S. Khademi
S. Khademi
Profiel
Biografie
Since April 2021, Seyran Khademi is an Assistant Professor at the faculty of Architecture and the Built Environment (ABE) and the co-director of AiDAPT lab (AI for Design, Analysis, and Optimization in Architecture and the Built Environment). She is working as an interdisciplinary researcher between Computer Vision lab and Architecture Department at ABE. Her research interest lies at the intersection of Data, Computer Vision and Deep Learning in the context of man-made imagery including illustrations and visual data for Architectural Design. In 2020 she was honored to be the research in residence fellow at the Royal Library of the Netherlands working on visual recognition for children’s book collection. In 2017 she was appointed as an postdoctoral researcher at Computer vision lab working on the ArchiMediaL project, regarding the automatic detection of buildings and architectural elements in visual data focusing on Computer Vision and Deep Learning methods for archival data and street-view imagery. Seyran received her Ph.D. in signal processing and optimization in 2015 from TU Delft, followed by postdoctoral research on Intelligent Audio and Speech algorithms. She received her MSc. degree in Signal Processing from the Chalmers University of Technology in Gothenburg, Sweden, in 2010 and her BSc degree in telecommunications from the University of Tabriz in Iran.
Expertise
Computer Vision, Deep Learning, Visual Data, Visual Similarity Learning, Data Science, Machine Learning, Optimization, Statistics, Quantitative Visual Analysis
Expertise
Publicaties
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2022
AmsterTime
A Visual Place Recognition Benchmark Dataset for Severe Domain Shift
Burak Yildiz / Seyran Khademi / Ronald Maria Siebes / Jan Van Gemert -
2022
Computer Vision and Human–Robot Collaboration Supported Design-to-Robotic-Assembly
Henriette Bier / Seyran Khademi / Casper van Engelenburg / J. Micah Prendergast / Luka Peternel
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2022
Reconstructing compact building models from point clouds using deep implicit fields
Zhaiyu Chen / Hugo Ledoux / Seyran Khademi / Liangliang Nan
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2021
Deep Learning from History
Unlocking Historical Visual Sources Through Artificial Intelligence
Seyran Khademi / Tino Mager / Ronald Siebes -
2020
On Sensitive Minima in Margin-Based Deep Distance Learning
Reza Serajeh / Seyran Khademi / Amir Mousavinia / Jan C. van Gemert
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