Amir Shahroudy
Product Owner at Zenseact
Gothenburg, Sweden
Summary
👤
Senior🎓
Top SchoolAmir Shahroudy is a Product Owner at Zenseact with two decades of experience bridging machine vision research and production software for autonomous driving. He holds a PhD in Machine Vision from Nanyang Technological University and has moved from hands-on roles—software developer, scrum master and senior machine vision engineer—into product leadership, enabling faster delivery of perception features. His expertise spans computer vision, pattern recognition and ML, and he has published practical tooling such as MATLAB visualization scripts for the widely used NTU RGB+D action recognition dataset. Based in Gothenburg, Amir pairs deep academic grounding with a decade of industry experience to translate complex vision research into customer-focused product roadmaps. He is particularly adept at aligning cross-functional teams to integrate novel perception algorithms into safety-critical automotive stacks.
10 years of coding experience
7 years of employment as a software developer
Master of Science (MS) Artificial Intelligence, Master of Science (MS) Artificial Intelligence at Sharif University of TechnologyBachelor’s Degree Computer Engineering, Bachelor’s Degree Computer Engineering at Isfahan University of TechnologyDoctor of Philosophy (Ph.D.) Machine Vision, Doctor of Philosophy (Ph.D.) Machine Vision at Nanyang Technological University Singapore
English, Persian, Swedish, Arabic
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (5)
computer-vision10
matlab10
data-visualisation9
data-visualization9
data-visualizations9
Programming languages (2)
Github contributions (5)
shahroudy/NTURGB-D
Apr 2016 - Mar 2021
Info and sample codes for "NTU RGB+D Action Recognition Dataset"
Role in this project:
Data Scientist
Data ScientistContributions:26 commits, 2 PRs, 24 pushes in 4 years 11 months
Contributions summary:Amir primarily focused on developing code for visualizing and analyzing 3D human pose data from the NTU RGB+D dataset. Their contributions involved writing and updating MATLAB scripts to draw skeleton data on depth, IR, and RGB frames. They utilized the data to create visual representations, enhancing the ability to interpret and analyze human activity from multiple modalities.
action-recognition
shahroudy/deep-head-pose
Sep 2019 - Sep 2019
:fire::fire: Deep Learning Head Pose Estimation using PyTorch.
Contributions:37 commits, 31 pushes in 6 days
deep-learningpose-estimationpytorch
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