A. Paulo Coimbra is an Assistant Professor at the Department of Electrical and Computer Engineering, University of Coimbra, where he has worked since 1996. He also holds a researcher position at the Institute of Systems and Robotics (ISR-Coimbra). His career spans electromagnetic and thermal analysis, robotics, and renewable energy systems. BSc and PhD in Electrical Engineering from University of Coimbra (1985, 1996) IEEE Member since 1995 Consultant at CWJ-Projeto SA since 2008 Co-author of 1 book, 3 book chapters, and 4 patents Research interests focus on electromagnetic compatibility , biped and hyper-redundant robotics , and vision-based navigation systems . Recent publications highlight applications in microgrid optimization , autonomous wildfire mitigation , and deep learning for environmental monitoring . Grant collaborations include projects like: SwitHome (post-stroke rehabilitation, 2018) FIREPROTECT (wildfire risk mitigation, 2017-2020) NEXTSTEP (smart substations, 2016-2020) Humanoid robot gait adaptation (2016-2019) He has contributed to over 120 conference papers and 35 journal publications, with work spanning robotics , energy systems , and environmental applications .
Pedro Manuel Quintas Aguiar is an Associate Professor at the Department of Electrical and Computer Engineering, Instituto Superior Técnico (University of Lisbon), specializing in Systems, Decision, and Control. His work integrates optimization, machine learning, and computer vision with applications in distributed systems, 3D reconstruction, and robotics. He leads research in structured prediction and distributed algorithms for sensor networks. Education: PhD in Electrical Engineering (assumed based on role) Not explicitly stated but inferred from academic rank Research Interests: Optimization algorithms for distributed systems Machine learning for computer vision (attention mechanisms, shape representation) 3D reconstruction from video and motion estimation Robotic perception and bin-picking applications His recent work emphasizes scalable distributed optimization (e.g., D-ADMM algorithms) and robust visual attention models. Grants & Labs: Collaborations on sensor networks and underwater communications Focus on real-time systems and low-light video processing Labs/Teams: Involved with ISR (Institute for Systems and Robotics) at IST.
Isabel Cecília Correia da Silva Praça Gomes Pereira is a Coordinator Professor at the School of Engineering of the Polytechnic Institute of Porto (ISEP), where she serves as Director of the Master on Informatics Engineering and Advisor of ISEP Presidency for R&D. She is also a Senior Researcher at GECAD (Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development), a research unit ranked as Excellent by the Portuguese Science & Technology Foundation. Dr. Praça holds a PhD in Electrical and Computer Engineering - Industrial Informatics from the University of Trás-os-Montes e Alto Douro, and completed her post-doctoral studies in Artificial Intelligence - Multi-Agent Systems with support from the Portuguese National Science Foundation (SFRH/BPD/30111/2006). Her academic journey includes progressive appointments from Assistant to Adjunct Professor and finally to Coordinator Professor at ISEP. Her research focuses on Artificial Intelligence applied to cybersecurity and security of AI , with significant contributions to applying AI in various domains including cybersecurity (projects like AIDA, SAFE, VESTA), industry (SeCoIIA, Cyberfactory), and energy systems (SPET, MAS-Society). She leads a research team at GECAD working on these topics and has strong connections with over 100 companies and technology transfer centers across more than 30 countries. Analysis of her recent publications reveals a strong trend toward AI security, with particular emphasis on adversarial attacks against AI systems, secure implementation of AI in critical domains, and privacy-preserving AI techniques. Her work spans both theoretical foundations and practical applications across multiple sectors including energy, healthcare, and network security, demonstrating her ability to bridge academic research with real-world applications. Dr. Praça has been recognized as an expert by several prestigious organizations including the European Union Agency for Cybersecurity (ENISA), where she contributes to working groups on Security of AI and the European Cybersecurity Skills Framework. She also serves as an expert for NATO's Defence Innovation Accelerator for the North Atlantic (DIANA) and represents ISEP in the European Cybersecurity Organization (ECSO). As an educator, she teaches Machine Learning and Multi-agent Systems in the MSc in AI program, and Security of Communications and Infrastructures in the Cybersecurity branch of the MSc in Informatics. She currently supervises 4 PhD students in AI security and privacy and has guided 45 Master's theses, demonstrating her commitment to developing the next generation of researchers and practitioners in her field. Her research is supported by numerous international and national grants, including multiple Horizon Europe and Horizon 2020 projects where she serves as Principal Investigator or Co-PI. She has participated in over 35 R&D projects totaling significant funding, with a strong track record of translating research into practical applications through industry partnerships.
Sérgio Filipe Carvalho Ramos serves as Coordinator Professor at the School of Engineering (ISEP) of the Polytechnic Institute of Porto (IPP), where he has held academic positions since 2001. Since 2021, he directs the Bachelor Program in Electrical Engineering - Electrical Power Systems and previously served as deputy director of the Electrical Engineering Department (2018-2020). He maintains active research collaborations with international institutions including Universidade de Vigo and Universidade da Coruña in Spain, and UNESP in Brazil. His educational background includes a PhD (2015) and MSc (2006) in Electrical and Computer Engineering from the University of Lisbon - IST. His research focuses on smart grids, energy communities, data mining applications in power systems, and renewable energy optimization . He has pioneered work in smart buildings optimization, shared PV generation, and flexible power contracts through significant R&D projects. The analysis of his recent publications reveals strong trends in energy community modeling , with 8 of the 15 most recent papers addressing peer-to-peer energy sharing, community optimization, and local market design. His work increasingly integrates machine learning for forecasting (particularly for EV integration and PV generation) and employs advanced optimization techniques including game theory and MBNLP methods. The research consistently targets practical implementation in smart grid contexts with emphasis on real-world applicability. Premio REN 2008 Scientific Publication Merit from IPP (2011, 2015, 2023) Ramos has supervised 2 PhD students (ongoing), 29 MSc, 6 ERASMUS, and 116 BSc students. His grant portfolio demonstrates exceptional success in securing competitive funding, including FCT projects BENEFICE and SPET as PI/Co-PI, the EU-funded SATCOMM project coordinating Atlantic energy pilots, and multiple Honda Research Institute consultancies. His research group has successfully coordinated over 20 national and international R&D projects with total funding exceeding €5M. As a core researcher in the GECAD research group, he leads initiatives in smart buildings and energy communities. He serves as General Secretary for IPP at ATHENA European University and participates in the CARPE Network's Sustainable and Smart Cities working group, demonstrating institutional leadership beyond his departmental responsibilities.
Daniel De Matos Silvestre is a researcher at COPELABS, Lusofona University, and the Institute for Systems and Robotics (ISR) at Instituto Superior Técnico, Lisbon. He holds a PhD in Electrical and Computer Engineering from IST (2017) with highest honors, an M.Sc. in Advanced Computing from Imperial College London (2009), and a B.Sc. in Computer Networks from IST (2008). His research focuses on fault detection and isolation, distributed systems, network control systems, and randomized algorithms. PhD in Electrical and Computer Engineering (IST, 2017) M.Sc. in Advanced Computing (Imperial College London, 2009) B.Sc. in Computer Networks (IST, 2008) His work spans critical areas such as state estimation , collision avoidance , and resilient control systems against adversarial attacks. Recent publications highlight advancements in constrained convex generators , flocking-based navigation , and gradient descent algorithm analysis . Collaborations include research stays at University of California, Santa Barbara, and joint works with Joao Hespanha. Key contributions include novel methods for set-membership estimation , autonomous vehicle coordination , and distributed control algorithms with applications in robotics and cybersecurity. He has published extensively in top journals like IEEE Transactions on Automatic Control , European Journal of Control , and Systems and Control Letters .
Rui Araújo is an Associate Professor at the University of Coimbra (UC) since 2024, with a Habilitation in Electrical Engineering and Intelligent Systems. He is a Senior Member of IEEE and founding member of the Portuguese Institute for Systems and Robotics (ISR-Coimbra) . His career spans over 25 years in academia and research. BSc (1991), MSc (1994), PhD (2000), and Habilitation (2024) - all from UC Research interests focus on computational intelligence , intelligent control , and machine learning , with applications in robotics, automation, energy systems, and industrial processes. Key subtopics include fuzzy systems, neural networks, optimization algorithms, and real-time control architectures. Recent publications demonstrate expertise in deep learning for additive manufacturing , fuzzy control of photobioreactors , and machine learning for wastewater treatment . His work integrates edge computing and evolving systems for industrial environments. Scientific recognition includes: 2005: Fumio Harashima Best Paper Award (ETFA) 2018: Best Paper Award (ICELTICS) 2021: Best Paper Award (INDIN) 2023: Honorable Mention (SIMPEP) Active in research grants (49 total), including EVAI Charge (electric vehicle AI), RELIABLE (robotic safety systems), and InGestAlgae (microalgae production). Supervises research teams at ISR-Coimbra and collaborates with European institutions.
Nuno Gonçalves is a Tenured Assistant Professor at the Department of Electrical and Computer Engineering and Senior Researcher at the Institute for Systems and Robotics (ISR) - Coimbra, University of Coimbra. He also serves as Innovation Manager at the Portuguese Mint and Official Printing Office (INCM) since 2018, focusing on technology transfer and product development. His roles include coordinating the AI group of APDSI and leading research in biometrics, facial recognition, and steganography. PhD in Computer Vision (University of Coimbra, 2008) His research spans computer vision, biometrics, and visual information security, with specific focus on facial recognition, morphing attack detection, presentation attack detection, graphical security, security coding, printer-proof steganography, and robotics. Recent work emphasizes deepfake detection, neural implicit representations, and robust biometric solutions. Article trends reveal expertise in 3D face reconstruction, multimodal deep learning for medical applications, and security-enhancing steganography. He leads projects integrating geometric modeling with neural networks for signed distance functions and develops benchmarks for morphing attack robustness in facial recognition systems. Member, IEEE He has coordinated grants from Fundação para a Ciência e a Tecnologia and private industry partners, including projects like BLOCKDFAKE (2025-2026) for deepfake detection in public administration and VISUAL-ID (2022-2024) for unique visual identities. His lab at ISR-Coimbra collaborates with INCM on security document validation and printer-proof steganography, with patents granted by EUIPO and USPTO.
Pedro Simões Coelho is a Full Professor and President of the Scientific Board at NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, where he also serves as an Integrated Researcher in the Information Management Research Center (MagIC). He is a senior expert for the European Commission in statistical methods and sampling techniques, and holds leadership roles in several national and European bodies, including the European Master of Official Statistics (EMOS) board and the Portuguese Health Technologies Commission. His research interests lie at the intersection of statistics, data analysis, and public policy. He specializes in sampling techniques, structural equation modeling, survey methodology, and data quality, with applications in health economics, business intelligence, and official statistics. His work bridges theoretical statistical innovation with real-world policy impact, particularly in health and administrative systems. His recent publications demonstrate a strong trend toward integrating artificial intelligence and advanced modeling techniques into public administration and health policy. Topics include AI-based detection of legislative burdens, economic burden assessments in dermatology, consumer segmentation via social media, and cardiovascular health optimization. These works reflect a multidisciplinary approach combining statistics, machine learning, and domain-specific knowledge to solve complex societal challenges. Senior Expert, European Commission (Statistical Methods & Sampling) Member, EMOS Board Member, Ischools Accreditation Committee Head of Information and Statistics, NOVA Clinical Research Unit (NOVA CRU) Former President, Portuguese Association for Classification and Data Analysis (CLAD) Former Member, Portuguese High Council for Statistics (CSE) Former President, Fiscal Board, CESD-Lisboa He has supervised numerous graduate and undergraduate courses and has been a consultant and trainer for major institutions including Eurostat, the Portuguese Statistical Office, and the Portuguese Central Bank. His work includes over 100 peer-reviewed publications and around 200 research projects resulting in more than 500 reports. He has delivered nearly 100 invited talks and conference presentations worldwide. He is actively involved in research labs and teams such as MagIC and NOVA CRU, where he leads initiatives in data-driven public health and statistical innovation. His ongoing projects focus on AI for policy assessment, sustainable health systems, and advanced statistical modeling for small area estimation and data integration.
Mathieu Blondel is a Researcher at Google DeepMind in Paris, France. He obtained his PhD in Machine Learning from Kobe University in 2013. From 2013 to 2019, he worked at NTT's Communication Science Laboratories in Kyoto, Japan. Research Interests: Differentiable programming Language models and deep learning Loss functions for structured prediction Optimization algorithms Optimal transport theory Polynomial networks Machine learning software Scientific Awards: Open-science honorable mention at ECML PKDD 2013 Software Contributions: Mathieu is a core contributor to scikit-learn , a foundational Python library for machine learning. He also maintains open-source projects on differentiable sorting, optimal transport, and Fenchel-Young losses.
Chulhee Yun is an Ewon Assistant Professor at KAIST Kim Jaechul Graduate School of AI (KAIST AI), with a joint affiliation starting September 2025 at KAIST Graduate School of AI for Math and a part-time Visiting Faculty Researcher position at Google Research. He directs the Optimization & Machine Learning (OptiML) Laboratory at KAIST AI. Education: PhD in Elec. Eng. & Comp. Sci., 2016–2021, Massachusetts Institute of Technology MSc in Electrical Engineering, 2014–2016, Stanford University BSc in Electrical Engineering, 2007–2014, KAIST Chulhee Yun's research focuses on deep learning theory, optimization, and machine learning theory. His work bridges theoretical foundations with practical applications in neural network training, addressing fundamental questions about optimization dynamics, generalization capabilities, and the mathematical properties of deep learning models. His research spans from theoretical analyses of gradient-based optimization methods to practical techniques for improving neural network training and performance. His recent publications demonstrate a strong focus on understanding the fundamental properties of neural network training dynamics, optimization algorithms, and generalization. The research spans theoretical analyses of stochastic optimization methods like SGD, investigations into the properties of transformers and language models, and practical techniques for improving neural network training efficiency. A notable trend is the combination of rigorous theoretical analysis with practical implications for deep learning systems. Scientific Awards: KAIA Outstanding Paper Award at KAIA Summer Conference 2025 (CKAIA 2025) KT Best Paper Award at KAIA Summer Conference 2025 (CKAIA 2025) Best Paper Award at KAIA Fall Conference 2024 (JKAIA 2024) KT Best Paper Award at KAIA Summer Conference 2024 (CKAIA 2024) KAIA Outstanding Paper Award at KAIA Summer Conference 2023 (CKAIA 2023) NAVER Outstanding Theory Paper Award at KAIA-NAVER Joint Fall Conference 2022 (JKAIA 2022) Honorable Mention at NYAS Machine Learning Symposium 2020 Poster Awards Notable AC for NeurIPS 2023 Top Reviewer at ICML 2025 Professor Yun actively mentors PhD, Master's, and undergraduate students through the OptiML Laboratory. His research group includes several PhD students (Jaeyoung Cha, Hanseul Cho, Yujun Kim, Junghyun Lee, Junsoo Oh, Baekrok Shin), Master's students, and undergraduate interns. He has successfully guided former students such as Geonhui Yoo, Jaewook Lee, and Dongkuk Si to completion. His service to the academic community includes serving as Social Chair for ICML 2026, Conference Area Chair for ICLR 2025-2026 and NeurIPS 2023-2025, and as a reviewer for numerous top conferences and journals. Chulhee Yun directs the Optimization & Machine Learning (OptiML) Laboratory at KAIST, which focuses on advancing the theoretical foundations of machine learning and optimization. The lab investigates fundamental questions about neural network training dynamics, optimization algorithms, and generalization properties, with the goal of bridging theoretical insights with practical applications in deep learning systems.
Greg Dongyoon Han serves as a Senior Research Scientist at NAVER AI Lab since January 2018 and an Adjunct Professor at KAIST's Graduate School of AI since September 2021. His work bridges industrial AI research and academic instruction, focusing on foundational advancements in large-scale machine learning systems without maintaining a dedicated KAIST laboratory. His research spans machine learning, deep learning, and multi-modal AI with emphasis on large language models, vision-language systems, and transformer architectures. Key themes include model efficiency through token compression, robustness against adversarial attacks, mathematical reasoning enhancement, and novel approaches to model merging and unlearning. His work consistently addresses scalability challenges in vision and language domains while exploring theoretical aspects of neural network dynamics. Recent publications (2024-2025) reveal three dominant trends: 1) Token-level innovations for model compression and efficiency, 2) Robust adaptation techniques for vision-language systems, and 3) Mathematical reasoning augmentation for large language models. These contributions appear in top venues including NeurIPS, ICML, CVPR, and ICLR, often featuring novel architectural modifications and training paradigms. Award highlights include: 4th place in ImageNet ILSVRC 2017 object localization Outstanding Reviewer at NeurIPS 2018 and CVPR 2021 Outstanding Paper Award at ICACT 2014 NAVER AI Lab's Best 2022 Paper for "Learning Features with Parameter-Free Layers" Though not currently advising KAIST students directly, Han actively co-mentors research projects as evidenced by multiple "co-mentored project" publications. His academic service is extensive: Area Chair for ICLR 2026 and NeurIPS 2025, plus continuous reviewing for NeurIPS (2018-2024), ICLR (2019-2025), and CVPR (2018-2026). He maintains strong industry-academia collaboration through NAVER internships rather than traditional university lab structures. Based at NAVER AI Lab, Han operates within South Korea's premier industrial research environment while contributing to KAIST's AI graduate education. His work demonstrates the growing synergy between corporate AI labs and academic institutions in advancing foundational machine learning research.
Slavisa Tomic is an Associate Professor at Universidade Lusófona, Centro Universitário de Lisboa (UL-CUL), and a senior researcher at COPELABS (Communication Technologies and Systems Laboratory). His work focuses on wireless sensor networks, target localization, tracking, and UAV navigation in GPS-denied environments. His research interests include: Target localization and tracking using hybrid measurements (RSS, AoA, TOA) Robust estimation techniques (ML, MAP, Kalman filters, particle filters) Optimization methods (SOCP, SDP, GTRS) for localization UAV navigation in satellite-less environments Machine learning applications in signal processing and localization Security in wireless networks (malicious node detection, spoofing) His recent publications show a strong trend toward integrating deep learning (e.g., LSTM, CNN) with classical signal processing for autonomous navigation and localization. He has developed tools like AutoNAV for UAV simulation and contributed to embedded implementations of optimization algorithms. His work spans both theoretical algorithm development and practical implementation. Scientific Recognition: Ranked among the top 2% most influential scientists globally in Information and Communication Technologies (sub-area: Networks and Telecommunications) from 2019 to 2024, based on Stanford University methodology. He has advised or collaborated on research involving energy-based acoustic localization, test-bench development for embedded systems, and drone-based environmental monitoring. He has also contributed to special issues and book chapters in his domain. His research is supported by active publication in IEEE, Sensors, and other high-impact journals. He is affiliated with COPELABS, a research lab focused on communication technologies, where he contributes to projects involving secure, robust, and intelligent wireless systems.
Inês Lynce is a Professor at the Department of Computer Engineering within the Instituto Superior Técnico (University of Lisbon) and a researcher at INESC-ID Lisboa . Her research focuses on Artificial Intelligence, Constraint Satisfaction and Optimization, Automated Reasoning, Formal Methods, and Bioinformatics. She leads multiple research projects, including RIGA (Indirect Discrimination Analysis), GOLEM (Automated Programming), and LAIfeBlood (AI for Blood Management), funded by FCT and EU programs. Education: While specific academic qualifications aren't listed, her roles and research output indicate advanced degrees in Computer Science/Engineering. Her work bridges theoretical computer science and practical applications, with a strong emphasis on Satisfiability (SAT) solving, constraint programming, and AI-driven solutions for complex systems. She has organized major conferences like SAT 2019 and ECAI 2025 , and serves on editorial boards for journals including Artificial Intelligence Journal and Journal on Satisfiability . Her awards include the INESC-ID Young Researcher Award (2009), APPIA PremiA Award (2009), and UTL/Deloitte Young Researcher Award (2008). Professional activities span program committee roles for AAAI , IJCAI , and CP conferences, reflecting her leadership in AI and constraint-based research. Teaching activities are managed through Fenix IST, and she collaborates with initiatives like CompSustNet for interdisciplinary research. Her work has been applied to diverse domains, including transportation scheduling, bioinformatics modeling, and cybersecurity protocol analysis.
João Silva Sequeira is an Assistant Professor at Instituto Superior Técnico (IST) and Senior Researcher at the Institute for Systems and Robotics (ISR) in Lisbon, Portugal. His primary affiliations include the Department of Electrical and Computer Engineering at IST, where he teaches Robotics and Social Robotics courses. He leads research in Social Robotics & Human-Robot Interaction, focusing on architectural aspects like robot modeling, control systems, and cooperative robotics. His work spans projects such as the Diabetic Foot Automated Assessment (DFAA) and the development of robotic canes for mobility assistance. Education: Extensive academic background in robotics and systems engineering (specific degrees not explicitly listed). Editorial Roles: Guest Editor for MDPI Sensors and Sustainability journals, Topic Editor for MDPI Robotics, and Review Editor for Frontiers in Robotics and AI. Research interests emphasize societal applications of robotics, including healthcare robotics, robotic inspection systems for power lines, and ethical considerations in AI. Notable awards include the Best Intelligence Paper Award (2022) and ICT 2015 Young Minds Grand Prix. His work integrates interdisciplinary approaches, collaborating with institutions like REN Portugal and European projects (e.g., MOnarCH). Key contributions include patents on reaction sphere actuators and power line inspection robots, as well as co-authoring books on robotics fundamentals and ethics. Current projects include the VIENNA autonomous vehicle initiative and the Helianto solar-powered train. He actively participates in conferences like ICRE and ICRESS, addressing robot ethics and safety standards.
Rita Alexandre Pinto Ribeiro is a Research Fellow at GECAD – Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, within the School of Engineering at the Polytechnic Institute of Porto (ISEP). She is currently pursuing her Ph.D. in Artificial Intelligence and Intelligent Systems Engineering at the same institution, focusing on non-invasive health monitoring using wearable devices and physiological signals. Her educational background includes: Ph.D. in Artificial Intelligence and Intelligent Systems Engineering (in progress, 2024-2028), Polytechnic Institute of Porto, School of Engineering. Master's in Biomedical Engineering (2021-2023), Polytechnic Institute of Porto, School of Engineering, with a thesis on "Automatic anonymization of clinical text". Bachelor's in Medical Imaging and Radiotherapy (2016-2020), Polytechnic Institute of Porto, School of Health. Ribeiro's research centers on artificial intelligence in healthcare, particularly the analysis of physiological signals (like PPG) from wearables for health monitoring. She employs explainable and robust machine learning methods to develop clinical decision support systems. Her work spans foundation models, signal processing, and the application of AI to improve patient outcomes in areas such as blood pressure prediction, sleep analysis, and medication adherence. Her recent publications reflect a strong focus on wearable-enabled remote health monitoring, explainable AI in biosignal analysis, and computer vision for healthcare. These works collectively advance the integration of AI into clinical practice, emphasizing reliability and interpretability. As a Research Fellow, she contributes to international projects including RM4HEALTH (Remote Monitoring in Health and Sports) and REMO (Remote patient-targeted health monitoring to reduce clinical workload), funded by COMPETE2030-FEDER. These projects involve collaboration with academic and industrial partners to develop and evaluate machine learning models for health applications. Ribeiro is an active member of the GECAD research group, which fosters interdisciplinary work in intelligent engineering and computing for healthcare innovation. She also participates in international events such as the DeepLearn 2025 summer school and the EAIA summer school to stay at the forefront of AI advancements.
