Despoina Paschalidou
Postdoctoral Researcher at Stanford University
Palo Alto, California, United States
Summary
👤
Senior🎓
Top SchoolDespoina Paschalidou is a computer vision researcher with 12 years of experience, currently a postdoctoral researcher at Stanford after completing a PhD at ETH Zürich under Andreas Geiger and Luc van Gool. Her work spans robotics, RGB-D perception, and probabilistic models—building everything from motion-detection and face-recognition pipelines for RoboCup Rescue to a novel supervised variant of Latent Dirichlet Allocation during her master’s research. She combines strong systems engineering (ROS-based, component architectures) with theoretical depth in learning systems, enabling rapid prototyping of perception modules that are research-ready and deployable. Based in Palo Alto, she brings cross-cultural academic training from Greece, Switzerland, and the US, and a proven track record of leading teams to competition success and high-impact publications.
12 years of coding experience
General Lyceum Graduation Certificate, 19.5, General Lyceum Graduation Certificate, 19.5 at Eniaio Lykeio MikrasBSc + MSc equivalent (10 semesters curriculum, 311 ECTS), Electrical and Computer Engineering, 8.20, BSc + MSc equivalent (10 semesters curriculum, 311 ECTS), Electrical and Computer Engineering, 8.20 at Aristotle University of Thessaloniki (AUTH)Doctor of Philosophy (Ph.D.), Computer Vision, Doctor of Philosophy (Ph.D.), Computer Vision at ETH ZurichPostodocoral Researcher, Postodocoral Researcher at Stanford University
English, Greek, German
Github Skills (18)
scene10
visualization9
3d-graphics9
threejs9
synthesis9
3d8
pytorch8
webgl8
transformers7
3d-reconstruction7
computer-vision7
ray6
machine-learning6
deep-learning5
python5
Programming languages (2)
Github contributions (5)
paschalidoud/superquadric_parsing
Apr 2019 - Jun 2020
Code for "Superquadrics Revisited: Learning 3D Shape Parsing beyond Cuboids", CVPR 2019
Contributions:18 commits, 19 pushes, 2 branches in 1 year 2 months
nv-tlabs/ATISS
Sep 2020 - Nov 2022
Code for "ATISS: Autoregressive Transformers for Indoor Scene Synthesis", NeurIPS 2021
Contributions:20 commits, 1 PR, 17 pushes in 2 years 3 months
pytorchtransformersneurips-2021synthesisneurips
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