Aya Saad

Aya Saad

Research Scientist

Department of Aquaculture
SINTEF Ocean (https://www.sintef.no/en/all-employees/employee/aya.saad/)

Trondheim, Norway

Research Scientist at the department of aquaculture in SINTEF Ocean participating in many projects working in Artificial Intelligent, Robotic Vision, Machine Learning, data analysis, modeling, knowledge representation, simulations, reasoning, optimization and robust decision support systems.

Research interests

Artificial Intelligence, robust artificial intelligence, reliable computing, constraint programming, constraint reasoning with uncertain data, data modeling, knowledge representation, ontology creation, decision support systems, data analysis, optimization, visualization, machine learning, robotic and computer vision.

Master thesis supervision for 10+ students

Other Activities

A member of the IDUN-ITK group at NTNU, which is part of the “IDUN – from Ph.D. to professor” project. The IDUN project main goal is to promote the female representation in academia. The ITK group main research focus in a 2-years journey is to develop methodologies of Robust AI learning and to ensure their applicability to a broad set of applications such as robotic control. (2020-2022)

Participate as a teacher assistant at the Probabilistic AI summer school, June 2021. The school aims at expanding the knowledge of state-of-the-art Machine learning and artificial intelligence by providing theoretical background and hands-on tutorials on new methodologies, implementations, and practical examples on: probabilistic modeling, variational inference, probabilistic programming, and deep generative models. The school is registered as a Ph.D. course at NTNU with 7.5 credit points. https://probabilistic.ai/.

Publication List

Theses

[T.1] Saad, A. CDF-intervals: A Probabilistic Interval Constraint Framework to Reason about Data with Uncertainty. Open Access Repositorium der Universität Ulm. Dissertation. http://dx.doi.org/10.18725/OPARU-3966, 2016.

Journal Publications

[J.4] Ansari, S., Desai, D.V., Saad, A., Stahl, A., Implications of single-stage deep learning networks in real-time zooplankton identification, Current Science, Volume 125, Issue 11, (2023), pp 1259-1266, doi: 10.18520/cs/v125/i11/1259-1266, 2023.

[J.3] Saad, A., Stahl, A., Våge, A., Davies, E., Nordam, T., Aberle-Malzahn, N., Ludvigsen, M., Johnsen, G. Sousa, J., Rajan, K. Advancing Ocean Observation with an AI-driven Mobile Robotic Explorer. Oceanography. Vol. 33 (3). https://doi.org/10.5670/oceanog.2020.307, 2020.

[J.2] Saad, A., Frühwirth, T., and Gervet, C. 2014. The P-Box CDF-Intervals: A Reliable Constraint Reasoning with Quantifiable Information. Theory and Practice of Logic Programming, vol 14, special issue 4-5: The 30th International Conference on Logic Programming, ICLP2014, July 2014, pp. 461-475. https://doi.org/10.1017/S1471068414000143, 2014.

[J.1] Saad, A., Gervet, C., and Abdennadher, S. 2010. Constraint Reasoning with Uncertain Data Using CDF-Intervals. International Conference on the Integration of AI and OR Techniques in Constraint Programming, CPAIOR 2010, 292–306, Springer Verlag. https://doi.org/10.1007/978-3-642-13520-0_32, 2010.

Conference Publications

[C.16] Saad, A., Su, B., Bjørnson, F.O., A Web-Based Platform for Efficient and Robust Simulation of Aquaculture Systems using Integrated Intelligent Agents. The 27th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems (KES 2023), 2023-09-06- 2023-09-8, Athens, Greece. Procedia Computer Science.

[C.15] Saad, A., Jakobsen, S., Bondø, M., Mulelid, M., Kelasidi, E., StereoYolo+DeepSORT: A Framework to Track Fish from Underwater Stereo Camera in Situ. Proceedings of SPIE, The 16th International Conference on Machine Vision (ICMV), 2023-11-15 - 2023-11-18, Yerevan, Armenia.

[C.14] Saad, A., Nissen, O., Eilertsen, E., Bjørnson, F.O., Hagtun, T., Aspaas, O., Baikas, A., Ohrem, S., Towards Improved Visualization and Optimization of Aquaculture Production Process. The 26th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems (KES 2022), 2022-09-07- 2022-09-9, Verona, Italy. Procedia Computer Science, Elsevier 207 (2022), pp. 3439-3448. https://doi.org/10.1016/j.procs.2022.09.531

[C.13] Saad, S., Håkansson, A., RAMARL: Robustness Analysis with Multi-Agent Reinforcement Learning - Robust Reasoning in Autonomous Cyber-Physical Systems. The 26th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems (KES 2022), 2022-09-07- 2022-09-9, Verona, Italy. Procedia Computer Science, Elsevier 207 (2022), pp. 3662-3671. https://doi.org/10.1016/j.procs.2022.09.426

[C.12] Teigen, A., Saad, A., Stahl, A., Mester, R. Few-Shot Open World Learner. Control Engineering Practice 2021, 13th IFAC Conference on Control Applications in Marine Systems, Robotics, and Vehicles, 2021-09-22 – 2021-09-24, Oldenburg, Germany, (2021), Elsevier, IFAC-PaperOnline Volume 54, Issue 16, (2021), pp 444-449. https://doi.org/10.1016/j.ifacol.2021.10.129.

[C.11] Haug, M., Saad, A., Stahl, A., CIRAL: a hybrid active learning framework for plankon taxa labeling. Control Engineering Practice 2021, 13th IFAC Conference on Control Applications in Marine Systems, Robotics, and Vehicles, 2021-09-22 – 2021-09-24, Oldenburg, Germany, Elsevier, IFAC-PaperOnline Volume 54, Issue 16, (2021), pp 450-457. https://doi.org/10.1016/j.ifacol.2021.10.130.

[C.10] Salvesen, E., Saad, A., Stahl, A. Robust Deep Unsupervised Learning Framework to Discover Unseen Plankton Species. Proceedings of SPIE, The 14th International Conference on Machine Vision (ICMV), 2021-11-08 - 2021-11-12, Rome, Italy, 2021. Volume 12084, pp 241-250, (2022). https://doi.org/10.1117/12.2622489.

[C.9] Borgersen, J., Saad, A., Stahl, A. MOG: a background extraction approach for data augmentation of time-series images in deep learning segmentation. Proceedings of SPIE, The 14th International Conference on Machine Vision (ICMV), 2021-11-08 - 2021-11-12, Rome, Italy, 2021, Volume 12084, pp 360-368, (2022). https://doi.org/10.1117/12.2622899.

[C.8] Anand, A., Seel, K., Gjærum, V., Håkansson, A., Robinson, H., Saad, S. Safe Learning for Control using Control Lyapunov Functions and Control Barrier Functions: A Review. The 25th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems (KES 2021), 2021-09-08- 2021-09-10, Szczecin, Poland. Procedia Computer Science, Volume 192, (2021), pp 3987-3997. https://doi.org/10.1016/j.procs.2021.09.173.

[C.7] Håkansson, A., Saad, S., Anand, A., Gjærum, V., Robinson, H., Seel, K. Robust Reasoning for Autonomous Cyber-Physical Systems in Dynamic Environments. The 25th International Conference on Knowledge-Based and Intelligent Information and Engineering Systems (KES 2021) , 2021-09-08- 2021-09-10, Szczecin, Poland. Procedia Computer Science Volume 192, (2021), pp 3966-3978. https://doi.org/10.1016/j.procs.2021.09.171.

[C.6] Haug, Martin; Saad, Aya; Stahl, Annette. A combined informative and representative active learning approach for plankton taxa labeling. Proceeding of SPIE, The 13th International Conference on Digital Image Processing (ICDIP), June 30th, 2021, Singapore, Volume 11878, (2021), pp 495-503. https://doi.org/10.1117/12.2601096.

[C.5] Saad, A., Bergum, S., Stahl, A. An Instance Segmentation Framework for In-situ Plankton Taxa Assessment. Proceedings of SPIE, The 13th International Conference on Machine Vision (ICMV), 1160511, Rome, Italy, 2020-11-02 - 2020-11-06, Volume 11605, (2021), pp 294-303. https://doi.org/10.1117/12.2587693.

[C.4] Bergum, S., Saad, A., Stahl, A. Automatic in-situ instance and semantic segmentation of planktonic organisms using Mask R-CNN. IEEE Oceanic Engineering Society & Marine Technology Society - Singapore, 2020-10-5 - 2020-10-14, (2020). https://ieeexplore.ieee.org/document/9389377.

[C.3] Salvesen, E., Saad, A., Stahl, A. Robust methods of unsupervised clustering to discover new planktonic species in-situ. IEEE Oceanic Engineering Society & Marine Technology Society - Singapore, 2020-10-5 - 2020-10-14, (2020). https://ieeexplore.ieee.org/document/9389188.

[C.2] Teigen, A., Saad, A., Stahl, A. Leveraging Similarity Metrics to In-Situ Discover Planktonic Interspecies Variations or Mutations. IEEE Oceanic Engineering Society & Marine Technology Society - Singapore, 2020-10-5 - 2020-10-14, (2020). https://ieeexplore.ieee.org/document/9388998.

[C.1] Mudawwar, M. and Saad, A. 2001. The k-ary n-cube Network and its Dual: a Comparative Study, in Proceedings of the 13th IASTED International Conference on Parallel and Distributed Computing and Systems, Anaheim, California, pages 254- 259, August 21-24, 2001.

Invited Talks and Workshops/Demos

[W8] Kiese, O., Saad, A., Stahl, A. Presentation: 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, USA, 2020-02-16 - 2020-02-21, 2020.

[W.7] Ansari, S., Saad, A., Stahl, A., Rajachandran, M. 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, https://doi.org/10.1002/essoar.10502404.1, 2020.

[W.6] Saad, A., Davies, E., Stahl, A. Poster presentation: 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., USA, 2020-02-16 - 2020-02-21, https://doi.org/10.1002/essoar.10502403.1, 2020.

[W.5] Saad, A., Stahl, A. Poster presentation: 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, Norway, 2019.

[W.4] Saad, A. 2014. CDF-Intervals: A Reliable Framework to Reason about Data with Uncertainty. The 10th ICLP Doctoral Consortium, Vienna, Austria, 2014.

[W.3] Saad, A., Gervet, C., and Frühwirth, T. CDF-Intervals: Reliable Constraint Reasoning with Quantifiable Information. The Proceeding of the Doctoral Program of the 18th International Conference on Principles and Practice of Constraint Programming, Quebec, Canada, 2012.

[W.2] Saad, A., Gervet, C., and Frühwirth, T. CDF-Intervals Revisited. The Eleventh International Workshop on Constraint Modelling and Reformulation – ModRef, Quebec, Canada, 2012.

[W.1] Saad, A., Invited Talk with title: From a Generic to a Customized Framework: Paving the Way for WebCT, Presented in Syllabus Fall 2002 Conference. November 2002, Boston Mariott Newton, Boston MA, USA, 2002.

Gemini article by Nancy Bazilchuk, A robotic microplankton sniffer-dog https://norwegianscitechnews.com/2021/05/a-robotic-microplankton-sniffer-dog/ (May 2021)

Conference Organizations and Participations

Chair of the Robust AI, an invited session at the International Conference of Knowledge-Based and Intelligent Engineering Systems (KES) since September 2021.

Member of the reviewing committees