جستجوی استاد راهنمای دانشگاهی

Professor David Wagg is a Professor of Nonlinear Dynamics and Departmental Director of Research and Innovation at the School of Mechanical, Aerospace and Civil Engineering, University of Sheffield. His research focuses on nonlinear structural dynamics, digital twins, vibration suppression, and real-time hybrid testing. He holds a BEng and PhD from University College London and previously served as a Professor at the University of Bristol (2008–2013). Notable awards include the EPSRC Advanced Research Fellowship (2004–2009). Education: BEng and PhD in Nonlinear Dynamics from University College London. Research Interests: Digital twins for dynamics applications, nonlinear structural dynamics, vibration control, real-time hybrid testing, and identification methods for nonlinear dynamics. His work emphasizes applying nonlinear models and control strategies to engineering challenges like wind turbines and large civil infrastructure. Grants & Leadership: Co-Investigator for EPSRC grants on CITCoM and Digitwin, coordinator of the Marie Curie ETN DyVirt, and PI for the EPSRC programme on Engineering Nonlinearity (2012–2017). He co-authored Nonlinear Vibration with Control (2015) and edited books on structural dynamics. Lab/Teams: Involved in the Laboratory for Verification and Validation (LVV) and leads research groups focused on digital twin applications, inerter-based systems, and structural health monitoring.

• Nonlinear Dynamics
• Structural Dynamics
• Vibration Control
• +۳ مورد دیگر

Benne de Weger is an Associate Professor in the Department of Coding Theory and Cryptology at Eindhoven University of Technology (TU/e). His research focuses on cryptology, information security, computational number theory, and lattice-based cryptography. He holds MSc and PhD degrees in Mathematics from Leiden University. Prior to his academic position at TU/e, he worked in industry roles as a cryptographic software engineer and information security consultant. Research interests include RSA cryptanalysis, hash collision applications, Diophantine equations, and the abc-conjecture. His work bridges theoretical mathematics with applied cryptography, particularly in lattice-based systems and security protocol analysis. He has contributed to over 98 research outputs, including peer-reviewed articles and conference proceedings. Notable recent work involves lattice vector analysis and hybrid algorithms for cryptographic problem-solving. Benne has supervised 45 academic works but no specific student names are listed in the provided text. No scientific awards are explicitly mentioned. His research collaborations span global institutions, focusing on cryptographic systems and number theory applications.

• Cryptology
• Information Security
• Cryptography Analysis
• +۵ مورد دیگر

Professor Wang Li-Lian is a faculty member in the Division of Mathematical Sciences at Nanyang Technological University (NTU), Singapore. He holds the rank of Professor of Applied Mathematics and has been affiliated with NTU since 2006, progressing through roles including Assistant and Associate Professor before his current position. His research focuses on spectral methods, computational acoustics/electromagnetics, and PDE-based image processing. He has supervised multiple PhD and Master's students, including Zhang Jing, Gu Ying, Yang Zhiguo, and others. Education and career highlights include a Postdoctoral Research Associate at Purdue University (2002-2003), followed by a Visiting Assistant Professorship (2003-2005). His academic work spans spectral element methods, fractional differential equations, and high-order numerical techniques for wave scattering problems. Notable contributions include advancements in spectral-Galerkin methods for nonlocal operators and exact nonreflecting boundary conditions for Maxwell's equations. Research interests emphasize high-accuracy numerical schemes, with applications to fractional PDEs, metamaterial simulations, and image processing. His publications (over 100+ articles) reflect expertise in spectral methods, computational physics, and mathematical modeling. Teaching responsibilities include courses on partial differential equations, numerical analysis, and scientific computing.

• Applied Mathematics
• Numerical Analysis
• Spectral Methods
• +۵ مورد دیگر

Michael A. Newton is a Professor and Chair of the Department of Biostatistics and Medical Informatics at the University of Wisconsin–Madison, School of Medicine and Public Health. His research focuses on statistical methodologies for high-dimensional biomedical data, including cancer biology, immunology, and genomics. He is renowned for developing empirical Bayesian methods, stochastic models, and computational tools for analyzing molecular data. His work integrates statistical theory with interdisciplinary collaborations, contributing to advancements in translational biomedicine. Newton has held prestigious awards, including the Mortimer Spiegelman Award (2003) and the COPSS Presidents' Award (2004). He is an elected Fellow of the American Statistical Association and an elected Member of the International Statistical Institute. He leads the Biostatistics and Epidemiology Research and Design (BERD) core at the Institute for Clinical and Translational Research and is affiliated with the Carbone Comprehensive Cancer Center and the Center for Genome Science and Innovation. His teaching includes advanced courses in computational statistics, Bayesian analysis, and statistical methods in molecular biology. Newton directs graduate programs in Statistics and Biomedical Data Science, emphasizing interdisciplinary training.

• Biostatistics
• Genomics
• Computational Statistics
• +۵ مورد دیگر

Hongyu Liu is a Chair Professor of Applied Mathematics at the Department of Mathematics, City University of Hong Kong. His research spans inverse problems, wave propagation, mathematical materials science, and game theory. He has authored books on spectral theory, inverse scattering, and numerical methods. Education: Not explicitly stated in text but inferred through academic rank and publications. Research Interests include: Inverse Problems and Imaging, Wave Propagation, Analysis and PDEs, Mathematical Materials Science, Scattering Theory, Game Theory, and Mathematical Biology. His work frequently addresses plasmon resonances, cloaking, and scattering theory applications. Publications focus on inverse scattering, mean field games, and elastic systems, with contributions to journals like Inverse Problems and SIAM Journal on Applied Mathematics. He is also an editor for Communications on Analysis and Computation . Advising and grants are not detailed here, but his research group's activities imply significant contributions to collaborative projects.

• Inverse Problems and Imaging
• Wave Propagation
• Analysis and PDEs
• +۵ مورد دیگر

Gunnar von Heijne serves as Professor of Theoretical Chemistry at Stockholm University, a position he has held since 1994, and as Director of the Center for Biomembrane Research since 2006. Previously, he directed the Stockholm Bioinformatics Center from 2000-2006. His academic career includes appointments at Karolinska Institute as Associate Professor (1989-1994), University of Munich as Full Professor of Computer Science (1999-2002), and ETH Zurich as Professor of Computer Science (2003). Dr. von Heijne earned his Ph.D. in Theoretical Physics from the Royal Institute of Technology, Stockholm in 1980. His research focuses on protein sorting and membrane protein biogenesis, with groundbreaking contributions including the discovery of the "(-1,-3)-rule" for signal peptide cleavage sites and the "positive inside" rule for membrane protein topology. He has developed essential bioinformatics tools such as SignalP, TargetP, and TMHMM that are widely used in the field. His work spans both computational methods development and experimental validation across E. coli and eukaryotic systems, with recent emphasis on dual-topology membrane proteins and their evolutionary significance. His research has established fundamental principles in membrane protein biology and provided essential tools for the global research community. Dr. von Heijne's publications have received approximately 75,000 citations with an h-index of 102, reflecting his significant impact on molecular biology, bioinformatics, and membrane protein research. The T. Svedberg Award, The Swedish Biochemical Society (1990) The Göran Gustafsson Prize, The Swedish Academy of Sciences (1995) The Arrhenius Medal, The Swedish Chemical Society (1997) Elected member of the Royal Swedish Academy of Sciences (1997) Elected member of the Academia Europaea (1998) "Excellence" Award, the Swedish Research Council (2002) Honorary Doctorate, Åbo Akademi (2008) The van Deenen Medal, Utrecht University (2009) The Accomplishment by a Senior Scientist Award of the International Society for Computational Biology (2012) Dr. von Heijne has supervised 16 Ph.D. students since 1991 and has served extensively on academic committees, including as faculty examiner for doctoral defenses at multiple international institutions. He has chaired the Nobel Committee for Chemistry since 2007 and has participated on editorial boards of leading journals including Journal of Molecular Biology and EMBO Journal. As Director of the Center for Biomembrane Research, he leads an interdisciplinary team investigating membrane protein structure, function, and biogenesis through integrated experimental and computational approaches, advancing our understanding of fundamental cellular processes.

• Molecular Biology
• Cell Biology
• Bioinformatics
• +۵ مورد دیگر

Dr. Jeffrey Lyons is an Associate Professor in the Department of Mathematical Sciences at The Citadel, part of the Swain Family School of Science and Mathematics. He specializes in fractional calculus, differential equations, and boundary value problems. His teaching spans from foundational courses like College Algebra and Calculus to advanced topics such as Applied Engineering Mathematics and Differential Equations. Dr. Lyons holds a Ph.D. in Mathematics (2011) from Baylor University, with prior positions at Trinity University, University of Hawaii, and Nova Southeastern University. His research focuses on theoretical and applied aspects of fractional boundary value problems, including fixed point theorems and solution differentiation under varying boundary conditions. Notable awards include the 2011 Chancellor’s Award for Excellence in Teaching and several research grants supporting collaborative work across institutions. His recent publications explore topics like positive solutions for fractional BVPs, Caputo derivatives, and dynamic equations on time scales. These contributions highlight advancements in analytical methods and their applications in mathematical modeling. Dr. Lyons actively participates in academic communities, presenting at international conferences such as the 10th AIMS Conference in Madrid, Spain.

• Fractional Calculus
• Differential Equations
• Boundary Value Problems
• +۳ مورد دیگر

Yannis Kevrekidis is a Professor at Princeton University with a distinguished career in computational mathematics and chemical engineering. He is currently a Hans Fischer Senior Fellow at the Technical University of Munich (TUM-IAS) and has held visiting positions at institutions like the Zuse Institute Berlin and Caltech. Education : National Technical University of Athens (Chemical Engineering) University of Minnesota (PhD in dynamical systems) Research Interests : Equation-Free and Variable-Free Modeling Complex Systems Dynamics Multiscale Computation Integration of Machine Learning with Scientific Computing Pattern Formation & Instability Analysis Key Article Trends : Advanced data-driven modeling of dynamical systems Manifold learning for reaction coordinates Projective integration methods Coarse-grained modeling across disciplines Applications in epidemiology, neuroscience, and fluid dynamics Scientific Awards : Guggenheim Fellowship Humboldt Research Award Computing in Chemical Engineering Award (AIChE) Bodossaki Academic Award Allan P. Colburn Award Collaborations : Extensive international collaborations with institutions in Germany, Austria, and the UK Key role in the Complex Systems Modeling and Computation focus group at TUM-IAS

• Complex Systems Modeling
• Computational Mathematics
• Applied Mathematics
• +۷ مورد دیگر

Dimitri Bertsekas is the Fulton Professor of Computational Decision Making at the School of Computing and Augmented Intelligence , Arizona State University (ASU), where he has been a full-time faculty member since 2019. Previously, he held the position of McAfee Professor of Engineering at MIT (1979–2019). He is renowned for his contributions to optimization, control theory, and artificial intelligence. Education: Bachelor's degree in Engineering from the National Technical University of Athens (Greece). MS in Electrical Engineering from George Washington University (1969). PhD in System Science from MIT (1971). Research Interests: Bertsekas' work bridges theory and application in optimization, control systems, and reinforcement learning. His research emphasizes large-scale computational methods, dynamic programming, and stochastic systems, with significant impacts on machine learning and AI. Publications & Awards: He has authored over 20 books and numerous papers, including seminal works like Neuro-Dynamic Programming and Dynamic Programming and Optimal Control . His accolades include the INFORMS John von Neumann Theory Prize (2018), the IEEE Control Systems Award (2022), and membership in the U.S. National Academy of Engineering. Affiliations: Bertsekas founded Athena Scientific (1995), a publisher of technical books, and serves as Chief Scientific Advisor at Bayforest Technologies (London). He has advised numerous students and held editorial roles for scientific journals.

• Optimization
• Control Systems
• Reinforcement Learning
• +۳ مورد دیگر

László Kozma is an Assistant Professor at the Theoretical Computer Science group of the Institute of Computer Science (Freie Universität Berlin). He obtained his PhD from Saarland University under Raimund Seidel, followed by postdoctoral positions at Tel Aviv University and TU Eindhoven. His research focuses on self-adjusting data structures , adaptive algorithms , and combinatorial optimization with applications to problems like the Traveling Salesman Problem, binary search trees, and geometric data structures. Academic Affiliation: Freie Universität Berlin (since 2018) Education: PhD in Computer Science (Saarland University, 2016); postdoc at Tel Aviv University and TU Eindhoven. His work explores the intersection of data structures, combinatorial algorithms, and geometric methods. He has made significant contributions to problems involving pattern-avoidance in inputs, saddlepoint detection , and self-adjusting heaps . Key areas include: Adaptive algorithms for pattern-avoiding inputs Optimal tree and heap structures Geometric and stochastic approaches to optimization Complexity analysis of classical algorithms Recent publications highlight efficient solutions for exponential cut problems (ESA 2025), balanced TSP partitioning (EuroCG 2025), and randomized saddlepoint algorithms (ESA 2024). His research often bridges theory and practice, exemplified by the smooth heap implementation and fun projects like Recursi and Cuckoo Hashing visualization.

• Data Structures
• Algorithms
• Algorithmic Adaptivity
• +۷ مورد دیگر

Andreas Rauber is an Associate Professor in the Department of Data Science at Technical University of Vienna. He serves as Curriculum Coordinator for Bachelor and Master programs in Business Informatics and Data Science, and chairs the Curriculum Commission for Business Informatics. His research focuses on Information Systems Engineering, Logic and Computation, and Visual Computing, addressing challenges in data management, digital preservation, and reproducibility in e-science. He leads projects like OS Trails and FAIR-AI, emphasizing FAIR principles and trustworthy research infrastructures. Rauber has contributed to over 150 publications, including works on data citation frameworks, adversarial ML defenses, and reproducibility in IR. His work bridges technical innovation with policy, exemplified through roles in the EOSC Support Office Austria and RDA Austria initiatives. Key projects include establishing FAIR data practices across universities and advancing digital preservation through repositories like DBRepo. He coordinates international collaborations, such as the EU-funded EOSC-Life and EGI Advanced Computing projects. His teaching spans courses in machine learning, information retrieval, and research methods, fostering next-generation data scientists.

• Data Management
• Digital Preservation
• Machine Learning
• +۴ مورد دیگر

Yong-Bin Kim is a Professor in the Department of Electrical and Computer Engineering at Northeastern University, part of the College of Engineering. He has held prior positions at Intel Corp., Hewlett Packard Co., Sun Microsystems, and the University of Utah. His research focuses on integrated circuit design, nanoelectronics, bio-chip interfaces, and low-power VLSI systems. He has contributed to initiatives like the HPVLSI Lab and Microsystems and Electron Devices Lab. Education includes a B.S. in Electronic Engineering from Sogang University (Seoul, South Korea), an M.S. from the New Jersey Institute of Technology, and a Ph.D. in Computer Engineering from Colorado State University (1996). Research interests encompass high-speed low-power VLSI design, system-on-chip (SoC), physical VLSI CAD, and nanoelectronics. Specific areas include bio-sensor interface circuits, electronic neuron design, and adaptive robot controllers. Key projects involve compact power-efficient integrated circuits and high-speed transceiver design. Outstanding Paper Award, 2020 IEEE ISOCC South Korean Patent for Autonomous Impedance Calibration (2021) Best Paper Award, 2016 International SoC Design Conference Patent for Improved Receiver Circuit (2020) Patent for Method to Detect Trojan Circuits (2018) Advisees include graduate student Yixuan He. He has led research projects funded by Winchester Technology and Hynix Semiconductor, focusing on semi-self-calibration transceivers and tunable RF inductors. Labs include the HPVLSI Lab and Microsystems and Electron Devices Lab at Northeastern University, which focus on high-speed/low-power IC design and microfabrication technologies.

• Integrated Circuit Design
• Nanoelectronics
• High-Speed System Integration
• +۴ مورد دیگر

Liu Derong is a distinguished academic holding the position of Chair Professor at Southern University of Science and Technology (Shenzhen, China) since 2022. He is also a Full Professor at the University of Illinois at Chicago (UIC) since 2006. His academic journey includes roles as Professor at Guangdong University of Technology (2017–2022) and the Chinese Academy of Sciences' Institute of Automation (2008–2016). He earned his Ph.D. in Electrical Engineering from the University of Notre Dame (1994), M.S. from the Chinese Academy of Sciences (1987), and B.S. from Nanjing University of Science and Technology (1982). His research focuses on neural networks, reinforcement learning, intelligent control, and adaptive dynamic programming. He has authored 19 books and 260+ journal papers, contributing significantly to computational intelligence and control systems. Notable works include Adaptive Dynamic Programming with Applications in Optimal Control (2017) and co-editing volumes like Frontiers of Intelligent Control and Information Processing (2014). Liu Derong has held leadership roles in professional societies, including Editor-in-Chief of Artificial Intelligence Review , Deputy Editor-in-Chief of the IEEE/CAA Journal of Automatica Sinica , and President of the Asia Pacific Neural Network Society (2018). He has organized major conferences such as the IEEE World Congress on Computational Intelligence (2014) and received prestigious awards like the Dennis Gabor Award (2018) and membership in Academia Europaea (2021). His contributions span theoretical advancements and practical applications in control systems, with a focus on optimization, robotics, and energy systems. He has also served on editorial boards of leading journals and as a keynote speaker at 30+ international conferences.

• Neural Networks
• Reinforcement Learning
• Intelligent Control
• +۵ مورد دیگر

Professor Vincent Sokalski is a faculty member in the Department of Materials Science and Engineering at Carnegie Mellon University (CMU), specializing in nanoscale magnetic and spintronic materials. He holds a B.S. from the University of Pittsburgh and advanced degrees (M.S., Ph.D.) from CMU. His research focuses on emerging phenomena in magnetic thin films for energy-efficient computing, particularly leveraging skyrmions and the Dzyaloshinskii-Moriya interaction. He chairs the Pittsburgh IEEE Magnetics Society and directs CMU's MSE Department's community and inclusion initiatives. Sokalski also leads outreach efforts through the College of Engineering. Education: Ph.D., Materials Science & Engineering, CMU (2011) M.S., Materials Science & Engineering, CMU (2009) B.S., Materials Science & Engineering, University of Pittsburgh (2007) Research emphasizes low-power memory solutions via spintronic materials, with a focus on stabilizing skyrmions for next-generation computing. His group employs combinatorial material design and advanced imaging techniques like Lorentz transmission electron microscopy. Recent work explores asymmetrical superlattices and DMI-driven phenomena. Publications highlight advancements in domain wall dynamics, skyrmion stabilization, and magnetic symmetry breaking. Awards include the 2021 Provost’s Inclusive Teaching Fellowship. Sokalski advises students like Nisrit Pandey and Maxwell Li, who have won national poster competitions. He co-leads the AMPED Consortium grant for advanced magnetic technologies and oversees MSE’s materials characterization facilities.

• Magnetic Materials
• Spintronics
• Computer Memory
• +۳ مورد دیگر

Giuliano Casale is a Professor in the Department of Computing at Imperial College London, leading the Quality of Service Research Lab (QORE). His research focuses on performance assurance, resource management, and fault-tolerance in distributed systems. He teaches courses on Probability and Statistics and Scheduling and Resource Allocation at undergraduate and Master’s levels. Casale’s work spans cloud computing, edge AI, and machine learning applications in system modeling. Key contributions include methodologies for performance engineering, anomaly detection, and automated resource management in large-scale systems. He actively participates in international conferences, delivering keynote speeches on topics such as performance evaluation and AI-driven systems. His research integrates queueing theory, machine learning, and generative models to address challenges in distributed software systems. Casale also engages in service activities like PhD admissions tutoring and collaborates on projects involving resilience planning and cloud service optimization. His lab, QORE, emphasizes practical solutions for real-world distributed systems, including edge federations and serverless architectures. Casale’s work bridges theoretical performance analysis with industrial applications, contributing to advancements in both academia and industry.

• Performance Engineering
• Cloud Computing
• Resource Management
• +۶ مورد دیگر