Dr. Aishwarya Venkataramanan
Conrad Philipp
Address: Computer Vision Group
Department of Mathematics and Computer Science
Friedrich Schiller University of Jena
Ernst-Abbe-Platz 2
07743 Jena
Room: 1223
Phone: +49 (0) 3641 9 46426
E-mail: aishwarya (dot) venkataramanan (at) uni-jena (dot) de
Curriculum Vitae
since Jun 2024 Research Associate / Postdoc
Computer Vision Group, Friedrich Schiller University Jena
2020 – 2023 PhD Student
University of Lorraine and IRL GT-CNRS, France
PhD Thesis: “Automatic Identification of Diatoms using Deep Learning to Improve
Ecological Diagnosis of Aquatic Environments”
2020 Research Engineer
DREAM Lab, Georgia Tech-CNRS IRL2958
2018 – 2020 M. Sc. Studies in Electrical and Computer Engineering
Georgia Institute of Technology, Metz, France
Master Thesis: “Generation of Realistic Tree Barks using Deep Learning”
2014 – 2018 B. Eng. Studies in Electrical and Electronics
Sri Sivasubramaniya Nadar College of Engineering (Anna University)
Research Interests
  • Computer Vision
  • Deep Learning
  • Uncertainty Quantification
Aishwarya Venkataramanan, Assia Benbihi, Martin Laviale, Cédric Pradalier:
Gaussian Latent Representations for Uncertainty Estimation using Mahalanobis Distance in Deep Classifiers.
ICCV Workshop on Workshop on Uncertainty Quantification for Computer Vision (ICCV-WS). 2023.
[bibtex] [web] [abstract]
Aishwarya Venkataramanan, Martin Laviale, Cédric Pradalier:
Integrating Visual and Semantic Similarity Using Hierarchies for Image Retrieval.
International Conference on Computer Vision Systems (ICVS). Pages 422-431. 2023.
[bibtex] [web] [doi] [abstract]
Aishwarya Venkataramanan, Pierre Faure-Giovagnoli, Cyril Regan, David Heudre, Cécile Figus, Philippe Usseglio-Polatera, Cédric Pradalier, Martin Laviale:
Usefulness of Synthetic Datasets for Diatom Automatic Detection using a Deep-learning Approach.
Engineering Applications of Artificial Intelligence. 117 : pp. 105594. 2023.
[bibtex] [web] [doi] [abstract]
Aishwarya Venkataramanan, Antoine Richard, Cédric Pradalier:
A Data Driven Approach to Generate Realistic 3D Tree Barks.
Graphical Models. 123 : pp. 101166. 2022.
[bibtex] [web] [doi] [abstract]
Aishwarya Venkataramanan, Martin Laviale, Cécile Figus, Philippe Usseglio-Polatera, Cédric Pradalier:
Tackling Inter-Class Similarity and Intra-Class Variance for Microscopic Image-based Classification.
International Conference on Computer Vision Systems (ICVS). Pages 93-103. 2021.
[bibtex] [web] [doi] [abstract]