Nirmalie Wiratunga is a Professor in Intelligent Systems at the School of Computing , Robert Gordon University, and serves as the Associate Dean for Research . She is also an Adjunct Professor at the Norwegian University of Science and Technology (IDUN program). Her academic excellence spans over two decades in Artificial Intelligence and Machine Learning , with a focus on Explainable AI (XAI) , Case-Based Reasoning (CBR) , and Natural Language Processing (NLP) . Her research explores innovative methodologies for knowledge-rich representations to automate decision-making through CBR for Retrieval-Augmented Q&A systems and human-centered AI platforms . She co-founded Attendr.app , a spinout for student and conference attendance tracking, and leads the Artificial Intelligence & Reasoning Research Group at RGU. Recent publications (2024–2025) highlight her work on LLM hallucination detection , counterfactual explanations in finance , cross-lingual biomedical review automation , and multi-query resolution in legal domains . Themes span AI explainability , NLP , CBR , and domain-specific knowledge integration across healthcare, law, and education. Her leadership extends to organizing international workshops on XAI , digital health , and Deep Learning , and co-chairing the ICCBR 2021 and 2022 conferences. She actively contributes to program committees for ECCBR , ECML/PKDD , and IJCAI .
Yubao Liu is a Professor at Sun Yat-sen University's School of Data and Computer Science, Department of Computer Science, with a prolific research career spanning over two decades in computer science. His work demonstrates significant contributions to database systems, data mining, and spatio-temporal analysis. Professor Liu's research interests focus on Data Mining , Database Systems , Traffic Flow Prediction , and Graph Neural Networks . His recent work has concentrated on developing advanced techniques for large-scale traffic flow prediction, crowd flow analysis, and spatio-temporal modeling using deep learning approaches. His research bridges theoretical computer science with practical applications in transportation systems and urban computing. Liu's publication record shows consistent high-impact contributions, with recent work emphasizing graph-based neural network architectures for traffic forecasting problems. His research demonstrates a clear evolution from foundational database work to cutting-edge applications of deep learning in transportation and social network analysis. Professor Liu has collaborated extensively with researchers including Weiyang Kong, Kaiqi Wu, Sen Zhang, Genan Dai, and Youming Ge, indicating a strong research group focused on spatio-temporal data analysis and deep learning applications. His academic advising is evident through publications where his students appear as first authors, suggesting an active mentorship role in training the next generation of computer scientists specializing in data-intensive applications.
Panagiotis Hadjidoukas is an Associate Professor and Head of the Laboratory for Computing at the Computer Engineering and Informatics Department, University of Patras, within the School of Engineering. His work focuses on high-performance computing systems and parallel programming models. His research spans parallel and distributed computing , runtime support for parallel programming models , and automation of AI/ML workloads . Key contributions include developing the torc runtime system for task parallelism and pioneering work in extreme-scale scientific simulations. His interests bridge theoretical computer science with practical applications in scientific computing and AI acceleration. Notable achievements include the ACM Gordon Bell Prize Winner (2013) for 11 PFLOP/s cloud cavitation simulations and Finalist (2015) for in-silico lab-on-a-chip microfluidics. His software tools ( torc_lite , torcpy ) enable efficient parallelism across diverse architectures. Doctor of Philosophy (2003), University of Patras Master of Science (2001), University of Patras Diploma in Computer Engineering (1998), University of Patras As Head of the Laboratory for Computing, he leads infrastructure development while maintaining active research collaborations with IBM Research and ETH Zurich. His teaching portfolio includes graduate courses on high-performance computing for data sciences and parallel processing principles.
Dr. Artur Basiura serves as an Assistant Professor in the Department of Applied Informatics at AGH University of Science and Technology's Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering in Kraków, Poland. His institutional affiliation centers on applied informatics research within electrical engineering and computer science domains. His research spans graph theory applications in lighting systems, energy efficiency optimization, and software engineering. Key interests include graph-based street lighting modernization, XBRL taxonomy integration, and adaptive control systems. His work bridges theoretical computer science with practical electrical engineering solutions for urban infrastructure. Analysis of his 14 publications (2004-2022) reveals an evolving research trajectory: early work (2004-2007) focused on software engineering fundamentals including e-commerce frameworks and UML diagram comprehension, while recent publications (2016-2022) demonstrate a strategic shift toward graph-theoretic approaches for lighting system optimization and energy reduction in urban environments.
R. Iris Bahar is a Professor of Computer Science and Engineering at Colorado School of Mines, where she serves as Department Head of Computer Science since 2022. Previously, she held dual appointments as Professor of Engineering and Computer Science at Brown University for 26 years. Her research focuses on energy-efficient and reliable computing across system-level to device-level domains, with applications in near-data processing, robust machine learning for robotics, and noise-immune nanoscale circuit design. Research Interests include computer architecture, low-power design, and robotic system optimization. She pioneers hybrid generative-discriminative inference techniques for robot scene perception, achieving 40% accuracy improvements in occluded environments through FPGA-based Monte-Carlo sampling implementations. Her work on subthreshold CMOS noise modeling provides 1000x simulation speedups over SPICE-based methods. Near-Data Processing Architectures Approximate Computing Hardware/Software Co-design Nanoscale Noise Modeling Robust AI for Robotics Scientific Awards include the 2024 IEEE Undergraduate Teaching Award, 2019 Marie R. Pistilli Women in Engineering Achievement Award, and NSF CAREER award. She is an IEEE Fellow and ACM Distinguished Scientist. Funded Research spans NSF grants on thermal noise effects ($360k), durable data structures ($500k), and Brown SEED grants for autonomous robotics. Her lab trains 12+ students in hardware acceleration and probabilistic computing.
Hiroyuki Niiro is a Professor in the Department of Information Engineering at Ibaraki University's College of Engineering, where he has served since joining as an Assistant in 1993, becoming Lecturer in 1997, Assistant Professor in 2001, and Professor in 2015. Born in Nagasaki in August 1961, he graduated from Tokyo Institute of Technology's Department of Information Science in 1985 and earned his Doctorate in Engineering from the same institution in 1997. His research focuses on Natural Language Processing using machine learning and statistics , with additional interests in image and voice pattern recognition and data mining. His work has evolved from traditional NLP techniques to contemporary transformer-based approaches, particularly specializing in Japanese language processing, word sense disambiguation, and domain adaptation methods. Niiro's recent publications (2021-2025) demonstrate his shift toward large language models, with research spanning BERT adaptations, data augmentation techniques, multimodal learning, and Japanese-specific NLP challenges. His work shows a consistent trajectory from foundational NLP research to current state-of-the-art language modeling approaches. He currently supervises graduate students including Yang Lei (D2) and Yu Onodera (M2) in his laboratory. Niiro has authored numerous books including Fine-tuning of LLM and RAG (2024), Introduction to Natural Language Processing Starting with Document Classification (2022), and multiple deep learning programming guides. His laboratory maintains active research in natural language processing, with members participating in major conferences including the Annual Conference of the Association for Natural Language Processing. Niiro has also contributed to educational resources including statistical examination preparation materials and RSS technology introductions.
Assistant Professor Mahmut Aykaç is affiliated with Gaziantep University, Faculty of Engineering, Department of Electrical and Electronics Engineering. He holds a Doctorate (2010-2018), Master's (2006-2009), and Licence (2001-2006) in Electrical and Electronics Engineering from Gaziantep University, specializing in Circuits and Systems Theory. His research spans Image Processing , Machine Learning , and Wireless Communication Systems , with a focus on RFID localization ZigBee-based navigation Chaos theory in cryptography Edge computing for neural networks Legacy textile system modernization Scientific contributions include 7 refereed journal articles and participation in 7 international conferences. Notable projects: ZigBee Campus Navigation System (2013-2017) Jacquard Loom Control System Modernization (2007-2009) Supervised master's theses on: Single-band GSM RF energy harvesting Automatic power factor correction systems Received certification as expert witness in Expropriation and Electrical Engineering fields.
Dr. Christos Antonopoulos is an Associate Professor at the Department of Electrical and Computer Engineering, University of Patras. He holds a Diploma and PhD in Electrical Engineering from the University of Patras (2002, 2008) and has participated in over 16 European research projects (FP5, FP6, FP7, Horizon 2020) and 6 national projects. Research Interests: Wireless Networks Cyberphysical Systems Embedded Software Architecture Internet of Things Cross-Layer Protocols Sensor Networks Technical Expertise: His work involves network simulation, power optimization, and reconfigurable computing. He has published >100 journal/conference papers and 13 book chapters with over 1000 citations.
Sabine Schulte im Walde is an Apl. Prof. (Adjunct Professor) at the Institute for Natural Language Processing (IMS) , University of Stuttgart. Her research bridges computational linguistics and cognitive semantics, focusing on noun compounds, semantic change, and multimodal representations. Key research areas: semantic drift, compositionality, German particle verbs Instrumental in creating datasets for semantic change detection (DWUGs, DURel) Recent work explores BERT's limitations in compound semantics and cross-lingual metaphor detection She contributes to major European NLP initiatives, with publications in top venues like ACL. The IMS group specializes in language-technology intersections, managing 6 professorships, 50+ researchers, and 200+ students in NLP programs.
Naim Dahnoun serves as Professor of Teaching and Learning in Signal Processing at the University of Bristol's School of Electrical, Electronic and Mechanical Engineering, where he contributes to the Visual Information Laboratory and Photonics and Quantum research theme. His career uniquely integrates advanced technical research with pedagogical innovation in engineering education. He holds an Ingenieurd'Etat degree from Oran and a Ph.D. from the University of Leicester, establishing his foundation in both theoretical and applied engineering. His academic journey reflects continuous engagement with cutting-edge signal processing technologies and educational methodologies. Research interests center on Signal Processing with specialized expertise in Digital Signal Processing implementation , mmWave Radar systems for human monitoring, Biomedical Signal Processing (particularly foetal EEG and vital sign detection), and Engineering Education pedagogy. His work develops real-time radar-based solutions for posture estimation, fall detection, and non-contact health monitoring while pioneering FPGA-based DSP teaching approaches. Analysis of his 91 publications reveals a distinct dual trajectory: since 2020, approximately 60% of his work focuses on mmWave radar applications in healthcare (including neonatal monitoring and elderly care), while the remainder advances DSP education through project-based FPGA implementations and e-learning tools. This synergy between technical innovation and teaching methodology defines his scholarly contribution. His recognition includes: Students’ Award for Outstanding Teaching (Engineering), 2018 As a research supervisor, he has guided 7 students through advanced projects while securing funding as co-investigator on interdisciplinary grants including a national database study on neonatal outcomes (2018-2019) and Bristol Harbour water quality monitoring (2017). His grant portfolio demonstrates consistent translation of signal processing techniques to healthcare and environmental applications. Within the Engineering Education Research Group, he leads initiatives in DSP pedagogy and digital learning tools, while his technical work in the Visual Information Laboratory drives radar-based sensing innovations. Current projects emphasize low-cost, real-time implementations for both medical monitoring systems and educational platforms.
Ricardo J. Machado is a Full Professor of Information Systems Engineering at the University of Minho, where he founded this disciplinary area. He currently serves as President of the CCG/ZGDV Institute and has held strategic roles including Vice-Rector of UMinho. He is a member of the Board of GraphicsVision.AI, the leading academic network in Europe in computer graphics and vision. DEng in Electrical and Computer Engineering (U.Porto) MSc and PhD in Computer Science and Engineering Dr. Habil in Information Systems Engineering (UMinho) Certified Software Product Manager (ISPMA) Cybersecurity and Crisis Management programme from Portuguese National Defence Institute Professor Machado's research focuses on modeling and requirements engineering, systems architecture, process and project management, information semantics, ontologies, and cognitive computing. He has developed several methods and tools including the 4SRS method and the shobi-PN meta-model for requirements analysis, software architecture, ontology computation, and project portfolio management. His work bridges theoretical frameworks with practical industrial applications across various sectors. His recent publications demonstrate a strong trend toward digital transformation across multiple domains including smart cities, healthcare systems, industry 4.0, and higher education. The research shows increasing integration of semantic interoperability, logical architecture design, and data management frameworks. His work consistently addresses the challenges of aligning business processes with technical implementations while focusing on practical applications in real-world settings. IEEE MGA Achievement Award APLOG Excellence Award TAA Textbook Award nomination by Springer Professor Machado has supervised nearly one hundred PhD and MSc students, many of whom now hold leadership roles in academia and industry. He has led over 50 R&I projects funded by FCT, ANI, IAPMEI, and the European Union, partnering with institutions including Carnegie Mellon University, MIT, Fraunhofer, and Bosch. His project portfolio spans requirements engineering, systems architecture, and digital transformation initiatives across multiple sectors including healthcare, manufacturing, and smart cities. He founded the SEMAG research group at ALGORITMI and the EPMQ department on Software Engineering and Intelligent Data at CCG/ZGDV. As Director of the ALGORITMI Research Centre, he coordinated UMinho's participation in strategic initiatives such as CEDT, CMU | Portugal, EIT Digital, EUHubs4Data, and Gaia-X Portugal. He also co-founded DTx and ProChild CoLabs, and TICE.pt and CSCP Clusters.
Måns Magnusson is an Associate Professor at the Department of Statistics, Uppsala University, with affiliations at the Institute for Analytical Sociology (Linköping University) and the Institute for Future Studies. His work bridges Bayesian statistics, probabilistic machine learning, and textual analysis, focusing on model evaluation, diagnostics, and inference algorithms. He contributes to computational social science and digital humanities through text-as-data methods. Research Themes: Bayesian inference, probabilistic machine learning, statistical inference from textual data Key Applications: Sociology, political science, law, education statistics, public health Current Projects: Improving probabilistic programming generalizability (Swedish Research Council grant), SWERIK research infrastructure (Riksbankens jubileumsfond) His recent publications emphasize text mining, model comparison, and legal data challenges. He has developed tools for national ID number validation, hate crime estimation, and parliamentary corpus construction. Notable awards include the Cramér Prize (2018), Statistician of the Year (2023), and membership in the Swedish Young Academy (2023) and ELLIS (2024). Scientific Contributions: Botten Ada Bayesian election model, 'loo' package for cross-validation Collaborative Work: AI4Research sabbatical (2024), Riksbankens jubileumsfond funding Industry Background: Statistician roles at Swedish Agency for Education, Crime Prevention, and Public Health
Youhua Shi is a full Professor in the Faculty of Science and Engineering at Waseda University, Japan. He obtained his Doctor of Engineering from Waseda in 2005 and is an active member of IEICE, IPSJ, IEEE, and two Japanese academic societies. His research portfolio integrates trustworthy computing, hardware security of AI accelerators, energy-harvesting interface circuits for triboelectric nanogenerators, and low-power VLSI design-for-test methodologies. Education: Doctor of Engineering, Waseda University (2005) Graduate studies, Waseda University, Division of Engineering (completed 2005) Research Interests: Prof. Shi pursues trustworthy and secure silicon systems, spanning hardware Trojans in automated AI-accelerator flows, radiation-hardened latch design for soft-error resilience, and power-efficient CNN accelerators exploiting zero-gating and data-reuse techniques. Parallel work targets energy-autonomous IoT through advanced interface circuits for triboelectric nanogenerators, achieving record energy-per-cycle beyond the classical CMEO limit. Publication Trends: Recent articles (2024-2025) emphasize two thrusts: (i) security of AI/FPGA accelerators—proposing stealthy hardware-Trojan frameworks embedded within design-space-exploration flows that can misclassify up to 97% of inputs—and (ii) power electronics for triboelectric harvesters—introducing dual-output rectifiers and Bennet-doubler biasing that multiply output power >150× over conventional full-wave rectifiers, enabling battery-free IoT nodes. Scientific Awards: APCCAS Best Student Paper Award – 2020 IEEK Best Paper Award – 2012 Students & Collaboration: He has mentored numerous doctoral and master’s scholars, including Yirui Su, Chao Guo, Jinghao Ye, Lin Ye, Saki Tajima, and Masaru Oya, many of whom serve as first authors on his high-impact publications, indicating an active and productive advising role. Labs & Teams: While the text does not name a specific laboratory, his continued affiliation with Waseda University’s Faculty of Science and Engineering and his extensive project output imply he leads a research group focused on secure & energy-efficient VLSI systems, collaborating closely with colleagues such as Prof. Masao Yanagisawa and Prof. Nozomu Togawa.
Oliver Bringmann is a full Professor and head of the Chair of Embedded Systems at the University of Tübingen, Germany, and a member of the board of directors at the FZI Research Center for Information Technology. His research integrates embedded-system design, energy-efficient AI accelerators, dependable automotive perception, and medical AI for capsule endoscopy. Education & Career Ph.D. in Computer Science, University of Tübingen, 2001 Diploma in Computer Science, University of Karlsruhe (KIT) Head, Chair of Embedded Systems, University of Tübingen (since 2012) Deputy spokesperson & spokesperson, Dept. of Computer Science, University of Tübingen (2014-2022) Board of Directors, FZI Research Center for Information Technology Research Interests Bringmann’s group pioneers hardware/software co-design for ultra-low-power Edge-AI , developing RISC-V based accelerators, compiler-aware neural-architecture search, and real-time perception systems for autonomous driving and medical devices. Key topics include: Energy-efficient AI architectures (“Edge AI”) and custom accelerator generation Robust collective perception under adverse weather (LiDAR, camera, V2X fusion) Timing/power-predictable embedded software and system-on-chip design automation Hardware-assisted security and safety for automotive & IoT systems AI-driven capsule endoscopy localization and anomaly detection Recent Publication Trends His 2024-2025 articles reveal a strong shift toward robust multimodal perception for automated driving (snow, fog, collective LiDAR fusion) and Edge-AI medical devices (capsule endoscopy with multi-task CNNs). Core contributions span dataset generation (SCOPE, SnowyLane), safety metrics (LSM), and fast performance modeling for DNN accelerators. Professional Service & Projects Executive/Steering Committees: IEEE/ACM DATE, CODES+ISSS, CASES, ITSS conferences EU CATRENE EDA roadmap chapter lead (Embedded Software & ESL-to-RTL) Principal investigator in Scale4Edge, OCEAN12, enerDAG and other national projects on energy-efficient sensorics and secure energy trading. His group maintains extensive collaborations with automotive and semiconductor industry, focusing on dependable, energy-aware embedded intelligence.
Andy D. Pimentel is a Full Professor at the University of Amsterdam, where he chairs the Parallel Computing Systems (PCS) group within the Systems and Networking Lab at the Informatics Institute. His work focuses on the design, programming, and run-time management of multi-core and multi-processor computer systems, with particular attention to performance, power/energy consumption, system dependability, and design productivity. His academic background includes: PhD in Computer Science, 1998, University of Amsterdam MSc in Computer Science, 1993, University of Amsterdam Professor Pimentel's research spans multiple critical areas in modern computing systems. His primary interests include multi-core embedded systems, system-level design and simulation, design space exploration, performance and power analysis, system dependability, hardware/software co-design, run-time resource management, and Edge AI. His work addresses the growing challenges of making computer systems faster, more sustainable, energy efficient, reliable, and secure in an era of increasing computational demands and climate concerns. The PCS group he leads performs research on the modeling, analysis and optimization of extra-functional aspects of computing systems, which play a pivotal role in their work. An analysis of Professor Pimentel's recent publications reveals a strong focus on edge computing, distributed AI, and energy-efficient system design. His work bridges theoretical computer architecture with practical implementation challenges, particularly in the context of resource-constrained environments. Key trends include the adaptation of AI models for edge devices, thermal management in advanced architectures, and optimization of multi-core systems for both performance and energy efficiency. His research increasingly addresses sustainability concerns in computing, reflecting broader industry and academic priorities. His notable scientific achievements include: IEEE CEDA Outstanding Service Recognition Award DATE Fellow Award Professor Pimentel has served in numerous leadership roles in the academic community, including as General Chair of Design Automation and Test in Europe (DATE) 2024, Vice General Chair of IEEE/ACM Embedded Systems Week 2025, and General Chair of IEEE/ACM Embedded Systems Week 2026. He has secured significant research funding for projects related to sustainable computing, edge AI, and multi-core system design. His professional service includes board membership with the ICT Research Platform Nederland (IPN) since 2020 and leadership roles in major conferences such as DATE, Embedded Systems Week, and SAMOS. The Parallel Computing Systems group he chairs is a vibrant research team within the Systems and Networking Lab at the Informatics Institute. The PCS group focuses on the challenges of modern computing systems, particularly addressing the extra-functional aspects like performance, power consumption, and system dependability. Their work is highly relevant to current technological challenges in edge computing, sustainable systems design, and the integration of AI into resource-constrained environments.
