جست‌وجوی استاد و پژوهشگر

• Control Systems
• Structural Systems Theory
• Biomedical Engineering
• +۵ مورد دیگر

Sérgio Pequito is an Associate Professor in the Department of Electrical and Computer Engineering at Instituto Superior Técnico, University of Lisbon, and a principal investigator at the Evolutionary Systems and Biomedical Engineering Lab (Institute for Systems and Robotics). His research bridges structural control systems theory with applications in neuroscience and biomedicine. Education: PhD in Electrical and Computer Engineering from Carnegie Mellon University and Instituto Superior Técnico (2014); MSc (2009) and BSc (2007) in Mathematics from Instituto Superior Técnico Research focuses on understanding large-scale systems through structural/parametric analysis, with applications to brain dynamics, personalized medicine, and control of complex networks. Key themes include fractional-order systems, distributed consensus, and brain-machine interfaces. Recent publications analyze fractional-order dynamical networks, controllability metrics, and privacy-preserving control algorithms, reflecting his expertise in both theoretical and applied domains. Keywords: Control Systems, Structural Theory, Biomedical Engineering, Neuroscience. Scientific recognition includes the 2016 O. Hugo Schuck Award (Theory Category) and a 2009 IEEE Conference on Decision and Control Best Student Paper Finalist. He leads interdisciplinary research teams and collaborates on neural engineering projects, particularly in epilepsy treatment and cortical activity modeling.

Daniel Silvestreمشاهده پروفایل

استادیار

• Distributed Systems
• Networked Control
• Set-Valued Estimation
• +۲ مورد دیگر

Daniel Silvestre is an Assistant Professor at NOVA University of Lisbon's School of Science and Technology, researching fault-tolerant distributed systems and networked control. His work develops set-valued estimation methods and resilient consensus algorithms for applications in multi-agent coordination and network security. As principal investigator of the FirePuma project (€840k funding), he designs sensor fusion systems for early wildfire detection. Research focuses on: Set-based fault detection in cyber-physical systems Resilient consensus under adversarial conditions Distributed medium access control Stochastic network algorithms He coordinates PhD projects on state estimation and decentralized control while teaching automation and control engineering courses.

Sérgio Nunesمشاهده پروفایل

استادیار

• Information Retrieval
• Web Technologies
• Entity-Oriented Search
• +۴ مورد دیگر

Sérgio Nunes is an Assistant Professor at the Faculty of Engineering (FEUP) of the University of Porto, Portugal, and a Senior Researcher at INESC TEC's Information and Computer Graphics Systems Unit. He holds a MSc (2004) and PhD (2010) in Informatics Engineering from FEUP, focusing on Information Retrieval. His research spans Information Retrieval, Web Technologies, Entity-Oriented Search, and Natural Language Processing. Key research interests include entity-oriented search models (e.g., Hypergraph-of-Entity), low-resource language datasets, narrative extraction, and misinformation detection. His work often integrates graph theory, visualization tools (e.g., MediaViz), and collaborative platforms for text analysis. Publications emphasize innovations in search algorithms, dataset construction, and ethical AI applications. Notable projects include Labadain-30k+ for Tetun language resources and Text2Story for narrative analysis in European Portuguese news.

Rui Lopesمشاهده پروفایل

پژوهشگر

• Sports Science
• Network Analysis
• Agent-Based Modeling
• +۳ مورد دیگر

Rui Lopes is a researcher affiliated with the Iscte Business School at ISCTE - Instituto Universitário de Lisboa. His work focuses on interdisciplinary research at the intersection of sports science, network analysis, and complex systems. Key research interests include team performance dynamics in football (soccer), spatial analysis using Voronoi diagrams, agent-based modeling of collective behavior, and applications of machine learning in sports analytics. His academic contributions span multiple domains: Sports Science: Investigating tactical performance, player coordination, and decision-making in elite football through quantitative methods. Network Analysis: Developing algorithms to detect fraudulent networks in telecom systems and analyzing urban mobility structures. Complex Systems: Exploring synergistic interactions in teams using hypernetworks and ecological dynamics frameworks. Recent research highlights include: A dynamic method using Voronoi diagrams to assess team passing behavior in football (2023) Analysis of synchronization tendencies in elite football teams (2023) Machine learning approaches to decode success features in soccer passes (2022) No scientific awards or grants are explicitly mentioned in available texts. His work often intersects with the Real-Life Learning initiative at ISCTE Business School, emphasizing practical applications of theoretical frameworks.

Geoffrey Nelissenمشاهده پروفایل

پژوهشگر

• Real-Time Scheduling Theory
• Real-Time Operating Systems
• Multiprocessor/Multi-Core Architectures
• +۳ مورد دیگر

Geoffrey Nelissen is a researcher scientist at CISTER, part of the Instituto Superior de Engenharia do Porto (ISEP) within the Polytechnic Institute of Porto (IPP). He holds a PhD in Electrical Engineering from Université Libre de Bruxelles (ULB, Belgium), awarded in 2012 under Professors Joël Goossens and Dragomir Milojevic. His research focuses on real-time scheduling theory, embedded systems, and multi-core architectures, with significant contributions to network-on-chip (NoC) design and cache contention analysis. He has been actively involved in advancing scheduling algorithms for real-time systems, particularly in partitioned and global scheduling contexts. Education: MSc in Electrical Engineering (ULB, 2008), PhD in Real-Time Systems (ULB, 2012). Research Interests : Real-Time Scheduling Theory, Real-Time Operating Systems, Multiprocessor/Multi-Core Architectures, Network-on-Chip (NoC), Embedded Systems, Cache Contention Analysis. His work emphasizes practical solutions for scheduling challenges in heterogeneous and real-time environments, addressing issues like memory contention, bus arbitration, and cache interference. Publications : Recent work includes advancements in 3-Phase Task Model analysis, deflection-based NoC architectures (e.g., IPDeN), and holistic memory contention frameworks. His contributions span conferences like RTCSA, RTSS, and ECRTS, with a focus on bridging theoretical scheduling models and practical embedded system implementations. Awards : Multiple Outstanding Paper Awards at RTCSA 2020, RTNS 2017, and ECRTS 2016, alongside Best Presentation and Open Problem Awards. His research has been recognized for its impact on real-time system reliability and performance. Labs/Teams : Active member of the CISTER Research Centre, focusing on embedded systems and real-time computing. Collaborates with international researchers on topics like FPGA-based NoC design and mixed-criticality systems.

Alberto Lopezمشاهده پروفایل

استادیار مهمان

• Mathematics
• Algebraic Geometry
• Economics
• +۳ مورد دیگر

Alberto Lopez is a Visiting Assistant Professor at the Higher Institute of Economics and Management (ISEG) within the University of Lisbon. He currently serves as Diretor-Adjunto do Laboratório de Inovação at Imprensa Nacional Casa da Moeda. His academic journey includes a Doctorate in Mathematics (2010) from ISEG, a Master in Economy, Monetary and Financial Economics (2022), and a Bachelor in Mathematics from Universitat Zurich. Professional Affiliations: ISEG, University of Lisbon; Imprensa Nacional Casa da Moeda; Instituto de Matemática Pura e Aplicada (IMPA). Research Focus: Algebraic geometry (Brill-Noether theory, moduli spaces), financial economics (central bank policies), and machine learning ethics. His work bridges pure mathematics with applied economics and computational science. Recent research includes studies on central bank asset purchases' macroeconomic impacts, algorithmic bias in machine learning, and combinatorial structures in algebraic geometry. Awards: Excellence Postdoctoral Fellowship at IMPA (2015). Teaching: Coordinates mathematics courses for economics and management programs at ISEG. He leads innovation initiatives at Laboratório de Inovação and has held roles such as Research Fellow at the Korea Institute for Advanced Study and Norbert Wiener Assistant Professor at Tufts University.

Pedro Alves Martins Rodriguesمشاهده پروفایل

استادیار

• Discrete Dynamical Systems
• Arithmetic Dynamics
• Algebraic Number Theory
• +۱ مورد دیگر

Pedro Alves Martins Rodrigues is an Assistant Professor at the Department of Mathematics, Instituto Superior Técnico . He earned his PhD in Mathematics from the same institution in 2003. His primary research focuses on Discrete Dynamical Systems and Arithmetic Dynamics, with secondary interests in Algebraic Number Theory and Combinatorics. He actively contributes to teaching undergraduate courses in combinatorics, graph theory, and number theory. PhD in Mathematics, Instituto Superior Técnico (2003) Research interests include advanced topics in: Enumerative Combinatorics Graph Theory applications Algebraic Number Theory Symmetry-based counting methods Modern graph algorithms Extremal graph problems Teaching roles (2019–2025): Combinatorics and Graphs (LMAC) Introduction to Number Theory (LMAC) Introduction to Coding Theory (MMA) Differential and Integral Calculus II Topics for Mathematics Projects/Seminars Contact: pedro.martins.rodrigues@tecnico.ulisboa.pt

• Multimedia Signal Processing
• Image and Video Coding
• Event-Based Imaging
• +۸ مورد دیگر

Catarina Brites is an Assistant Professor in the Department of Information Science and Technology at ISCTE - Instituto Universitário de Lisboa and an Integrated Researcher at the Institute of Telecommunications - IUL, where she contributes to the Multimedia Signal Processing Group (MSP-Lx). She holds a PhD in Electrical and Computer Engineering from Instituto Superior Técnico (IST), University of Lisbon. PhD, Electrical and Computer Engineering, Instituto Superior Técnico (IST), 2011 M.Sc., Electrical and Computer Engineering, Instituto Superior Técnico (IST), 2005 B.Sc., Electrical and Computer Engineering, Instituto Superior Técnico (IST), 2003 Her research focuses on multimedia signal processing , particularly 2D/3D visual data coding , event-based imaging , plenoptic imaging (light fields and point clouds) , machine learning for image coding , and visual quality assessment . She has authored over 70 scientific publications with more than 3,890 citations (Google Scholar) and an h-index of 30. Her recent publications highlight a consistent focus on point cloud quality assessment , event-based vision coding , and deep learning applications in image and video compression . Trends include the development of novel metrics for 3D visual data and the application of neural networks to lossless and efficient coding paradigms. Best Paper Award Finalist at 2021 IEEE International Workshop on Multimedia Signal Processing Best Paper Honorable Mention Award at 2019 IEEE International Conference on Multimedia and Expo Top 8% Reviewer at 2018 IEEE International Conference on Multimedia and Expo Top 10% Paper Award at 2017 IEEE International Workshop on Multimedia Signal Processing Best Reviewer Award at 2012 IEEE Visual Communications and Image Processing Catarina Brites has co-supervised several M.Sc. theses at IST in areas including image/video coding and deep learning-based coding. She has been actively involved in multiple national and European R&D projects, serving as Co-Principal Investigator in the DARING project and Technical Coordinator in the EVASION project. She has also contributed as an Associate Editor for IEEE journals and served on technical and organizing committees for major conferences including IEEE ICIP, ICME, VCIP, and ACIVS. She is a key member of the Multimedia Signal Processing Group (MSP-Lx) at the Institute of Telecommunications, where she leads research in next-generation visual data representation and compression. She is also involved in the Technical Commission CT-120 - Image, Audio and Multimedia Information Coding, contributing to national standardization efforts.

• Signal and Image Processing
• Machine Learning
• Artificial Intelligence
• +۷ مورد دیگر

João Pedro Oliveira is an Associate Professor in the Department of Information Science and Technology at ISCTE – Instituto Universitário de Lisboa, where he also coordinates the Pattern and Image Analysis Group. He is an Integrated Researcher at the Institute of Telecommunications – IUL, contributing to cutting-edge research in signal and image processing and machine learning. His academic background includes a PhD and a Bachelor's degree in Electrical and Computer Engineering from the Higher Technical Institute - UTL. His research interests span Signal and Image Processing , Machine Learning , AI in Healthcare , Cultural Heritage Informatics , and Mobile Application Recommendation Systems . His recent publications demonstrate a strong interdisciplinary focus, combining AI with archaeology, healthcare diagnostics, and mobile computing. The analysis of his 15 most recent publications reveals a consistent trend in applying machine learning and computer vision techniques to real-world problems, particularly in cultural heritage digitization, medical imaging, and mobile user modeling. He frequently collaborates with researchers in diverse domains, publishing in high-impact journals such as Engineering Applications of Artificial Intelligence and IEEE Access . While no scientific awards are listed in the provided text, his extensive publication record and leadership in research groups underscore his academic impact. João Pedro Oliveira advises students in computer science and engineering domains, though specific names are not mentioned. He is involved in multiple research projects, particularly those integrating AI with societal applications. He leads and participates in research initiatives related to image analysis, semantic modeling, and mobile systems. His work often involves collaboration with multidisciplinary teams focusing on digital transformation in public and health sectors.

Mário Pereiraمشاهده پروفایل

استادیار

• Deductive Software Verification
• Program Verification
• Formal Methods
• +۴ مورد دیگر

Mário Pereira is an Assistant Professor at NOVA School of Science and Technology, NOVA University of Lisbon. He is also an Integrated Member of NOVA LINCS (NOVA Laboratory of Computer Science and Informatics). Dr. Pereira's research focuses on Deductive Software Verification , with particular expertise in program verification techniques and tools. His work centers on the deductive verification of OCaml programs, where he has developed tools and techniques as part of his Marie Sklodowska-Curie project "Cameleer." He is an enthusiast of the Why3 verification tool and the auto-active approach to program verification. His research spans formal methods, type systems (including refinement types and intersection types), and the development of verified software libraries. Among his notable achievements, Dr. Pereira was a Marie Sklodowska-Curie Fellow from June 2020 to May 2022, working on the Cameleer project at NOVA-LINCS. This prestigious fellowship supported his research in developing tools for the deductive verification of OCaml programs. Dr. Pereira has extensive teaching experience, having served as a teaching assistant since 2016. At FCT/Universidade Nova de Lisboa since 2020, he has taught courses including Theory of Computation and Analysis and Design of Algorithms. Previously, he taught at École Polytechnique in France, where he assisted in courses such as Introduction to Computer Science, Programming and Algorithms Basis, Advanced Programming, and Compilers. As an active member of the research community, Dr. Pereira contributes to several open-source software projects related to program verification, including Cameleer (a deductive verification tool for OCaml), Why3 (a software verification framework), OCamlGraph (a graph library for OCaml), and VOCaL (the Verified OCaml Library). He maintains an active GitHub presence under the username mariojppereira.

Ricardo Gonçalvesمشاهده پروفایل

استادیار

• Artificial Intelligence
• Knowledge Representation and Reasoning
• Logic Programming
• +۲ مورد دیگر

Ricardo Gonçalves is an Assistant Professor at the Department of Computer Science, Faculdade de Ciências e Tecnologia , Universidade Nova de Lisboa. He is also a researcher at NOVA LINCS , focusing on knowledge representation and reasoning within artificial intelligence. His research interests include Knowledge Representation and Reasoning Logic Programming Non-Monotonic Reasoning Answer Set Programming He has published extensively on forgetting mechanisms in logic programming and knowledge integration techniques. Recent publications emphasize Answer Set Programming optimizations Knowledge forgetting operators Neural-symbolic integration Stream reasoning with deep learning

Helena Galhardasمشاهده پروفایل

دانشیار

• Databases
• Data Cleaning
• Data Integration
• +۴ مورد دیگر

Helena Galhardas is an Associate Professor at the Department of Computer Science and Engineering at Instituto Superior Técnico, University of Lisbon , and a researcher at INESC-ID . Her work focuses on data cleaning, data integration, information extraction, and ETL systems , with applications in clinical data management and big data frameworks. Academic Affiliation: Associate Professor, Department of Computer Science and Engineering, Instituto Superior Técnico, University of Lisbon Research Unit: INESC-ID (Information and Decision Support Systems) Contact: helena.galhardas@tecnico.ulisboa.pt | +351 21 423 32 46 Her research spans theoretical and applied domains, including clinical data systems like Umedicine for structured medical data management and PiCaSSo for pediatric palliative care. She has developed frameworks like AJAX for data cleaning, REEL for relation extraction, and ∂u∂u for distributed duplicate detection. Her work integrates machine learning with database systems , particularly in workflow optimization and one-to-many data transformations. Scientific contributions include: Best Student Paper Award (2009) Nomination for Best Paper Award (2009) Research projects: PiCaSSo (2025-26): Pediatric Palliative Care Support System LAIfeBlood : AI for Blood Management Data2Help : Data Science for EMS Optimization DataStorm : Cloud-based Data Management

• Business Analytics
• Data Science
• Decision Support Systems
• +۴ مورد دیگر

Paulo Alexandre Ribeiro Cortez is a Full Professor at the Department of Information Systems, School of Engineering, University of Minho. He serves as Associate Director of the ALGORITMI Research Centre and is a member of both the IST R&D Group and IDS R&D Lab. With a Dr habil academic degree, his work bridges theoretical research and practical applications across multiple domains. Dr. Cortez's research focuses on Business Analytics, Data Science, Decision Support Systems, Neural Networks and Machine Learning, Evolutionary Algorithms, and Artificial Intelligence Applications. His work demonstrates a strong emphasis on applying these techniques to real-world problems in manufacturing, transportation, healthcare, marketing, and energy systems. He has developed practical tools like the rminer R package and authored the book 'Modern Optimization with R' (Springer, 2021), showing his commitment to making advanced techniques accessible. His recent publication trends reveal a strategic focus on solving industrial challenges through data-driven approaches, with particular emphasis on predictive maintenance, anomaly detection, and optimization problems across various sectors. His work consistently combines technical innovation with practical business applications, particularly in Industry 4.0 contexts and smart city infrastructure. Top 2% world influential researcher in Artificial Intelligence and Information Technology (2018-2023, Stanford PLOS Biology study) Associate Editor of Decision Support Systems (A* CORE, JCR Q1) Associate Editor of Expert Systems (JCR Q2) Associate Editor of Data Science and Analytics (JCR Q2) Dr. Cortez has supervised 6 Postdoctoral works, 16 PhD theses, 60 MSc theses, and 18 BSc final projects in Intelligent Data Analysis. He has participated in over 40 R&D projects and has evaluated 96 R&D project proposals for scientific entities across multiple countries including Canada, France, Israel, and Portugal. His research has attracted significant attention with over 15,400 Google Scholar citations (h-index of 52) and 6,500 Scopus citations (h-index of 33). As Associate Director of the ALGORITMI Research Centre, he leads multiple research teams including the IST R&D Group and IDS R&D Lab, focusing on innovative applications of data science and artificial intelligence to solve complex problems in sustainable industries, smart cities, healthcare, and public administration.

Seunghoon Hongمشاهده پروفایل

دانشیار

• Machine Learning
• Computer Vision
• Deep Learning
• +۴ مورد دیگر

Seunghoon Hong is an Associate Professor at the School of Computing, Korea Advanced Institute of Science and Technology (KAIST), where he leads the KAIST Vision and Learning Lab (VLLab@KAIST). His research focuses on advancing machine learning and computer vision with emphasis on reducing human supervision requirements and enhancing model interpretability. Prior to joining KAIST, he was a visiting faculty researcher at Google Brain and completed a postdoctoral fellowship at the University of Michigan working with Professor Honglak Lee. Education: PhD in Computer Science and Engineering, 2017, POSTECH, Korea (Supervised by Professor Bohyung Han) BS in Computer Science and Engineering, 2011, POSTECH, Korea Professor Hong's research primarily explores scaling up machine learning algorithms for visual perception while minimizing human supervision requirements. He investigates techniques to make machine learning algorithms more interpretable, enabling users to better understand and participate in the decision-making process. His work spans few-shot learning, generative models, vision transformers, and equivariant neural networks, with applications across various computer vision tasks. His recent publications reveal a strong focus on advancing foundation models for computer vision with emphasis on data efficiency, interpretability, and generalization. Key themes include developing novel approaches for few-shot learning, improving model efficiency through token merging and compositionality, and creating robust vision systems capable of handling real-world challenges like weather degradation. His work frequently appears in top-tier conferences including NeurIPS, ICLR, CVPR, and ECCV. Scientific Awards: Outstanding Paper Award at ICLR 2023 Professor Hong actively mentors a substantial group of students, including multiple Ph.D. candidates, master's students, and undergraduates. He serves in leadership roles for major conferences, including as Senior Area Chair for NeurIPS 2025 and Workshop Chair for ICCV 2025. His research has significant practical implications for developing more efficient, interpretable, and adaptable computer vision systems. He leads the KAIST Vision and Learning Lab, which focuses on cutting-edge research at the intersection of machine learning and computer vision, with particular emphasis on developing algorithms that require minimal human supervision while maintaining high performance and interpretability.

Cristina Requejoمشاهده پروفایل

دانشیار

• Operational Research
• Integer Programming
• Combinatorial Optimization
• +۷ مورد دیگر

Cristina Requejo is an Associate Professor at ISEG – Lisbon School of Economics and Management, University of Lisbon, and a researcher at CEMAPRE/REM. Her academic career spans institutions including the University of Coimbra and the University of Aveiro, where she held teaching and research roles in mathematics and operational research. Education: PhD in Mathematics, Operations Research – Faculty of Sciences, University of Lisbon (2004) Master's in Operational Research – University of Lisbon Degree in Mathematics – University of Coimbra Her research centers on optimization, particularly integer programming, combinatorial optimization, robust optimization, vehicle routing, and healthcare logistics. She applies advanced mathematical programming techniques to real-world problems in logistics, scheduling, and network design. Her work bridges theoretical algorithm development and practical applications in sectors like healthcare and transportation. The most recent publications reflect a strong focus on MIP-based heuristics, Lagrangian duality, and vehicle routing with skill and temporal constraints. Her research consistently appears in high-impact operations research journals, demonstrating sustained contributions to algorithmic and modeling advances in optimization. Scientific Awards: No awards explicitly mentioned in the text. She has supervised multiple graduate students, including doctoral candidates funded by FCT scholarships, and has contributed to active research projects promoting international collaboration. Her teaching includes advanced courses in decision making, optimization, and operational research at the doctoral and master’s levels. She is affiliated with research labs such as CEMAPRE/REM, contributing to a vibrant research environment in applied mathematics and economics.