Research topics: Trustworthy Machine Learning; Interpretable Machine Learning for Healthcare; Trustworthy models for 3D point clouds; Robust Machine Learning for Space Debris; Google Scholar profile: https://scholar.google.com/citations?user=Y9CArh4AAAAJ Claudia Soares received a B.Sc., M.Sc., and Ph.D. in Electrical and Computer Engineering from Instituto Superior Tecnico, Portugal, as well as a Diploma in modern languages and literature from Nova University of Lisbon, Portugal. Since 2021 she is a researcher with NOVALINCS and an Assistant Professor at NOVA School of Science and Technology, Portugal. Her research focuses on using optimization, physics, and probability theory to develop trustworthy machine learning models and algorithms for various applications, including environmental monitoring with 3D point clouds, trustworthy healthcare applications, and space. PhD supervision experience Prof. Soares has experience supervising and co-supervising Ph.D. students and collaborating with industry partners such as Epic Games, CUF Health, GMV, and Neuraspace, as well as international researchers from institutions like Carnegie Mellon University, the University of Milan, the University of Novi Sad, and Washington University. Successful PhD graduates include Filipa Valdeira, Assistant Researcher in the Tenure Track, Stevo Rackovic, who submitted a PhD thesis for an industrial PhD with Epic Games, and Pedro Valdeira with the CMU-Portugal dual degree, starting a Post-Doc in industry. Main projects: 1. Coordinator. (2025-2026) HyCARE - HyCARE: Hybrid Clinical Decision Support System Integrating Rule-based and Machine Learning Approaches (FCT,I.P. €120,439.04) 2. Coordinator. (2022-2023) MD2TRUST - Trustworthy data science for improving healthcare efficiency: the case of the medical referral process (FCT,I.P. CMU Portugal €69,046.70) 3. Local coordinator. (2018-2022) H2020-MSCA-ITN BIGMATH: BIG data challenges for MATHematics (EU €1,747,505.16) 4. Co-Principal investigator (2019-2021) Identifying and Predicting Emergency Admissions (FCT.I.P. Data Science for the Public Administration €283,103.75) 5. Local coordinator. (2022-2025) Artificial Intelligence Fights Space Debris (EU PRR total: €24,395,404.00 local: €736,000.00) Selected publications: 1. Caldas, F. M., Soares, C. (2022). A Temporal Fusion Transformer for Long-term Explainable Prediction of Emergency Department Overcrowding. In Joint European Conference on Machine Learning and Knowledge Discovery in Databases (pp. 71-88). Grenoble. Cham: Springer Nature Switzerland. 2. Rackovic, Stevo, Dušan Jakovetic, Cláudia Soares (2024). "Refined Inverse Rigging: A Balanced Approach to High-fidelity Blendshape Animation." SIGGRAPH Asia 2024 Conference Papers. 3. Metelo, Frederico, Soares, Claudia, Rackovic, Stevo, Costa, Pedro, (2024) "PeersimGym: An Environment for Solving the Task Offloading Problem with Reinforcement Learning." Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Cham: Springer Nature Switzerland. 4. Valdeira, P., Xavier, J., Soares, C., Chi, Y. (2025). Communication-efficient vertical federated learning via compressed error feedback. IEEE Transactions on Signal Processing. 5. Caldas, Francisco; Soares, Claudia, (2024) Machine Learning in Orbit Estimation: a Survey, Acta Astronautica. 6. Valdeira, F. M., Ferreira, R., Micheletti, A., Soares, C. (2023). Probabilistic Registration for Gaussian Process Three-Dimensional Shape Modelling in the Presence of Extensive Missing Data. SIAM Journal on Mathematics of Data Science, 5(2), 502-527. 7. Rackovic, S., Soares, C., Jakovetic, D. (2023). Distributed Solution of the Blendshape Rig Inversion Problem. In SIGGRAPH Asia 2023 Technical Communications (pp. 1-4). Sidney. Selected to appear in ACM Kudos (https://www.growkudos.com/publications/10.1145%25252F3610543.3626166/reader)