Bojana Rosic is a Full Professor specializing in Applied Mechanics & Data Analysis. Her research spans Artificial Intelligence, Machine Learning, Robotics, and Uncertainty Quantification, with a focus on integrating computational methods into mechanical systems and materials science. Key Research Areas: Machine Learning, Uncertainty Quantification, Robotics, Soft and Compliant Mechanisms, Materials Simulation. Recent Work: Contributions to neural network-based constitutive modeling for anisotropic materials, real-time control systems for robotic manipulators, and uncertainty quantification techniques using Polynomial Chaos Expansion. Collaborations: Active in interdisciplinary research with applications in energy, sustainability, and biomedical engineering. Her work emphasizes practical implementations of AI in mechanical engineering, including autonomous systems and collaborative robots (cobots). While no specific awards or educational background are detailed here, her extensive research output (68 publications) highlights her leadership in computational methods and machine learning integration.
A.Q.L. Keemink serves as an Assistant Professor in Biomechatronics and Biorobotics at the University of Twente, within the Faculty of Engineering Technology's Department of Biomechanical Engineering. He is a member of the Biomechatronics and Rehabilitation Technology (ET-BE-BRT) research group and the TechMed Centre. His academic credentials include an MSc (cum laude) in Mechatronics and a PhD in human power augmentation systems and interaction control. Research interests encompass optimization-based control for human-robot interaction, specifically exoskeletons for upper and lower limb support. His work integrates machine learning, optimal motion planning, model-predictive control, and neuromechanics imitation to address rehabilitation challenges for individuals with movement deficits. Key domains include biomechatronics, biorobotics, and rehabilitation engineering. Keemink collaborates within the Biomechatronics and Rehabilitation Technology group, contributing to the University of Twente's TechMed Centre for medical technology innovation.
Gerwin Hoogsteen is an Assistant Professor at the University of Twente, affiliated with the Computer Architecture for Embedded Systems chair. He focuses on smart grids, cyber-physical systems, and applying theoretical research in field-tests. PhD in Decentralized Energy Management (2017, University of Twente) His research integrates machine learning , distributed coordination , and cybersecurity into smart grid optimization. Recent work emphasizes multi-objective optimization for EV charging hubs, energy community resilience, and congestion management. Key article trends include EV charging algorithms , decentralized control , and hybrid storage systems . He contributes to UN SDGs like Climate Action and Affordable Energy . Founder of DEMKit and ALPG open-source software Collaborator in EU projects (SUSTENANCE, SERENE, LocalRES)
Prof. Dr. Ir. Herman van der Kooij is a leading academic in Biomechatronics and Rehabilitation Technology , affiliated with the University of Twente (0.8 FTE) and Delft University of Technology (0.2 FTE). He chairs the Biomechatronics group at UT and has made groundbreaking contributions to wearable robotics for medical and industrial applications. His research focuses on human balance control , neuromechanical modeling , and exoskeleton-assisted mobility . He develops technologies like the LOPES gait rehabilitation robot and the Mindwalker exoskeleton , combining experimental and computational approaches to advance wearable robotics. His work spans soft robotics , real-time EMG-driven control , and low-cost sensor integration . Van der Kooij has published over 170 peer-reviewed works and received prestigious Dutch VIDI and VICI grants . He leads national programs in Wearable Robotics and 4TU Soft Robotics , and serves as associate editor for IEEE journals. He founded two specialized labs: the Rehabilitation Robotics Laboratory (with Roessingh Research and Development) and the Virtual Reality Human Performance Lab , which integrates robotics, motion capture, and VR for testing. He emphasizes active learning in courses like Biorobotics and Biomechatronics , encouraging students to learn through hands-on projects and mistakes. His work also explores non-medical applications of exoskeletons, including industrial ergonomics and entertainment technology .
Antonios Liapis is an Associate Professor at the Institute of Digital Games, University of Malta. He completed his PhD in September 2014 under the supervision of Georgios N. Yannakakis at the IT University of Copenhagen. His academic journey includes an M.Sc. in Information Technology from the same institution and a 5-year Diploma in Electrical and Computer Engineering from the National Technical University of Athens. Dr. Liapis has held various academic positions at the University of Malta: Post-doctoral Researcher (2014-2015), Lecturer (2015-2020), Senior Lecturer (2020-2022), and currently Associate Professor (2022-present). He has served as General Chair for multiple international conferences including FDG (2020), GALA (2019), and EvoMusArt (2018-2019). He is an Associate Editor of the IEEE Transactions on Games and a member of the Games Technical Committee of the IEEE Computational Intelligence Society. His research focuses on Artificial Intelligence as an autonomous creator and as a facilitator of human creativity. Key areas include computationally intelligent tools for game design and computational creators that blend semantics, visuals, sound, plot, and level structure to create various game genres including horror, adventure, shooter, and dungeon crawler games. His work has resulted in over 150 peer-reviewed publications and several research awards. Dr. Liapis has secured multiple research grants from the European Commission, including projects on AI-powered robotic material recovery, virtual reality aided design, and learning science through coding and play. His notable project series "Data Adventures" demonstrates the use of open data from Wikipedia, DBpedia, Wikimedia Commons, and OpenStreetMap to automatically generate adventure games with complete plots, characters, items, and locations. His scientific contributions have been recognized with several awards including Best Paper Awards at major conferences, Best Reviewer Award, and Runner-Up Best Student Paper Award. Dr. Liapis has also co-organized 16 workshops in diverse conferences throughout his career. Research interests include: Artificial Intelligence for creative applications Procedural Content Generation in games Computational Creativity systems Machine Learning for game design Affective Computing in virtual environments Human-AI collaboration in creative processes His recent work shows a strong trend toward integrating Large Language Models with game design, exploring quality diversity algorithms for creative applications, and advancing affect modeling for improved player experience. The research spans computer science, artificial intelligence, game studies, and human-computer interaction, with practical applications in education, entertainment, and design.
Dr. Mojtaba Rostami Kandroodi is a University Lecturer at Tilburg University's Tilburg School of Humanities and Digital Sciences (TSHD) within the Department of Intelligent Systems. His academic profile demonstrates active engagement in both teaching and research, with courses including Machine Learning, Mathematics for Premasters DSS, and supervision of Master's theses through the Data Science in Action program. Dr. Rostami Kandroodi's research centers on the intersection of cognitive neuroscience, computational modeling, and psychiatric disorders. His work investigates how motivational biases influence decision-making processes, with particular focus on reinforcement learning mechanisms in both healthy individuals and those with psychiatric conditions. He examines how neurochemical factors like dopamine and substances such as LSD modulate learning processes, seeking to understand the neurocognitive basis of conditions including depression, anxiety, and addiction. His research employs sophisticated computational models to analyze behavioral data from probabilistic reversal learning tasks, revealing nuanced insights into reward-punishment asymmetries and cognitive control mechanisms. Analysis of his publication record shows a consistent focus on reinforcement learning dynamics across multiple contexts. His work spans from theoretical computational models of asymmetric updating in volatile environments to clinical investigations of learning biases in psychiatric populations and pharmacological studies examining how substances alter learning processes. This research trajectory demonstrates both methodological sophistication and clinical relevance, bridging computational neuroscience with practical applications for understanding and potentially treating psychiatric disorders. Dr. Rostami Kandroodi maintains active collaborations with researchers across institutions, as evidenced by his co-authorship with prominent figures like Hanneke den Ouden. His teaching responsibilities in Machine Learning and data science methodologies complement his research focus, creating a cohesive academic profile centered on quantitative approaches to understanding cognition and behavior.
Dr. Inga Schwabe is a University lecturer in the Department of Methodology at Tilburg School of Social and Behavioral Sciences, Tilburg University. She specializes in advanced quantitative methods and their application to psychological research questions, particularly in maternal mental health and forensic psychology contexts. Her research interests include: Structural Equation Modeling and advanced statistical techniques Machine learning applications in psychological research Maternal bonding and postpartum depression Mindfulness interventions during pregnancy Psychometric validation of assessment tools Analysis of large language models through psychological frameworks Dr. Schwabe's recent publications demonstrate a strong trajectory of methodologically sophisticated research addressing critical questions in perinatal mental health and the intersection of psychology with artificial intelligence. Her work on identifying prenatal risk factors for postpartum depression using machine learning represents a significant contribution to early intervention strategies, while her critical examination of 'cognitive phantoms' in large language models challenges assumptions about applying human psychometric tools to AI systems. She teaches courses in Structural Equation Modeling & Analysis and Construction and analysis of questionnaires, providing essential methodological training for social and behavioral science researchers. Her collaborative work spans multiple departments including Medical and Clinical Psychology, Developmental Psychology, and Tranzo, reflecting the interdisciplinary nature of her research approach.
Dr. Sandjai Bhulai is a Researcher at Centrum Wiskunde & Informatica (CWI) in Amsterdam, The Netherlands, and affiliated with Vrije Universiteit Amsterdam. He works in the Stochastics department within the Faculty of Science, focusing on applying machine learning and optimization techniques to solve complex real-world problems across diverse domains including telecommunications, mental health crisis intervention, and operations research. His research spans multiple critical areas of applied mathematics and computer science: Development of novel machine learning evaluation frameworks (Dutch Draw, Dutch Scaler) Optimization algorithms for facility location and supply chain problems Telecommunications signal processing using deep learning Mental health applications, particularly suicide prevention helpline analysis Natural language processing for conversation analysis in crisis intervention Dr. Bhulai's recent work demonstrates exceptional interdisciplinary impact. His Dutch Draw framework provides a universal baseline for binary classification problems that has been adopted across multiple research communities. His mental health research has directly influenced suicide prevention practices through machine learning analysis of helpline conversations, identifying specific counselor interventions that improve outcomes. In telecommunications, he's developed neural network approaches that simultaneously handle multiple modulation schemes, improving efficiency in wireless communications. His scientific contributions appear in leading venues including: Computers and Operations Research Journal of Classification IEEE Open Journal of the Communications Society JMIR Mental Health Journal of Applied Probability Dr. Bhulai maintains strong collaborative relationships with researchers across institutions, particularly with Rob van der Mei at CWI. He has secured funding for impactful projects including the collaboration between Stichting 113 Suicide Prevention and CWI, which applies machine learning to improve crisis intervention services. His work exemplifies how theoretical advances in machine learning can address critical societal challenges.
Ahmed Elazab serves as an Associate Researcher at Shenzhen University's School of Biomedical Engineering since January 2021, following a Postdoctoral Research Fellowship at the same institution from January 2018 to April 2020. He holds a Ph.D. in Pattern Recognition and Intelligent Systems from the Shenzhen Institutes of Advanced Technology, University of Chinese Academy of Sciences (2017). Education Ph.D. in Pattern Recognition and Intelligent Systems, Shenzhen Institutes of Advanced Technology, University of Chinese Academy of Sciences, China (2017) Research Focus Dr. Elazab's work centers on machine learning and deep learning applications in biomedical contexts, with specialized expertise in medical image analysis , brain anatomy analysis , and computer-aided diagnosis . His research integrates computer vision, bioinformatics, and data science to develop AI-driven solutions for complex medical challenges including neurodegenerative diseases and infectious outbreaks. Publication Trends Analysis of his recent publications (2020-2023) reveals a concentrated focus on deep learning for medical diagnostics, particularly in Alzheimer's disease staging from MRI data and COVID-19 detection from X-ray images. His work consistently incorporates domain-specific knowledge into neural architectures, with growing emphasis on generative models for medical image segmentation and vaccine development. Cross-cutting themes include handling multi-modal biomedical data and addressing real-world clinical constraints. Scientific Recognition Active Academic Editor for PeerJ Computer Science with 1,600 contribution points Reviewer for prestigious international journals across computer science and biomedical domains Author/co-author of over 80 peer-reviewed publications Academic Engagement Dr. Elazab maintains significant scholarly involvement through editorial work at PeerJ Computer Science, where he has handled manuscripts on deep learning applications in medical imaging since 2020. His extensive publication record demonstrates consistent research productivity, though specific grant funding or student supervision details are not documented in available sources. He contributes to multiple subject areas including Artificial Intelligence, Bioinformatics, and Computational Biology. Research Environment As part of Shenzhen University's School of Biomedical Engineering, Dr. Elazab operates within a multidisciplinary research ecosystem focused on AI-driven medical solutions. His work intersects with ongoing initiatives in medical image computing and computational diagnostics, leveraging institutional resources for biomedical data analysis without specified laboratory affiliations.
Harry Hochheiser is an Assistant Professor in the Department of Biomedical Informatics at the University of Pittsburgh School of Medicine, where he also serves as Associate Director of the Biomedical Informatics Training Program. His academic career spans multiple disciplines at the intersection of computing and healthcare, with a strong focus on human-computer interaction, bioinformatics, and medical informatics. Dr. Hochheiser's research interests include human-computer interaction, information visualization, bioinformatics, universal usability, security, privacy, and public policy implications of computing systems. His work particularly focuses on NIH-funded projects related to bioinformatics research portals, visualization for review of chart records, and tools for aiding the discovery of animal models of human diseases. His research demonstrates a commitment to developing technology that addresses real clinical needs while considering user experience and ethical implications. His publication record shows consistent scholarly output across multiple domains, with recent work emphasizing biomedical informatics, machine learning applications in healthcare, epidemiological modeling, and academic publishing practices. His research demonstrates a strong interdisciplinary approach that bridges computer science with clinical medicine and public health, with particular attention to human-centered design principles in healthcare technology. Editorial Board Member, PeerJ - the Journal of Life & Environmental Sciences Editorial Board Member, PeerJ Computer Science Over 2,900 points on PeerJ representing 2,800 editorial contributions and 105 reviews Dr. Hochheiser has made significant contributions across numerous subject areas including Science and Medical Education, Science Policy, Statistics, Human-Computer Interaction, Computational Science, Bioinformatics, and many others. His work has practical implications for healthcare technology development, particularly in creating more effective, user-friendly systems that address real clinical needs while considering privacy and security concerns.
Jannis Teunissen is a researcher in the Multiscale Dynamics group at Centrum Wiskunde & Informatica (CWI), the Dutch national center for mathematics and computer science. He also serves as a visiting lecturer at the Centre for mathematical Plasma Astrophysics at KU Leuven. Education: BSc in Physics & Astronomy and Master in Computational Science from University of Amsterdam PhD in computational plasma physics at CWI (obtained "cum laude") Postdoctoral research at KU Leuven's Centre for mathematical Plasma Astrophysics Dr. Teunissen's research focuses on computational plasma physics, particularly on simulating electric discharges. His work bridges theoretical modeling, computational methods, and experimental validation. He develops advanced computational techniques for studying streamer discharges, which are fast-moving ionized channels that form the first stage of sparks. These phenomena have important applications in environmental technology, high-voltage engineering, and atmospheric science. His research employs a range of computational methods including adaptive mesh refinement (AMR), plasma fluid modeling, particle-in-cell simulations, geometric multigrid solvers, and high-performance computing techniques. More recently, he has been applying machine learning methods to space weather research. Analysis of his publication history shows a strong focus on streamer discharge phenomena across different gas mixtures, with emphasis on macroscopic parameterization, electric field measurements, and radio emission calculations. Hershkowitz Early Career Award and Review (2024) from Plasma Sources Science and Technology Early Career Scientist Prize on Plasma Physics (2023) from IUPAP Student Award of Excellence of the Gaseous Electronics Conference (2015) PhD obtained "cum laude" (2015) Dr. Teunissen has been actively involved in several research projects including "Reliable nExt GENERation Actuation sysTEms (REGENERATE)" and "Plasma for Plants: Towards controlled and efficient plasma-activated water generation for a cleaner environment." His work has resulted in numerous publications focusing on streamer discharges in various gas mixtures, their radio emissions, electric field measurements, and computational modeling approaches. His research has significant implications for understanding natural phenomena like lightning and developing more environmentally friendly alternatives to traditional insulating gases used in high-voltage technology.
Pengcheng Liu is an Associate Professor in the Department of Computer Science at the University of York, holding this position since January 2020. He maintains active memberships in IEEE, IEEE Robotics and Automation Society (RAS), IEEE Control Systems Society (CSS), and the International Federation of Automatic Control (IFAC), while serving on the IEEE Technical Committee for Bio Robotics, Soft Robotics, Robot Learning, and Safety, Security and Rescue Robotics. His research spans robotics, machine learning, automatic control, and optimization, with specialization in humanoid robotics, rehabilitation systems, agricultural applications, and human-computer interaction. Key focus areas include developing lightweight neural networks for embedded agricultural systems, bionic-companionship frameworks for service robots, EMG-controlled rehabilitation devices, and precise control of robotic manipulators using ROS/Gazebo. His work consistently bridges theoretical control systems with practical implementations in healthcare and precision agriculture. Analysis of his publication trends reveals strong emphasis on applying machine learning to real-world robotics challenges, particularly in resource-constrained environments (e.g., agricultural robotics with efficient neural networks) and human-centered applications (e.g., rehabilitation gloves and brain-computer interfaces). Recent work demonstrates increasing integration of computer vision with control systems for autonomous operation. His notable scientific awards include: Global Peer Review Awards from Web of Science (2019) Outstanding Contribution Awards from Elsevier (2017) Dr. Liu has secured and managed research funding through major programs including EPSRC, Newton Fund, Innovate UK, Horizon 2020, Erasmus Mundus, FP7-PEOPLE, and NSFC. He serves as a regular reviewer for EPSRC, NIHR, and NSFC grant panels while reviewing for over 30 flagship journals and conferences in robotics, AI, and control systems. His editorial roles include Associate Editor for IEEE Access and PeerJ Computer Science, where he has edited 17 publications. Though specific lab affiliations aren't detailed, his research in agricultural robotics, rehabilitation systems, and humanoid platforms suggests active collaboration with York's robotics and AI research groups, particularly in developing practical implementations of control algorithms and machine learning models for real-world deployment.
Paulo Jorge Coelho serves as an Adjunct Professor in the Electrical Engineering Department at the School of Technology and Management, Polytechnic University of Leiria, and as an integrated researcher with the ROBiTECH (Advanced Robotics and Smart Factories) group at INESC Coimbra's Leiria delegation. With over 20 years of academic experience since 2004, he specializes in Microprocessors, Industrial Automation, and Computer Vision instruction. Education: Ph.D. in Informatics (2019), Trás-Os-Montes and Alto Douro University Specialization in Automation and Control (2007), Coimbra University Bachelor of Electrical Engineering (2004), Coimbra University Research Focus: His work bridges industrial automation and computer vision with cutting-edge machine learning applications in biomedical imaging, ambient assisted living, and assistive technologies. Current projects emphasize practical implementations for reducing physical impairments and enhancing healthcare solutions through deep learning frameworks. Publication Trends: Recent work (2024-2025) reveals strong interdisciplinary convergence between healthcare diagnostics (schizophrenia/EEG analysis, perinatal depression prediction) and industrial/computer vision systems (sports analytics, activity recognition). His research consistently leverages sensor fusion and deep learning architectures to solve real-world problems across medical and engineering domains. Professional Engagement: Active member of the Portuguese Engineers Order and Portuguese Association for Pattern Recognition, with significant editorial contributions (89+ edited articles) across AI and computer vision domains. Previously served as course director and Scientific-Pedagogical Committee member for the Master's in Electrical and Electronic Engineering. Research Infrastructure: Operates within ROBiTECH's advanced robotics ecosystem at INESC Coimbra, focusing on smart factory solutions and human-robot interaction systems. His lab environment integrates industrial automation testbeds with biomedical sensor networks for cross-domain innovation.
Jeffrey Dellosa serves as a Professor at Caraga State University in the College of Engineering and Geosciences, Butuan, Philippines. His academic career focuses on renewable energy research with particular emphasis on solar photovoltaic systems for rural development applications in the Philippines. He holds a Doctor of Engineering degree specializing in Renewable Energy from Ateneo de Davao University (2019-2023). Education: Doctor of Engineering in Renewable Energy, Ateneo de Davao University (2019-2023) Professor Dellosa's research spans multiple domains within renewable energy engineering, with particular expertise in solar photovoltaics, energy conversion systems, and power generation technologies. His work bridges theoretical research with practical applications for rural electrification and sustainable development. Current research directions include floating solar photovoltaic systems, IoT-based energy monitoring, and renewable energy integration for healthcare facilities. Analysis of his publication record reveals a strong emphasis on practical implementation of renewable energy solutions in the Philippine context, with increasing focus on interdisciplinary approaches combining AI, IoT, and traditional energy engineering. Recent publications demonstrate a shift toward comprehensive system design that addresses both technical and socioeconomic aspects of renewable energy deployment in rural communities. Professor Dellosa leads research in Nelson Jr Enano's Lab and collaborates extensively with regional institutions on renewable energy projects. His work has resulted in 67 publications with significant readership (60,773 reads) and citations (286 citations), demonstrating impactful contributions to the field of renewable energy engineering in Southeast Asia.
