Dr. Jan Salmen is a researcher at Ruhr University Bochum's Faculty of Computer Science, affiliated with the Institute of Neuroinformatics (INI). His work focuses on real-time systems, computer vision, and machine learning. Doctoral thesis: Efficient video-based driver assistance systems Salmen's research spans autonomous driving, traffic sign recognition, stereo vision, and sports analytics. He has contributed to benchmarks in traffic sign detection and soccer analysis. Publications highlight his expertise in image processing, pattern recognition, and sensor fusion for autonomous systems. Key trends include optimization of machine learning algorithms for real-time applications. He collaborates with interdisciplinary teams at INI, which integrates experimental psychology, neurophysiology, and robotics into artificial cognitive systems research.
Delphine Périé-Curnier is a Full Professor in the Department of Mechanical Engineering at Polytechnique Montréal and Director of Graduate Studies. Her research focuses on developing quantitative MRI techniques for non-invasive characterization of living tissue mechanical properties, particularly in cardiotoxicity detection and musculoskeletal mechanobiology . She leads the Bioperformance Analysis and Innovation Laboratory (LIAB) and contributes to the Institute of Biomedical Engineering. Education: Ph.D. from Paul Sabatier University, Toulouse, France Her work bridges medical imaging , biomechanical modeling , and finite element analysis to predict disease progression through pathomechanism understanding. Key projects include exercise-induced cardiac changes in childhood cancer survivors and spinal biomechanics in scoliosis. Recent publications (2023-2024) emphasize cardiovascular MRI for childhood cancer survivorship and hemodynamic modeling in left ventricle analysis. She supervises 26 graduate students, with completed theses spanning topics like doxorubicin cardiotoxicity , knee replacement stability , and spatial cardiac MRI protocols . Teaching includes graduate courses in biomedical design , advanced biomechanics , and modeling techniques .
Dr. Carlo Cavicchia is an Assistant Professor of Statistics at the Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam. He holds a PhD in Methodological Statistics from La Sapienza University of Rome and has held roles such as Research Fellow at UnitelmaSapienza University and Consultant for NGOs in Zanzibar. His research focuses on latent variable models, composite indicators, and unsupervised classification, with applications in environmental policy, sports analytics, and teacher job satisfaction. Cavicchia teaches statistics and data science courses at undergraduate and graduate levels and actively contributes to academic communities through journal reviewing, conference organizing, and editorial roles. Education: PhD in Methodological Statistics (La Sapienza University of Rome, 2020) MSc in Statistics and Decision Sciences (La Sapienza University of Rome, 2016) BSc in Statistics (La Sapienza University of Rome, 2013) Dutch University Teaching Qualification (BKO, 2022) Research Interests: Cavicchia’s work emphasizes hierarchical models, non-parametric statistics, and data science applications. He develops methodologies for composite indicators, including ultrametric Gaussian mixture models and disjoint principal component analysis. His research bridges theoretical advancements with real-world problems, such as waste management in Italian municipalities and ranking European football teams using composite metrics. Grants & Awards: 2024: IFCS Chikio Hayashi Award 2023: ESE Starter Grant (€300,000) 2017: Research Grant for Junior Researchers (€1,270) 2016: PhD Scholarship, La Sapienza University Academic Engagement: Cavicchia serves as IASC Data Analysis Competition Officer (2023–2025), co-edits the ISI Magazine , and organizes conferences like DSSV 2020 and DSSV-ECDA 2021. He is an elected member of the International Statistical Institute and contributes to SVQS’s Sustainability initiatives. Labs & Teams: He co-organizes the Econometrics internal seminars at Erasmus University and collaborates with researchers at University of Naples Federico II on hierarchical models and convex clustering.
Kyle O'Keefe is a Professor in the Department of Geomatics Engineering at the University of Calgary's Schulich School of Engineering. He holds dual B.Sc. degrees in Geomatics Engineering (University of Calgary, 2000) and Honours Physics (University of British Columbia, 1997), and a Ph.D. in Geomatics Engineering (University of Calgary, 2004). He is a Professional Engineer (P.Eng.) registered with the Association of Professional Engineers and Geoscientists of Alberta since 2005. His research focuses on positioning and navigation technologies, including Global Navigation Satellite Systems (GNSS) advancements Ultra-wideband (UWB) ranging for vehicle/pedestrian navigation Indoor positioning using wireless signals Wearable sensor integration for biomechanics and navigation GNSS spoofing detection and cybersecurity Notable projects include: Development of UWB-augmented GNSS for RTK surveying (2007–present) Wearable sensor systems for rowing/kayaking motion analysis (2017–present) CanX-2 nanosatellite GPS receiver operations (2008) Igliniit project with Inuit hunters for Arctic environmental monitoring (2006–2009) Multi-constellation GNSS evaluation across 20+ years He has received prestigious awards including the Michael Richey Medal (2011) and multiple Best Paper Awards at IPIN and ION conferences. His teaching includes courses like Advanced GNSS Theory and Wireless Location. Active in professional organizations, he co-edits special journal issues and advises industry on emerging navigation technologies.
Prof. Dr. Kai Essig is a Professor of Human Factors and Interactive Systems at the Faculty of Communication and Environment, Rhine-Waal University of Applied Sciences, Kamp-Lintfort, Germany. He has a strong interdisciplinary background combining computer science, cognitive science, and human-computer interaction, with a focus on eye tracking, visual perception, and assistive technologies. Master of Science in Computer Science and Chemistry, Bielefeld University (1998) Ph.D. in Computer Science, Bielefeld University (2007) His research centers on eye tracking, human-computer interaction, usability engineering, visual attention, and cognitive interaction technology . He investigates how movement expertise influences visual perception and how multimodal software can support real-time human actions. His work integrates computer vision, machine learning, and neuroscience to develop intelligent systems that adapt to user behavior. The 15 most recent publications reflect a consistent trend in eye movement analysis, mental representations, brain-machine interfaces, and assistive technologies . These works span domains such as sports psychology, robotics, augmented reality, and cognitive neuroscience, demonstrating a strong interdisciplinary approach. Key themes include gaze-based interaction, automated annotation of visual behavior, and the implementation of smart systems for daily living assistance. Scientific recognition includes: Landmark in the Land of Ideas (2018) – for the ADAMAAS project, awarded by 'Land of Ideas', a joint initiative of the German government and the Federation of German Industries Prof. Essig has been actively involved in research projects such as ADAMAAS (Adaptive and Mobile Action Assistance in Daily Living Activities), which received national recognition. He has collaborated extensively with the Neurocognition and Action-Biomechanics Research Group at Bielefeld University and the Excellence Cluster CITEC. While no formal advising of students is listed, his publications suggest mentorship and collaboration with junior researchers. His lab work is centered on eye-tracking systems, multimodal interaction, and cognitive modeling , particularly within applied environments like smart glasses and assistive technologies.
Raghavendra Ramachandra is a Professor at the Department of Information Security and Communication Technology (IIK) , within the Faculty of Information Technology and Electrical Engineering at the Norwegian University of Science and Technology (NTNU) in Gjøvik. His research focuses on biometric systems, particularly in face, fingerprint, and finger vein recognition, with emphasis on presentation attack detection, morphing attack detection, and deep learning applications. Current research projects include: SALT (2022-2026) : Developing privacy-preserving facial biometric authentication systems. OffPAD (2022-2025) : Creating cryptographic tools and presentation attack detection for fingerprint biometrics. SWAN (2015-2020) : Developing biometric countermeasures against presentation attacks. His recent publications demonstrate technical expertise in: Face morphing attack detection using vision transformers and point cloud networks Image fusion techniques for multispectral biometrics GAN-based synthetic data generation for security evaluation Explainable AI approaches for biometric verification Professor Ramachandra also supervises PhD and Master’s students, and has extensive experience in leading national and EU research initiatives.
Hans Vernooij is a Lecturer in Farm Animal Health at the Faculty of Veterinary Medicine, Utrecht University. He specializes in statistical methods and data science applications in veterinary epidemiology and animal health. His areas of expertise include: Statistical methods for veterinary research Applied Data Science in Life Sciences Epidemiological modeling Machine learning applications in animal health Vernooij has extensive experience in developing statistical models for animal health applications. His research focuses on applying advanced statistical techniques and data science methods to solve problems in veterinary epidemiology and farm animal health. He has particular expertise in Random Forest models, as demonstrated during his sabbatical at the Human Sciences Research Council in Pretoria where he developed a model for HIV status prediction based on demographic information and knowledge of HIV prevention from large-scale survey data. His publication record shows consistent contributions across veterinary epidemiology, with recent work emphasizing machine learning applications and big data analytics in animal health surveillance. The research demonstrates a clear trajectory from traditional statistical methods toward more advanced data science approaches. Vernooij is actively involved in teaching and mentoring: Teaches statistics to Bachelor students at the veterinary faculty Supports PhD candidates and Master students during data analysis phases of their research Provides statistics education for the Master of Epidemiology program at the Julius Centre of University Medical Centre
Dr. Nathalia Costa is a Senior Research Fellow at the University of Queensland's cLinical TRials cApability (ULTRA), located within the Centre for Clinical Research in Herston, and affiliated with the Centre for Innovation in Pain and Health Research (CIPHeR). She holds appointments in the School of Health and Rehabilitation Sciences within the Faculty of Health, Medicine and Behavioural Sciences. Her research focuses on theoretically grounded qualitative and mixed methods approaches to understanding low back pain, pain management, and healthcare systems. Costa is particularly known for her groundbreaking work on low back pain flares, which established the first rigorous definition of pain flares through stakeholder engagement with over 70 experts and consumers. Her methodological expertise spans systematic reviews, qualitative methodologies (interviews, ethnography, Delphi studies), embedding qualitative research in clinical trials, and systems-thinking frameworks. Costa's recent publications demonstrate a growing focus on culturally responsive care, healthcare access for migrant populations, and the intersection of legal services with mental health. Her work bridges micro-level patient experiences with macro-level healthcare systems, aiming to make pain care more nuanced, equitable, and evidence-based. She has published over 55 papers spanning musculoskeletal conditions, pain research, health policy, and qualitative methodologies. Her scientific achievements include the 2021 ISSLS Prize for Lumbar Spine Research, recognition from the International Society for the Study of the Lumbar Spine. Her research has influenced international pain frameworks, including adoption by the International Association for the Study of Pain (IASP) in their online Fact Sheet (July 2023). Costa actively supervises multiple PhD students, serves as Associate Editor for Qualitative Health Research and the Journal of Humanities in Rehabilitation, and has taught across disciplines including research methods, musculoskeletal physiotherapy, and health policy. She has secured over AUD$7.5M in research funding, currently leading the FORENSIC trial investigating lumbar fusion surgery effectiveness for persistent severe low back pain.
Raul Giraldez Rojo is a Professor at the University of Pablo de Olavide in Seville, Spain, working in the Department of Sports and Information Technology within the area of Computer Languages and Systems. He is affiliated with the DASE (Data Analytics Science & Engineering) research group and contributes to PhD programs in Engineering, Data Science, and Bioinformatics. His research interests focus on computer science with specialization in machine learning, evolutionary algorithms, and data analytics. Dr. Giraldez Rojo has made significant contributions to supervised learning techniques, clustering algorithms, and genetic programming applications. His work bridges theoretical computer science with practical applications in bioinformatics and data mining. His publication record demonstrates expertise in developing algorithms for data analysis, particularly in biclustering methods for expression data and supervised clustering techniques. His research shows a consistent focus on improving efficiency and effectiveness of computational methods for handling complex datasets. Dr. Giraldez Rojo collaborates extensively with researchers including Jesús Salvador Aguilar-Ruiz and José Cristobal Riquelme Santos, indicating strong research partnerships within the Spanish academic community specializing in computational intelligence.
PD Dr. Kaspar Riesen is the Head of the Pattern Recognition Group at the Institute of Computer Science, University of Bern. His research focuses on graph-based methods for pattern recognition, with applications in document analysis, environmental modeling, and healthcare. Key interests include graph matching, neural networks, and spatio-temporal modeling. His work spans structural pattern recognition, graph embeddings, and keyword spotting in historical documents. Recent projects involve river network analysis using graph regression and hypoglycemia prediction via LSTM-GNN hybrid models. Publications emphasize graph theory advancements, such as normalized graph compression and geometric similarity learning. Collaborations include developing specialized algorithms for automated error detection and improving decision-making in simulated sports. Labs/Teams: Pattern Recognition Group (PRG) at the University of Bern.
Martin Ingvar is a Senior Professor at Karolinska Institutet's Department of Clinical Neuroscience, affiliated with the Pain and Brain Imaging research group led by Karin Jensen. He holds a Medical Degree from Lund University (1984) and a Doctor of Medical Science degree (1982), specializing in experimental neurological research. His research focuses on knowledge processes in healthcare, integrating cognitive science, information theory, and medical informatics to develop clinical information systems that enhance patient care. He leads the Vinnova Demonstrator project (2023–2027) on multi-use health data and has held prominent roles such as Dean of Research at Karolinska Institutet (2010–2013) and Board Chair of Swelife (2013–2017). Ingvar’s academic career includes leadership positions like Deputy Head and Head of the Department of Clinical Neuroscience (2004–2010), and directorships of facilities like the Karolinska MR Center (1998–2022). His grants span topics like psychiatric prediction systems, chronic pain mechanisms, and healthcare data innovation. He has contributed to over 400 publications, emphasizing brain imaging, psychiatric disorders, and health informatics. Key contributions include pioneering work on the National MEG Center and advancing integrative medicine. His research bridges clinical neuroscience with societal health challenges, emphasizing data-driven solutions for healthcare systems.
Sao Mai Nguyen is an Enseignante-Chercheuse (Lecturer-Researcher) at ENSTA Paris, affiliated with the Unité d'Informatique et d'Ingénierie des Systèmes (U2IS). Her research bridges robotics, artificial intelligence, and cognitive science, focusing on cognitive developmental robotics, intrinsic motivation in learning, and human-robot interaction. She explores how robots can adapt to social and physical environments, particularly in physical rehabilitation and smart home applications. Research Interests: Nguyen’s work integrates machine learning, robotic embodiment, child psychology, and neuroscience. Key areas include human-robot interaction, assistive robotics for chronic low back pain rehabilitation, activity recognition in smart homes using IoT sensors, and intrinsic motivation-driven learning frameworks. Recent Contributions: Her 2024 HDR thesis on reinforcement and imitation learning for sequential tasks underscores her expertise in strategic learning systems. Recent publications address bio-inspired robotics models, hierarchical reinforcement learning, and benchmark environments like 'Open the Chests' for activity recognition. Collaborations include projects like the R-COOL randomized trial for robot-coached physical exercises. Labs & Teams: She contributes to the U2IS lab, advancing interdisciplinary research in AI and robotics. Her work spans experimental platforms for sensorimotor learning and healthcare robotics applications.
Franck Courchamp is a Senior Researcher at CNRS affiliated with the University of Paris Sud . His work focuses on ecology, conservation biology, and invasion biology , blending theoretical and applied approaches to address biodiversity loss and climate change impacts. Research Highlights: Investigates population dynamics, biological invasions, and their economic costs; pioneers ethical frameworks for conservation decisions. Awards: Recipient of the CNRS Silver Medal (2011), Fulbright Fellowship (2014), and Thompson Scientific’s Highly Cited Scientist recognition. Teaching & Outreach: Regular teaching engagements and author of two popularization books, including 'Ecology for Dummies' (in French). Leadership: Supervised over 60 students, served on editorial boards (Ecology Letters, Ecological Research), and contributed to global invasive species policy assessments. His recent publications emphasize economic costs of invasions , ethical dilemmas in conservation , and cross-regional invasion trends , reflecting his commitment to actionable ecological science. Scientific Impact: With an h-index of 35 and over 5200 citations, Courchamp’s work spans peer-reviewed journals, monographs, and book chapters, influencing both academic and public discourse on biodiversity.
Dr. Harshala Gammulle is a Research Fellow at Queensland University of Technology (QUT), School of Electrical Engineering & Robotics. She holds a PhD in Computer Vision from QUT (2019), receiving the QUT Executive Dean's Commendation for Outstanding Doctoral Thesis. Her expertise spans machine learning, computer vision, and spatio-temporal modeling for human behavior understanding. She leads interdisciplinary projects with funding from DST Group, SmartSat CRC, QLD DESI, and others. Research focuses include: human action recognition, medical anomaly detection, satellite image analysis, and AI for environmental monitoring. Key projects involve quantum-classical hybrid ML for biomedical signal analysis, disaster forecasting via hyperspectral data, and autonomous combat vision systems. She has supervised PhD/MPhil candidates in ML and quantum hybrid ML. Education: PhD (Computer Vision, QUT 2019), BSc (University of Peradeniya, Sri Lanka). Awards: WiT Emerging Achiever Technology Award finalist (2021), University Award for Academic Excellence (2015). Teaching includes units like Digital Signals and Image Processing (EGH444), and Computing & Data for Engineers (EGB103). Current grants involve QLD DESI, SmartSat CRC, and Rheinmetall Defence Australia collaborations. Active in labs like SAIVT and QUT's Early Career Research schemes.
Shawn W. O'Driscoll, M.D., Ph.D. is a Professor of Orthopedics at the Mayo Clinic College of Medicine and Science and a Consultant in the Department of Orthopedic Surgery at Mayo Clinic in Rochester. He leads the Elbow and Shoulder Laboratory, focusing on elbow biomechanics, surgical techniques, and arthroplasty. His academic roles include directing the Shoulder and Elbow Fellowship program and serving on several research committees. Education: MD and PhD from the University of Toronto, with postdoctoral training in orthopedic biomechanics under K-N An and BF Morrey. Research Interests: Elbow instability, prosthetic radial head replacement, minimally invasive surgical techniques, and cartilage repair. Awards: ICRS Honorary Lifetime Membership, Mayo School of Continuous Professional Development's Course of the Year Award, and multiple clinical research awards. Dr. O'Driscoll has pioneered advancements in radial head arthroplasty and elbow contracture management, and his work emphasizes translating research into clinical practice to improve patient outcomes.
