Adriano José Conceição Tavares is an Associate Professor at the School of Engineering, University of Minho, Portugal. He serves as a Senior Researcher at Centro ALGORITMI, where he is affiliated with both the IE R&D Group and the ESRG R&D Lab. His academic credentials include a PhD in Industrial Electronics from the University of Minho, a Master of Science in Information Technology from the University of Coimbra, and an undergraduate degree in Informatics from the University of Coimbra. Professor Tavares specializes in embedded systems with particular expertise in: Embedded systems modeling and design System software design System-on-chip design Real-time operating systems Hardware acceleration and FPGA design Virtualization for embedded systems IoT frameworks and protocols His publication record demonstrates a strong research trajectory in hardware-software co-design, with recent work showing an increasing integration of machine learning techniques into embedded systems. His publications span theoretical frameworks to practical implementations addressing real-time performance, resource constraints, and security challenges. Among his scholarly metrics: h-index of 18 126 publications with 1148 citations 20 publications in Q1/Q2 journals Author of a book on microcontroller programming Professor Tavares has supervised students including Miguel Ângelo Fernandes Silva and has established international collaborations through the Erasmus Program with institutions in China, Iran, Thailand, Jordan, and Cambodia. He teaches advanced courses on embedded and real-time systems modeling, compiler design, system-on-chip design, real-time operating system design, and advanced computer architectures at University of Minho.
Filipe Pereira Pinto Cunha Alvelos is an Associate Professor at the University of Minho's School of Engineering, Department of Production and Systems. He holds a PhD in Operational Research from the University of Minho and specializes in mathematical optimization models and metaheuristics for complex systems. His research focuses on: Operations Research and Optimization Integer programming and branch-and-price methods Local search metaheuristics for complex problems Applications in wildfire management, healthcare logistics, and telecommunications Recent work emphasizes wildfire prevention through optimization frameworks, resource dispatch algorithms, and fire spread modeling. He leads the O3F project (2021-2024) for forest fire reduction and chairs the Optimization and Wildfire Conference (2024). Scientific contributions include publications in European Journal of Operational Research , International Transactions in Operational Research , and conference proceedings of Lecture Notes in Computer Science . His work combines column generation techniques with metaheuristics for practical optimization.
Lino António Antunes Fernandes Costa is an Associate Professor (with tenure) at the Department of Production and Systems, School of Engineering, University of Minho, Portugal. He conducts his research activities at the ALGORITMI R&D Centre as a member of the Systems Engineering and Operational Research (SEOR) research group. His academic credentials include a PhD in Production and Systems Engineering, an MSc in Informatics, and a DEng in Informatics and Systems Engineering. These qualifications form the foundation for his interdisciplinary research bridging engineering, computer science, and mathematical optimization. Dr. Costa's research focuses on multi-objective optimization, nonlinear optimization, evolutionary algorithms, and applied statistics. His work demonstrates significant application to real-world industrial problems, particularly in manufacturing optimization, agricultural technology (with emphasis on olive cultivation), emergency response systems, and quality control. His methodological approaches often combine traditional optimization techniques with modern machine learning and artificial intelligence methods. His publication record shows a clear evolution toward Industry 4.0 applications, with recent work emphasizing the integration of optimization techniques with smart manufacturing systems, agricultural technology, and emergency management. His research demonstrates strong interdisciplinary connections between operations research, computer science, and domain-specific applications in manufacturing and agriculture. h-index: 17 Total Publications: 126 Total Citations: 1,193 Q1/Q2 Journal Publications: 40 Dr. Costa serves as a regular reviewer for prestigious journals including IEEE Transactions on Evolutionary Computation, Evolutionary Computation, IEEE Intelligent Systems, and European Journal of Operational Research. He has contributed to over 40 international scientific events as a program committee member, including major conferences such as ACM GECCO, IEEE CEC, and EMO. His professional activities extend to project-based learning initiatives in industrial engineering education, demonstrating his commitment to both research and teaching excellence. His laboratory work centers around the ALGORITMI Centre's SEOR research group, where he collaborates on developing optimization frameworks for distributed manufacturing systems, agricultural technology applications, and emergency resource management. Current projects include applying multi-objective optimization to olive cultivation challenges and developing intelligent systems for firefighting resource allocation.
Ana Cristina Luz Broega is an active Assistant Professor at the University of Minho's School of Engineering, Azurém Campus, where she directs the Master's in Fashion Communication Design program and teaches courses spanning Fashion Design Project, Advanced Apparel Design, and Ethical Fashion. Her academic career at the institution began in 1997 as an Intern Assistant, progressing through Assistant Professor positions since 2008, with significant leadership roles including membership on the Master's in Design and Marketing Steering Committee. PhD in Textile Engineering - Textile Physics (2008), University of Minho School of Engineering Master's in Design and Marketing (2001), University of Minho School of Engineering Bachelor's in Textile Engineering (1994), University of Beira Interior Broega's research centers on sustainable fashion systems with particular emphasis on psychological comfort dimensions in clothing, circular economy implementation, and creative process innovation. Her work bridges textile engineering with human-centered design, examining how emotional, social, and cultural factors influence garment acceptance and usage patterns. Current investigations explore aging populations' clothing needs, gender-neutral fashion frameworks, and waste valorization through surface design. Analysis of her recent publications reveals a strong trajectory toward systemic sustainability solutions, with increasing focus on circular business models, emotional durability, and cross-sensory design methodologies. Her work consistently connects material science with consumer behavior, demonstrating how technical textile properties intersect with psychological comfort perception to drive sustainable adoption. Curricular Merit for Research in Textile Field (2005) Award for Best Final Average in Bachelor's Engineering (1994), Order of Engineers Portugal Broega actively mentors doctoral and master's students across fashion design, sustainability, and communication fields while leading European Commission-funded projects including Fashion Alive (2022-2034) and TEXSTRA (2017-2020). Her research grants focus on sustainable education frameworks, textile innovation, and cross-border design collaboration. As principal organizer of the International Congress on Fashion and Design (CIMODE) since 2012, she facilitates global knowledge exchange among designers, researchers, and industry professionals. Her work integrates closely with the Textile Science and Technology Center (2C2T), leveraging university-industry partnerships to advance sustainable textile applications while directing the Fashion Communication Design program that connects academic research with market innovation through collaborative platforms like A Malha.
Luis Loures is Full Professor and President of Polytechnic Institute of Portalegre, Portugal. A landscape architect and agronomic engineer by training, he holds a PhD in Planning (University of Algarve) and post-doctoral qualification in Agronomy (Universidad de Extremadura). His distinguished career includes international research positions at University of Toronto and Michigan State University. His research encompasses: Urban/regional planning and landscape reclamation Sustainable development frameworks Cross-border cooperation mechanisms Climate-smart agriculture systems Mediterranean environmental management Recent publication trends (2021-2025) show concentrated focus on: Sustainable tourism in rural border regions Agroecological soil management techniques Spatial analysis of landscape patterns Climate adaptation in agriculture Urban-periurban ecosystem services Awards include: Honoris Causa Doctorate from Universidad Tecnológica ECOTEC (Ecuador) Research leadership includes: Principal investigator for 34 grants including EU COST Actions VALORIZA Research Centre affiliation Projects spanning irrigation efficiency, circular economy, and regenerative landscapes Laboratory affiliation: VALORIZA - Research Centre for Endogenous Resource Valorization
Tiago Martins is an active academic at the University of Coimbra where he serves as an Invited Assistant Professor in the Department of Informatics Engineering and researches at the Computational Design and Visualization Lab (CMS/CISUC). His academic affiliations include current and previous positions at the University of Coimbra and Universidade Católica Portuguesa. Martins holds three degrees from the University of Coimbra: PhD in Information Science and Technology (2013-2021) MSc in Design and Multimedia (2011-2013) BS in Design and Multimedia (2008-2011) His research explores the convergence of computational methods and creative practices with focus areas in: Evolutionary computation applied to design systems Generative design methodologies Computational creativity frameworks Machine learning for artistic applications Prototyping computational artifacts Recent publications demonstrate strong focus on evolutionary algorithms in creative applications, generative adversarial networks, computational typography, and AI-powered design systems. His work consistently bridges technical innovation with artistic expression. Martins has received academic recognition including: The 3% Best Students Award (2012/2013) The 3% Best Students Award (2010/2011) He has secured research funding from: Portuguese Foundation for Science and Technology Imprensa Nacional-Casa da Moeda SA QREN Mais Centro Program At the Computational Design and Visualization Lab, Martins develops generative systems exploring the intersection of computation, design, and art. His installations and artworks have been exhibited internationally, showcasing applications of his research in computational creativity.
Ângela Silva is an Assistant Professor at the Department of Production and Systems , School of Engineering, University of Minho, and a researcher at the ALGORITMI Research Centre . She holds a PhD (2008), MSc (2003), and BSc (2000) in Industrial Engineering from the University of Minho. Former director of Industrial Engineering and Management degrees at Lusíada University (2012–2017) and Distribution/Logistics programs at Polytechnic Institute of Viana do Castelo (2018–2023) Coordinated research groups in Industrial Processes (CLEGI, 2013–2018) and Organizational Processes (AdiT-Lab, 2021–2023) Her research focuses on Operations Management , Logistics , and Supply Chain Modeling , with over 60 publications. Recent work addresses: Lean manufacturing via SMED methodology in automotive and cable production Reverse logistics integration with continuous improvement Eco-friendly intermodal transportation and electric vehicle infrastructure Optimization of textile layouts and fire station scheduling via linear programming Regional development through SDG 9/11 frameworks in Alto Minho Publications span international journals and conference proceedings , emphasizing practical applications in logistics, sustainable operations, and industrial problem-solving. Supervised/co-supervised over 40 MSc dissertations in operations/logistics Active in editorial boards (e.g., International Journal of Engineering and Industrial Management, 2014–2018) She contributes to ALGORITMI Research Centre (integrated member since 2023) and has participated in funded projects from Fundação para a Ciência e a Tecnologia (e.g., contracts UID/CEC/00319/2019, UID/EMS/04005/2019).
Aline Ganninger is a researcher at the Cooperative State University Baden-Wuerttemberg (DHBW), with additional collaborations at Dresden University of Technology and Brandenburg University of Technology (BTU) Cottbus-Senftenberg. Her work primarily addresses industrial engineering challenges in global production environments through empirical research and practical frameworks. Her research focuses on intercultural shopfloor management and adaptive production systems , examining how cultural diversity impacts manufacturing efficiency and leadership strategies. Key contributions include developing methodologies for multinational workforce integration and holistic production frameworks responsive to market volatility. Her 2022 work on DHBW's research data repository demonstrates expanding expertise in AI-driven academic infrastructure. Collaborating extensively with Annette Hoppe and engineering teams across German institutions, Ganninger's publications reveal consistent engagement with the Society for Ergonomics and Cooperative Research Center for Technology Stress. Her work bridges theoretical industrial engineering with practical implementation in real-world manufacturing settings.
Adriano Jorge Cardoso Moreira is an Associate Professor with Habilitation at the Department of Information Systems, School of Engineering, University of Minho, Portugal. He holds multiple leadership roles including Scientific Coordinator of Urban Computing at Centro de Computação Gráfica and Director of the MAP-tele PhD Program. His research is conducted primarily through the Urban Computing Lab , focusing on smart place technologies. Education: PhD in Electrical Engineering (1997) and Bachelor's in Electronic and Telecommunications Engineering (1989), both from University of Aveiro, Portugal. Research Focus: His work spans indoor positioning, mobile/context-aware systems, urban computing, and wireless network simulation. Key innovations include fingerprinting algorithms for localization, multi-sensor fusion techniques, and human mobility analysis. Research outputs consistently address real-world industrial challenges such as warehouse management, factory automation, and urban infrastructure. Research Output Trends: Recent publications (2021-2023) emphasize practical applications of Wi-Fi/LoRaWAN fingerprinting, machine learning for sensor calibration, and industrial vehicle tracking. Over 70% of recent works involve experimental validation in real environments, reflecting a strong applied research focus. Key thematic clusters include radio map optimization, multi-sensor datasets, and scalability of positioning systems. Awards & Recognition: First Prize, EvAAL-ETRI Indoor Localization Competition (Off-site track, 2015 & 2017) Second Prize, EvAAL-ETRI Indoor Localization Competition (2016) IEEE Senior Member status Patent in computational geometry Projects & Funding: He leads/participates in numerous EU/national projects including: ORIENTATE (2021-2023): Low-cost indoor positioning for factories Lab4U&Spaces (2021-2023): Urban space solutions AR WARE (2018-2022): AR for warehouse management SAMU (2015-2018): Smart autonomous mobile units Lab & Team: He established/leads the Urban Computing Lab developing technologies for smart environments. Previously headed the Computer Communications and Pervasive Media Group (until 2016). Current team includes PhD/Master students working on wireless positioning and mobility analysis.
Marco Vasconcelos is an active researcher at the University of Aveiro, Portugal, where he serves as an Integrated Member of the Cognition Team. With a PhD from the University of Aveiro, he has established himself as a prominent figure in animal cognition and decision-making research, with 95 publications, 9,723 reads, and 1,322 citations to his name. His research spans multiple institutions and collaborations across Europe, particularly with colleagues at the University of Aveiro and international collaborators including Armando Machado, Tiago Monteiro, and Alex Kacelnik. Vasconcelos's primary research focus centers on decision making and rationality, integrating concepts and techniques from operant and developmental psychology, comparative cognition research, optimal foraging theory, and microeconomics. His work examines how animals, particularly pigeons and starlings, make choices between alternatives, modeling how these options are valued and how valuation drives decisions. He employs optimality and learning theory to address valuation, using empirically supported algorithms to translate valuation into choice. His research often involves developing mathematical models of causative processes and testing them through controlled experiments, with particular emphasis on timing mechanisms, suboptimal choice phenomena, and information processing in animal decision making. Analysis of Vasconcelos's recent publications reveals a strong focus on temporal cognition and decision processes in animals. His work consistently explores how timing mechanisms interact with reinforcement structures to shape behavior, with particular attention to midsession reversal tasks, suboptimal choice phenomena, and numerical discrimination. The research demonstrates sophisticated integration of theoretical models from economics with empirical behavioral data, creating a bridge between normative decision theory and descriptive behavioral observations. His 2022-2025 publications show increasing methodological sophistication with variable trial spacings, differential reinforcement probabilities, and advanced modeling approaches. Vasconcelos maintains active collaborations with researchers across multiple institutions, particularly with Armando Machado at the University of Aveiro and Alex Kacelnik at the University of Oxford. His research has been supported through various grants that enable extensive behavioral experimentation with animal subjects. While specific grant details aren't provided in the available text, his consistent publication output across numerous high-impact journals indicates sustained research funding. As part of the Cognition Team at the University of Aveiro, Vasconcelos contributes to a vibrant research environment focused on animal learning and behavior. His work with the team has produced significant insights into how animals process temporal information, make decisions under uncertainty, and develop cognitive representations of numerical quantities. The research group employs sophisticated behavioral paradigms to investigate fundamental questions about the nature of decision processes and cognitive mechanisms in non-human animals.
Beomjoon Kim is an Associate Professor at the Graduate School of AI at Korea Advanced Institute of Science and Technology (KAIST). He directs the Humanoid Generalization (HuGe) lab, which focuses on creating general-purpose humanoids capable of efficient decision-making in complex environments. Education: Ph.D. in Computer Science from MIT CSAIL M.Sc. in Computer Science from McGill University B.Math in Computer Science and Statistics from University of Waterloo Professor Kim's research spans multiple areas of robotics and artificial intelligence, with a particular focus on humanoid robotics, task and motion planning, and robot learning. His work aims to bridge the gap between high-level task planning and low-level motion control, enabling robots to operate effectively in complex, real-world environments. He has made significant contributions to the field of geometric task and motion planning, developing novel algorithms that improve the efficiency and effectiveness of robot decision-making processes. Analysis of Professor Kim's recent publications reveals a strong focus on humanoid robotics, with particular emphasis on motion planning, object manipulation, and learning-based approaches. His work increasingly integrates deep learning techniques with traditional robotics algorithms, demonstrating a trend toward more data-driven approaches in robotics research. Many of his papers address the challenge of generalization in robotics, seeking to develop systems that can handle novel objects and environments without extensive retraining. Scientific Awards: Best Cognitive Robotics Paper award at ICRA 2017 Oral presentation (top 6% of accepted papers) at AAAI 2020 Oral presentation (top 6% of accepted papers) at AAAI 2019 Oral presentation (top 6% of accepted papers) at AAAI 2018 Spotlight presentation (top 3.5% of accepted papers) at NeurIPS 2018 Spotlight presentation (top 4% of accepted papers) at NeurIPS 2013 Plenary talk (top 12% of accepted papers) at CoRL 2020 Professor Kim actively mentors students at various levels, currently advising multiple Ph.D. and Master's students in the HuGe lab. His research is supported by grants that enable his team to pursue ambitious projects in humanoid robotics and AI. Through collaborations with institutions like MIT and industry partners, his work has significant impact on both academic research and practical applications of robotics technology. The HuGe lab, under Professor Kim's direction, has established itself as a leading research group in humanoid robotics. The lab maintains strong connections with other research institutions and regularly publishes in top-tier robotics and AI conferences. Current research directions include developing more efficient motion planning algorithms, improving robot manipulation capabilities, and exploring the integration of large language models with robotic systems.
Kijung Shin is an Associate Professor at KAIST (Korea Advanced Institute of Science and Technology), holding dual appointments in the Kim Jaechul Graduate School of AI and the School of Electrical Engineering (Computer Division). He leads the Data Mining Lab and teaches multiple courses including Graph Mining and Social Network Analysis, Data Mining and Search, and other foundational courses in electrical engineering and AI. Education Ph.D. in Computer Science, Carnegie Mellon University (February 2019) M.S. in Computer Science, Carnegie Mellon University (December 2017) B.S. in Computer Science and Engineering, Seoul National University (August 2015) B.A. in Economics (Double Major), Seoul National University (August 2015) Research Interests Professor Shin's research primarily focuses on data mining, graph algorithms, and network science, with particular expertise in hypergraph analysis, tensor decomposition, and graph neural networks. His work bridges theoretical foundations with practical applications, developing algorithms that can efficiently analyze complex real-world networks. His recent research has expanded into multimodal learning, integration of large language models with graph neural networks, and applications in recommendation systems, satellite imagery analysis, and biological data analysis. His approach combines rigorous mathematical analysis with practical implementation, resulting in numerous open-source software tools that have been widely adopted in both academia and industry. His research has significant implications for social network analysis, fraud detection, recommendation systems, and scientific discovery in various domains. Research Trends Professor Shin's recent publications show a clear trajectory toward more complex network structures, particularly hypergraphs that capture higher-order interactions beyond simple pairwise relationships. His work increasingly integrates traditional graph algorithms with deep learning approaches, especially focusing on how graph neural networks can be improved and made more interpretable. There's also a growing emphasis on practical applications in areas like satellite imagery analysis, medical data, and recommendation systems that address real-world challenges. Scientific Awards Received the PAKDD Best Survey Paper Award for 'Multi-Behavior Recommender Systems: A Survey' (2025) Selected as one of the best short paper candidates of ACM RecSys 2024 (top 7) for 'Revisiting LightGCN' (2024) Selected for oral presentation (2.6% of accepted papers) at AAAI 2024 for 'VITA: 'Carefully Chosen and Weighted Less' Is Better in Medication Recommendation' (2024) Received the IEEE ICDM Best Student Paper Runner-up Award for 'TensorCodec: Compact Lossy Compression of Tensors without Strong Data Assumptions' (2023) Received the SIGKDD Best Research Paper Award and CogX Award for Best Student Paper in AI for 'FRAUDAR: Bounding Graph Fraud in the Face of Camouflage' (2016) Received the Best Senior Thesis Award from Seoul National University (2015) Received the Samsung Humantech Paper Award (1st in Computer Science) (2015) Teaching and Mentoring Professor Shin has taught multiple graduate and undergraduate courses at KAIST since 2019, including Graph Mining and Social Network Analysis, Data Mining and Search, and foundational courses in electrical engineering. He has also co-organized tutorials at major conferences including AAAI, KDD, ICDM, and CIKM on advanced topics in hypergraph neural networks and real-world hypergraph analysis. As the leader of the Data Mining Lab, he mentors numerous graduate students and postdoctoral researchers, fostering a collaborative research environment that has produced significant contributions to the field of data mining and network analysis. Research Leadership Professor Shin leads the Data Mining Lab at KAIST, which focuses on developing novel algorithms for analyzing complex networks and high-dimensional data. The lab has produced numerous influential software tools including D-Cube, M-Zoom, CoreScope, and DenseAlert, which are widely used in both academic research and industry applications. His research group maintains active collaborations with institutions worldwide and has received funding from various sources to support their innovative work in data mining and network analysis.
Prof. Gabriel Pestana holds a PhD in Information Systems from the Technical University of Lisbon and specializes in applied knowledge management and collaborative decision-making platforms. His expertise centers on optimizing organizational decision processes through data governance workflows, data semantics, context awareness, and role-based alert systems using information visualization and analytics models. Education: PhD in Information Systems, Technical University of Lisbon Research Interests: Gabriel Pestana's work integrates Insight Knowledge and Data Analytics to enhance Information Systems in Health & Well-Being and critical infrastructure domains. His methodology focuses on modeling data governance workflows to improve organizational decision-making, with specific contributions in context-aware systems and role-based alert design. Current projects emphasize practical applications of information visualization for complex data environments. Scientific Awards: No scientific awards were documented in the provided text. Advising and Grants: Actively involved in national and international research projects targeting Health & Well-Being systems Research initiatives focused on critical infrastructure optimization through data analytics Labs and Teams: Associated with R&D units including DICE-Algorithm and ECP (Ecoceramics and Crystalwork of Portugal), contributing to interdisciplinary projects in data-driven decision support systems.
