time robotics research engineer positions starting Autumn 2026, passionate about advancing robotics and AI systems that solve real-world challenges and bridge the gap between research and deployment.
If you are interested in introducing robotics and AI technology into Device Art or any form of art expression — please feel free to reach out!
Open to discussing industry opportunities and research collaborations in AI and robotics — feel free to connect!
Education
PhD in Robotics and AI for Healthcare
Imperial College London — The Hamlyn Centre for Robotic Surgery
Research topic: Autonomous Robotic System for Medical Instrument Manipulation
Thesis: Robotic-Assisted Diffuse Reflectance Spectroscopy Sampling for Tissue Assessment: From Model-Driven Control to Learned Policies
London, UK
MRes in Medical Robotics & Image-guided Intervention
Imperial College London — The Hamlyn Centre for Robotic Surgery
Thesis: Imitation Learning for Robotic Radio Sensor Manipulation
London, UK
BEng in Mechatronics and Robotic Systems
University of Liverpool (2+2 with Xi'an Jiaotong-Liverpool University)
Final year project: Deep learning segmentation tools for lung X-ray images (COVID-19)
Liverpool, UK & Suzhou, China
Research Experience
An ongoing research project developing a complete autonomous robotic learning pipeline for surgical instrument manipulation, integrating multimodal sensing with advanced generative policy architectures. Demonstrated significant real-world performance improvements on a clinical surgical robot platform. Details withheld pending publication.
- Employed Reinforcement Learning to train a robust policy in simulation with domain randomisation.
- Proposed teacher-student training to distil policy with privilege information and extend perception capabilities.
- Improved spatial generalisation for uncovered workspace by 81%.
- Developed an adaptive scanning method to enhance initial detection robustness.
- Designed a deep reinforcement learning framework with reward shaping to optimise radiotracer detection precision and operational efficiency.
- Validated on simulated and real surgical robot systems, demonstrating an 80% success rate.
- Collaborated across interdisciplinary teams (clinicians, engineers, researchers) to translate clinical needs into technical solutions.
- Designed a complete robotic system integrating a KUKA arm with multiple sensors.
- Proposed a vision-based precise contact state estimation module for millimetre-level control.
- Minimised variance to human-level intra-observer variance as gold-standard manual sampling.
Industry Experience
Robotics Research Engineer — Intern
Robot Learning Team, Kinisi Robotics — Bristol & London, UK
- Integrated and trained Vision-Language-Action (VLA) models including PI0, PI0FAST, and GR00T for autonomous warehouse manipulation tasks in simulation and on physical robots.
- Developed simulation environments for benchmarking bimanual humanoid robotics policies.
- Optimised ML policy training pipelines and evaluation infrastructure.
Technical Skills
Core tools and methods I use across surgical robotics, robot learning, and computer vision.
Publications
"Overcoming Imperfect Kinematics in Surgical Robotics Through Sim-to-Real Visuomotor Learning"
ICRA 2026 IEEE International Conference on Robotics and Automation, 2026 — Accepted
"Automatic Robotic-Assisted Diffuse Reflectance Spectroscopy Scanning System"
ICRA 2025 IEEE International Conference on Robotics and Automation, 2025
ICRA 2025 IEEE International Conference on Robotics and Automation, 2025
"Deep imitation learning for automated drop-in gamma probe manipulation"
HSMR 2023 15th Hamlyn Symposium on Medical Robotics, Jun. 2023
ICRA 2024 IEEE International Conference on Robotics and Automation, 2024, pp. 8180–8186
"TransBridge: A lightweight transformer for left ventricle segmentation in echocardiography"
MICCAI 2021 Workshop Simplifying Medical Ultrasound (ASMUS), MICCAI Workshop, 2021, pp. 63–72
Mentoring
Supervised and provided hands-on research guidance for 10 MRes students at the Hamlyn Centre, Imperial College London, across thesis projects in medical robotics and robot learning.