Thomas Winkler is an Associate Professor at the Division of Micro and Nanosystems, KTH Royal Institute of Technology, Sweden, and collaborates with TU Braunschweig, Germany. His research focuses on solving life science challenges using microsystems tools, particularly in neuropsychiatric disorders like schizophrenia. He develops organ-on-chip models, engineered microfluidic platforms, and biosensors for point-of-care diagnostics. Winkler leads an interdisciplinary ERC-funded team addressing metabolic coupling in neurovascular units and oxidative stress biomarkers. Key achievements include the ERC Starting Grant (2023) and work on electrochemical sensors for clozapine monitoring. He teaches courses such as Microsystem Technology (EK2350) and supervises PhD and postdoctoral researchers. Current projects include machine learning-guided robotic organoid maturation and electrochemical technology development for the CHIPzophrenia initiative. His lab actively seeks talent through open positions in Stockholm and Braunschweig. Scientific awards include the ERC Starting Grant and Marie Skłodowska-Curie Actions Fellowship. Research spans sensor development, microfabrication, and biomaterials, with a focus on translating lab technologies to clinical applications. Collaborations bridge engineering and life sciences, emphasizing personalized mental healthcare solutions.
Dr James Herbert-Read is an Associate Professor and Whitten Lecturer in Marine Biology at the Department of Zoology, University of Cambridge. He serves as Deputy Head of Department (Postgraduate Education) and leads the Marine Behavioural Ecology Group. His research focuses on understanding how animals, particularly marine organisms, collect and process information from their environments to make behavioral decisions, with emphasis on social interactions, adaptation mechanisms, and ecological constraints. His group employs theoretical frameworks, controlled experiments, and quantitative field studies to investigate behavioral diversity in marine species. Key themes include collective behavior, predator-prey dynamics, camouflage strategies, and the impacts of environmental stressors on animal decision-making. Recent publications highlight work on lionfish vocalization mechanisms, cuttlefish camouflage, citizen science applications in marine research, and behavioral responses to visual and acoustic noise. Scientific awards and affiliations include: Whitten Lecturer in Marine Biology Associate Professor, University of Cambridge He has supervised research projects on topics such as: Social attraction in invasive fish species Evolution of coordinated movement Neurophysiological basis for leadership in shoals Maternal effects on offspring exploration
Chantal Pellegrini is a Lecturer and PhD student at the Chair of Computer Aided Medical Procedures (Prof. Navab) at Technical University of Munich (TUM). Her research focuses on Deep Learning applications in medical imaging, including explainable AI for radiology report generation and Vision-Language Models for clinical decision support. She actively contributes to the DHM, NARVIS Lab, and RobUSt research groups. Teaching responsibilities include courses such as 'Computer Aided Medical Procedures', 'Medical Augmented Reality', and 'Surgical Robotics'. She supervises student projects in medical AI and healthcare innovation, with recent projects involving multimodal report generation and graph pretraining for medical applications. Education: BSc/MSc Computer Science (TUM), current PhD student since 2022 Labs: DHM (German Heart Center), NARVIS Lab, RobUSt Robotics & Ultrasound Research Keywords: Medical Image Understanding, Radiology Reports, LLMs in Healthcare Her publications span surgical OR dataset development, reinforcement learning for clinical decisions, and explainable X-ray diagnosis systems. She mentors MA/BA students in medical AI and project management for healthcare applications.
C. S. George Lee is a Professor of Electrical and Computer Engineering at Purdue University's Elmore Family School of Electrical and Computer Engineering, located in West Lafayette. His research focuses on Robotics, Transfer Learning, Neuro-fuzzy Systems, Automatic Controls, and Computer Engineering. He holds a BSEE (1973), MSEE (1974) from Washington State University, and a PhD (1978) from Purdue University. His work integrates computational intelligence with AI, robotics, and education technology, emphasizing human-machine co-learning models and bilingual systems. His contributions span domains like quantum computing, generative AI, and knowledge graph applications. He leads the Art Lab at Purdue and has published extensively on topics ranging from humanoid robotics to cross-cultural educational platforms. His research areas include developing intelligent agents for edutainment, robotic assistants for student learning, and advanced machine learning techniques. Notable trends in his publications involve computational intelligence applied to bilingual language models (e.g., Taiwanese/English co-learning), quantum-based AI systems, and human-centric robotics. He has explored applications in healthcare (e.g., blood donor analysis), autonomous navigation, and game AI (e.g., Go). His work often bridges theoretical advancements with real-world implementations, such as Java software tools for motor activity assessment (JKinect) and AI-driven platforms for skill evaluation. Lee's research emphasizes interdisciplinary collaboration, with contributions to IEEE conferences and cross-institutional projects. His lab develops tools for adaptive e-learning, robotic task performance evaluation, and human pose estimation using neural networks. Despite prolific publishing, no specific grants or awards are explicitly mentioned in the provided text. His work continues to explore the intersection of human intelligence and smart machines through platforms like Metaverse integration and BCI (Brain-Computer Interface) applications.
Tural Khudiyev is an Assistant Professor at the Department of Materials Science and Nanotechnology , National University of Singapore (NUS), focusing on multimaterial electronic fibers for human-fabric interaction, soft robotics, and neural probes. He previously worked as a postdoctoral researcher at MIT's Research Laboratory of Electronics. B.Sc. in Nuclear Engineering, Hacettepe University Ph.D. in Materials Science and Nanotechnology, Bilkent University His research spans implantable and wearable electronics , stimuli-responsive drug delivery , and AI in healthcare , with a strong emphasis on scalable nanomanufacturing techniques. His publications in journals like Nature , Nature Nanotechnology , and Advanced Materials highlight innovations in thermally drawn fibers and soft bioelectronic interfaces . Key trends include flexible electronics , machine learning in textiles , and bio-integrated devices , with applications in neural engineering and power textiles . His work has been featured in media outlets such as Reuters and Business Insider . Selected as MIT’s top research story of 2021 #1 Most Read Article in Applied Optics (2015) Featured in Virtual Journal of Nanoscale Science & Technology Khudiyev's contributions to thermally drawn fibers and soft robotics demonstrate interdisciplinary collaboration between materials science, neuroscience, and wearable technology.
James Tung is an Associate Professor at the University of Waterloo’s Faculty of Engineering, Department of Mechanical and Mechatronics Engineering. His research focuses on assistive technology, rehabilitation engineering, and mobility solutions for individuals with disabilities. He leads the Neural and Rehabilitation Engineering (NRE) Lab, which develops wearable sensors, robotics, and machine learning tools to enhance mobility and monitor motor rehabilitation. He teaches courses including BME 355 (Physiological Systems Modelling), BME 540 (Neural and Rehabilitation Engineering), and ME/MTE engineering modules. The lab collaborates with clinical and industry partners to translate research into practical solutions, addressing real-world mobility challenges and aging demographics. His research spans real-world gait analysis, fall risk assessment, and prosthetic design, with a focus on pediatric neurodevelopmental disorders and elderly mobility. The NRE Lab emphasizes interdisciplinary work, combining biomechanics, robotics, and data science to improve healthcare outcomes. Lab Alumni: Includes researchers like Robin Murdock (Myant Inc.), Andrew Hart, and Raj Senthilkumar, contributing to prosthetics and gait analysis. Partnerships: Engages clinical and industry stakeholders for knowledge translation and commercialization. Current projects include developing smart rollators, biofeedback prosthetics, and sensor-based assessment tools to address mobility limitations in aging populations and individuals with disabilities.
Andrzej Majkowski is an Associate Professor at the Institute of the Theory of Electrical Engineering, Measurement and Information Systems, Faculty of Electrical Engineering, Warsaw University of Technology. His career spans over two decades of research in biomedical engineering, focusing on brain-computer interfaces, signal processing, and emotion recognition. Active in both teaching and research, he contributes to advancing methodologies in electrophysiological signal analysis. Warsaw University of Technology Institute of the Theory of Electrical Engineering, Measurement and Information Systems Faculty of Electrical Engineering Specializing in biomedical engineering , Majkowski's research bridges control systems and information technologies with neuroscience applications. His work explores brain-computer interfaces , EEG/EMG signal processing , and emotion recognition using multimodal physiological data. Recent studies focus on deep learning architectures for artifact removal and classification tasks. Recent publications highlight trends in CNN-LSTM hybrid models for signal denoising, convolutional networks for seizure detection, and machine learning applications in visual evoked potential analysis. His work spans both clinical applications (epilepsy monitoring) and human-computer interaction (emotion recognition, sign language detection). With over 98 documented publications and significant bibliometric indicators (h-index 13 in Scopus), Majkowski has supervised 95 promoted theses. His research includes one funded project and collaborations in biomedical instrumentation, though specific award details remain unspecified in available records.
Randall D. Beer is a Provost Professor at Indiana University with affiliations across multiple departments and centers, including the Cognitive Science Program , Program in Neuroscience , School of Informatics, Computing, and Engineering , and the Center for Complex Networks and Systems Research . His research focuses on understanding how organisms function as integrated wholes, emphasizing the interplay between brains, bodies, and environments. He develops computational models of neuromechanical systems, biologically-inspired robotics, and dynamical systems approaches to cognition. Education: While formal educational details are not explicitly listed, Beer's academic trajectory is reflected in his extensive publications and roles in interdisciplinary research programs. Research Interests: Beer investigates: - Embodied cognition and enaction frameworks - Neurodynamics and central pattern generators - Evolution of behavior in artificial agents - Metabolic and developmental systems biology - Dynamical systems theory - Computational modeling of C. elegans locomotion Software Contributions: Beer has developed tools like Dynamica (for dynamical systems analysis), CTRNN (neural network simulation), and Evolutionary Agents (robotics control frameworks). These tools are widely used in computational neuroscience and robotics research. Advising and Teams: He supervises a large group of graduate students and postdocs, contributing to projects such as neuromechanical modeling and evolutionary robotics. His work is supported through grants focusing on embodied cognition and systems biology. Labs and Collaborations: Active in the Center for Complex Networks and Systems Research and collaborates with interdisciplinary teams exploring topics like autopoiesis, viability theory, and robotic embodiment.
Suyi Li is an Associate Professor in the Department of Mechanical Engineering at Virginia Tech's College of Engineering, where he leads the Dynamic and Architected Robot and structurE (DARE) Lab. Previously, he served as an Assistant Professor at Clemson University from 2016-2022 after completing postdoctoral research at the University of Michigan. Ph.D. in Mechanical Engineering, University of Michigan, Ann Arbor (2014) M.Sc. in Mechanical Engineering, Pennsylvania State University (2008) B.S. Summa Cum Laude in Mechanical Engineering, University of Michigan, Ann Arbor (2006) Dr. Li's research focuses on pioneering new paradigms of intelligent robots and functional structures by exploiting the interplay between geometry, mechanics, actuation, and computation. His work spans origami-inspired morphing structures, physically computing materials that perform machine learning tasks without traditional electronics, and soft/reconfigurable robots that can move like animals or grow like plants. His innovative approach combines mechanical engineering principles with computational thinking to create systems with 'mechano-intelligence'. Analysis of Dr. Li's recent publications reveals a strong trajectory toward embodied intelligence and mechanical computing, where physical structures themselves perform computational tasks. His work increasingly integrates origami/kirigami principles with advanced materials to create systems that can sense, process information, and actuate without conventional electronics. The research shows progression from fundamental mechanics of adaptive structures to sophisticated applications in robotics and computing. Dean's Awards of Excellence – Faculty Fellow, Virginia Tech (2024) C.D. Mote Jr Early Career Award, ASME Design Engineering Division (2022) Gary Anderson Early Achievement Award, ASME Aerospace Division (2021) Junior Researcher of the Year Award, College of Engineering, Clemson University (2020) CECAS Dean's Faculty Fellow, Clemson University (2018) CAREER Award, National Science Foundation (2018) ASME Freudenstein Young Investigator Award Dr. Li has secured nearly two million dollars in research funding, including the prestigious NSF CAREER award and an NSF EFRI project to build mechano-bio hybrid reservoir computers. He advises multiple Ph.D. and Master's students in the DARE Lab, with recent successes including Vishrut Deshpande's Ph.D. defense. His research has generated close to 80 journal and conference papers, demonstrating significant impact in the fields of adaptive structures and materials systems. Dr. Li also serves on editorial boards for several prominent journals including Journal of Intelligent Material Systems and Structures and Philosophical Transactions of the Royal Society A. The DARE Lab at Virginia Tech comprises a multidisciplinary team of researchers working on origami-inspired meta-structures, physically computing materials, and soft robotics. Current projects include developing electronics-free crawling robots with mechanical central pattern generators, creating kirigami-based wearable medical devices, and engineering metamaterials with programmable mechanical properties. The lab actively collaborates with institutions across the country and has received recognition for its innovative approaches to combining mechanical design with computational capabilities.
Dr. Aniket Bera is an Associate Professor in Computer Science at Purdue University and holds an Adjunct Associate Professor role at the University of Maryland at College Park (UMIACS). He directs the IDEAS Lab at Purdue and previously served as a Research Assistant Professor at UNC Chapel Hill. His research focuses on Affective Computing, Computer Graphics (AR/VR), AI & Robotics, Social Robotics, and medical AI applications for mental health diagnostics. Affiliations: Purdue University (Primary), University of Maryland (Adjunct), UMIACS Career: Joined Purdue in 2017, extensive industry collaborations with Disney Research, Intel, and C-DAC Research Interests: Affective Computing: Emotion perception via gait analysis, speech, and facial/body expressions AR/VR: Redirected walking, virtual environments, and human motion modeling Medical AI: AI-driven mental health detection systems (e.g., VidSole dataset) in collaboration with medical schools Key Contributions: Developed Project Dost (mental health initiative) Received 2020 Brain & Behavior Seed Grant ($X) for emotion-gait research Authored 65+ papers (1,800+ citations) with awards at IEEE VR 2021 Funding & Leadership: Serves as Senior Editor for IEEE RA-L (Planning/Simulation) Conference Chair for ACM SIGGRAPH MIG 2022 Labs/Teams: IDEAS Lab (Purdue), UMD GAMMA Group
Prof. Raimon Jané Campos is a leading figure in biomedical signal processing at the Universitat Politècnica de Catalunya (UPC) and Universitat de Barcelona (UB). As co-director of UPC's Biomedical Signal and System Group (CREB) and coordinator of the Biomedical Engineering PhD Programme, he bridges engineering and clinical applications. His work focuses on respiratory and sleep disorder diagnostics, with significant contributions to COPD and sleep apnea monitoring through wearable devices and machine learning. PhD in Biomedical Engineering (UPC, 1989) Visiting researcher at Université de Nice-Sophia Antipolis Vice-president of Spanish Society of Biomedical Engineering Research spans respiratory mechanics , sleep-disordered breathing , acoustic biomarkers , bioimpedance , and machine learning in biomedical contexts . His 2025 work on microcalorimetric pathogen classification and 2024 spiking neural networks for apnea detection demonstrate cutting-edge integration of computational methods with physiological monitoring. Articles from 2017-2024 reveal consistent focus on non-invasive diagnostics , cardiorespiratory synchronization , and smartphone-based health solutions . Awarded the Barcelona City Technology Research Award (2005) and serving on the International Advisory Board for Physiological Measurement since 2010, his career combines academic leadership with real-world clinical translation through IBEC's technology transfer initiatives.
Lawrence H. Staib is a Professor of Biomedical Engineering at Yale University, with additional academic appointments in Electrical & Computer Engineering and Radiology & Biomedical Imaging. He holds a Ph.D. from Yale University and specializes in automated medical image analysis, including techniques like model-based segmentation, nonrigid registration, and diffusion tensor imaging (DTI). His research focuses on applications in neuroscience, cardiology, and cancer imaging, emphasizing machine learning and functional MRI analysis. His key contributions include advancements in white matter tractography via anisotropic wavefront evolution, real-time neural tract parcellation (Fasciculography), and noise reduction in diffusion tensor fields. Staib is a Fellow of the American Institute for Medical and Biological Engineering (2015), recognizing his impactful work in medical imaging technologies. Staib's research also encompasses statistical deformation models, perturbation-based shape analysis, and 3D deformable models for volumetric segmentation. He has developed patented 3D ultrasound computed tomography systems (USPTO #6878115, 7025725). His work bridges clinical needs with computational methods, addressing challenges in image registration, structural connectivity analysis, and medical robotics.
Eliot R. Smith is a Professor in the Department of Psychological and Brain Sciences at Indiana University Bloomington's College of Arts and Sciences. With over 126 publications and an h-index of 50, he has established himself as a leading scholar in social psychology, particularly in the areas of intergroup emotions and social cognition. His Scopus profile shows 10,991 citations across 8,796 documents, reflecting significant impact in his field. Smith's research focuses on how group membership shapes emotional experiences and social judgments. His work has significantly contributed to Intergroup Emotions Theory, exploring how people experience emotions on behalf of their social groups and how these group-based emotions influence intergroup relations. Recent research examines gender bias in hiring decisions through a large meta-analysis spanning 44 years of field experiments, human-robot interaction through social psychological lenses, and the dynamics of group-based emotions over time. His research integrates theoretical advances in embodied cognition with social psychological phenomena, examining how physical and social contexts shape cognitive and emotional processes. Analysis of his recent publications (2019-2024) reveals three dominant research trajectories: 1) gender bias and discrimination in organizational contexts, 2) extension of social psychological theories to human-robot interaction, and 3) refinement of Intergroup Emotions Theory with attention to temporal dynamics and regulatory processes. His work increasingly incorporates methodological innovations including meta-analyses, multi-analyst approaches, and cross-cultural comparisons.
Chao Liu is a Research Scientist at CNRS (French National Center for Scientific Research) since 2008, affiliated with the DEXTER team and the Department of Robotics, LIRMM at University of Montpellier, France. He earned his Ph.D. in Electrical & Electronic Engineering from Nanyang Technological University, Singapore (2006). Current research focuses on surgical robotics , haptics , teleoperation , and nonlinear control theory with applications in computer vision. His work addresses challenges in robotic-assisted telesurgery, including: Stable and transparent human-robot interaction through wave variable compensators and passivity filters Physiological motion compensation using spatio-temporal LSTM and dual Kalman filters EMG-based motion recognition for surgical skill assessment 3D soft-tissue reconstruction with stereo-endoscopes and deep learning Dr. Liu leads European and French projects like: TS2RT (CNRS-funded): Safer teleoperation with motion compensation ROBACUS (ANR-funded): Needle positioning with MPC control HaTUMoCo (CNRS-funded): Haptic teleoperation with uncertainty handling ARAKNES (EU-funded): Microrobotic systems for endoluminal surgery Scientific honors include Senior Member of IEEE and Member of Sigma Xi . He supervises Ph.D. and Master's students working on topics such as concentric tube robot optimization, haptic teleoperation, and EMG-based force estimation. Dr. Liu serves on IEEE Technical Committees for Telerobotics and Haptics , and as Technical Editor of IEEE/ASME Transactions on Mechatronics.
Sebastian Risi is a Professor at the IT University of Copenhagen , where he directs the Creative AI Lab and co-directs the Robotics, Evolution and Art Lab (REAL) . His work bridges computational evolution, deep learning, and collective intelligence for applications in robotics, art, and video game design. His research focuses on self-organizing AI systems that grow or assemble through local interactions, inspired by biological development. Key areas include neuroevolution , neural cellular automata , and generative modeling , with applications in adaptive robotics, game content creation, and damage-resilient AI. Recent publications highlight trends in self-assembling neural architectures (NDPs) and 3D functional machine generation (Minecraft experiments). Awards include ERC Consolidator Grant (2022), Best Paper at FDG’21 , and Google Faculty Award (2019). Scientific Awards : ERC Consolidator Grant (GROW-AI), Best Paper FDG’21, Runner-Up IEEE Games’20, GECCO 2017 Competition Winner, Sapere Aude Grant, Amazon/Google Faculty Awards He advises on projects like GROW-AI (EU-funded), AI-TESTER (game testing), and C2SIM (military systems). Media coverage includes Science , Wired , and Popular Science .
