Aya Saad
Background and activities
Postdoc researcher working on automating and embedding methods in microscopy into mobile robotic platforms, for real-time targeted plankton-taxa identification and classification, that feeds the process of autonomous navigation control and mapping, which assists in modeling oceanographic phenomena. A new framework for this task is under development. Examples of ML methods explored are listed but not limited to: Mask-RCNN and attention segmentation to perform real-time segmentation; Learning the Bag-of-features to better classify identified objects; Active learning to minimize the labeling task effort; C-GAN to solve the class imbalance problem; and, methods of unsupervised learning to identify unknown extracted objects in-situ.
Scientific, academic and artistic work
Displaying a selection of activities. See all publications in the database
Journal publications
- (2021) MOG: a background extraction approach for data augmentation of time-series images in deep learning segmentation. Proceedings of SPIE, the International Society for Optical Engineering.
- (2021) A combined informative and representative active learning approach for plankton taxa labeling. Proceedings of SPIE, the International Society for Optical Engineering. vol. 11878.
- (2021) CIRAL: a hybrid active learning framework for plankon taxa labeling. IFAC-PapersOnLine.
- (2021) Robust Reasoning for Autonomous Cyber-Physical Systems in Dynamic Environments. Procedia Computer Science. vol. 192.
- (2021) An instance segmentation framework for in-situ plankton taxa assessment. Proceedings of SPIE, the International Society for Optical Engineering. vol. 11605.
- (2021) Safe Learning for Control using Control Lyapunov Functions and Control Barrier Functions: A Review. Procedia Computer Science. vol. 192.
- (2021) Robust Deep Unsupervised Learning Framework to Discover Unseen Plankton Species. Proceedings of SPIE, the International Society for Optical Engineering.
- (2021) Few-Shot Open World Learner. IFAC-PapersOnLine.
- (2020) Advancing Ocean Observation with an AI-driven Mobile Robotic Explorer. Oceanography. vol. 33 (3).
Others
- (2020) Vision-based Real-time Zooplankton Detection and Classification using Faster R-CNN. Ocean Sciences Meeting 2020 . AGU, ASLO and TOS; San Diego, CA. 2020-02-16 - 2020-02-21.
- (2020) Automatic in-situ instance and semantic segmentation of planktonic organisms using Mask R-CNN. Global OCEANS 2020 . IEEE Oceanic Engineering Society & Marine Technology Society; Singapore. 2020-10-05 - 2020-10-14.
- (2020) Towards a Balanced-Labeled-Dataset of Planktons for a Better In-Situ Taxa Identification. Ocean Sciences Meeting 2020 . AGU, ASLO and TOS; San Diego, CA. 2020-02-16 - 2020-02-21.
- (2020) Recent Advances in Visual Sensing and Machine Learning Techniques for in-situ Plankton-taxa Classification. Ocean Sciences Meeting 2020 . AGU, ASLO and TOS; San Diego, CA. 2020-02-16 - 2020-02-21.
- (2020) Robust methods of unsupervised clustering to discover new planktonic species in-situ - poster. Global OCEANS 2020 . IEEE Oceanic Engineering Society & Marine Technology Society; Singapore. 2020-10-05 - 2020-10-14.
- (2020) Leveraging Similarity Metrics to In-Situ Discover Planktonic Interspecies Variations or Mutations. Global OCEANS 2020 . IEEE Oceanic Engineering Society & Marine Technology Society; Singapore. 2020-10-05 - 2020-10-14.
- (2019) AILARON - Autonomous Imaging and Learning Ai RObot identifying plaNkton taxa in-situ. Geilo Winter School 2019: Learning from Data . SINTEF; Geilo. 2019-01-20 - 2019-01-25.