Benjamin Biggs
Research Scientist at Luma AI
San Francisco, California, United States
Summary
👤
Senior🎓
Top SchoolBenjamin Biggs is a research scientist with a decade of experience building generative AI and computer vision systems, currently focused on diffusion models and LLMs at Luma AI after senior applied-science roles at Amazon Web Services. He holds a PhD from the University of Cambridge where he developed 3D reconstruction methods for challenging object classes and has applied that expertise across pose estimation, segmentation and action recognition. Benjamin blends academic rigor with product-focused engineering—transitioning research prototypes into production during roles at GSK and Amazon—and has led cross-functional efforts and supervised junior researchers. A first-class Discrete Mathematics graduate with departmental awards from Warwick, he brings strong mathematical foundations to modern deep learning problems. Outside work he is an accomplished musician and theatre-goer, and his recent enthusiasm for skiing hints at a willingness to take on steep learning curves.
10 years of coding experience
10 years of employment as a software developer
Doctor of Philosophy - PhD Computer Vision and Machine Learning, Doctor of Philosophy - PhD Computer Vision and Machine Learning at University of CambridgeBachelor’s Degree Discrete Mathematics, Bachelor’s Degree Discrete Mathematics at University of WarwickHigh School A Level, High School A Level at The Bishop's Stortford High School
English
Github Skills (90)
3d10
expectation-maximization10
keypoint10
rendering9
benchmark9
python9
model-fitting9
optimization9
whatsapp9
pytorch9
machine-learning9
reinforcement-learning9
odometry9
pyqt9
llm-inference8
Programming languages (10)
Github contributions (5)
benjiebob/BADJA
Nov 2018 - Oct 2022
Benchmark Animal Dataset of Joint Annotations (BADJA) with example code, as introduced in "Creatures Great and SMAL: Recovering the shape and motion of animals from video" (ACCV 2018).
Contributions:24 commits, 26 pushes, 1 branch in 3 years 11 months
pytorchannotationsdeep-learningdatasetmotion
benjiebob/StanfordExtra
Jul 2020 - Oct 2022
12k labelled instances of dogs in-the-wild with 2D keypoint and segmentations. Dataset released with our ECCV 2020 paper: Who Left the Dogs Out? 3D Animal Reconstruction with Expectation Maximization in the Loop.
Contributions:30 commits, 13 pushes, 5 comments in 2 years 3 months
3dexpectation-maximizationkeypoint
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