جستجوی استاد راهنمای دانشگاهی

• Astrophysics
• Galactic Archaeology
• Stellar Dynamics
• +۴ مورد دیگر

Dr. Payel Das is a UKRI Future Leaders Fellow in the Astrophysics research group at the University of Surrey's School of Mathematics and Physics. She holds a PhD in Astrophysics from the Max Planck Institute for Extraterrestrial Physics. Previously, she explored energy-efficient housing design at UCL and worked as a Postdoctoral Researcher (PDRA) at the University of Oxford. Her research focuses on galactic archaeology, combining dynamical modeling, machine learning, and phylogenetic methods to study stellar populations and dark matter distributions in galaxies. Education: PhD in Astrophysics (Max Planck Institute), MSc/BA (not explicitly stated). Research Interests: Chemical and dynamical properties of stars, forensic studies of galaxy evolution, interdisciplinary methods for big data analysis, and past work in sustainable housing. She uses techniques like equilibrium dynamical models, PCA/XD clustering, and evolutionary biology-inspired phylogenetics. Publications: Over 30 peer-reviewed articles in top journals like Monthly Notices of the Royal Astronomical Society, spanning topics from Milky Way structure to dwarf galaxy dynamics. Key contributions include uncovering Sagittarius satellite interactions and developing machine learning tools for stellar parameter estimation. Awards: UKRI Future Leaders Fellowship (2021–present). Labs/Teams: Leads projects on the GLEAM survey and collaborates with international teams on GALAH and APOGEE surveys.

Angelika Sebaldمشاهده پروفایل

پژوهشگر ارشد

• Nuclear Magnetic Resonance Spectroscopy
• Unconventional Computation
• Artificial Chemistries
• +۳ مورد دیگر

Dr. Angelika Sebald is an Honorary Fellow in the Department of Chemistry at the University of York. Her research focuses on computation and nuclear magnetic resonance (NMR) spectroscopy, with two core areas: (1) exploiting nuclear spin dynamics for unconventional computation using solution-state and solid-state NMR; and (2) developing artificial chemistries inspired by physical and chemical laws to examine algorithms with emergent properties. She collaborates with the Department of Computer Science and leads a project providing validated information on oral/maxillofacial surgery via maxfacts.uk . Her work bridges theoretical computation and experimental NMR, emphasizing interdisciplinary approaches. No scientific awards are listed, and no formal advisees are noted. Collaborations include cross-departmental projects and public-facing initiatives aimed at disseminating specialized medical knowledge.

• Machine Learning
• Computer Vision
• Pattern Recognition
• +۳ مورد دیگر

Richard Wilson is a Professor of Pattern Analysis in the Department of Computer Science at the University of York. He holds roles including Member of the Departmental Research Committee and Research Group Lead for Artificial Intelligence. With degrees from the University of Oxford (BA) and the University of York (DPhil), his research focuses on Machine Learning, Computer Vision, and Pattern Recognition with Graphs and Networks. He has contributed over 238 publications and led projects such as the UKRI AI Centre for Doctoral Training (SAINTS) and the Royal Society-funded Graphical Modeling of Brain project. Wilson has also been recognized with the IAPR Fellow Award (2010). His work spans theoretical advancements and applied research in network analysis, quantum algorithms, and interdisciplinary applications like drug discovery and healthcare informatics. His research interests integrate computational methods with complex systems analysis, emphasizing graph theory, entropy-based network modeling, and AI-driven solutions. Recent publications highlight innovations in semi-supervised learning, graph kernels, and quantum-inspired network dynamics. Wilson’s contributions to editorial roles, such as with the journal Pattern Recognition , and activities like PhD external examinations further underscore his academic leadership. Key Projects: UKRI AI Centre for Doctoral Training in Safe AI (SAINTS) – Principal Investigator (2024–2032) Graphical Modeling of Brain – Principal Investigator (2020–2023) His lab focuses on AI and computational methodologies, collaborating across disciplines to address challenges in healthcare, computer vision, and network science.

Manasa Kandulaمشاهده پروفایل

استادیار

• Soft Matter Physics
• Active Matter
• Colloids and Polymers
• +۴ مورد دیگر

Manasa Kandula is an Assistant Professor in the Department of Physics at the University of Massachusetts Amherst, leading the Bio-inspired Soft Matter Lab . Her research focuses on understanding soft matter systems through experimental and computational approaches, particularly exploring active colloids, glass transitions, and bio-inspired materials. She uses advanced microscopy techniques like liquid-phase TEM to observe dynamic processes at nanometer scales and develops algorithms for data analysis. Current research projects include studying circular microswimmers, colloidal filament dynamics, and polymer adsorption at interfaces. Her interdisciplinary work bridges physics, materials science, and engineering, with applications in designing adaptive materials and understanding living matter analogs. Affiliations: College of Natural Sciences, Department of Physics, UMass Amherst Techniques: Electron microscopy, fluorescence spectroscopy, custom image analysis Key Themes: Non-equilibrium systems, self-organization, soft materials engineering Opportunities exist for graduate, master’s, and undergraduate students to participate in experimental research on soft matter physics. Collaborations are encouraged across disciplines to explore new research frontiers in bio-inspired materials and advanced imaging technologies.

Chris Bakerمشاهده پروفایل

استاد

• Radar Systems
• Quantum Radar
• Signal Processing
• +۴ مورد دیگر

Professor Chris Baker holds the Chair of Intelligent Sensor Systems at the University of Birmingham's Department of Electronic, Electrical and Systems Engineering. He is also Chief Technology Officer at Aveillant Ltd (a Thales company). With over 35 years in radar research, he led development of UK military systems like Sentinel and MSTAR, and now pioneers quantum radar through the UK National Quantum Technology Hub. A Fellow of IEEE and IET, Baker authored over 250 publications and co-authored the textbook 'Stimson’s introduction to airborne radar'. His research spans cognitive radar, quantum sensing, bio-inspired systems, and automotive radar. Education includes BSc/Dip App Phys/PhD from UK institutions. He served as Dean of ANU's College of Engineering and held Royal Academy of Engineering chairs. Teaching includes courses based on his textbook. Awards include the IEE Mountbatten Premium (twice), and leadership roles in IEEE and IET journals. Current projects focus on quantum radar, distributed sensing, and bio-inspired sensor systems. He collaborates with global universities and industries, advising on technical strategy and engineering solutions. Grants supported applied research worldwide, with consultancy for governments and defense.

Herbert Jaegerمشاهده پروفایل

استاد

• Computer Science
• Artificial Intelligence
• Neuromorphic Computing
• +۴ مورد دیگر

Herbert Jaeger is a Professor of Computing in Cognitive Materials at the University of Groningen's Faculty of Science and Engineering, within the Artificial Intelligence department at the Bernoulli Institute. He holds editorial roles, including membership on the Neural Networks journal's editorial board and serving as Secretary General of the Interdisciplinary College. His expertise spans machine learning, dynamical systems modeling, neuromorphic computing, and reservoir computing. Education: PhD in Computer Science (Artificial Intelligence), University of Bielefeld Studies in Mathematics and Psychology, University of Freiburg Research Interests: His work focuses on theoretical and applied aspects of computing across disciplines, including unconventional computing models, neuromorphic systems, and reservoir computing. He explores how physical substrates and dynamical systems can advance computational paradigms, with applications in signal processing and robotics. Publications: Recent work emphasizes formal theories for physics-based computing, timescales in neuromorphic systems, and interdisciplinary computing landscapes. These studies highlight novel approaches to hardware-software integration and cross-disciplinary innovations. Projects & Grants: Leads research through the MINDS group, focusing on funded projects in cognitive materials and computing. Collaborates on initiatives like the AMARSi project, exploring modular architectures for rich motor skills in robotics. Labs/Teams: Active within the Bernoulli Institute and the Artificial Intelligence group, fostering interdisciplinary collaborations in cognitive science and engineering.

Roel Verstappenمشاهده پروفایل

استاد

• Computational Fluid Dynamics
• Scientific Computing
• Turbulent Flow
• +۶ مورد دیگر

Roel Verstappen is a Professor in the Department of Computational and Numerical Mathematics at the University of Groningen's Faculty of Science and Engineering. His expertise spans Scientific Computing and Computational Fluid Dynamics, with a focus on turbulence simulation, large-eddy simulation (LES), and multiphase flows. He actively contributes to advancing numerical methods and models that preserve physical symmetries and conservation laws. Research Interests: His work centers on developing low-dissipation simulation techniques, regularization modeling, and symmetry-preserving discretization for complex fluid dynamics problems. Key areas include turbulent flow analysis, multiphase flow dynamics, and applications in robotics and renewable energy systems. He has pioneered methods to ensure energy and momentum conservation in computational models and explored bio-inspired designs for soft robotic swimmers. Articles Trends: Recent publications highlight innovations in LES for multiphase flows, fluid-structure interaction in biological systems, and robotics. He has also investigated uncertainty quantification in wind-farm simulations and collaborated on projects involving magnetic actuation and natural convection flows. These works reflect a blend of theoretical advancements and practical engineering applications. Advising & Grants: He supervises graduate research in computational fluid dynamics and numerical methods but no specific student names are listed. His funding sources and grants are not explicitly mentioned in the provided texts. Labs/Teams: Verstappen is affiliated with the Bernoulli Institute, which focuses on interdisciplinary research in mathematics, computer science, and engineering. His collaborations include researchers from institutions like TU Eindhoven and the University of Zaragoza, addressing challenges in maritime engineering, soft robotics, and sustainable energy systems.

Mo Liمشاهده پروفایل

استاد

• Computational Materials Science
• Mechanical Properties
• Phase Transition
• +۷ مورد دیگر

Mo Li is a Professor in the School of Materials Science and Engineering at Georgia Institute of Technology. He received his Ph.D. in Applied Physics from California Institute of Technology in 1994, with postdoctoral work at Caltech and Argonne National Laboratory before joining academia. A Member/Fellow of APS and MRS, he leads the Computational Materials Science and Engineering group. His research spans ceramics, composites, metals, and nanostructures, addressing challenges in energy, environment, infrastructure, and transportation. Key methodologies include computational modeling, material characterization, and analysis of mechanical properties, phase transitions, and non-equilibrium phenomena. Recent publications focus on mathematical modeling of deformable systems, mechanical behavior of knitted materials, bio-inspired tissue mimics, and computational geometry applications. Trends include strong interdisciplinary integration of materials science with topology, biomechanics, and statistical mechanics. No scientific awards are explicitly mentioned. He advises graduate researchers in computational materials science within the CMSE laboratory group.

• Cloud Computing
• Cybersecurity
• Internet of Things (IoT)
• +۷ مورد دیگر

Gokop Goteng is a Senior Lecturer at Queen Mary University of London's School of Electronic Engineering and Computer Science, specializing in cloud computing, IoT security, and middleware systems. He holds a PhD in Grid Computing from Cranfield University (2009) and has held postdoctoral roles at Qatar Computing Research Institute (2012-2014) and King Abdullah University of Science and Technology (2012-2014). Currently, he leads the BUPT joint program's Cloud Computing and Middleware modules, focusing on industry-relevant skills in distributed systems and software engineering. His research integrates cloud technologies, cybersecurity, and AI to address challenges in smart cities, energy systems, and digital healthcare. Notable projects include malware detection in IoT devices and optimizing cloud service latency via deep learning. He is an Oracle Certified Professional (OCP), Java Certified Programmer (JCP), and active member of the British Computer Society (BCS) and IEEE. Teaching responsibilities include Cloud Computing (BUPT joint programme) Middleware for IoT Software Engineering , emphasizing hands-on programming with APIs, secure middleware design, and team-based software development. His work emphasizes industry collaboration, as seen in AWS Academy partnerships to enhance student employability. He maintains a research website at gotengphdstudents for doctoral supervision and project updates.

Sabre Kaisمشاهده پروفایل

استاد

• Quantum Computing
• Quantum Information Science
• Quantum Chemistry
• +۵ مورد دیگر

Sabre Kais is the Goodnight Distinguished Chair in Quantum Computing at North Carolina State University's Department of Electrical and Computer Engineering. He previously held roles at Purdue University, including Distinguished Professor of Chemistry and courtesy professorships in Computer Science and Physics. His research focuses on quantum information science, quantum computing for complex systems, and quantum chemistry simulations. Education: BSc, MSc, and PhD in Chemical Physics, Theoretical Chemistry, and Chemistry from the Hebrew University of Jerusalem (1983–1989). Postdoctoral research at Harvard University (1989–1994). Research interests include quantum algorithms, open quantum systems, quantum machine learning, and quantum simulation. He directs the NSF-funded Center for Quantum Technology and has led major initiatives like the Quantum Information for Quantum Chemistry center. Notable awards: Fellow of the American Physical Society (2006), AAAS Fellow (2007), Guggenheim Fellow (2005), and the 2019 Herbert Newby McCoy Award (Purdue’s highest natural science honor). He also serves on editorial boards and is a visiting professor at Berkeley (2024–2025). His work bridges quantum computing with applications in chemistry, finance, and healthcare, emphasizing interdisciplinary collaboration. Recent publications explore quantum federated learning, energy teleportation protocols, and defect-induced information scrambling in quantum systems.

• Nanotechnology
• Atomic, molecular and optical physics
• Electronics, sensors and digital hardware
• +۳ مورد دیگر

Dr. Soon Hock Ng is an Industry Research Fellow at Swinburne University of Technology's School of Science, Computing and Emerging Technologies. His research focuses on nanotechnology, atomic/molecular/optical physics, and electronics, with particular emphasis on laser-based materials processing and advanced imaging techniques. He leads projects involving femtosecond laser ablation, coded aperture imaging algorithms, and thermal management materials. His work integrates experimental and computational methods, leveraging synchrotron facilities and advanced spectroscopic tools. Key research themes include energy-efficient materials, optical nanostructures, and bio-inspired material characterization. Dr. Ng has secured significant external grants from the Australian Research Council (ARC) and the Australian Academy of Science, supporting initiatives like X-ray nanolithography and remote synchrotron data platforms. Grants: Web platform for remote synchrotron data analysis (2021-2022), X-ray nanolithography facility (2020-2022), Photonic crystals for solar cell efficiency (2019-2022) Supervises 8 PhD candidates focusing on metamaterials, laser fabrication, and freeform optical elements Notable contributions include developing non-linear imaging algorithms for IR applications and creating laser-patterned sapphire substrates for radiative cooling systems. His multidisciplinary approach bridges optics, materials science, and computational engineering for real-world applications in energy and healthcare sectors.

• Machine Learning
• Deep Learning
• Neural Networks
• +۳ مورد دیگر

Claudio Gallicchio is an Associate Professor of Deep Learning at the Department of Computer Science, University of Pisa, Italy. His research focuses on Machine Learning, Deep Learning, Neural Networks, and Dynamical Systems, with specialization in Dynamical Neural Networks, Reservoir Computing, and Graph Learning. He actively contributes to IEEE Task Forces on Reservoir Computing and Randomized Neural Networks, and is a member of several AI societies including AAAI and ENNS. He holds a Ph.D. in Computer Science from the University of Pisa. His work emphasizes addressing challenges in graph neural networks—such as oversquashing—and advancing energy-efficient neural architectures using memristive systems. He supervises students on topics like Recurrent Neural Networks, Neural ODEs, and Deep Learning for Digital Humanities. Gallicchio's recent publications explore Reservoir Computing applications in industrial processes, non-dissipative graph networks, and federated learning. His research bridges theoretical foundations (e.g., Hamiltonian architectural bias) with practical implementations (e.g., memristor-based models). He has pioneered frameworks like GRAMA for adaptive graph time-series analysis and the TEACHING platform for human-centric autonomous systems.

Uriel Levyمشاهده پروفایل

استاد

• Nanophotonics
• Silicon Photonics
• Plasmonics
• +۵ مورد دیگر

Uriel Levy is a Professor at the Department of Applied Physics of The Hebrew University of Jerusalem (HUJI), where he serves as director of the Harvey M. Krueger Family Center for Nanoscience and Nanotechnology. His research focuses on nanophotonics, silicon photonics, plasmonics, and light-matter interactions for applications in communication, sensing, and energy systems. PhD in Electrical Engineering from Tel Aviv University Postdoctoral work at University of California, San Diego Levy’s research spans advanced photonic technologies, including metasurface design, terahertz imaging, and quantum sensing. His work integrates theoretical and applied approaches to develop novel optical devices with applications in healthcare, automotive, and industrial systems. His recent publications highlight innovations in: tunable metasurfaces, SWIR sensing, photonic integrated circuits for quantum applications, and nonlinear optical devices. These contributions demonstrate his focus on scalable, CMOS-compatible photonic platforms. Elected Fellow of the Optical Society of America (OSA) Associate Editor for Science Advances (AAAS) ERC grant recipient Levy co-founded TriEye, an Israeli startup commercializing automotive sensing technology from his lab. His work also explores atomic-scale interactions in nanophotonic systems, including magnetometer development and light-vapor coupling for quantum applications.

Sunil Khatriمشاهده پروفایل

استاد

• VLSI Design
• Secure Computing Systems
• Machine Learning Hardware
• +۷ مورد دیگر

Sunil Khatri is a Professor in the Department of Electrical and Computer Engineering at Texas A&M University, affiliated with the College of Engineering and the Computer Engineering and Systems Group. He holds a Ph.D. from the University of California, Berkeley (1999), an M.S. from the University of Texas at Austin (1989), and a B.S. from the Indian Institute of Technology Kanpur (1987). His academic progression includes roles as Assistant Professor (2000–2004) at the University of Colorado Boulder and Associate Professor (2010–2015) at Texas A&M. Educational Background: Ph.D., University of California, Berkeley, 1999 M.S., University of Texas at Austin, 1989 B.S., Indian Institute of Technology Kanpur, 1987 Research Interests: Intelligent and Secure Computing Systems: Focused on hardware architectures for machine learning (e.g., neuromorphic circuits, NoCs, sub-threshold circuits) and secure hardware design (blockchain, physically unclonable functions). Logic Synthesis and Applications: Includes VLSI CAD, gene regulatory network modeling, noise-based logic, and Boolean satisfiability (SAT) solvers. Interdisciplinary Extensions: Explores routing table compression, radar signal processors, optical networking (DWDM), and LDPC decoders. Research Trends in Publications: Recent work emphasizes neuromorphic hardware acceleration (e.g., CapsPDNet for insulator discharge prediction, FFIR filters for IoT), secure protocols (multi-factor authentication frameworks), and energy-efficient circuits (flash-based designs, low-voltage regulators). His contributions span VLSI, machine learning, and cybersecurity, often leveraging FPGA/GPU acceleration. Grants & Labs: Leads projects in the Computer Engineering and Systems Group, focusing on secure computing, machine learning hardware, and interdisciplinary VLSI applications. Active in grant-driven research with a focus on practical implementations in industry.

Dylan Shellمشاهده پروفایل

استاد

• Distributed AI
• Multi-Robot Systems
• Robotics
• +۶ مورد دیگر

Dylan Shell is a Professor in the Department of Computer Science & Engineering at Texas A&M University, affiliated with the College of Engineering. His research focuses on distributed AI, multi-robot systems, and autonomous planning. He holds a B.Sc. from the University of the Witwatersrand and advanced degrees from the University of Southern California. Education: PostDoc in Robotics, University of Southern California (2009) Ph.D. Computer Science, University of Southern California (2008) MSc Computer Science, University of Southern California (2006) B.Sc. (Hons) Computer Science, University of the Witwatersrand (2001) B.Sc. Computational & Applied Mathematics and Computer Science, University of the Witwatersrand (2000) Research Interests: Distributed AI and multi-robot coordination Biologically-inspired systems for robotics Autonomous planning and decision-making Crowd modeling and multi-agent systems Recent Research Trends: His work emphasizes sensor-based planning, resilient systems, and privacy-preserving robotics. Recent papers explore optimal planning under uncertainty, combinatorial filter reduction, and multi-robot task allocation. His research often bridges theory and practice, with applications in autonomous systems, disaster response, and human-robot interaction. Awards: Montague-CTE Scholar for teaching excellence (2013–2014) Faculty Service Excellence Award (2010) Advising and Grants: Shell advises a diverse cohort of graduate and undergraduate students in the Distributed AI Robotics Lab . He has led NSF-funded projects on adaptive sampling, contact models, and privacy-aware multi-robot systems. His lab collaborates with industry partners like Intelligent Automation, Inc. Labs & Teams: He directs the Distributed AI Robotics Lab, which develops algorithms for autonomous systems and experiments in multi-robot coordination. The lab emphasizes interdisciplinary work, blending robotics, AI, and control theory.