Louise Parkin serves as a Lecturer in the Department of Networks and Telecommunications at the University Institute of Technology of Blois (IUT Blois), University of Tours, with additional affiliations at the Polytechnic School of the University of Tours (EPU) and the Fundamental and Applied Computer Science Laboratory of Tours (LIFAT). Her academic profile centers on advancing cooperative methods for knowledge base interrogation and semantic data management. Her research specializes in Semantic Web technologies, focusing on resolving critical challenges in RDF and SPARQL query processing including plethoric answer sets, unexpected results, and scalability limitations. She develops cooperative frameworks that enhance user interaction with knowledge bases like Wikidata through query explanation mechanisms, adaptive result filtering, and on-demand analysis architectures. Her work bridges theoretical database concepts with practical knowledge engineering applications. Within LIFAT laboratory, Parkin contributes to cutting-edge research in semantic data management systems. Her recent projects include Rankingdom's cooperative architecture for Wikidata analysis and scalable techniques for knowledge base exploitation, demonstrating sustained innovation in query processing methodologies from 2020 through 2025 publications.
Dr Majid Zamani is a Lecturer in the School of Electronics & Computer Science at the University of Southampton. His research focuses on implantable and wearable biomedical devices, applied AI in biomedical engineering, and hardware-efficient processing frameworks. Member of: Digital Health and Biomedical Engineering Institute for Life Sciences Member of: Centre for Internet of Things and Pervasive Systems Member of: Centre for Health Technologies Member of: UKRI AI Centre for Doctoral Training in AI for Sustainability (SustAI) Current research addresses key challenges in scalability, signal processing, sensing, energy efficiency, and miniaturization for next-generation implantable brain-machine interfaces (iBMI). He explores hardware-efficient computational platforms for biomedical applications, including AI-driven algorithms for neural signal/image processing, augmented navigation in constrained anatomical spaces, and low-power real-time processors in 180/90/45 nm CMOS technologies. Recent publications focus on deep learning for spike sorting, binarized neural networks, noise-aware speech enhancement, and AI integration in surgical navigation. His work bridges biomedical engineering with AI and hardware optimization. Teaching: Digital system design (ELEC6236) 42+ publications in journals like IEEE Transactions on Medical Imaging, Journal of Neural Engineering, and IEEE Access
Distinguished Professor Jie Lu AO is an internationally renowned scientist in computational intelligence at the University of Technology Sydney, where she serves as Associate Dean (Research Excellence) in the Faculty of Engineering and Information Technology and Director of the Australian Artificial Intelligence Institute (AAII), the largest AI hub in Australia with 35 researchers and 230 PhD students. She has been a Professor at UTS since 2007 after serving as Associate Professor from 2004-2006. Professor Lu earned her PhD from Curtin University, Perth, Australia. Her research focuses on computational intelligence with significant contributions to fuzzy transfer learning, concept drift, data-driven decision support systems, and recommender systems. She has developed machine learning models, intelligent recommender systems, and AI-driven decision support systems through collaborations with industry partners including Optus, Sydney Trains, Domain Holdings Australia Ltd, and Workforce Health Assessors Transport NSW. Her recent publications demonstrate a strong focus on addressing challenges in non-stationary environments, out-of-distribution detection, multi-stream concept drift, and applying AI to healthcare applications such as stroke risk prediction and cancer risk assessment. She has pioneered approaches combining traditional AI techniques with large language models for more robust and explainable systems, particularly in legal case recommendation and women's health applications. Officer of the Order of Australia (AO) IEEE Fellow, IFSA Fellow, Australian Computer Society Fellow Australian Laureate Fellow in AI and Industry Laureate Fellow in AI-for-Health UTS Chancellor's Research Medal for Research Excellence (2019) IEEE Transactions on Fuzzy Systems Outstanding Paper award (2019, 2022) Australian Most Innovative Engineer award (2019) NeurIPS 2022 Paper Award Australasian AI Distinguished Research Contribution Award (2022) Australian NSW Premier Prize on Excellence in Engineering (2023) Professor Lu has supervised 60 PhD students to graduation and serves as Editor-In-Chief for Knowledge-Based Systems journal. She has secured 47 ARC grants and over 110 industry projects since 2017, with funding from ARC Discovery projects, ARC Laureate Fellowships, and industry partners. Her leadership has established UTS as a leading center for AI research in Australia, with significant impact across multiple sectors including transportation, telecommunications, healthcare, and education. As Director of the Australian Artificial Intelligence Institute, Professor Lu has built a thriving research ecosystem that bridges academic research with practical industry applications. Her work on concept drift and transfer learning addresses fundamental challenges in adapting machine learning models to changing environments, with direct applications to real-world problems requiring continuous learning and adaptation.
Benjamin W. Domingue is an Associate Professor at the Graduate School of Education , Stanford University, with affiliations including the Stanford Center on Early Childhood and Policy Analysis for California Education (PACE) . His research bridges psychometrics and sociogenomics , focusing on statistical tools for measuring educational outcomes and integrating genetic data into social science research. Education: PhD in Education, University of Colorado Boulder (Advisor: Derek Briggs, 2012) MA in Mathematics, University of Texas at Austin (Advisor: Uri Treisman, 2006) BS in Mathematics, University of Texas at Austin (2001) His work examines test score properties , response time analysis , and genetic influences on educational attainment . Recent projects include developing the Item Response Warehouse (IRW) resource and analyzing oral reading fluency during the pandemic . Key methodological contributions involve the InterModel Vigorish (IMV) for predictive accuracy and speed-accuracy tradeoffs in real-world settings. Scientific Awards Jacobs Foundation Research Fellow (2022–2024) Outstanding Reviewer for AERA Open (2019, 2018) AERA Division D Quantitative Dissertation Award (2013) Faculty Advising Award (2018–2019) His research spans collaborations with institutions like the University of California, Berkeley, University of Colorado Boulder, and The University of Texas at Austin. He has contributed to genetic moderation studies , item response theory , and healthcare provider behavior through psychometric approaches.
Prof. Dr. Osman Kukrer is a full-time faculty member at Eastern Mediterranean University (EMU), Faculty of Engineering, Department of Electrical and Electronics Engineering. He has been actively supervising graduate students in power electronics, control systems, and renewable energy integration since the 1990s. His research spans advanced power conversion topologies, including quasi-Z-source inverters multilevel converters active power filters grid-connected systems adaptive beamforming algorithms electric vehicle grid integration Notable contributions include the EMU Publication Citation Award (2017) and extensive supervision of 41 graduate theses, with research interests aligning with modern energy systems and signal processing techniques.
Professor Yaprak Yalçın is affiliated with Istanbul Technical University as a faculty member in the Department of Control and Automation Engineering. With an h-index of 7, her research has generated significant academic impact. She has published extensively in control systems and automotive engineering domains. PhD in Control and Automation Engineering from Istanbul Technical University (2004-2010) Research Interests: Specializing in control theory and mechatronics, her work focuses on: Model Predictive Control systems Vehicle dynamics and path tracking Energy management optimization Nonlinear control methodologies Adaptive control applications Automotive automation technologies Research Trends: Recent publications demonstrate technical expertise in: Developing explicit nonlinear MPC for vehicle path tracking Implementing Kalman filters for autonomous motorcycle control Advancing energy management systems for electric buses Optimizing trajectory following in heavy-duty vehicles Integrating deep learning with control systems Improving computational efficiency in real-time control Grants & Projects: Currently leading three BAP-funded projects including: Virtual MATLAB laboratory for control systems Probabilistic MPC for autonomous vehicles Consensus control with adaptive MPC
Michael Kessler is a Researcher at the University of Zurich , affiliated with the Department of Systematic and Evolutionary Botany and Botanical Garden. His work focuses on tropical plant biodiversity and biogeography, investigating the interplay of biotic and abiotic factors with evolutionary processes in regions such as the Andes, Central America, Africa, Indonesia, and the Philippines. Key research areas include fern flora systematics, epiphytic plant ecology, and climate-vegetation interactions in alpine and tropical ecosystems. He employs GIS modeling to analyze plant distribution-climate relationships and explores functional diversity drivers in fern communities. His recent publications highlight global patterns in fern phylogenetics, conservation challenges in tropical cloud forests, and elevational biodiversity dynamics. Collaborations span institutions in the USA (A.R. Smith, J. Grant), Germany (A. Widmer), and Switzerland (G. Zotz, D. Croll).
Lauren Sullivan is an Assistant Professor at Michigan State University’s Kellogg Biological Station, Department of Plant Biology, and Ecology, Evolution & Behavior Program. She earned her Ph.D. from Iowa State University and specializes in plant population/community ecology, movement ecology, species invasions, and conservation/restoration ecology. Her research combines field experiments and theoretical models to understand how global change drivers (habitat fragmentation, nutrient loading, herbivore shifts) affect plant dispersal and ecosystem outcomes. Her lab focuses on the interplay between seed dispersal, nutrient enrichment, and herbivory in shaping grassland biodiversity. Recent work explores wind seed dispersal mechanics, fire impacts on Ozark woodlands, and climate-dispersal interactions in Geum triflorum . Her team emphasizes inclusivity, antiracist practices, and mental health support for lab members. Lauren’s publications (2025–2018) span seed bank limitations in prairie restoration, soil microbial responses to nitrogen, forb diversity conservation, and modeling frameworks for dispersal and productivity. Key themes include global change impacts, ecological modeling, and sustainable restoration strategies. She has mentored students like Laís, Ale, and Kate, while advocating for equity in STEM through lab initiatives. Scientific contributions include advancements in understanding biodiversity-productivity relationships, herbivore-assisted reinvasions, and ethical specimen collection. Her work often bridges theoretical and applied ecology, with applications in conservation policy and ecosystem management. Lab updates highlight her mentorship of graduate students and ongoing efforts to foster diversity in plant ecology, aligning with her commitment to creating inclusive scientific environments.
Christina Lioma is a Full Professor at the Department of Computer Science (DIKU), University of Copenhagen . She has held academic positions including Associate Professor (2014-2018) and Freja Fellow/Assistant Professor (2012-2013) at the same institution. M.Hons (University of Glasgow, 2001) M.Sc. (University of Manchester, 2003) Ph.D (University of Glasgow, 2007) Her research focuses on Information Retrieval , Text Analytics , and Recommender Systems within Applied Machine Learning and Natural Language Processing . Recent work examines fairness-relevance tradeoffs in recommendation systems and neural mechanisms for knowledge conflict tracing. Recent publications in Nature Communications and top conference proceedings (WWW, SIGIR) explore hybrid computation architectures, brain-based language generation, and fairness evaluation metrics. She actively participates in academic conferences as organizer and speaker, including the European Conference on Information Retrieval.
Jalil Noroozi is a researcher at the Department of Botany and Biodiversity Research within the Faculty of Life Sciences. His work focuses on plant biogeography, climate change impacts on alpine flora, and biodiversity conservation in the Irano-Anatolian global biodiversity hotspot. He leads projects on phylogeography and conservation status assessment of high mountain plants. Active projects: Alpine plants of different micro-habitats and climate change (2024–2027), Comparative phylogeography in Iranian high mountain ranges (2019–2022) Key collaborations: University of Vienna, international biodiversity databases Research interests include: Endemism and species distribution in high-altitude ecosystems Climate change effects on alpine plant communities Vegetation classification and conservation gap analysis Phylogenetic and functional diversity patterns Recent publications analyze climate change threats to endemic species, comparative phylogeography, and vegetation patterns in Iranian mountains. His work has contributed to the IranVeg vegetation database and global alpine biodiversity frameworks. He studies interactions between environmental stressors and plant trait adaptations in mountainous regions.
Dr. James Thorburn is an Associate Professor in Marine Ecology at Edinburgh Napier University's School of Applied Sciences, where he leads research on the spatial ecology and biology of large marine vertebrates. His work focuses on conserving threatened elasmobranchs (sharks, skates, rays) through field research, spatial modeling, telemetry, and habitat assessment, aiming to inform evidence-based conservation policy. He serves as Principal Investigator on national projects, collaborating with academic, NGO, and government partners, while shaping international conservation strategies. PhD: University of Aberdeen on movement and connectivity of spurdog and tope sharks Leadership: People, Culture and Environment Lead; convenor of Animal Welfare and Ethical Review Body Projects: SIORC Report (NatureScot, £20,001), Tiree Tope (NatureScot, £15,568), BlueFish (Scottish Government, £496,331) His recent publications address critical topics in elasmobranch conservation, including habitat assessment for endangered batoids, particle algorithms for tracking, AUV-based ecological studies, and ethical research methodologies. His research supports strategic leadership in institutional governance and mentoring early-career scientists, with a strong emphasis on public engagement and science communication.
Dr. Jan Grewe is a Senior scientific employee at the Institute for Neurobiology, Department of Biology, Faculty of Science at Eberhard-Karls University Tübingen. His research focuses on neuroethology and electrophysiology, particularly studying sensory information processing in weakly electric fish. He is affiliated with the Neuroethology research group led by Prof. Dr. Jan Benda. Dr. Grewe's primary research interests include neural coding, electrosensory processing, and computational neuroscience. He investigates how sensory information is processed by the nervous system to guide behavior, with a specific focus on weakly electric fish as a model system. His work combines electrophysiological recordings with computational approaches to understand neural mechanisms. He is also a strong advocate for open science and open data practices. His recent publications demonstrate expertise in neural coding, electrosensory systems, and data management. The research spans from basic neural mechanisms in weakly electric fish to developing computational models and data standards. His work shows a consistent focus on understanding how neural systems process information efficiently, with applications to both biological and artificial systems. Dr. Grewe has been actively involved in teaching and mentoring, including organizing the G-Node Course on Neural Data Analysis and mentoring in the Google Summer of Code program. His GitHub profile shows active contributions to neuroscience software projects like NIX and odML, which facilitate data sharing and reproducibility in neuroscience research.
Murtaza Ahmed Siddiqi is an Assistant Professor of Computer Science at the University of Texas at San Antonio (UTSA), affiliated with the College of Sciences. His expertise spans cybersecurity, machine learning, and network intrusion detection systems (NIDS). He holds a Ph.D. in Engineering from Yeungnam University and an M.S. from Mohammad Ali Jinnah University. His research focuses on optimizing NIDS through machine learning techniques, analyzing social engineering threats, and addressing security challenges in emerging technologies like 5G, drones, and smart cities. He has published extensively on topics including feature selection methods, agile normalization approaches, and tactile internet frameworks. Dr. Siddiqi’s work bridges theoretical advancements with practical applications, emphasizing real-world security solutions. His publications highlight interdisciplinary approaches combining machine learning with network security, emphasizing efficiency and adaptability. His recent studies address IoT device security in 5G networks and vulnerability analysis in unmanned aerial systems. He actively contributes to educational and technical advancements in cybersecurity through teaching and collaborative research initiatives. His academic contributions include over 15 peer-reviewed articles since 2015, covering domains such as intrusion detection optimization, WSN security, and APT defense strategies. While no specific awards are listed, his prolific publication record reflects sustained scholarly engagement. He collaborates on grants and lab initiatives focused on improving cyber resilience in modern communication systems.
Giacomo Boracchi is a researcher at the Polytechnic University of Milan , focusing on machine learning, computer vision, and signal processing. His work spans anomaly detection, change detection in data streams, 3D imaging, and biomedical applications. Key Collaborations : Diego Carrera, Luca Magri, Cesare Alippi Industries : Embedded systems, medical imaging, environmental monitoring Recent research explores adaptive Kalman filtering for battery estimation, zero-shot anomaly detection, and explainable AI for vision-language models. Publications highlight applications in waste sorting, histological data generation, and cardiac monitoring. His technical focus includes convolutional networks, ensemble learning, and domain adaptation. Boracchi's work bridges theoretical advancements with real-world systems in fraud detection, semiconductor manufacturing, and wearable health devices.
Kyoung-Don (KD) Kang is a Professor in the School of Computing at Binghamton University, State University of New York. He specializes in real-time embedded systems, cyber-physical systems, and IoT security. His research focuses on enhancing real-time data services' timeliness and power efficiency, supported by NSF grants and industry collaborations. Education: BS, MS (Kyungpook National University); MS, PhD (University of Virginia). Research Interests: Real-time embedded systems, cyber-physical systems, IoT, security, edge computing, and wireless sensor networks. His work emphasizes control-theoretic approaches, feedback mechanisms, and adaptive algorithms for real-time systems. Grants & Projects: NSF-funded projects include 'Enhancing Timeliness of Real-Time Data Services' and 'QoS-Aware Data Management.' He leads the Real-Time Embedded Systems Laboratory, developing frameworks like Chronos and RTMR. Awards: Best Paper nominations (DCOSS'19), NSF grants, and patents in surveillance systems. Over 16 MS students and 9 PhD students have graduated under his mentorship, many joining top tech firms. Labs & Teams: Director of the Real-Time Embedded Systems Lab, focusing on edge computing, real-time stream processing, and secure IoT applications. Collaborates with industry partners like Samsung and Intel.
