جست‌وجوی استاد و پژوهشگر

• Aerodynamics
• Fluid Mechanics
• Aeroacoustics
• +۴ مورد دیگر

Prof. Dr. Rinie Akkermans is Professor of Aerodynamics and Flight Mechanics at Hamburg University of Applied Sciences (HAW Hamburg), affiliated with the Department of Automotive and Aeronautical Engineering. He holds leadership roles as Vice-Director of the Research and Technology Transfer Center 'Future Air Mobility' and serves on the university Senate. His academic background includes a PhD in Physics from TU Eindhoven and an MSc in Aerospace Engineering from TU Delft. Research expertise spans computational and experimental fluid dynamics with emphasis on: Aeroacoustics and noise reduction techniques Turbulent flow modeling using DNS/LES methods Aerodynamic optimization of wings, propellers, and high-lift systems Bio-inspired flow control and vortex dynamics He actively supervises PhD candidates from TU Braunschweig, Volkswagen AG, and Beihang University. Recent publications focus on advancing computational methods (Lattice Boltzmann, Overset-LES) and experimental validations in aeroacoustics, flow control, and propeller/wing optimization. Work frequently appears in leading aerospace journals including AIAA Journal and Aerospace Science and Technology . Professional service includes doctoral committee memberships and industry collaborations with Volkswagen AG on automotive aeroacoustics. No awards are documented in the provided text.

Joost C. Dessingمشاهده پروفایل

مدرس

• Eye-hand coordination
• Human-robot interactions
• Sports performance analysis
• +۳ مورد دیگر

Joost C. Dessing is a Lecturer at Queen's University Belfast's School of Psychology, affiliated with the EPIC research group. He holds a Marie Curie FP7 Career Integration Grant to study eye-hand coordination in spatial and temporal contexts, extending to applications in sports and human-robot interactions. His academic journey includes an MSc (2001) and PhD (2005) from Amsterdam's Faculty of Human Movement Sciences, followed by postdoctoral work in the Netherlands and Canada. Education: MSc in Human Movement Sciences, Vrije Universiteit Amsterdam (2001) PhD in Human Movement Sciences, Vrije Universiteit Amsterdam (2005) Research interests focus on neural mechanisms underlying eye-hand coordination, particularly in dynamic tasks like catching, hitting, and robotics collaboration. He employs behavioral experiments, TMS, and computational modeling in real/virtual environments. Recent work includes VR-based goalkeeper studies and deep learning for human-robot intention inference. Key research trends revealed through publications include advancements in gaze estimation frameworks, collaborative robotics datasets, and applied sports biomechanics. His work bridges fundamental neuroscience with practical applications in manufacturing and sports. Awards include the Best PhD Thesis award (2005), Center for Open Science Challenge Award (2018), and QUB Research Seed Fund (2021). He actively promotes Open Science, serving as co-lead for the UK Reproducibility Network. Advising/grants: Marie Curie FP7 Grant (2012-) Supervising 3 PhD students (e.g., Valkanidis on goalkeeping, Adebayo on human-robot learning) Labs/teams: EPIC research group, collaborations with EEECS department (Prof. McLoone).

• Computer Vision
• Machine Learning
• 3D Reconstruction
• +۵ مورد دیگر

Andreas Geiger is Professor and Head of the Department of Computer Science at the University of Tübingen, Germany, where he leads the Autonomous Vision Group (AVG) within CyberValley. He serves as core faculty at the Tübingen AI Center, Principal Investigator in the Excellence Cluster ML in Science and CRC Robust Vision, and ELLIS fellow coordinating the ELLIS PhD program. Prof. Geiger's research centers on machine learning for computer vision, natural language processing, and robotics, with emphasis on 2D/3D scene representations, geometry reconstruction, and robust model development. His group pioneers neural rendering techniques like Gaussian splatting while addressing real-world challenges in autonomous driving, VR/AR, and scientific document analysis through discriminative and generative approaches. Recent publications (2025) demonstrate strong focus on 3D scene understanding using Gaussian splatting, autonomous driving simulation with world models like ReSim and Vista, and generative methods for controllable 3D scenes. The group maintains Scholar Inbox for personalized academic recommendations while advancing reinforcement learning for self-driving (CaRL) and robust perception evaluation (EMPERROR). Major recognitions include: Best Paper Awards at CVPR 2024/2021, 3DV 2017/2015, GCPR 2015 Sage 10-Year Impact Award 2024 and Longuet-Higgins Prize 2022 ERC Starting Grant 2019 and IEEE PAMI Young Researcher Award 2018 nuPlan Challenge Winner 2023 and German Pattern Recognition Prize 2017 Prof. Geiger actively supervises PhD students including Reiser (3D view synthesis), Baur (Lidar scene flow), Jaeger (reinforcement learning), and Liao (urban scene transfer). His group secures substantial funding through the ERC Starting Grant, Vector Stiftung's MINT Innovation Program, ML in Science Cluster, and CRC Robust Vision, while collaborating with industry via Waymo and nuPlan challenges. The Autonomous Vision Group operates from CyberValley's new campus building with a diverse international team (12+ nationalities). They maintain Scholar Inbox for academic discovery and organize regular technical retreats and unique team-building activities like deep cave expeditions, fostering innovation in 3D vision and autonomous systems.

James Yangمشاهده پروفایل

استاد

• Biomechanics
• Human Modeling and Simulation
• Robotics
• +۵ مورد دیگر

Dr. James Yang is a Professor and Associate Chair for Graduate Affairs in the Department of Mechanical Engineering at Texas Tech University . He directs the Human-Centric Design Research Lab and holds Fellowships from SAE and ASME, along with IEEE Senior Membership. His academic journey includes a Ph.D. in Mechanical Engineering from the University of Iowa (2003), an M.S. in Automobile Engineering from Jilin University (1992), and a B.S. in Vehicle Engineering from Jilin University (1989). Research Focus: Human simulation models for engineering, sports, and medicine; digital human modeling; biomechanics; robotics; healthcare engineering; autonomous vehicle dynamics; finite element analysis; muscle fatigue modeling Teaching: Undergraduate and graduate courses in dynamics, control systems, engineering design, and digital human modeling Leadership: Associate Chair for Graduate Affairs (2025-), previously Associate Chair for Undergraduate Studies (2015-2017) Dr. Yang's research lab develops skeletal, musculoskeletal, and finite element human models for ergonomic assessment, injury prediction, exoskeleton interaction, and medical device design. His work spans autonomous vehicle driver behavior , spine surgery optimization , fall prevention , and soft robotics , funded by the US Army, DOE, DOD, NSF, NIST, NIOSH, and industry partners like Honda R&D. Recent publications explore muscle fatigue controllers, 3D lifting optimization, and scissor door mechanism wear analysis. Key scientific awards include: Fulbright U.S. Scholar (2017-2018), Chancellor’s Council Distinguished Research Award (TTU), SAE Arch T. Colwell Merit Award, ASME Best Paper Awards, Air Force Summer Faculty Fellowship, and Whitacre Faculty Fellowship. He serves as Associate Editor for IEEE Transactions on Human-Machine Systems , ASME Journal of Mechanisms and Robotics , and other journals. Advising: Mentored 22+ graduate students including Juan, Jazmin, Ritwik, Shadman, and Erik Grants: NSF REU Site (2017-2019), US Army, DOD (DHA, USARIEM), DOE, NPSF

Matteo Lisiمشاهده پروفایل

مدرس

• Visual Perception
• Decision-Making Under Uncertainty
• Psychophysics
• +۳ مورد دیگر

Matteo Lisi is a Lecturer in the Department of Psychology at Royal Holloway University of London. His research examines how humans process uncertainty in decision-making across perceptual, financial, and medical contexts, combining behavioral experiments with computational modeling. He actively shares code and data on GitHub and OSF. Role: Lecturer in Psychology Institution: Royal Holloway University of London Research Focus: Visual perception, interoception, and uncertainty computation Research Outputs: His recent work includes studying error correction in diverse populations, mapping visual contrast sensitivity with fMRI, and analyzing cardiac interoception in infants. Articles explore motion perception extrapolation, visual hallucinations from Ganzflicker, and prior knowledge effects in childhood vision development. Collaborative Projects: Currently involved in an MRC-funded study on sex differences in interoception and mental health across the menstrual cycle, alongside Dr. James Shinskey and others.

• Perception
• Psychophysics
• Human Performance
• +۵ مورد دیگر

Prof. Stefan Mateeff is a full-time Professor at the Department of Cognitive Science and Psychology at New Bulgarian University. His academic career spans decades of research in perception, psychophysics, and visual motion processing , with a focus on human performance and ocular pursuit mechanisms. He earned his DSc in 1990 for work on visual spatial orientation and voluntary eye movements. Education: BS in Physics (1968, Sofia University), PhD in Psychophysics (1973), DSc in Psychology (1990) Previous Roles: Senior Research Fellow at the Institute of Neurobiology, BAS, and Head of the "Perception Mechanisms" Lab Current Courses: PSYB201 Psychological Inference, PSYB502 Perception Languages: German, English, Russian His research explores visual motion perception, spatial localization during eye movements, and reaction time dynamics . Key projects include national grants (e.g., OHN 1003/2000) and a long-term DFG-funded international collaboration (1988–2008) on motion perception and localization. The 15 most recent publications emphasize motion detection thresholds, ocular pursuit compensation, and multisensory integration , with recurring themes in visual processing, directional sensitivity, and perceptual constancy. Emails: mateeff@dir.bg , smateev@nbu.bg .

• Binocular Vision
• Amblyopia
• Stereopsis
• +۴ مورد دیگر

Alexandre Reynaud serves as a Junior Scientist at the Research Institute of the McGill University Health Centre (RI-MUHC) at the Montreal General Hospital site and holds an Assistant Professor position in the Department of Ophthalmology and Visual Sciences within McGill University's Faculty of Medicine and Health Sciences. He is actively affiliated with the Brain Repair and Integrative Neuroscience (BRaIN) Program and Centre for Translational Biology, directing the Binocular Integration in Space and Time research group under the McGill Vision Research Unit. Dr. Reynaud's research centers on elucidating binocular vision mechanisms through psychophysical experiments and brain imaging techniques applied to both normal and clinical populations. His work spans amblyopia pathophysiology, stereopsis processing, visual plasticity, and spatial vision analysis including texture and color perception. He develops computational models to explain neural correlates of visual processing deficits and investigates therapeutic applications like dichoptic training protocols. Analysis of his recent publications reveals a dominant focus on amblyopia treatment innovation, particularly dichoptic training methodologies for adults beyond critical periods. His work consistently examines binocular imbalance quantification, interocular suppression dynamics, and stereopsis restoration mechanisms. Current trends show increasing emphasis on translating laboratory findings into clinical tools like the Dichoptic Contrast Ordering Test and home-based therapeutic applications. Dr. Reynaud leads the Binocular Integration in Space and Time research initiative within the McGill Vision Research Unit, collaborating across the Brain Repair and Integrative Neurosciences Program to develop vision-strengthening systems and investigate neural mechanisms underlying visual perception deficits.

Brian Curlessمشاهده پروفایل

استاد

• Computer Graphics
• Computer Vision
• 3D Reconstruction
• +۶ مورد دیگر

Professor Brian Curless is a faculty member at the Paul G. Allen School of Computer Science & Engineering at the University of Washington , where he has been since 1998. His research focuses on 3D reconstruction , computer graphics , computer vision , and computational photography , with a particular emphasis on neural rendering , human shape modeling , and augmented reality . He leads the Graphics and Imaging Laboratory (GRAIL) and co-founded the UW Reality Lab . Education : B.S. in Electrical Engineering (University of Texas at Austin), M.S. and Ph.D. in Computer Science (Stanford University) His recent work includes NeRF-based view extrapolation , real-time background matting , and generative 3D modeling using photo collections. He has contributed to CVPR , SIGGRAPH Asia , and ECCV conferences, focusing on dynamic scene animation , light diffusion , and shadow removal . Honors : Best Paper Award (3D Vision 2013) Professor Curless has taught courses in computer graphics (CSE 457/557/558) and co-developed software tools like VripPack for volumetric range image processing. He maintains collaborations with institutions like Stanford University and Microsoft , and his work spans applications in digital art preservation , virtual production , and interactive technologies .

• Machine Learning
• Deep Learning
• Robustness in Neural Networks
• +۲ مورد دیگر

Dr. Grigorios Chrysos is a faculty member in the Department of Electrical & Computer Engineering at the University of Wisconsin-Madison. His research focuses on reliable machine learning, emphasizing robustness to noise, out-of-distribution generalization, and theoretical understanding of neural/polynomial networks. Education: PhD in Machine Learning, Imperial College London (2020) M.Eng. in Electrical Engineering, National Technical University of Athens (2014) Key research interests include robustness in deep networks , inductive bias analysis , and polynomial network design for high-order input interactions. His work explores adversarial robustness, fair model generalization, and extrapolation properties in generative frameworks. Recent awards include the prestigious DAAD AInet Fellowship (2023), Best Reviewer Awards at NeurIPS (2022), ICLR (2022), and IMCL (2021), alongside Amazon Cloud Credits and Nvidia GPU donations (2019). Scientific Contributions: Advancing tensor methods in machine learning Developing polynomial networks for robustness Designing efficient EEG seizure analysis algorithms

Derek Arnoldمشاهده پروفایل

استاد

• Sensory Processing
• Conscious Perception
• Predictive Coding
• +۸ مورد دیگر

Professor Derek Arnold is a cognitive neuroscientist at the School of Psychology, University of Queensland. His research focuses on sensory processing, predictive coding, and the neural mechanisms underlying conscious perception, particularly in visual and temporal domains. Current projects explore aphantasia, synaesthesia, and the relationship between brain activity and imagery vividness. Education: Macquarie University, University of Sydney, University College London Research Interests: Sensory Processing and Predictive Coding Time Perception and Neural Oscillations Confidence in Perceptual Decisions Integration of Multisensory Experiences His recent work examines how imagined experiences and neural oscillations correlate with subjective perception, alongside critiques of unscientific theories of consciousness. Key grants include multiple ARC Discovery Projects and a Future Fellowship. Scientific Awards ARC Future Fellowship UQ Research Excellence Award He supervises PhD students on topics like aphantasia, emotional stimuli prediction, and visual acuity. His lab at UQ investigates perceptual illusions and brain-computer interfaces.

Esra Yukselمشاهده پروفایل

مدرس

• Nuclear Physics
• Theoretical Physics
• Nuclear Astrophysics
• +۳ مورد دیگر

Dr. Esra Yuksel is a Lecturer in Theoretical Nuclear Physics at the University of Surrey's School of Mathematics and Physics. She holds a PhD from Yildiz Technical University (2016) and is a Fellow of the Higher Education Academy (FHEA, 2024). Her roles include Physics Department Demonstrator Coordinator (2023–present) and School Liaison (2024–present). Her research focuses on nuclear properties under extreme conditions, energy density functionals, and machine learning applications. Key areas include octupole collectivity in rare-earth nuclei, finite-temperature effects on nuclear stability, and relativistic energy density functional optimization. Recent work highlights include groundbreaking studies on Gadolinium nuclei's pear-shaped deformations and Pb-208's structural anomalies. Publications emphasize experimental-theoretical synergy, such as parity-violating electron scattering constraints on nuclear models and machine learning predictions for nuclear masses. She leads supervision of PhD and MSc students, with ongoing projects in nuclear astrophysics and computational methods. Education: PhD in Nuclear Physics, Yildiz Technical University, 2016 Fellow of Higher Education Academy (FHEA), 2024 Awards: FHEA 2024 Research Themes: Nuclear energy density functionals Finite-temperature nuclear dynamics Machine learning for nuclear data Weak interaction processes in stellar environments

• Cognitive Psychology
• Visual Cognition
• Attention and Perception
• +۳ مورد دیگر

Dr. Anna Heuer is a Researcher at the Humboldt University of Berlin, affiliated with the Institute of Psychology within the Faculty of Life Sciences. Her role is part of a DFG-funded project focusing on Active Perception and Cognition in General Psychology. She is based at Rudower Chaussee 18, Room 2.205, and can be reached via anna.heuer@hu-berlin.de. Her research emphasizes spatial and temporal reference frames in visual working memory, attentional control, and cognitive processes related to perception and action. Her work explores how predictable motion and feature-based attention influence memory retention, as well as the neural mechanisms underlying distractor suppression and decision-making. Notable studies include investigations into impaired attentional disengagement in Internet Gaming Disorder and the integration of spatiotemporal information in visual events. Her research bridges cognitive psychology, neuroscience, and clinical applications. Labs/Teams: Active participant in the 'Active Perception and Cognition' research group at the Institute of Psychology. Collaborates with interdisciplinary teams studying visual cognition and neural mechanisms of attention.

• Computational Neuroscience
• Neural Networks
• Motion Processing
• +۳ مورد دیگر

Selma Souihel is a PhD Candidate at INRIA Sophia Antipolis within the Biovision team, affiliated with Université Côte d’Azur. Her research in Computational Neuroscience focuses on motion encoding, anticipation, and retino-cortical integration. She is funded by the ANR project “Trajectory” (ANR-15-CE37-0011) and collaborates with researchers from Institut de Neurosciences de la Timone, Institut de la Vision, and Institut des Neurosciences Paris-Saclay. Education: PhD in Computer Science (2019, Université Côte d’Azur); Engineering degree in bioinformatics (2016, Ecole des Mines de Saint Etienne) Her research bridges retinal and cortical modeling, emphasizing computational principles for motion extrapolation and long-range cellular interactions. Articles span mean-field approaches, motion anticipation models, and retinal dynamics, presented at international conferences like Bernstein, AREADNE, and ICMNS. She received a poster prize at the GDR Neural Net conference (2018). Teaching roles include courses on Digital Transmissions, Networks and Security, and Advanced Networking at Université Côte d’Azur (2016–2019). She also organized PhD seminars and events like MOMI2018 and Grets’17, while participating in science outreach at Inria.

• Stochastic Programming
• Mathematical Modeling
• Financial Mathematics
• +۷ مورد دیگر

Prof. habil. dr. Leonidas Sakalauskas is an Affiliated Scientist at the Smart Technologies Research Group within the Institute of Data Science and Digital Technologies at Vilnius University. His extensive research career spans mathematical modeling, stochastic programming, and statistical analysis with significant contributions across multiple disciplines. He maintains an active research profile with recent publications extending into 2025. Professor Sakalauskas' research focuses on stochastic programming, mathematical modeling, financial mathematics, statistical analysis, optimization, and queueing theory. His work demonstrates a consistent pattern of interdisciplinary research, bridging theoretical mathematics with practical applications in finance, social sciences, and computer science. He has developed innovative approaches to agent-based modeling, fractal analysis, and Monte Carlo methods, contributing significantly to both theoretical frameworks and practical implementations. His recent publications reveal a continuing evolution of research interests, with increasing emphasis on data science applications, computational social science, and advanced statistical modeling techniques. The articles demonstrate sophisticated methodological approaches to complex problems across diverse domains including financial modeling, social cohesion analysis, and network performance optimization. Throughout his career, Professor Sakalauskas has maintained a strong publication record in high-impact journals and conference proceedings, reflecting his ongoing engagement with the international research community. His work shows consistent methodological rigor combined with practical relevance across multiple application domains. Professor Sakalauskas has collaborated extensively with researchers across Lithuania and internationally, demonstrating strong teamwork and interdisciplinary engagement. His research has practical applications in financial modeling, social science analysis, and computational systems optimization, contributing to both theoretical advances and real-world implementations.

Sylvain Calinonمشاهده پروفایل

مدرس

• Robotics
• Robot Learning
• Human-Robot Collaboration
• +۴ مورد دیگر

Sylvain Calinon is a Senior Research Scientist at the Idiap Research Institute and a Lecturer at the Ecole Polytechnique Fédérale de Lausanne (EPFL). He specializes in robot learning, human-robot collaboration, and optimal control. He holds a PhD from EPFL (2007) and has led research teams at IIT (2009–2014) and Idiap (2014–present). His work focuses on enabling robots to learn skills from demonstrations and interactions, emphasizing geometric and model-based approaches. Affiliations: Idiap Research Institute (Senior Scientist), EPFL (Lecturer in Microengineering) Research Interests: Robot learning, manipulation planning, optimal control, human-robot interaction, and industrial applications. Calinon's research has been supported by grants from the European Commission, Swiss National Science Foundation, and others. He has received multiple awards, including the ABB Award and Best Paper Awards at ICRA, IROS, and RSS. He has advised over 20 PhD students and contributes to editorial roles in top journals like IEEE Transactions on Robotics. His current projects include the H2020 MEMMO (memory of motion) and COLLABORATE (human-robot co-production systems). He also leads initiatives in geriatronics and industrial cobotics, emphasizing real-world applications.