جستجوی استاد راهنمای دانشگاهی

• Data Visualization
• Human-Computer Interaction
• Big Data Analytics
• +۱۳ مورد دیگر

Huamin Qu is a Chair Professor in the Department of Computer Science and Engineering at the Hong Kong University of Science and Technology (HKUST). He serves as the Founding Dean of the Academy of Interdisciplinary Studies (AIS), Founding Head of the Division of Emerging Interdisciplinary Areas (EMIA), and was the Founding Acting Head of Computational Media and Arts (CMA) at HKUST(GZ). Qu directs the VisLab and coordinates the Human-Computer Interaction (HCI) group. He obtained his BS in Mathematics from Xi'an Jiaotong University and MS/PhD in Computer Science from Stony Brook University. Qu's research integrates Data Visualization , Human-Computer Interaction , and Human-Centered AI , with applications in urban informatics, social networks, and explainable AI. His work focuses on developing interactive systems for big data analytics, visual storytelling, and AI-driven decision support. Research extends to multimodal communication, fintech, and augmented reality applications. His publications emphasize visual analytics for complex datasets (mobility, social media, financial), interaction techniques for immersive environments, and AI-enhanced visualization tools. Recent works explore explainable AI interfaces and large-scale data communication frameworks. IEEE Visualization Academy (2020) IEEE VGTC Technical Achievement Award AI 2000 Most Influential Scholar (2019, 2023, 2024) 21 Best Paper/Honorable Mention awards IBM Faculty Award (2009) APICTA Merit Award (2015) Yelp Dataset Grand Prize (2018) Qu has advised 48 PhD graduates (21 now faculty at institutions like UC Davis, University of Minnesota, Texas A&M) and 30 MPhil students. He secured major grants including RGC theme-based projects (digital citizenship, air pollution), UGC AoE (slope safety), and China's 973 Program. As VisLab director, he leads 20+ researchers in visualization/HCI projects adopted by Microsoft, IBM, Huawei, and Tencent.

Cao Jiannongمشاهده پروفایل

دانشگاهی

• Cloud and edge computing
• Parallel and distributed computing
• Big data analytics
• +۲ مورد دیگر

Cao Jiannong is currently a Chair Professor and Director of the University Research Facility in Big Data Analytics at Hong Kong Polytechnic University . He has held academic roles including Assistant Professor at City University of Hong Kong and University Lecturer at the University of Adelaide and James Cook University. His research spans Cloud and Edge Computing , Parallel and Distributed Systems , Big Data Analytics , and Wireless Sensing . Ph.D. in Computer Science, Washington State University (1990) MSc in Computer Science, Washington State University (1986) BSc in Computer Science, Nanjing University, China (1982) His work focuses on solving theoretical and practical challenges in distributed computing , mobile cloud systems , and wireless sensor networks . Recent projects include coupled network embedding models for heterogeneous networks and SDN architectures for vehicular communication. His research also pioneers WiFi-based non-invasive health monitoring and fault-tolerant sensor deployment for structural health applications. Dr. Cao's publications highlight advancements in network embedding , edge computing , and WSN optimization . Key papers address multi-user computation partitioning , energy-efficient SHM systems , and consensus protocols for mobile networks. These works have been cited over 15,000 times, with an h-index of 60. Ministry of Education (China) Natural Science Award (2018) Distinguished Member, ACM (2017) Fellow, IEEE (2014) Best Paper Awards at IEEE DSAA, SMARTCOMP, and WCNC Dr. Cao has advised multiple PhD students, including Linchuan Xu and Weigang Wu , whose research on WSN-based SHM and coupled network embedding has practical impact. His leadership includes directing Hong Kong Polytechnic University's Big Data Research Facility and serving on technical committees for IEEE INFOCOM and ACM/IEEE conferences.

Yunjie Yangمشاهده پروفایل

دانشیار

• AI-powered sensing and imaging
• Machine learning
• Soft sensors & electronics for robotics
• +۵ مورد دیگر

Dr. Yunjie Yang is an Associate Professor at the University of Edinburgh's School of Engineering, with affiliations at the Edinburgh Futures Institute (EFI), the Edinburgh Generative AI Laboratory (GAIL), and the Edinburgh Centre for Robotics. He previously held the Chancellor's Fellow in Data Driven Innovation (2018-2023) and Bayes Innovation Fellow (2023-2024) positions. His research focuses on AI-powered sensing and imaging, machine learning, and soft sensors & electronics for robotics. Yang received his PhD in Engineering Electronics from the University of Edinburgh, MSc in Control Science & Engineering from Tsinghua University, and BEng in Measurement & Control Engineering from Anhui University. After his PhD, he worked as a Postdoctoral Research Associate in Chemical Species Tomography before securing his lectureship. His research interests center on developing intelligent sensing systems that replicate human perception capabilities for robotics and intelligent systems. He pioneers flexible sensing and imaging technologies across various scales through innovative multi-modal sensors, soft electronics, and their modeling using machine learning approaches. His work aims to enable autonomous physical artificial intelligence by bridging the gap between robotic systems and human-like perception. Analysis of his recent publications reveals a strong focus on soft robotics perception, particularly through electrical impedance tomography (EIT) and transformer-based architectures. His research spans medical imaging applications, digital twin modeling for industrial processes, and machine learning approaches for sensor data interpretation. The trend shows increasing integration of physics-informed deep learning with traditional tomographic techniques to achieve higher accuracy and efficiency. European Research Council (ERC) Starting Grant (2024) IEEE J. Barry Oakes Advancement Award (2024) IEEE I&M Society Graduate Fellowship Award (2015) Multiple Best Paper Awards Senior Member of IEEE Fellow of the International Society for Industrial Process Tomography Fellow of the Higher Education Academy ESI highly cited papers Dr. Yang serves as Associate Editor for IEEE Transactions on Instrumentation and Measurement and holds editorial positions with Scientific Reports and IEEE Sensors Journal. His research has been licensed to overseas research institutes and industry partners and received wide media coverage including BBC, EFE, USA Today, and STV. He has secured significant grant funding including the prestigious ERC Starting Grant. He leads the Edinburgh SMART Lab (Sensing/imaging + Machine Learning + Robotics), which aims to replicate human perception capabilities for robotics and advance flexible sensing technologies through innovative multi-modal sensors and machine learning approaches. The lab focuses on enabling autonomous physical artificial intelligence with applications spanning medical diagnostics, industrial monitoring, and advanced robotics systems.

Martin Messnerمشاهده پروفایل

استاد

• Management Accounting
• Performance Measurement
• Accounting and Managerial Work

Prof. Martin Messner is a University Professor at the University of Innsbruck, leading the Controlling Institute within the Department of Organization and Learning. His work focuses on management accounting, performance measurement, and the intersection of accounting practices with organizational behavior. He holds editorial leadership roles, including Editor-in-Chief of Accounting, Organizations and Society (2021–present) and previously served on the Management Accounting Research editorial board (2015–2019). Prior to his current position, he was an Assistant Professor at HEC Paris (2006–2011). Research Interests: His research explores performance measurement systems, ethics in management control, and the evolving roles of accounting in organizational narratives and governance. He investigates topics like algorithmic decision-making, narrative reporting, and entrepreneurial governance structures. Professional Activities: Member of the European Accounting Association’s Publications Committee Co-organizer of the MASOP Workshops (2008–2023) His publications span academic journals and edited volumes, addressing themes such as zero-based budgeting, data scientist identities, and the ethical implications of control systems. He actively bridges theoretical and practical accounting issues through interdisciplinary collaboration.

Ian Horrocksمشاهده پروفایل

استاد

• Knowledge Representation
• Description Logics
• Automated Reasoning
• +۴ مورد دیگر

Ian Horrocks is a Professor of Computer Science at the University of Oxford and a Fellow of Oriel College. His research focuses on knowledge representation, description logics, automated reasoning, and semantic web technologies. He has held academic positions at the University of Manchester (2003–2007) and served as Chief Scientist at Cerebra Inc. (2001–2006). Horrocks earned his BSc (1st class), MSc, and PhD in Computer Science from the University of Manchester (1981–1997). His work includes foundational contributions to ontology languages (e.g., OWL) and reasoning systems such as HermiT and ELK. He has supervised over twenty doctoral students and postdoctoral researchers. His honors include Fellowships from the Royal Society (2011), ECCAI (2009), and the British Computer Society (2005). He serves as Editor-in-Chief of the Transactions on Graph Data and Knowledge and leads initiatives in semantic web standards and knowledge graph applications. Key Roles: Editor-in-Chief (Journal of Web Semantics), Co-Chair (W3C OWL Working Group) Grants: EPSRC Senior Research Fellowship (2005), numerous international collaborations Labs: Oxford Semantic Technologies, involvement in projects like RDFox and PAGOdA

Zhou Zhi-Huaمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Data Mining
• +۳ مورد دیگر

Zhou Zhi-Hua is a Professor at Nanjing University's Department of Computer Science & Technology, serving as Standing Deputy Director of the National Key Lab for Novel Software Technology and Founding Director of LAMDA (Institute of Machine Learning and Data Mining). He holds simultaneous fellowships from ACM, AAAI, AAAS, IEEE, IAPR, IET/IEE, and CCF, reflecting his exceptional contributions to computational intelligence. His educational background includes: B.Sc. in Computer Science from Nanjing University (1996) M.Sc. in Computer Science from Nanjing University (1998) Ph.D. in Computer Science from Nanjing University (2000) Zhou's research pioneers fundamental advances in machine learning theory and applications. His seminal work on ensemble methods established new frameworks for classifier combination, while innovations in multi-label learning and anomaly detection addressed critical challenges in complex data analysis. His research bridges theoretical rigor with practical implementations across diverse domains including biometrics, data mining, and computer vision, resulting in over 150 publications and 18 patents. His textbooks "Ensemble Methods" (2012) and "Machine Learning" (2016) have become standard references in the field. Analysis of his publication trajectory reveals sustained leadership in core machine learning challenges: evolving from neural network ensembles (2002) through semi-supervised learning breakthroughs (2005) to foundational work on multi-instance learning (2012) and theoretical margin analysis (2013). His recent focus demonstrates increasing sophistication in handling complex data structures while maintaining theoretical soundness. His scientific excellence is recognized through: National Natural Science Award of China (2013) PAKDD Distinguished Contribution Award (2016) IEEE ICDM Outstanding Service Award (2016) IEEE CIS Outstanding Early Career Award (2013) Microsoft Professorship Award (2006) Simultaneous fellowships from 7 major international societies Zhou provides extraordinary service to the academic community as Executive Editor-in-Chief of Frontiers of Computer Science and Associate Editor-in-Chief of Science China Information Science. He founded the ACML conference and has chaired premier events including ICDM'16 and PAKDD'14. His leadership extends to serving as General Chair for ICDM'16, Program Chair for IJCAI'15 Machine Learning Track, and Area Chair for multiple top conferences. The available text does not specify student advising details or research grants. He directs LAMDA research group at Nanjing University, which has established itself as a global powerhouse in machine learning research, and contributes significantly to the National Key Lab for Novel Software Technology's mission of developing next-generation intelligent systems.

Vladimir Kazeevمشاهده پروفایل

استادیار

• Numerical Analysis
• Tensor Methods
• Partial Differential Equ to the data-driven solution of PDEs
• +۳ مورد دیگر

Vladimir Kazeev is an Assistant Professor at the Faculty of Mathematics, University of Vienna , where he has held a faculty position since 2019. He also held previous academic appointments as a Szegő Assistant Professor at Stanford University (2017–2019), a postdoctoral researcher at the University of Geneva (2015–2017), and research positions at ETH Zurich (2011–2015), Russian Academy of Sciences (2008–2011), and Moscow Institute of Physics and Technology (2009). His research focuses on adaptive, data-driven numerical methods for differential equations, nonlinear low-parametric approximation, and numerical linear algebra. His work intersects computational mathematics, tensor methods, and high-dimensional problem-solving, particularly in the context of partial differential equations (PDEs) and stochastic modeling. The 15 most recent publications reveal a strong emphasis on quantized tensor-structured methods for PDEs, low-rank approximations, and high-dimensional numerical analysis. His research spans theoretical advancements in tensor decomposition, practical applications in chemical reaction networks, and novel discretization techniques for multiscale and degenerate diffusion problems. Scientific awards include the prestigious ETH Medal for outstanding doctoral theses (2016) Russian Academy of Sciences Medal for outstanding student works in mathematics (2011) Advising and teaching activities include supervising Jason Zhu (Stanford, 2019) and Simon Etter (ETH Zurich, 2014), as well as teaching advanced courses in tensor methods, numerical analysis, and PDEs at the University of Vienna, Stanford University, and the University of Geneva. His service to the community includes peer review for 15+ journals and co-organizing minisymposia at SIAM meetings.

Jiannong Caoمشاهده پروفایل

استاد

• Cloud and edge computing
• Parallel and distributed computing
• Big data analytics
• +۲ مورد دیگر

Jiannong Cao is a Chair Professor and Director of the University Research Facility in Big Data Analytics at the Department of Computing, Hong Kong Polytechnic University. He has held various academic roles since 1990, including Assistant Professor at City University of Hong Kong and Lecturer at Australian universities. PhD in Computer Science, Washington State University (1990) MSc in Computer Science, Washington State University (1986) BSc in Computer Science, Nanjing University (1982) His research focuses on cloud and edge computing , parallel and distributed computing , and mobile computing , with significant contributions to wireless sensor networks (WSN) for structural health monitoring (SHM) and software-defined networking (SDN) for vehicular communications. Recent work includes WiFi-based non-invasive health monitoring systems and multi-user computation partitioning in mobile cloud environments. Dr. Cao’s publications demonstrate trends in WSN optimization , SDN architectures , and cognitive modeling for network embedding , with applications in smart healthcare , transportation systems , and industrial IoT . Ministry of Education Natural Science Award (2018) ACM Distinguished Member (2017) IEEE Fellow (2014) Best Paper Awards at IEEE DSAA, SMARTCOMP, WCNC He has mentored numerous researchers, including Linchuan Xu , Xuefeng Liu , and Weigang Wu , who have authored key publications in top venues like ACM WSDM and IEEE INFOCOM . His professional roles include chairing IEEE committees and serving on grant panels for the Hong Kong Research Grant Council.

Alan Bovikمشاهده پروفایل

استاد

• Image quality
• Video compression
• Streaming video
• +۷ مورد دیگر

Alan Bovik is the Cockrell Family Regents Endowed Chair Professor in the Department of Electrical and Computer Engineering at The University of Texas at Austin's Cockrell School of Engineering. He also holds positions at The Institute for Neurosciences and serves as Director of the Laboratory for Image and Video Engineering (LIVE). With a career spanning over three decades at UT Austin, he has progressed from Assistant Professor (1984-1988) to Associate Professor (1988-1994) to his current position as Full Professor (1994-present). Dr. Bovik received his Ph.D. in Electrical and Computer Engineering in 1984 from the University of Illinois, Urbana-Champaign. Professor Bovik's research focuses on image and video quality assessment, visual perception, and digital media processing. He is renowned for developing groundbreaking algorithms including the Structural Similarity (SSIM) index, Visual Information Fidelity (VIF), and various blind quality assessment models like BRISQUE and NIQE. His work bridges engineering and neuroscience, creating perception-based models that optimize visual media delivery while reducing bandwidth consumption. These innovations have had profound industry impact, with his algorithms processing a significant proportion of global internet video traffic. His recent publications demonstrate continued leadership in perceptual quality assessment, with increasing focus on AI-generated content, high dynamic range (HDR) video, and novel applications in medical imaging. The research shows a clear trajectory toward more sophisticated, neural network-based quality metrics that better align with human visual perception across diverse content types. Professor Bovik has received numerous prestigious awards recognizing his contributions to the field: John Fritz Medal (2024) IEEE Edison Medal (2022) IAMB BaM Award (2022) Elected to the United States National Academy of Engineering (2022) Technology and Engineering Emmy Award (2021) IEEE Fourier Award for Signal Processing (2019) Progress Medal from The Royal Photographic Society (2019) Named Honorary Fellow of The Royal Photographic Society (2019) Primetime Emmy Award (2015) Edwin H. Land Medal from The Optical Society (2017) As Director of the Laboratory for Image and Video Engineering (LIVE), Professor Bovik has secured substantial research funding from organizations including the National Science Foundation and the National Institute for Standards and Technologies. His lab has produced numerous influential datasets including the LIVE Image and Video Quality Databases. He has mentored many successful students who have gone on to make significant contributions in academia and industry, though specific student names are not provided in the source materials. The Laboratory for Image and Video Engineering (LIVE) under Professor Bovik's direction has become a world-renowned center for research in perceptual image and video quality. The lab maintains close collaborations with major technology companies including Netflix, Amazon, and YouTube, ensuring that research has direct practical applications. LIVE has developed numerous influential tools and databases that are widely used in both academic research and industrial applications worldwide.

• Data Mining
• Machine Learning
• Clustering Algorithms
• +۶ مورد دیگر

Claudia Plant is a Professor in the Faculty of Computer Science , leading the Research Group Data Mining and Machine Learning . Her research focuses on clustering algorithms, data mining, and machine learning applications in areas like biomedical data, wind energy, and causality inference. She has contributed to projects such as Knowledge-infused Deep Learning for Natural Language Processing (2020–2028) and Hybrid Computational Sciences (2021–2021). Plant has authored over 160 publications, with recent work emphasizing deep learning, anomaly detection, and GPU-optimized algorithms. She actively engages in academic activities, including talks on clustering methods and interdisciplinary projects like Governing Algorithms: The Politics of Data and Decision-Making . Her research interests span clustering algorithms , graph neural networks , causality discovery , and ethical digital transformation . Notable projects include causal analysis of wind farm dynamics and AI-enhanced education tools. Plant’s work bridges computational methods with societal challenges, such as empowering marginalized communities through ethical technology adoption.

Cesare Franchiniمشاهده پروفایل

استاد

• Quantum Materials
• Computational Physics
• First Principles Methods
• +۷ مورد دیگر

Cesare Franchini is a full Professor at the University of Vienna's Faculty of Physics, leading the Computational Materials Physics research group. His work focuses on theoretical understanding and computational modeling of quantum materials using first principles methods, particularly VASP. He maintains an active research program with numerous postdocs, PhD students, and collaborations across multiple institutions including the University of Bologna. Professor Franchini's research centers on quantum materials with many interacting degrees of freedom (lattice, spin, and electron orbital) that enable novel electronic and magnetic phases. His specific interests include metal-insulator transitions, polaron physics (electron-phonon interactions), non-collinear spin orderings, topological Dirac/Weyl phases, multiferroism, and superconductivity. He has increasingly incorporated machine learning data-driven tools and diagrammatic Monte Carlo techniques into his computational approaches. Analysis of his recent publications (2024-2025) reveals a strong focus on polaron physics across multiple material systems, with significant work on hematite, titanium dioxide, and quantum paraelectrics like KTaO3. His research increasingly integrates machine learning with traditional first-principles methods, particularly for studying hydrogen diffusion, surface science phenomena, and electronic structure calculations. There's also substantial work on single-atom catalysis and the application of advanced computational techniques to understand fundamental charge transport mechanisms in energy materials. Professor Franchini actively supervises numerous PhD students and postdocs, including Andrea Angeletti, Viktor Birschitzky, Lorenzo Celiberti, and several others working on diverse aspects of computational materials physics. He leads or participates in major research projects including TACO (Taming Complexity in Materials Modeling), DCAFM (Doctoral College Advanced Functional Materials), and the recently launched Spin-orbit entangled anharmonic polarons project. His group maintains strong collaborations with experimentalists at Charles University, Technical University of Vienna, and other international institutions.

Senad Bećirovićمشاهده پروفایل

استاد

• Artificial Intelligence in Education
• Digital Technologies in Education
• Intercultural Education
• +۳ مورد دیگر

Senad Bećirović serves as a full Professor at the University of Education Lower Austria (Pädagogische Hochschule Niederösterreich), having progressed from assistant professor (2014) to associate professor (2017) before attaining his current rank in 2023. His academic career spans teaching research methodology, pedagogy, and advanced statistics across undergraduate, graduate, and doctoral programs at international institutions, delivered through both online and in-person formats. Bećirović's research focuses primarily on Artificial Intelligence and digital technologies in education , with significant contributions to intercultural education , gifted education , and foreign language pedagogy . His work demonstrates a consistent trajectory toward understanding how emerging technologies reshape educational practices and outcomes. Through four books published by Springer and Nova Science and over 60 articles in top-tier journals, he has established himself as a leading voice in digital educational transformation. His recent publications (2023-2025) reveal a pronounced emphasis on AI applications in education, with multiple studies examining AI literacy, policy frameworks, and implementation strategies across European higher education contexts. This research demonstrates both theoretical depth and practical relevance to current educational challenges. Bećirović actively contributes to the scholarly community as editor and reviewer for numerous Q1 journals including Smart Learning Environments (Springer), Education and Information Technologies (Springer), and TESOL Quarterly (Wiley). He participates in multiple European Commission-funded initiatives such as ENRICH (Enhancing Teaching and Research through Innovative Digital Technologies) and Erasmus+ projects focused on peace learning and English education. His professional activities extend to international collaboration through membership in the European Network Ethical Use of AI and the American Educational Research Association (AERA). He frequently delivers keynote addresses at international conferences, sharing expertise on AI in education, digital transformation, and intercultural competencies.

Celso Grebogiمشاهده پروفایل

استاد

• Nonlinear and complex dynamical systems
• Chaos theory
• Fractal geometry
• +۵ مورد دیگر

Professor Celso Grebogi, Sixth Century Chair in Nonlinear & Complex Systems at the University of Aberdeen, is a globally recognized leader in nonlinear dynamics , chaos theory , and systems biology . He founded the Institute for Complex Systems and Mathematical Biology and co-founded the Aberdeen-Lanzhou-Tempe Research Centre. His career spans institutions including University of Maryland, University of São Paulo, and Max-Planck-Society (External Scientific Member since 1998).

• Computational Systems Biology
• Multi-omics Data Analysis
• Single-cell Sequencing
• +۵ مورد دیگر

Nikolaus (Nik) Fortelny is a Group Leader in Computational Biology at the University of Salzburg, Austria, where he leads the Computational Systems Biology research group within the Department of Biological Sciences & Medical Biology. His research focuses on understanding biological systems at the molecular level through advanced computational approaches. Dr. Fortelny's research interests include: Computational Systems Biology Multi-omics data integration and analysis Single-cell and spatial biology Machine learning applications in biology Network science approaches to biological regulation Immune system modeling His recent publications demonstrate a strong focus on applying computational approaches to understand complex biological systems, particularly in immunology and cellular regulation. His work often involves collaboration with experimental biologists to generate and analyze large-scale datasets from multi-omics experiments collected at single-cell or spatial resolution. Dr. Fortelny is actively involved in research recruitment and is currently hiring for professor positions in Medical Systems Biology and Animal Physiology at the University of Salzburg, with an application deadline of April 19th, 2025. His group regularly seeks students, PhD candidates, postdocs, and staff scientists to join their team.

Vikram Deshpandeمشاهده پروفایل

استاد

• Mechanobiology
• Solid mechanics
• Data-driven mechanics
• +۴ مورد دیگر

Vikram Deshpande is a Professor in the Department of Engineering at the University of Cambridge, UK, where he has been employed since 2010. He also maintains significant international connections, having served as a Visiting Professor at the Technical University of Eindhoven (2009-2017) and previously holding positions at the University of California, Santa Barbara and Brown University. His research spans multiple disciplines within solid mechanics and materials science, focusing on fundamental mechanisms that govern material behavior across different scales. His research interests encompass Mechanobiology , where he explores cellular organization mechanisms; Solid mechanics with applications to impact and failure; Data-driven mechanics approaches; Microarchitectured solids including mechanical metamaterials; Fluid-structure interaction in impact scenarios; Chemo-mechanics of battery materials; and Dislocation mechanics for understanding material deformation. His work uniquely bridges fundamental physics with practical engineering applications, particularly in developing materials with tailored mechanical properties. The analysis of his recent publications reveals a strong focus on mechanical metamaterials, cellular mechanics, and electro-chemo-mechanical phenomena in energy storage systems. His research demonstrates a consistent pattern of addressing fundamental scientific questions while maintaining strong connections to practical engineering applications, particularly in materials design, protective systems, and energy technologies. His publications frequently combine experimental approaches with sophisticated modeling techniques across multiple scales. 2024 Zdeněk P. Bažant Medal for Failure and Damage Prevention 2023 Fellow, Royal Academy of Engineering and International Member US National Academy of Engineering 2022 Warner T. Koiter Medal and William Prager Medal 2022 European Research Council (ERC) Advanced Grant 2021 Gili Agostinelli Prize and IIT Bombay Distinguished Alumnus Award 2020 Fellow, Royal Society of London and Rodney Hill Prize Professor Deshpande has served on numerous editorial boards including the Journal of the Mechanics and Physics of Solids (current Associate Editor), Modelling and Simulation in Materials Science and Engineering, and Proceedings of the Royal Society A. He chairs the Royal Society Sectional Committee 4 and serves on the Advisory Board of the European Mechanics Society EUROMECH. His leadership extends to directing the International Conference on Fracture and chairing the EUROMECH Mechanics of Materials Conference committee. His research group at Cambridge, accessible through cambridgesolidmechanics.co.uk, focuses on developing fundamental understanding of material behavior to enable the design of next-generation engineering materials.