Expert Research Scientist at United Imaging Intelligence
New York City Metropolitan Area United States
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Summary
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Senior
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Top School
Benjamin Planche is an expert research scientist specializing in computer vision and machine learning, with 13 years of experience tackling data-scarce industrial and medical imaging problems. Based in the New York City metropolitan area, he translates frontier research into practical product solutions, leading rapid prototyping and feasibility studies for AI-enabled medical devices, imaging, and robotics. He has guided research teams at United Imaging Intelligence and UII America, Inc., and previously advanced robust vision systems at Siemens, focusing on training under constrained data and large-scale industrial/government projects. His academic pedigree includes a summa cum laude PhD in Computer Science from Universität Passau, along with master's degrees from Passau and Luleå University of Technology. He is an active open-source contributor, including work on Hands-On Computer Vision with TensorFlow 2 for Packt, implementing and refining CV models and notebooks for MNIST in TF2. This blend of rigorous research, leadership, and hands-on engineering helps him turn complex requirements into reliable AI-backed solutions at scale.
13 years of coding experience
5 years of employment as a software developer
Master's degree, Computer Science, Diplomed with Honors, Master's degree, Computer Science, Diplomed with Honors at Institut national des Sciences appliquées de Lyon
Master of Science (M.Sc.), Computer Science, Master of Science (M.Sc.), Computer Science at Luleå University of Technology
Diplomed with Honors (Summa Cum Laude), Diplomed with Honors (Summa Cum Laude) at Universität Passau
French, English, German, Japanese, Chinese, Swedish
Hands-On Computer Vision with TensorFlow 2, published by Packt
Role in this project:
ML Engineer
Contributions:22 commits, 6 PRs, 43 pushes in 1 year 6 months
Contributions summary:Benjamin's contributions focused on implementing and refining computer vision models within the TensorFlow 2 framework. Their work included adding code and descriptive notebooks for computer vision concepts, with an emphasis on building neural networks from scratch and applying them to classification tasks like MNIST. Furthermore, they adapted the code to the target TF2 framework, indicating hands-on experience with the specified library.
Contributions:43 commits, 7 pushes in 5 years 5 months
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