Danny Bickson
CEO at Visual Layer
Jerusalem District, Israel
Summary
🤩
Rockstar🎓
Top SchoolDanny Bickson is a serial founder and technical leader with 15 years building large-scale machine learning and computer vision systems, currently serving as General Manager of the Camtek Gen AI Research Center after Visual Layer’s acquisition. He co-founded GraphLab/Turi (acquired by Apple), led applied deep learning teams at Apple for manufacturing inspection, and now focuses on GenAI-powered visual intelligence that indexes massive image and video collections for rapid semantic search and investigation. Comfortable across research and production, he has deep expertise in distributed ML (PowerGraph/GraphLab), PyTorch-based CV models, and practical data pipelines that drive deployments at scale. An active open-source contributor, his work spans backend algorithm implementation and dataset tooling such as fastdup, reflecting a hands-on approach to improving data quality and model readiness. Based in Jerusalem with a PhD in computer science, he blends academic rigor with repeated startup exits and selective angel investments in the AI infrastructure ecosystem.
15 years of coding experience
16 years of employment as a software developer
PHD, PHD at HUJI
Github Skills (13)
object-detection10
matrix-factorization10
algorithms10
machine-learning10
jupyter-notebook10
c-language10
cprogramming-language10
python10
image10
data-analysis10
computer-vision9
linear-algebra9
datasets8
Programming languages (6)
Github contributions (5)
jegonzal/PowerGraph
Oct 2010 - Aug 2015
PowerGraph: A framework for large-scale machine learning and graph computation.
Role in this project:
Back-end Developer
Back-end DeveloperContributions:1011 commits, 3 PRs, 4 pushes in 4 years 11 months
Contributions summary:Danny contributed to the PowerGraph project by fixing IT++ compilation errors and adding user-KNN and item-KNN functionalities, indicating a focus on backend development and algorithm implementation. They addressed compilation issues related to the use of float types in the demo apps for clustering, as well as adding the ability to incorporate user-KNN and item-KNN algorithms within the project's framework. Further, they added comments, and fixed outputs of existing and added functionalities within the demo.
computationcomputation-graphmachine-learningscalelarge-scale-machine-learning
visual-layer/fastdup
May 2022 - Jan 2023
fastdup is a powerful, free tool designed to rapidly generate valuable insights from image and video datasets. It helps enhance the quality of both images and labels, while significantly reducing data operation costs, all with unmatched scalability.
Role in this project:Data Scientist
Data ScientistContributions:135 releases, 24 reviews, 50 commits in 8 months
Contributions summary:Danny's primary contribution is creating and modifying a Jupyter Notebook, specifically an example using the UAV Drone Dataset. The notebook involves downloading, extracting, and analyzing data, including bounding box annotations, implying an interest in object detection and potentially image analysis. Based on the code, the user is working with images and annotation data and using Python libraries for data processing and analysis.
data-curationkaggleduplicate-imagespythonclusters
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