Jingwen Wang
PHD Student at UCL
London, England, United Kingdom
Summary
👤
Senior🎓
Top SchoolJingwen Wang is a PhD student at UCL with nine years of experience focused on 3D computer vision, specializing in object-oriented semantic SLAM under the supervision of Prof. Lourdes Agapito and Prof. Niloy Mitra. Affiliated with UCL’s Foundational AI CDT, she combines rigorous academic research with hands-on teaching experience in image processing and robot vision navigation. Her background spans a Distinction MRes in Robotics and a first-class electrical engineering undergraduate foundation, reflecting strong theoretical and engineering skills. She has industrial research experience from Emotech Ltd and a track record of translating vision research into practical systems. Based in London, Jingwen is open to long-term opportunities while currently dedicated to her doctoral work. An understated strength is her ability to bridge robotics curricula and cutting-edge 3D vision research, mentoring students while advancing novel SLAM methods.
9 years of coding experience
Bachelor's Degree, Electrical Engineering and Electronics, All First Class. Year 2 average 89.3, Year 3 average 79.2, Bachelor's Degree, Electrical Engineering and Electronics, All First Class. Year 2 average 89.3, Year 3 average 79.2 at University of LiverpoolBachelor's Degree, Electrical and Electronics Engineering, BEng with First class honours, Bachelor's Degree, Electrical and Electronics Engineering, BEng with First class honours at Xi'an Jiaotong-Liverpool UniversityUniversity College London
English, Chinese
Github Skills (17)
3d-object-detection9
3d-reconstruction8
lidar8
kitti-dataset8
point-cloud7
slam7
object-detection6
depth6
pytorch6
radiance6
computer-vision5
openmmlab5
depth-estimation5
neural-rendering4
deep-learning4
Programming languages (3)
Github contributions (5)
JingwenWang95/DSP-SLAM
Oct 2021 - Mar 2022
Contributions:16 commits, 12 pushes, 37 comments in 4 months
JingwenWang95/home-platform
Aug 2018 - Mar 2019
HoME: a Household Multimodal Environment is a platform for artificial agents to learn from vision, audio, semantics, physics, and interaction with objects and other agents, all within a realistic context.
Contributions:100 pushes, 4 branches in 7 months
physicsinteractionvisiondeep-learningreinforcement-learning
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