جست‌وجوی استاد و پژوهشگر

Sven Scheweمشاهده پروفایل

استاد

• Automata Theory
• Game Theory
• Formal Verification
• +۱۲ مورد دیگر

Sven Schewe is a Professor in the Department of Computer Science at the University of Liverpool, affiliated with the School of Electrical Engineering, Electronics and Computer Science. He leads the AI Section and is a founding member and former leader of the Verification Group. He also has secondary affiliations with the Algorithms, Complexity Theory and Optimisation Group and the Institute for Risk and Uncertainty. Research Interests: His research centers on automata theory and game theory, particularly their applications in the verification and synthesis of reactive and safety-critical systems. He investigates infinite-duration games, automata over infinite words and trees, and develops algorithms and tools for automated verification, synthesis, and learning of optimal control strategies. His work extends to reinforcement learning with formal guarantees, cyber-physical systems, and AI safety. Recent Research Trends: His recent publications demonstrate a strong integration of formal methods with machine learning, particularly in adversarial training, neural network robustness, and model-free reinforcement learning under omega-regular objectives. He also applies formal reasoning to interdisciplinary domains such as chemical space exploration and materials science. Scientific Awards: Finalist for the ERCIM Cor Baayen Award 2010 Dr. Eduard Martin Preis 2009 GI Dissertation Award 2008 Advising and Grants: He actively supervises numerous PhD students and postdoctoral researchers. He is Principal Investigator (PI) or Co-Investigator (CI) on multiple major grants, including EPSRC Programme Grants, Royal Society Fellowships, and Horizon Europe projects. His funded research spans topics such as game theory, verification, synthesis, reinforcement learning, and risk analysis. He has hosted visiting researchers and collaborated internationally with institutions in Germany, France, India, Taiwan, and the US. Labs and Teams: He co-founded and led the Verification Group and previously led the AI Section at the University of Liverpool. These groups focus on formal methods, automata, games, and their applications in AI and safety-critical systems.

• Stochastic Simulation
• Optimization
• Machine Learning
• +۵ مورد دیگر

Giulia Pedrielli is an Associate Professor at Arizona State University's School of Computing and Augmented Intelligence, with affiliations as a Senior Global Futures Scientist. She holds a Ph.D., M.Sc., and B.S. in Mechanical and Industrial Engineering from Politecnico di Milano, Italy. Her research focuses on stochastic simulation, optimization, and machine learning applications in biomanufacturing, digital twins, and cyber-physical systems. She has contributed to frameworks for pandemic modeling (PySIRTEM), RNA design, and high-dimensional optimization. Pedrielli has collaborated with institutions like NUS and UC Berkeley, and her work spans biomedical engineering, supply chain resilience, and security of critical infrastructure. Recent articles emphasize co-simulation tools, AI-driven logic translation, and falsification methods for CPS security. Her teaching includes advanced stochastic simulation courses and thesis supervision across multiple disciplines. Education: Ph.D. Mechanical Engineering, Politecnico di Milano, Italy M.Sc. Industrial Engineering, Politecnico di Milano, Italy B.S. Industrial Engineering, Politecnico di Milano, Italy Research interests integrate simulation-based optimization with machine learning, particularly in safety-critical systems like healthcare and smart infrastructure. She develops digital twin methodologies for real-time control under uncertainty, leveraging Gaussian processes and Bayesian approaches. Pedrielli's work bridges theoretical advancements (e.g., BASSO optimization algorithm) with practical applications in biomanufacturing and pandemic response.

Xianzhi Liمشاهده پروفایل

دانشیار

• 3D Vision
• Computer Graphics
• Point Cloud Processing
• +۳ مورد دیگر

Xianzhi Li is an Associate Professor at the School of Computer Science and Technology, Huazhong University of Science and Technology (HUST). Before joining HUST, he was a postdoctoral fellow at The Chinese University of Hong Kong (CUHK) and the Hong Kong Center for Logistics Robotics. He earned his Ph.D. in Computer Science and Engineering from CUHK under Professors Pheng-Ann Heng and Chi-Wing Fu, preceded by an M.Sc. in Biomedical Engineering (CUHK, 2015) and a B.Eng. in Biomedical Engineering from Sichuan University (2014). His research focuses on 3D vision, computer graphics, point cloud processing, and deep learning, with a specific emphasis on designing machine learning algorithms for 3D data analysis. Recent work includes advancements in 3D point cloud analysis, semantic segmentation, and sim-to-real robotics applications. Key research trends include the integration of large language models with 3D data (e.g., MiniGPT-3D), open-world semantic segmentation (PDF), and joint 2D-3D learning frameworks (Joint-MAE). His contributions span robotics, computer vision, and graphics, with notable applications in industrial bin picking and 3D reconstruction. Awards: Teaching Assistant of Merit (2018), Biomedical Engineering Scholarship (2015), and National Scholarship in China (2013). Teaching: Courses include Computer Vision (Spring 2022), Advanced Topics in Computer Graphics and Visualization, and Principles of Computer Graphics.

Sándor Ádányمشاهده پروفایل

استاد

• Structural Engineering
• Computational Mechanics
• Finite Element Modeling
• +۳ مورد دیگر

Sándor Ádány is a Professor at the Department of Structural Mechanics , Budapest University of Technology and Economics. His work focuses on advanced structural analysis of thin-walled members and systems-based design methodologies. Research Interests: Specializes in buckling behavior of thin-walled structures, finite element modeling, cold-formed steel stability, and modal decomposition techniques. Key areas include Lateral-torsional buckling Displacement mapping in constrained FEM Stiffener optimization in plate structures Combined loading stability of tubular members Prebuckling deformation effects Fourier-based numerical methods Article Trends (2023-2025): Recent publications emphasize elastic stability analysis of thin-walled beams and tubular structures under complex loading conditions, with particular attention to prebuckling deflections, torsional rigidity effects, and numerical validation of analytical models. Innovations include Fourier-series displacement approximations and constrained finite element methodologies.

Sofia Kyratziمشاهده پروفایل

استادیار

• Geometric Modeling
• Computer-Aided Design (CAD)
• Parametric Modeling
• +۵ مورد دیگر

Sofia Kyratzi is an Assistant Professor of Geometric Modeling and Computer-Aided Design at the University of the Aegean's Department of Product and Systems Design Engineering. She holds a PhD (2007) in Computer Graphics and Geometric Modeling from the same university, an MSc in Medical Informatics (2002) from Aristotle University of Thessaloniki, and a BSc in Mathematics (2000) from Aristotle University. Roles: Teaches courses like Computer Graphics, Computer-Aided Design, and related graduate topics. Research Focus: Parametric modeling, sketch-to-solid algorithms, design intent recognition, and Industry 4.0 integration in manufacturing. Her work includes developing algorithms for reconstructing 3D models from sketches, leveraging cloud computing for CAD applications, and optimizing footwear design via biomechanical modeling. Key contributions span over 15 peer-reviewed publications in journals like Computer-Aided Design and conferences addressing geometric modeling challenges. She collaborates on projects exploring Industry 4.0 technologies for SMEs, emphasizing cloud-based CAD tools and parallel computing optimizations. Her research bridges theoretical geometric principles with practical industrial applications.

Babak Ayazifarمشاهده پروفایل

استاد آموزشی

• Signal Processing
• Graph Signal Processing
• Education Technology
• +۴ مورد دیگر

Babak Ayazifar is a Teaching Professor at UC Berkeley's EECS department since 2005. He holds a BS from Caltech (1989) and SM/PhD from MIT (2003). His work focuses on signal processing, education innovation, and curriculum development. He has won prestigious teaching awards including the Goodwin Medal (1999) and IEEE’s Mac Van Valkenburg Award (2012). His research bridges graph signal processing and pedagogical methodologies, with notable contributions to scalable educational tools like Jupyter notebooks. Education: BS Electrical Engineering, California Institute of Technology (1989) SM/PhD Electrical Engineering and Computer Science, MIT (2003) Key Roles: Visiting Senior Lecturer at MIT (2013–2014) Faculty co-advisor for Tau Beta Pi since 2009 Co-inventor of U.S. Patent 5,387,940 (video compression) Teaching Contributions: Developed EECS 16A curriculum Pioneer of interactive lab tools for signal processing education Advocate for TA mentoring and teaching excellence His research integrates signal processing fundamentals with educational innovation, emphasizing practical applications and student engagement. Recent work includes leveraging graph theory for signal analysis and Fourier pedagogy reform.

• Microelectromechanical Systems (MEMS)
• Compliant Mechanisms
• Carbon Nanotubes
• +۳ مورد دیگر

Dr. Brian D. Jensen is a Professor and Department Chair in the Department of Mechanical Engineering at Brigham Young University (BYU). He holds a Ph.D. in Mechanical Engineering from the University of Michigan and has also worked as a micromechanism designer at Sandia National Laboratories. His research focuses on Microelectromechanical Systems (MEMS) , Compliant Mechanisms , and Carbon Nanotube Applications in biomedical and mechanical contexts. Education: B.S. and M.S. in Mechanical Engineering, Brigham Young University M.S. in Electrical Engineering, University of Michigan Ph.D. in Mechanical Engineering, University of Michigan Dr. Jensen's research spans MEMS design , carbon nanotube-based biofilm resistance , and compliant mechanism optimization . His work has been recognized with the BYU Young Scholar Award and the Utah Engineers Council Educator of the Year Award . He has supervised numerous graduate students and holds 12 U.S. patents in MEMS and compliant mechanisms. His recent publications analyze compliant mechanism design , biofilm inhibition via CNT coatings , and microscale manufacturing techniques . Key subfields include boundary condition modeling , antimicrobial surfaces , and miniature spring design . Scientific Awards: Best Paper Awards BYU Young Scholar Award Utah Engineers Council Educator of the Year Award Dr. Jensen teaches undergraduate and graduate courses such as Dynamic System Modeling (MeEn 335), Microelectromechanical Systems (MeEn 550), and mentors capstone teams in MeEn 475/476 . He has developed 12 U.S. patents in MEMS and compliant mechanisms.

• Mathematical Statistics
• Probability Theory
• Applied Statistics
• +۳ مورد دیگر

Ernst Hansen is an Associate Professor at the Department of Mathematical Sciences, University of Copenhagen . He is based at University Park 5, Copenhagen Ø, and his work spans mathematical statistics, probability theory, and applied statistical modeling in public health and finance. Email: erhansen@math.ku.dk Research areas: Public health interventions, Markov chain applications, measure-theoretic probability, and statistical education His publications include textbooks and peer-reviewed articles on topics such as neck/shoulder pain prevention , continuous-time rating transition probabilities , and geometric drift analysis . While his work intersects with causal inference and stochastic processes, no formal awards or student advising records are documented in the provided texts.

• Robotics
• Underwater Robotics
• Computer Vision
• +۷ مورد دیگر

Claire Dune is an Assistant Professor at the University of Toulon, affiliated with the COSMER Laboratory (Mechanical and Robotic Systems Design Laboratory). Her research focuses on robotics, computer vision, and underwater systems, with applications in environmental monitoring and human-robot interaction. She teaches computer science, numerical methods, image processing, and visual servoing. Institution: University of Toulon Laboratory: COSMER (Mechanical and Robotic Systems Design Laboratory) Academic Rank: Assistant Professor Email: claire.dune@univ-tln.fr Her research centers on perception for robot control, particularly in underwater robotics and computer vision. Key interests include visual servoing, SLAM, gesture recognition for diver-robot interaction, and autonomous capabilities in real-world marine environments. She applies deep learning and sensor fusion techniques to enhance underwater visual perception and navigation. The recent publications demonstrate a strong trend in underwater robotics, with focus areas including tether dynamics (catenary modeling), ROV localization using umbilicals and IMUs, long-term visual localization in deep-sea environments, and color restoration in underwater imagery. Her work bridges theory and real-world application, contributing datasets like 'Eiffel Tower' for benchmarking and advancing multi-agent SLAM systems. Claire Dune has contributed to leading journals such as IEEE Robotics and Automation Letters, Ocean Engineering, and The International Journal of Robotics Research. Her editorial and survey work highlights her leadership in the domain of deformable object manipulation. Retrieval of benthic habitat abundance and bathymetry from hyperspectral data (DESIS) in shallow waters ROV localization using ballasted umbilical equipped with IMUs MAM3SLAM: Towards underwater robust multi-agent visual SLAM Eiffel Tower: A Deep-Sea Underwater Dataset for Long-Term Visual Localization Challenges and Outlook in Robotic Manipulation of Deformable Objects Claire Dune actively collaborates with researchers such as Vincent Hugel, Juliette Drupt, and Andrew Comport. She has supervised or co-supervised numerous research projects and publications, particularly in underwater robotics and assistive technologies. Her work involves experimental robotics and system integration, often validated in real marine environments. She leads research in the COSMER laboratory focused on underwater robotics, including projects on tethered ROVs, diver-robot communication via gesture recognition, and environmental monitoring using visual and hyperspectral data. Her team develops practical solutions for marine science and offshore operations, emphasizing robustness and autonomy.

Matteo Pezzullaمشاهده پروفایل

دانشیار

• Fluid-Structure Interactions
• Soft Hydraulics
• Elastic Instabilities
• +۳ مورد دیگر

Matteo Pezzulla is an Associate Professor at Aarhus University within the Department of Mechanical and Production Engineering Mechanics and Materials. His research focuses on fluid-structure interactions, soft hydraulics, and elastic instabilities, with applications in biomechanics and solid mechanics. Areas of expertise: Soft hydraulics, Fluid-structure interactions, Elastic instabilities, Biomechanics, Solid mechanics Teaching: Theory of Elasticity (BSc), Slender Structures (MSc) Memberships: American Physical Society (APS), Education Committee at AU Projects: The Architecture of Photosynthesis (2023–2026), Smart Fluidic Channels (2023–2025) His work bridges analytical, numerical, and experimental methods to study shell mechanics and biological systems. Publications highlight fluid-induced buckling, magneto-elastic materials, and biomimetic designs. Contact: matt@mpe.au.dk | +45 20 69 75 22

Maria Alberich Carramiñanaمشاهده پروفایل

استاد

• Algebraic Geometry
• Singularity Theory
• Geometric and Topological Methods in Robotics
• +۱ مورد دیگر

Maria Alberich Carramiñana is a Full Professor (Catedrática) at the Universitat Politècnica de Catalunya (UPC), Department of Mathematics, affiliated with the Faculty of Mathematics and Statistics. She leads the GEOMVAP research group (Geometry of Varieties and Applications) and is part of the Institut de Robòtica i Informàtica Industrial (CSIC-UPC). Her expertise lies in Algebraic Geometry, Singularity Theory, and their applications to robotics, alongside Mathematics Outreach. She holds a Ph.D. from the Universitat de Barcelona and has held roles including Ramón y Cajal Fellow and Associate Professor at UPC. Roles: Deputy Director at CFIS-UPC, member of the IMAGINARY Committee (RSME). Education: M.Sc. (1994), Ph.D. (2000) in Mathematics from UB. Research focuses on analytic and algebraic singularities, robotic perception and manipulation (e.g., cloth modeling), and geometric-topological methods in robotics. Key projects include Interactions of Geometry with Algebra and Applications and GeomVAP . Awards include Sant Jordi IEC Awards (2022, 2024), Vincent Caselles RSME-BBVA (2022), and supervision of award-winning students (e.g., Guillem Blanco, Franco Coltraro). Grants: Competitively funded projects (e.g., Geometria de Varietats i Aplicacions ) and non-competitive initiatives (e.g., LabORA consolidation). Active collaborations in robotics and algebraic geometry.

Florian Albrecht-Schoeckمشاهده پروفایل

پژوهشگر

• Photography
• Digital Art
• Artistic Research
• +۴ مورد دیگر

Florian Albrecht-Schoeck is a German artist and Researcher at the Offenbach University of Art and Design . His work bridges Photography , Artificial Intelligence , and Machine Learning , exploring themes of roots, identity, origin, and belonging within societal, political, and individual contexts . He co-teaches the Photography Fundamentals course with Prof. Martin Liebscher. Albrecht-Schoeck’s artistic practice involves analog, digital, and virtual photography . His series like Not Alone (2021/2022) and Squares (2023) analyze the medium’s evolving role in the age of AI. He integrates research into teaching , helping students navigate complex technologies through projects like No way Back: Photography without Photographs (2024/2025). He has received notable awards and scholarships , including the Brückenstipendium der Hessischen Kulturstiftung (2021) and Deutsche Börse Fotoförderpreis (2011). His works are housed in collections such as the Art Collection Deutsche Börse and the Museum für Kunst und Kulturgeschichte in Marburg . Key exhibitions include Roots Retropia Rage Repeat (2018) and Fotografie ohne Fotografie (2025). He also contributes to pedagogical projects , such as the annual ZuTaten publications and exhibitions (2024/2025).

Qiyang Hanمشاهده پروفایل

دانشیار

• Mathematical Statistics
• High Dimensional Probability
• Empirical Process Theory
• +۷ مورد دیگر

Qiyang Han is an Associate Professor in the Department of Statistics at Rutgers, The State University of New Jersey, within the School of Arts and Sciences. His research lies at the theoretical interface of statistics, probability, and algorithms, with a strong focus on foundational and high-dimensional problems. His research interests include mathematical statistics , high-dimensional probability , empirical process theory , nonparametric and shape-restricted inference , and Bayesian nonparametrics . He also investigates high-dimensional statistics , convex optimization , and large-scale iterative algorithms , with increasing attention to gradient descent dynamics and message passing methods. His recent publications reveal a consistent focus on precise asymptotic analysis, universality phenomena, and inference under geometric constraints. Themes across his work include robustness, adaptivity, and the theoretical underpinnings of modern statistical learning in overparameterized regimes. Ph.D. in Statistics, University of Washington, 2018 Supervised by Professor Jon A. Wellner He has collaborated with prominent researchers such as C.-H. Zhang, Bodhisattva Sen, Kengo Kato, and Richard J. Samworth. His work is published in top journals including The Annals of Statistics , Journal of the Royal Statistical Society Series B , and IEEE Transactions on Information Theory . Although no formal advising list is provided, his collaborative output suggests active mentorship and research leadership. He is involved in theoretical research with implications for machine learning, signal processing, and statistical inference. His lab or research group focuses on developing rigorous mathematical frameworks for understanding complex statistical algorithms and models, particularly in high-dimensional settings.

Liya Zhaoمشاهده پروفایل

مدرس ارشد

• Energy Harvesting
• Nonlinear Dynamics
• Smart Materials and Structures
• +۱۴ مورد دیگر

Dr. Liya Zhao is a Senior Lecturer in the School of Mechanical and Manufacturing Engineering at the University of New South Wales (UNSW Sydney), where she leads the Dynamic Smart Structures and Energy Harvesting Laboratory. She previously held academic positions at the University of Technology Sydney (UTS), first as a Lecturer (2017) and later promoted to Senior Lecturer (2021), before joining UNSW in 2022. Her research is highly interdisciplinary, focusing on smart structures, nonlinear dynamics, and sustainable energy technologies. Education: Ph.D. in Structures & Mechanics, Nanyang Technological University, Singapore (2015) B.Eng. in Civil Engineering, Tongji University, China (2009) Research Interests: Dr. Zhao's work centers on energy harvesting from ambient sources (wind, vibration, human motion, waves), smart materials (piezoelectric, triboelectric), metamaterials, and nonlinear dynamics. She designs adaptive structures for broadband energy harvesting and vibration suppression, with applications in self-powered wireless sensor networks, structural health monitoring, and wearable devices. Her research integrates theoretical modeling, numerical simulation, and experimental validation. Publication Trends: Over the past decade, her research has evolved from fundamental electromechanical modeling toward multifunctional metastructures and real-world deployment. Her recent work emphasizes hybrid energy harvesting, nonlinear tuning for broadband response, and integration with self-powered sensing systems, reflecting a strong trend toward practical, sustainable technologies. Scientific Awards: ARC Discovery Early Career Researcher Award (DECRA), 2021–2024 World's Top 2% Scientists (Stanford University), 2020–2023 Nanyang Engineering Doctoral Scholarship (NEDS), NTU Singapore Grants & Supervision: Dr. Zhao has secured multiple competitive grants, including ARC Discovery Projects and internal university funding, as both sole and chief investigator. She actively supervises research students and welcomes motivated candidates in mechanics, dynamics, and energy systems. Her lab, Dynamic Smart Structures and Energy Harvesting Lab , fosters innovation through experimental and computational research. She also contributes to teaching, including Mechanics of Solids II and Introduction to Aircraft Engineering. Lab & Team: She leads a dynamic research group focused on next-generation smart structures, supported by state-of-the-art facilities at UNSW. Her team develops novel materials and systems for sustainable energy and sensing, aiming to bridge the gap between fundamental science and real-world applications.

• Quantum Chemistry
• Computational Photochemistry
• Spectroscopy
• +۳ مورد دیگر

Prof. Tadeusz Andruniów serves as a full Professor at the Institute of Advanced Materials within the Faculty of Chemistry at Wrocław University of Science and Technology. His research program integrates quantum chemical methodologies with photobiological systems, focusing on light-induced processes in biological chromophores. With continuous publication output from 2004 to 2025, he maintains an active research trajectory evidenced by contributions to high-impact journals including the Journal of the American Chemical Society and Proceedings of the National Academy of Sciences. His core research interests encompass: Quantum Chemistry of Biological Systems Photoisomerization Dynamics in Rhodopsin Resonance Raman Spectroscopy Two-Photon Absorption Phenomena Protein-Chromophore Interactions Computational Photobiology These interests drive his investigations into excited-state behavior, energy storage mechanisms, and spectroscopic signatures of chromophores in complex biological environments. Analysis of his 2020-2025 publications reveals three dominant research thrusts: (1) Precision modeling of two-photon absorption in rhodopsin and fluorescent protein chromophores using high-level coupled-cluster methods; (2) Elucidation of protein environmental effects on spectroscopic properties through QM/MM simulations; and (3) Extension of computational frameworks to abiotic systems like sequence-defined polyurethanes. His work consistently demonstrates methodological innovation in handling excited-state dynamics and nonlinear optical properties. While specific grant details and student supervision records are not documented in available sources, his collaborative publication pattern involving international co-authors indicates active research networking. His technical contributions to computational protocols for artifact removal in polarizable embedding calculations and quantum region optimization represent significant methodological advances for the field.