جست‌وجوی استاد و پژوهشگر

Chua Tat Sengمشاهده پروفایل

استاد

• unstructured data analytics
• multimedia information retrieval
• recommendation systems
• +۶ مورد دیگر

Chua Tat Seng is a Professor at the School of Computing, National University of Singapore (NUS), holding the KITHCT Chair Professorship since 2009. He serves as co-Director of the NExT++ Center, a joint research center between NUS and Tsinghua University focused on Extreme Search. His academic career spans over three decades at NUS, where he has held various leadership positions including Acting Dean of the School of Computing (1998-2000) and Acting Head of the Department of Information Systems & Computer Science (1996-1998). Professor Chua's research spans unstructured data analytics , multimedia information retrieval , recommendation and conversation systems , and emerging applications in e-commerce and fintech . He established the Lab for Media Search (LMS) at the School of Computing and has been instrumental in advancing multimodal learning and search technologies. His work bridges theoretical foundations with practical applications, particularly in developing trustable AI systems for real-world deployment. His recent publications demonstrate a strong focus on large language models for recommendation systems , multimodal learning , and generative AI applications . The research trends show increasing emphasis on LLM-based recommendation, multimodal understanding, and addressing fundamental challenges in AI reliability, fairness, and efficiency. His work spans theoretical advancements in representation learning to practical applications in e-commerce, finance, and healthcare domains. ACM SIGMM Technical Achievement Award 2015 Multiple Best Paper Awards across ACM Multimedia, IEEE Transactions, and MMM conferences (2007-2020) Professor Chua has supervised 37 PhD students since 2004, establishing himself as a dedicated mentor in the academic community. His research has been supported by substantial grants including NExT++ ($12 million), Base Metals Price Forecasting ($200,000), and Multilingual Multimodal Knowledge Graph ($500,000). He maintains active collaborations with Tsinghua University, University of Southampton, and industry partners like Four Elements Capital and Singapore Press Holdings. As co-Director of the NExT++ Center, he leads a major research initiative focused on Web Intelligence and User Empowerment. His visiting professorships at Tsinghua University (2017-present) and Zhejiang University (2021-present) reflect his international impact in the field of multimedia and AI research.

• Mathematics of Data
• Numerical Linear Algebra
• Randomized Algorithms
• +۵ مورد دیگر

Liza Rebrova is an Assistant Professor in the Department of Operations Research and Financial Engineering (ORFE) at Princeton University's School of Engineering and Applied Science. She is also an associated faculty member of the Program in Applied and Computational Mathematics (PACM) and the Center for Statistics and Machine Learning (CSML). Her research focuses on randomized numerical linear algebra and the mathematics of data science, with connections to high-dimensional probability and stochastic optimization. She develops mathematically justified randomized algorithms for large-scale data, particularly interested in settings where data exhibits mathematical structure such as spectral decay, multi-modality, or non-negativity. Her work addresses challenges in data compression, recovery, and optimization under structured noise conditions. Dr. Rebrova's recent publications show a strong trend toward developing robust algorithms for linear systems, tensor data compression, and nonnegative matrix factorization. Her work bridges theoretical foundations with practical applications in machine learning and data science, with emphasis on memory efficiency and handling corrupted or incomplete data. NSF DMS-2309685 (Single PI, 2024-2026): "Outliers are not what they seem: data-aware, flexible, and robust randomized iterative methods" NSF DMS-2108479 (Collaborative, 2022-2024): "Fast, Low-Memory Embeddings for Tensor Data with Applications" (co-PIs Mark Iwen and Deanna Needell) She currently advises three PhD students (Jackie Lok, Shambhavi Suryanarayanan, and Sofiia Shvaiko) and has previously advised Abraar Chaudhry (PhD 2024) and Nicolo Grometto (Masters thesis 2023). At Princeton, she teaches graduate courses including ORF526 (Graduate Probability), ORF387 (Networks), and ORF523 (Convex and Conic Optimization).

Kian-Lee Tanمشاهده پروفایل

استاد

• Query Processing
• Big Data Analytics
• Database Systems
• +۵ مورد دیگر

Kian-Lee Tan is a Tan Sri Runme Shaw Senior Professor and Professor of Computer Science at the School of Computing, National University of Singapore (NUS). He holds a Ph.D. (1994), M.S. (1992), and B.Sc. (1st Class Honours) from NUS. His academic career spans decades of contributions to database systems and data analytics. Ph.D. in Computer Science, National University of Singapore (1994) M.S. in Computer Science, National University of Singapore (1992) B.Sc. in Computer Science (1st Class Honours), National University of Singapore As a leading researcher in database systems, Tan focuses on query processing and optimization in multiprocessor/distributed systems, database performance, security, and multimedia information retrieval. His work extends to computational biology applications like genome databases and real-time influence analysis on social streams. His recent publications highlight trends in GPU-accelerated graph analytics, trajectory pattern mining, and computational journalism. These works emphasize parallel processing, performance optimization, and social/media data analysis. IEEE Technical Achievement Award (2013) President Science Awards, Singapore (2011) NUS Graduate School Excellent Mentor Award (2010/2011) Outstanding University Researchers Award (1997/1998) Tan has supervised numerous research projects and mentored students contributing to database systems. He secured significant grants including a US$1 million Ripple Foundation grant (2024) for financial technology education. His editorial roles include ACM Transactions on Database Systems and IEEE Transactions on Knowledge and Data Engineering. He leads the FinTech Lab at NUS Computing and has served on the VLDB Endowment Board (2012-2017). His work bridges database foundations with emerging applications in AI, fintech, and computational journalism.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۶ مورد دیگر

Timothy M. Hospedales is a Professor of Artificial Intelligence at the Institute of Perception, Action and Behaviour within the School of Informatics at the University of Edinburgh . He also serves as VP AI and Head of Samsung AI Research Centre Europe . His research focuses on efficient and robust AI , emphasizing meta-learning , lifelong transfer-learning , and domain adaptation in both probabilistic and deep learning frameworks. Applications span computer vision , vision and language , reinforcement learning for robotics , and finance . Professor at University of Edinburgh (2020–present) ELLIS Fellow (2021) Head of Samsung AI Research Europe (2020–present) Founding Director of Applied Machine Learning Lab at QMUL (2012–2016) His work includes pioneering contributions to meta-learning , few-shot learning , and self-supervised methods , with notable awards such as the Best Paper Prize at ICML AutoML 2018 and Best Student Paper at ICPR 2018 . He has co-authored 15+ recent papers on topics like Vision-Language Models , Medical AI Fairness , and Diffusion Model Optimization . He served as Program Co-Chair for BMVC 2018 and AAAI 2022 , and authored a book on Visual Adaptation in the Deep Learning Era (2022). Co-Chair, BMVC 2018 Guest Editor, IET CV Special Issue (2016) Keynote Speaker at TASK-CV Workshop (ECCV 2016) Special Issue on Fewer Labels (IEEE PAMI 2020) His leadership extends to organizing workshops like the Learning-to-Learn Workshop at ICLR 2021 , Meta-Learning Workshop at NeurIPS 2020 , and Domain Generalisation Workshop at ICLR 2023 . Current projects include Meta-Omnium (CVPR 2023) for general-purpose meta-learning and MetaAudio (ICANN 2022) for few-shot audio classification benchmarks.

• Financial Engineering
• Operations Research
• Stochastic Modeling
• +۳ مورد دیگر

Liming Feng is an Associate Professor at the Department of Industrial and Enterprise Systems Engineering, University of Illinois at Urbana-Champaign, and has served as Director of the Master of Science in Financial Engineering (MSFE) program since 2022. His academic career at the university spans from Assistant Professor (2006-2012) to his current role. He earned his Ph.D. in Industrial Engineering and Management Sciences from Northwestern University (2006), an M.S. in Mathematics from Northwestern University (2000), and a B.S. in Mathematics from Beijing Normal University (1997). Ph.D., Industrial Engineering and Management Sciences, Northwestern University, 2006 M.S., Mathematics, Northwestern University, 2000 B.S., Mathematics, Beijing Normal University, 1997 Feng’s research focuses on Financial Engineering, Stochastic Modeling, and Computational Methods. He has contributed extensively to quantitative finance, particularly in options pricing, portfolio optimization, and market impact models. His work leverages advanced numerical methods, Fourier transforms, and stochastic calculus to solve complex financial problems. The trends in his publications highlight expertise in Levy processes, jump diffusion models, and numerical algorithms for financial derivatives. He has developed innovative techniques for Bermudan options pricing, discretely monitored barrier options, and portfolio deleveraging strategies. His articles often intersect Operations Research with Financial Engineering, emphasizing computational efficiency and mathematical rigor. ISE Faculty Fellow (2025) INFORMS Financial Services Section Best Student Research Paper (2013) First runner-up of the 2012 Morgan Stanley Prize for Excellence in Financial Markets Feng has served on editorial boards for Operations Research Letters and Mathematical Finance . He has been recognized repeatedly for teaching excellence, including the Sharp Outstanding Teaching Award (2011, 2022) and multiple entries in the List of Teachers Ranked as Excellent by Their Students (2007-2024). He currently leads the MSFE program and contributes to curriculum development through courses like IE 522 (Statistical Methods in Finance) and IE 527 (MSFE Professional Development).

Jakob Foersterمشاهده پروفایل

دانشیار

• Multi-Agent Learning
• Deep Reinforcement Learning
• Human-AI Coordination
• +۵ مورد دیگر

Jakob Foerster is an Associate Professor at the University of Oxford's Department of Engineering Science and a Supernumerary Fellow at St Anne's College. He leads the FLAIR lab, focusing on multi-agent reinforcement learning (MARL), human-AI coordination, and AI foundational research. Previously, he was a Research Scientist at Facebook AI Research (FAIR) and holds a DPhil from Oxford. His work has been cited over 5,000 times and includes seminal contributions like QMIX and the Hanabi Challenge. Research interests span compute-efficient scaling of AI, MARL applications in finance and bio, and ethical AI. He actively collaborates across academia and industry, co-organizing workshops like NeurIPS' Emergent Communication. His lab emphasizes open-ended RL, environment design, and scalable algorithms. Notable awards include the CIFAR AI Chair (2019) and NeurIPS Best Paper Runner-Up (2018). Current efforts include FLAIR's research on zero-shot coordination and the JaxMARL framework. He advises students in Oxford's Engineering DPhil and AIMS CDT programs.

Deval Desaiمشاهده پروفایل

دانشیار

• Law and Development
• Administrative Law
• Regulation
• +۷ مورد دیگر

Deval Desai serves as Reader in International Economic Law at the University of Edinburgh's Edinburgh Law School, a position he has held since 2020 after serving as Lecturer from 2020-2023. His academic career includes research positions at Harvard Law School and the Geneva Graduate Institute. Dr. Desai also brings extensive practical experience from a decade working with the World Bank on rule of law and governance in sub-Saharan Africa, as well as advising the United Nations on rule of law issues. Dr. Desai's educational background includes an MA in History and French Literature from Oxford University, followed by legal training at City University/BPP Law School (PGDipL and PGDipLP), and advanced law degrees (LLM and SJD) from Harvard Law School. At Harvard, he held prestigious fellowships including the Byse Fellowship, Program on Negotiation Next Generation Fellowship, and Institute for Global Law and Policy Fellowship. MA (Oxon), History and French Literature PGDipL, PGDipLP, City University/BPP Law School LLM, Harvard Law School SJD, Harvard Law School Dr. Desai's research centers on law and development, administrative law and regulation, theories of the state, and patterns of knowledge and authority across the Global North and South. His work examines institutional reform processes, particularly how expertise and knowledge operate within rule of law initiatives. His interdisciplinary approach draws from legal and social theory, development studies, international relations, and performance studies, as evidenced in his monograph Expert Ignorance (Cambridge University Press). His recent work focuses on fiscal sociology, administrative law in the Global South, and the politics of social welfare provision. Dr. Desai's publications span leading journals in law, political science, and development studies, revealing a consistent focus on the intersections between legal institutions, governance, and development. His work demonstrates increasing engagement with comparative methodologies and critical perspectives on knowledge production in global governance. Recent articles show particular attention to fiscal policy, welfare systems, and responses to global crises like the Covid-19 pandemic, with comparative studies between India and European contexts. Dame Muriel Spark Medal (2024, Royal Society of Edinburgh) Chancellor's 'Rising Star' Award (2023, University of Edinburgh) Member of the Young Academy of Scotland Fellow of the Royal Society of Arts Fellow of the Young Academy of Europe Dr. Desai has secured significant research funding from diverse international sources, including a £2.1 million Swiss National Science Foundation Sinergia grant titled 'Reversing the Gaze: Towards Post-Comparative Area Studies' where he serves as PI. He also leads projects funded by the British Academy, University of Edinburgh's Big Ideas Accelerator, Scottish Funding Council, and Foreign, Commonwealth, and Development Office. His commitment to mentoring is evident through his recognition with the Susan Manning Award for Inspiring Mentor and his leadership in establishing programs like the Global Scholars' Academy at the Geneva Graduate Institute and the Global Justice summer school at CEU. As editor-in-chief of the Cambridge University Press book series 'Elements in Legal Theory and the Global South' and serving on multiple editorial boards, Dr. Desai plays a significant role in shaping scholarly discourse. His current research projects examine 'loss' in legal institutions guiding transitional processes, constitutional implications of fiscal arrangements in fragile contexts, and social welfare provision in India and Italy.

• Supply Chain Management
• Transportation Systems
• Logistics
• +۴ مورد دیگر

Dr. Jérôme Verny is an Associate Professor specializing in transport, logistics, and supply chain management. He is the founder and director of the research institute in innovative transport and logistics, as well as the co-founder of the DISC Master's program (Digital & Innovative Supply Chain) in Paris and the Mobility Accelerator. His expertise spans digitalization in logistics, blockchain applications, and sustainable development. Educated at the University of Lille Nord de France (PhD in Economics and Management) and engineering schools like École Nationale des Ponts et Chaussées, he advises both public institutions (OECD, EU) and private enterprises on transportation strategies. Research Interests: Dr. Verny focuses on supply chain innovation, digital transformation, and strategic logistics. His work integrates blockchain technology for supply chain transparency, optimizes last-mile delivery in urban environments, and analyzes the impact of geopolitical actors like China on global trade networks. He also explores sustainable practices in transportation, including CO2 reduction strategies and pandemic response logistics. Awards: Recipient of the 2009 OECD-FIT Young Researcher Prize in Transport. His contributions bridge academic research with practical applications in industry, policy, and international trade. Key Activities: Co-founded the DISC Master’s program and leads research initiatives on blockchain adoption, Arctic shipping routes, and Mediterranean trade dynamics. He actively contributes to conferences such as the International Association of Maritime Economists (IAME) and publishes in journals like Structural Change and Economic Dynamics and International Journal of Shipping and Transport Logistics . Labs/Teams: Directs the Institut de Recherche en Transport et Logistique Innovante and collaborates with institutions like the OECD and European Commission on transport policy. His interdisciplinary approach involves engineering, economics, and data science to address global supply chain challenges.

Ricardo Caballeroمشاهده پروفایل

استاد

• Macroeconomics
• Financial Economics
• International Economics
• +۳ مورد دیگر

Ricardo Caballero is the Ford International Professor of Economics at the Massachusetts Institute of Technology's School of Humanities, Arts, and Social Sciences, where he previously served as Chairman of the Economics Department from 2008 to 2011. A leading scholar in macroeconomics and financial economics, his research focuses on safe assets, monetary policy, financial crises, and international economics. His research interests center on the macroeconomic implications of financial frictions, with particular emphasis on safe asset shortages, risk premium dynamics, and monetary policy transmission mechanisms. Caballero's work has pioneered the risk-centric approach to macroeconomics, explaining phenomena such as the Wall Street/Main Street disconnect, global imbalances, and the collapse of interest rates through the lens of safe asset scarcity and risk intolerance. His research bridges theoretical modeling with empirical analysis of financial crises and policy interventions. Caballero's recent publications demonstrate a consistent focus on financial conditions indexing, monetary policy frameworks, and the interaction between financial markets and the real economy. His work increasingly examines how central banks can target financial conditions directly and how risk premia evolve during crises, with applications to pandemic-era economic policy and zero lower bound environments. 2002 Frisch Medal of the Econometric Society Smith Breeden Prize by the American Finance Association Journal of Finance 2014 Brattle Group Prize 2022 Banque de France-TSE Senior Prize in Monetary Economics and Finance Elected Fellow of the Econometric Society (1998) Elected Fellow of the American Academy of Arts and Sciences (2010) As an NBER Research Associate and frequent policy advisor, Caballero has influenced central bank thinking globally through his work on financial stability, monetary policy frameworks, and global imbalances. His research has informed policy discussions at the Federal Reserve, IMF, and multiple central banks regarding crisis management, safe asset creation, and the appropriate monetary response to financial shocks. Caballero maintains active collaborations with major financial institutions and central banks worldwide, translating theoretical insights into practical policy frameworks.

Na Duمشاهده پروفایل

استادیار

• Human-Computer Interaction
• Human Factors in Smart City Technologies
• Human-Centered Computing
• +۴ مورد دیگر

Na Du is an Assistant Professor in the Department of Informatics and Networked Systems at the University of Pittsburgh's School of Computing and Information. She holds a PhD in Industrial & Operations Engineering from the University of Michigan (2021) and a Graduate Certificate in Data Science. Her research focuses on human factors in smart cities, human-centered computing, and user experience design. She is affiliated with the Intelligent Systems Program, Pitt Cyber, and the Center for Governance and Markets. Education: PhD in Industrial & Operations Engineering (University of Michigan, 2021); Undergraduate in Psychology (Zhejiang University). Research emphasizes explainable AI, human-AI teaming, and smart technologies. Recent grants include funding from Honda Research Institute and Pitt Cyber Accelerator for projects on emotions in Human-AI interaction and Metaverse privacy awareness. Her work has been recognized with awards like the HFES Best Paper Award and the IOE Outstanding Student Award. Advising includes PhD students and researchers in human factors and UX design. The HAT Lab under her leadership explores interdisciplinary challenges in human-computer interaction and smart systems.

Euijin Chooمشاهده پروفایل

استادیار

• Data-driven Cybersecurity
• Big Data Analytics
• Graph Mining
• +۶ مورد دیگر

Dr. Euijin (Alley) Choo is an Assistant Professor in the Department of Computer Science at the University of Alberta, specializing in data-driven cybersecurity and big data analytics. Her research focuses on AI-based cybersecurity solutions, anomaly detection in network traffic, and adversarial attacks on federated learning systems. She holds a Ph.D. from North Carolina State University and has held roles at Qatar Computing Research Institute, Korea University, and the University of Missouri-Rolla. Education: Ph.D., Computer Science, North Carolina State University (2015) M.S., Computer Science & Engineering, Korea University (2008) B.S. Dual Degree in Computer Science & Mathematics, Korea University (2006) Research Interests: Security and big data analysis intersections Federated learning security and privacy Anomaly detection in network logs and enterprise systems Malware/phishing detection using graph inference AI-driven threat intelligence aggregation Recent Grants: Mitacs Accelerate Program Grant: Fraud Detection in Financial Graphs ($60,000, 2025) National CyberSecurity Consortium Grant: IntruderInsight ($2M, 2025-2027) NSERC Discovery Grant: Threat Detection Framework ($180,000, 2025-2031) Awards: Best Paper Award at DBSEC 2015 NSERC Early Career Researcher Award (2025) Provost Fellowship (NC State, 2009-2010) Labs/Teams: Leads the Data-driven Network and Cyber Security (DANS) Lab, focusing on federated learning defenses, compromised entity detection, and malicious domain analysis.

Tyler Wryمشاهده پروفایل

دانشیار

• Social Enterprise
• Hybrid Organizations
• Microfinance
• +۷ مورد دیگر

Tyler Wry is an Associate Professor of Management at the Wharton School, University of Pennsylvania . His research focuses on hybrid ventures and social enterprises that balance conflicting commercial and social goals. He has published in top-tier journals such as Academy of Management Annals , Academy of Management Journal , and Organization Science . He serves on the editorial boards of Administrative Science Quarterly , Academy of Management Journal , and Academy of Management Review . Research Highlights : Hybrid organizations, social entrepreneurship, nanotechnology startups, disaster philanthropy, microfinance dynamics, institutional logics, and strategic adaptation during crises. Teaching : Courses on entrepreneurship, social impact, venture creation, and research methods, including project-based learning and hands-on venture development. Scientific Awards include the IACMR Presidential Award, ONE/NBS Research Impact Award, Carolyn Dexter Award, and multiple best paper recognitions. His work has also been recognized through fellowships and teaching awards. Notable Contributions : Demonstrated how corporate disaster involvement accelerates recovery, how patriarchal societal logics affect microfinance lending to women, and how contextual factors shape social-financial tradeoffs in hybrid organizations.

Ying Chenمشاهده پروفایل

دانشیار

• Financial Statistics
• Machine Learning
• Quantitative Finance
• +۷ مورد دیگر

Assoc Prof Ying Chen is an Associate Professor at the National University of Singapore , affiliated with the Department of Mathematics, Asian Institute of Digital Finance (as Academic Director of PhD Program in Digital FinTech 2022–2024), Risk Management Institute (2019–2023), Department of Statistics and Data Science (2019–2023), and Department of Economics (2018–2023). She also contributes to NUS Graduate School for Integrative Sciences and Engineering since 2016. Research Interests include: AI forecasting and quantum computing for finance Nonstationary time series and functional data analysis Energy data analytics and precision medicine Network autoregression and spatial-temporal modeling Explainable AI and citation metrics Portfolio liquidation and market-making algorithms Article Trends demonstrate expertise in: Adaptive forecasting for gas flows and electricity prices Blockchain network influence detection Quantum computing applications in finance Functional autoregression with mixed predictors Credit rating fairness and explainability High-resolution implied volatility modeling Scientific Awards include: ISI Elected Member (2016–) International Statistical Institute Council (2023–2027) IASC Scientific Secretary (2017–2019, 2023–2025) Advisory roles for EU FIN-TECH and xAIM projects

• Nonparametric Statistics
• High Dimensional Data Analysis
• Statistical Methods in Finance
• +۷ مورد دیگر

Vladimir Spokoiny is a Professor at the Departments of Mathematics and Economics of the Humboldt University of Berlin and Head of the Research Group "Stochastic Algorithms and Nonparametric Statistics" at the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) in Berlin, Germany. His research spans multiple areas of statistics, machine learning, and financial mathematics, with significant contributions to nonparametric statistics, high-dimensional data analysis, and statistical methods in finance. Spokoiny received his M.Sc. in applied mathematics from the Moscow Institute of Railway Engineering in 1981 and his Ph.D. in mathematics from Lomonosov Moscow State University in 1988. He completed his Habilitation at Humboldt University in 1996. His academic career includes positions at the All-Union Institute of Railway Transport in Moscow, the Institute for Information Transmission Problems in Moscow, and the Institute for Applied Analysis and Statistics in Berlin before joining the Weierstrass Institute and Humboldt University where he has been a professor since 2002. Spokoiny's research focuses on adaptive nonparametric smoothing and hypothesis testing, high dimensional data analysis, statistical methods in finance, image analysis with applications to medicine, classification, and nonlinear time series. His work often addresses the challenges of nonstationarity in time series data and develops innovative methods for volatility estimation and risk management. He has made significant contributions to the development of adaptive weights smoothing procedures, which have applications in image processing, community detection, and manifold learning. His recent work has expanded into high-dimensional statistics, Bayesian inference, and optimization methods for machine learning, with publications demonstrating novel approaches to Gaussian approximation, Laplace methods, and statistical inference in non-Euclidean spaces. Spokoiny has supervised numerous PhD students including Oliver Reiss, Danilo Mercurio, Ying Chen, Elmar Diederichs, and Mstislav Elagin, whose research has focused on mathematical finance, time series analysis, and statistical methods. He serves as an Associate Editor for The Annals of Statistics (since 2004) and Statistics and Decisions (since 2002), and has previously served on the editorial board of the Journal of Statistical Planning and Inference. His professional activities include reviewing for major statistical journals including Annals of Statistics, Bernoulli, Econometrica, and Journal of American Statistical Association, as well as reviewing grant proposals for the National Science Foundation (USA), German Research Foundation, and Netherlands Organisation for Scientific Research. Spokoiny is a member of several professional societies including the International Statistical Institute, American Statistical Association, Institute of Mathematical Statistics, and Bernoulli Society. He is fluent in Russian (mother tongue), English, and German, and has good knowledge of French. His research group at WIAS focuses on developing novel statistical methodologies with applications across various scientific domains, particularly emphasizing adaptivity and robustness in complex data environments. The group's work has significant implications for financial risk management, medical imaging, and machine learning applications, with recent publications addressing fundamental questions in high-dimensional statistics and nonparametric inference.

• Computer Hardware
• FPGA Acceleration
• Artificial Intelligence
• +۸ مورد دیگر

Professor Wayne Luk is a Professor of Computer Engineering at the Department of Computing, Faculty of Engineering, Imperial College London. He leads the Programming Languages and Systems Section and the Custom Computing Research Group, and directs the EPSRC Centre for Doctoral Training in High-performance Embedded and Distributed Systems and the Centre for Advanced Financial Engineering. He previously served as a Visiting Professor at Stanford University from 2006 to 2009. His research spans FPGA acceleration, quantum computing, deep learning optimization, and algorithm-hardware co-design, with affiliations to the CRUK Convergence Science Centre and the Engineering Secure Software Systems group. His research interests include computational modeling for particle physics, causal discovery in agent-based systems, and high-throughput digital electronics. Notable contributions include FPGA-accelerated algorithms for neural networks, quantum circuit simulation, and Bayesian optimization frameworks. His work emphasizes practical applications of reconfigurable hardware in fields like medical imaging, high-energy physics, and financial systems. Professor Luk is a Fellow of the Royal Academy of Engineering, IEEE, and BCS. His publications focus on advancing hardware-aware machine learning, FPGA-based acceleration techniques, and scalable design methodologies. His research bridges theoretical computer science with applied engineering, addressing challenges in real-time systems, embedded computing, and next-generation computing architectures. His academic leadership includes directing interdisciplinary centers and training programs, fostering collaboration across computing, engineering, and physics. Current projects explore quantum computing tools, causal inference systems, and high-performance graph neural networks for particle physics applications.