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.
Ermanno Aparo is a Professor and Principal Investigator at the Instituto Politécnico de Viana do Castelo (School of Technology and Management) and Universidade de Lisboa (Faculty of Architecture). His PhD in Design (Universidade de Aveiro) and background in architecture inform his transdisciplinary research spanning design innovation, cultural storytelling, and industry collaboration. Research Focus: He pioneers methodologies in creative processes, lighting/product design, and arts-business integration. Recent projects include MUSAE (exploring music/visual arts synergies) and industrial collaborations with FURNOR Lda on sustainable prototypes. His work emphasizes: Hermeneutic analysis of urban/cultural identity Co-creation in transartistic practices User-centered sustainable systems Awards & Recognition: Gold Muse Design Award (2023) Iron Design Award (2020) Gold Design Award (2019) Supervision & Grants: He has advised 15+ MSc/PhD theses and secured funding for 8 projects, including FCT-backed initiatives like MUSAE (UIDB/04008/2020) and Pat.Tech (POCI-01-0246-FEDER-181306). His lab prototypes (e.g., Deithy lighting, Shatron mute) highlight industry-academia impact.
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.
Mário Gonzalez Pereira is an Assistant Professor in the Department of Physics at the School of Science and Technology, University of Trás-os-Montes and Alto Douro (UTAD), Portugal. He has been continuously employed at UTAD since 1994, progressing from Junior Teaching Assistant to Assistant Professor in 2005, where he remains active in research and teaching. His educational background includes a PhD (2005), MSc (1998), and Licenciatura (1993) in Physics from Universidade de Lisboa. His research spans climate science, wildfire dynamics, drought assessment, and environmental physics, with particular focus on Portugal and Southern Africa. Pereira's publication record reveals strong emphasis on climate-fire relationships, drought monitoring using remote sensing, and climate change impacts on ecosystems. His recent work shows increasing focus on machine learning applications for wildfire prediction and interdisciplinary approaches combining meteorology, ecology, and agriculture. The research demonstrates consistent funding support and international collaboration, particularly with researchers from Portugal, Spain, and Southern Africa. He has served as principal investigator or team member on 13 research grants funded by Fundação para a Ciência e a Tecnologia, including projects on wildfire prediction (FLAIR), drought assessment, forest fire dynamics, and climate change impacts on agriculture. His work connects with CITAB (Centre for the Research and Technology of Agro-Environmental and Biological Sciences) and other research networks. Pereira actively contributes to the scientific community through extensive peer review activities across 15 journals, including Agricultural and Forest Meteorology, Climate, International Journal of Wildland Fire, and others. His expertise is sought in multiple disciplines reflecting his interdisciplinary approach to environmental challenges.
Alvaro Barradas serves as Assistant Professor at the University of Algarve's Faculty of Science and Technology under a permanent public service contract with exclusive dedication. His career is deeply rooted at UAlg, where he completed both undergraduate and doctoral studies. His educational qualifications include: Bachelor's in Computer Science - Management (1995) from University of Algarve - Gambelas Campus PhD in Electronic and Computer Engineering (2009) from University of Algarve, Faculty of Science and Technology Professor Barradas' research integrates Power Systems engineering with advanced networking technologies, specializing in substation automation (IEC 61850), interoperability testing, and simulator development. His secondary focus applies ICT solutions to educational contexts across vocational and secondary schooling. This dual-track approach connects electrical infrastructure innovation with pedagogical technology implementation. His scholarly output comprises 15 journal articles and one academic book, demonstrating consistent contribution to engineering literature. Supervision experience includes one completed Master's thesis, reflecting his commitment to graduate training within his technical domains.
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.
Vinicius Silva is an Assistant Professor with dual appointments at the University of Minho (Department of Industrial Electronics, School of Engineering) and the Polytechnic Institute of Cávado and Ave (Department of Technologies, School of Technology). He also serves as a Research Engineer at the International Iberian Nanotechnology Laboratory since January 2024, bringing his expertise in robotics and educational technology to this prominent research institution. Dr. Silva's research focuses on the intersection of robotics, human-computer interaction, and special education. His primary areas of investigation include: Human-robot interaction for autism therapy Emotion recognition systems for children with special needs Development of assistive technologies and educational applications Gesture recognition and action recognition systems Playware technology for emotional learning Engineering education methodologies His publication trends over the past decade show a strong emphasis on robotics applications for children with Autism Spectrum Disorder, with numerous studies examining how humanoid robots can facilitate social communication and emotional recognition. His 2023-2024 output includes significant work on customizable robotic exoskeletons, machine learning for educational prediction, and the effectiveness of educational technology in engineering contexts. Dr. Silva has established recognition in his field through peer review activities for journals including Computers in Biology and Medicine, Information Processing and Management, and Research in Autism Spectrum Disorders, demonstrating his standing within the academic community. His research is conducted through the Robotica-Autismo Research Group, which develops and tests innovative technologies to support children with autism in educational and therapeutic settings. The group's work spans web applications for emotion learning, serious games for emotional recognition, robotic systems for social interaction, and engineering education tools, reflecting a comprehensive approach to assistive technology development.
Ricardo Ferreira serves as Research Scientist Group Leader of the Spintronics research group at the International Iberian Nanotechnology Laboratory (INL). With a PhD in Physics Engineering from Instituto Superior Técnico (IST) completed in 2008, his career has focused on advancing spintronics technology through Magnetic Tunnel Junctions (MTJs). His research group develops high-yield fabrication processes for both micro-scale (down to 1μm²) and nano-scale (down to Dr. Ferreira's research interests span multiple cutting-edge areas in spintronics, with primary focus on nano-oscillators, neuromorphic computing architectures, and advanced magnetic field sensors. His work bridges fundamental physics with practical applications, particularly in developing devices that exploit spin transfer and spin Hall effects for next-generation computing paradigms. The group has made significant contributions to RF signal processing, true random number generation, and physical unclonable functions using spintronic devices. His recent publications reveal a strong trend toward neuromorphic computing applications, with multiple papers on spin-torque nano-oscillators for reservoir computing, weighted neural networks, and RF signal classification. The research demonstrates increasing sophistication in controlling vortex states and coupled oscillator dynamics for computational applications, while maintaining strong contributions to fundamental spintronics phenomena and sensor development. Co-author of 150+ peer-reviewed publications Co-inventor in 4 patent applications submitted at INL Participant in 6 European projects in the last decade Leader of multiple high-TRL industrial application projects Ferreira actively mentors PhD students and postdoctoral researchers, with several alumni having completed their doctoral work under his supervision. His group engages in both fundamental research and applied projects targeting Industry 5.0 ready production systems and space applications. The laboratory maintains advanced fabrication capabilities including UHV sputtering systems, ion milling equipment, and comprehensive characterization tools for spintronic device development.
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.
Myoungsoo Jung is the KAIST Endowed Chair Professor and Full Professor at Korea Advanced Institute of Science and Technology, holding primary appointment in the School of Electrical Engineering with additional affiliations in the School of Semiconductor System Engineering, Graduate School of AI Semiconductor, Graduate School of System Architect, and Graduate School of AI. His research focuses on cutting-edge computer architecture and operating systems with specialization in memory and storage systems. Professor Jung's research interests span computer architecture, operating systems, flash memory, solid-state drives, non-volatile memory, file systems, parallel processing, and heterogeneous computing. He has pioneered work in CXL-based memory expansion, computational SSDs, and memory disaggregation technologies that are transforming modern data centers and AI infrastructure. His recent publications demonstrate significant advancements in CXL-driven architectures, computational storage, and memory systems. The research trends show increasing integration of storage and memory technologies with AI workloads, particularly in large-scale graph processing, federated learning, and billion-scale data management. His team's work frequently appears in top-tier venues including ISCA, HPCA, SOSP, and USENIX ATC. Hall of Fame, IEEE/ACM ISCA (2024) Digital Innovation Award from Minister of Science and ICT (2024) CES Innovation Award Winner, CXL-Enabled AI Accelerator (2025) Korea Innovative Startup Award, Ministry of Science and ICT (2025) Samsung Best Paper Award Winner (Grand Prize) (2022) Professor Jung has successfully advised numerous PhD students including Miryeong Kwon (recipient of KAIST Outstanding PhD Dissertation Award) and Donghyun Gouk. His CAMEL research lab has secured over $13M in funding from sources including DOE, NSF, and Korean government agencies. The lab maintains strong industry partnerships with Samsung, SK Hynix, and Panmnesia, focusing on translating research into practical systems. Current projects include CXL-based memory expansion, computational SSDs for AI acceleration, and next-generation storage architectures for hyperscale data centers.
Bruno Miguel Nunes da Silva is an Adjunct Professor at Polytechnic Institute of Setúbal, where he teaches computer science and conducts research in artificial intelligence and data science. He completed his Doctoral Program in Computer Science from Universidade Nova de Lisboa in 2016. His research interests include: Artificial Intelligence Machine Learning Data Mining Artificial Neural Networks Ubiquitous Computing Data Streams Dr. da Silva's scholarly work focuses on developing innovative methodologies for complex data challenges, particularly in dynamic environments. His research on self-organizing maps for non-stationary data streams represents a significant contribution to the field, addressing the evolving nature of modern data landscapes. His work bridges theoretical computer science with practical applications across various domains. As an active contributor to the academic community, he has served as a reviewer for esteemed journals and conferences, ensuring scholarly rigor. He has also shared his expertise through book chapters, extending his influence beyond traditional journal publications. Dr. da Silva maintains a strong presence in the software development community through GitHub, where he contributes to several open-source projects related to his research interests, including frameworks for neural networks and data visualization tools that demonstrate practical implementations of his theoretical work.
Fernando Manuel Fontinha Camilo serves as a Visiting Adjunct Professor at the School of Technology of Setúbal within the Polytechnic Institute of Setubal. He maintains dual research affiliations as a collaborator at INESC-ID (Institute for Systems and Computer Engineering, Technology and Science) and as an integrated member of the MARE (Marine and Environmental Sciences Centre) research center through the MARE-IPSetubal unit. His academic credentials include: PhD in Electrical and Computer Engineering from Technical University of Lisbon (2020) Bachelor's degree in Electrical and Computer Engineering from Setúbal School of Technology (2011) Camilo's research program centers on energy systems with specialized expertise in photovoltaic energy conversion, grid integration of renewable sources, and operational dynamics of high-penetration renewable systems. His scholarly contributions span power systems engineering, smart grid technologies, and energy storage solutions, evidenced by publications in premier journals including International Journal of Electrical Power & Energy Systems, Solar Energy, and Journal of Energy Storage. This work addresses critical challenges in modernizing electrical infrastructure for sustainable energy transitions. He has co-authored scientific papers with thirteen collaborators across international research networks. His laboratory affiliations include the MARE-IPSetubal unit and INESC-ID, where he contributes to interdisciplinary projects focused on renewable energy system optimization and grid stability under variable renewable generation scenarios.
José António Moinhos Cordeiro serves as a Professor at the Setúbal School of Technology, Polytechnic Institute of Setúbal, with research spanning Computer Science, Electrical Engineering, Electronic Engineering, and Computer Engineering. Education: PhD in Computer Science (2011) from the University of Reading, UK Master's in Electrical and Computer Engineering (1997) from the University of Lisbon Bachelor's degree in Marine Systems Engineering, Electrical Engineering and Telecommunications (1992) from Escola Superior Náutica Infante D Henrique Bachelor's degree in Marine Systems Engineering, Electrical Engineering and Telecommunications (1984) from Escola Superior Náutica Infante D Henrique Research Profile: His work bridges Exact Sciences (Computer and Information Sciences) and Engineering Technologies (Electrical/Electronic/Computer Engineering), emphasizing practical applications in computing and electronics. Despite extensive publication output including seventeen books and seven book chapters, his journal article count remains minimal with only one peer-reviewed publication. He maintains a broad collaborative network with eighty co-authors across academic events and research projects. Professional Engagement: Cordeiro has participated in five academic events and led at least one research project as Principal Investigator, demonstrating active involvement in institutional R&D initiatives despite limited formal award recognition documented in the source material.
