جست‌وجوی استاد و پژوهشگر

• Functional data analysis
• Non-Euclidean data analysis
• High-dimensional data analysis
• +۳ مورد دیگر

Associate Professor LIN Zhenhua serves as a Presidential Young Professor in the Department of Statistics and Data Science at the National University of Singapore (NUS), with additional affiliation at the Institute of Data Science since 2021. His research develops cutting-edge statistical methodologies for complex data structures across multiple domains. Dr. LIN completed his Ph.D. at the University of Toronto in 2017 under Fang Yao's supervision, following M.Sc. degrees from Simon Fraser University (2013, 2010) and a B.Sc. from Fudan University (2008). Ph.D., University of Toronto, 2017 (Advisor: Fang Yao) M.Sc., Simon Fraser University, 2013, 2010 B.Sc., Fudan University, 2008 His research program spans functional data analysis (developing techniques for curves and surfaces), non-Euclidean data analysis (statistical methods on manifolds), high-dimensional statistics (p > n problems), and constrained statistical modeling. LIN's work bridges theoretical statistics with practical applications through rigorous mathematical frameworks and computational implementations. Recent publications reveal strong emphasis on bootstrap methods for high-dimensional inference, Riemannian geometry approaches for manifold-valued data, and innovative functional data techniques. His research shows consistent output with multiple 2025 publications in top journals including Biometrika, Bernoulli, and Journal of the American Statistical Association. Professional Recognition Presidential Young Professor, NUS (2019-present) Associate Editor, Bernoulli (2022-2024) Associate Editor, Statistics (2023-present) Young Researchers Committee, Bernoulli Society (2020-2024) Professor LIN actively mentors graduate students as evidenced by numerous collaborative publications with trainees. He teaches advanced courses including ST5215 Advanced Statistical Theory, DSA4211 High-dimensional Statistical Analysis, and ST5223 Statistical Models across multiple academic years. His research group develops specialized software packages including hdanova, matrix-manifold, synfd, mcfda, and iRFDA, making advanced statistical methods accessible to practitioners.

• Transport Modelling
• Economic Analysis of Transport
• Sustainable Transportation
• +۳ مورد دیگر

Anders Karlström is a Professor at KTH Royal Institute of Technology, specializing in Transport Modelling and Economics. His research focuses on sustainable transportation systems, emissions reduction, and energy efficiency. Key interests include activity-based modelling, dynamic discrete choice frameworks, and policy analysis for urban mobility. He has contributed to studies on travel behavior, infrastructure planning, and environmental impacts of transport systems across multiple international cities. His work integrates advanced methodologies such as recursive logit models, spatial regression, and machine learning for predictive analytics. Notable research areas involve evaluating weather variability effects on travel patterns, optimizing traffic state estimation with sensor data, and developing scenario-based models for future employment growth. Karlström collaborates with industries to enhance the competitiveness of sustainable transport solutions globally.

Nelly V. Litvakمشاهده پروفایل

استاد

• Algorithms for Complex Networks
• Applied Probability
• Stochastic Operations Research
• +۵ مورد دیگر

Nelly V. Litvak is a Full Professor in Algorithms for Complex Networks at Eindhoven University of Technology (Mathematics and Computer Science). She works on mathematical methods and algorithms for complex networks (social networks, WWW) using random graph models. She joined TU/e as a part-time professor in 2017 after being an Associate Professor at the University of Twente since 2012. Affiliations: 4TU Applied Mathematics Institute, Data Science Center Eindhoven, CTIT Industry Partners: ABN-AMRO Bank, Philips Lighting, Thales Editorial Role: Managing Editor of Internet Mathematics Her research focuses on extracting value from network data across three areas: (1) Information extraction and prediction, (2) Mathematical analysis of network characteristics, and (3) Efficient algorithms for incomplete network data. Key topics include PageRank, HITS algorithm, random graphs, homophilic networks, and network epidemiology. Recent work (2022-2025) spans network growth mechanisms, fairness in ranking algorithms, educational pedagogy, and pandemic forecasting dashboards. She contributes to SDGs through data-driven approaches to societal challenges. Teaching activities include course development at TU/e and earlier institutions, with innovative methods for computer engineering students' statistical understanding.

Rachel Pottingerمشاهده پروفایل

استاد

• Data Management
• Database Systems
• Data Integration
• +۷ مورد دیگر

Rachel Pottinger is a Professor in the Department of Computer Science at the University of British Columbia within the Faculty of Science. She has been at UBC since 2004, progressing from Assistant Professor to Associate Professor in 2012 and to full Professor in 2021. She is affiliated with research centers including CAIDA (Centre for Artificial Intelligence Decision-making and Action) and DFP (Designing for People), and is part of ICICS (Institute for Computing, Information and Cognitive Systems). Her research focuses on data management, particularly semantic data integration, metadata management, and making data more accessible and understandable to users. She leads the Data Management and Mining Lab and has supervised numerous doctoral and master's students. Her work addresses three main areas: helping people understand and explore their data, managing data not well supported by databases, and coordinating data across multiple databases. Her recent publications demonstrate strong trends in database usability, data provenance visualization, query recommendation systems, and building information modeling integration. Her work bridges theoretical database concepts with practical human-centered applications, particularly in making complex data systems more accessible to non-expert users. UBC Computer Science Department Faculty Teaching Award 2013 Computer Science Department Teaching Award 2010 CS Department Teaching Award Denice Denton Emerging Leader Award 2007 Pottinger has supervised numerous PhD and Master's students, with research focusing on data provenance, database usability, and data coordination. She has been involved in significant research projects related to data lakes, open data navigation, and query recommendation systems. Her current research explores table annotation and discovery in data lakes, query refinement for aggregation queries, and query prediction based on past user behavior. She is actively involved in the academic community, serving as Secretary-Treasurer for SIGMOD, on the VLDB Journal editorial board, and as a member of the Computing Research Association's Board of Directors. She previously served as General Co-Chair of SIGMOD 2020 and as Associate Head for the Undergraduate Program of the Department of Computer Science from 2018-2020.

Gerd Stummeمشاهده پروفایل

استاد

• Data Science
• Artificial Intelligence
• Formal Concept Analysis
• +۶ مورد دیگر

Gerd Stumme is a Full Professor of Computer Science at University of Kassel , leading the Chair on Knowledge and Data Engineering . He serves as Executive Director of the Research Center for Information Systems Design (ITeG) , director of the International Centre for Higher Education Research (INCHER) , and founding member of the Hessian Institute for Artificial Intelligence (hessian.AI) . His research spans the intersection of Data Science, AI, and Mathematics , focusing on semantic/structural analysis of social networks, concept hierarchies, and mathematical structures (graphs, ordered sets) for knowledge acquisition. He pioneered work on Semantic Web, Web Mining, Social Bookmarking , and Recommender Systems , and has recently revisited mathematical foundations for knowledge representation. Recent publications analyze ordinal motifs in lattices , controversy mapping , and social network structures , with applications to business models, journalism, and AI. His work often integrates graph theory and formal concept analysis . He is a core developer of BibSonomy , a social bookmarking and publication-sharing system, and has contributed to FolkRank and TriAS algorithms for collaborative knowledge management.

Pietro Ortolevaمشاهده پروفایل

استاد

• Decision Theory
• Behavioral Economics
• Experimental Economics
• +۵ مورد دیگر

Pietro Ortoleva is a Professor of Economics and Public Affairs at Princeton University , affiliated with the Department of Economics and the School of Public and International Affairs. His research spans Decision Theory , Behavioral Economics , Experimental Economics , and Political Economy , with a focus on understanding deviations from traditional economic models. Education: PhD in Economics, New York University (2009); BA in Economics, Università degli Studi di Torino (2004). Professional Roles: Coeditor of the American Economic Review (since 2021), former Editor of the Journal of Economic Theory (2018–2020), and editorial board member for multiple journals. His work investigates stochastic choice , ambiguity aversion , and reference-dependent preferences , often through incentivized experiments. Recent studies include the role of social norms in vaccine uptake , cautious utility models , and non-Bayesian belief updating . He has secured multiple National Science Foundation grants for projects on behavioral economics and decision-making under uncertainty. His 15 most recent publications reveal trends in behavioral decision theory , with emphasis on randomization preferences , time lotteries , cognitive biases , and political behavior . These studies frequently bridge economics, psychology, and public policy.

Pierre-Henri Parisمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Knowledge Graphs
• Information Extraction
• +۲ مورد دیگر

Pierre-Henri Paris is an Associate Professor (Maître de Conférences) at Paris-Saclay University since September 2024. Previously, he worked as a Postdoctoral Researcher at Telecom Paris (Institut Polytechnique de Paris) from September 2020 to August 2024. His academic journey includes a PhD in Artificial Intelligence from Sorbonne University and CNAM (Conservatoire National des Arts et Métiers) completed in 2020. Education: PhD in Artificial Intelligence, 2020, Sorbonne University and CNAM M.Sc. in Artificial Intelligence, 2016, CNAM M.Sc. in Mathematics, 2008, CY Cergy Paris University (incomplete) Pierre-Henri Paris's research focuses on the intersection of artificial intelligence, knowledge representation, and natural language processing. His work particularly emphasizes knowledge graphs, entity linking, and data quality. He has made significant contributions to projects like YAGO 4.5, which enhances knowledge bases with cleaner, logically consistent structures, and MAFALDA, a benchmark for fallacy classification. His research often bridges theoretical foundations with practical applications, particularly in how knowledge can be effectively represented, extracted, and utilized in complex systems. His recent publications reveal a strong focus on knowledge graph enhancement, semantic representation, and natural language understanding. The work on YAGO 4.5 demonstrates his commitment to creating more robust knowledge bases, while MAFALDA shows his interest in the intersection of language understanding and logical reasoning. His research trajectory indicates a consistent exploration of how structured knowledge can be integrated with linguistic analysis to create more intelligent systems. Advising: PhD students: Simon Coumes (2022-), Chadi Helwe (2022-2024), François Amat (2022-) Master's students: Syrine El Aoud (2021), Ayoub Mountassir (2013-2015) Bachelor's students: Khalil Halloul (2013-2014) Pierre-Henri Paris is actively involved in teaching at Paris-Saclay University, where he instructs courses including Introduction to Machine Learning, Introduction to Neural Networks, Algorithms for Data Science, Databases, and Data Warehousing. His teaching reflects his research expertise, providing students with both theoretical foundations and practical applications in artificial intelligence and data science.

Jiaoyan Chenمشاهده پروفایل

مدرس

• Neural-symbolic Knowledge Representation
• Knowledge Graphs
• Ontology Engineering
• +۳ مورد دیگر

Jiaoyan Chen is a Lecturer (Assistant Professor) in the Department of Computer Science at The University of Manchester, set to become a Senior Lecturer (Associate Professor) from July 2025. Previously, she served as a Senior Researcher at the University of Oxford and held postdoctoral roles at Heidelberg University. Her research focuses on neural-symbolic knowledge representation, ontology engineering, and integrating large language models with knowledge graphs. Education: PhD in Knowledge Reasoning and Predictive Analytics (Zhejiang University, 2011-2016) and BEng in Computer Science (Zhejiang University, 2007-2011). She also spent time as a visiting scholar at Zurich University (2014-2015). Research Interests include: Knowledge Graphs, Ontologies, Large Language Models, Retrieval Augmented Generation, and Machine Learning applications in knowledge-aware systems. She leads major grants such as the EPSRC New Investigator Award (EP/Y017706/1) and collaborates internationally through initiatives like the Manchester-Melbourne-Toronto Fund. Teaching: Leads units like 'Data Engineering Technologies' and 'Advanced Topics in Knowledge Representation'. She actively advises PhD students and co-develops tools like OWL2Vec* and DeepOnto. Service roles include Associate Editor of Transactions on Graph Data and Knowledge (TGDK), membership in the EPSRC Peer Review College, and leadership in ontology alignment initiatives like OAEI Bio-ML Track.

Imad El Haddadمشاهده پروفایل

پژوهشگر

• Atmospheric Chemistry
• Aerosol Science
• Air Pollution Source Apportionment
• +۴ مورد دیگر

Dr. Imad El Haddad serves as Group Head of the Molecular Cluster and Particle Processes group at the Laboratory of Atmospheric Chemistry (LAC), part of the Center for Energy and Environmental Sciences at Paul Scherrer Institute (PSI), Switzerland, since 2018. Previously, he held positions as Tenured Scientist and Deputy Head (2018-2019), Senior Scientist in the Smog Chamber group (2015-2018), and Postdoctoral Fellow (2011-2015) at PSI. His research aims to quantify how anthropogenic emissions alter atmospheric pollutant composition and impact Earth's climate and public health through molecular-level analysis using advanced mass spectrometry techniques. His academic background includes: Ph.D. in Atmospheric Chemistry, University of Provence, Marseille (2007-2011) Master's in Environmental Sciences (with distinction, rank 1/9), University of Provence (2006-2007) Master's in General Chemistry (with distinction, rank 1/10), Saint-Joseph University of Beirut (2005-2006) Bachelor of Science in Chemistry (with distinction, rank 1/14), Saint-Joseph University of Beirut (2002-2005) El Haddad's work centers on molecular fingerprinting of atmospheric aerosols , utilizing mass spectrometry (GC/MS, HPLC/APCI-MS2, HPLC/ESI-MS2) to identify primary and secondary molecular markers. He conducts smog chamber experiments to characterize emissions from wood burning, traffic, and cooking processes, determining secondary organic aerosol potential and oxidation state evolution. His group also studies in-cloud aqueous-phase aging and collaborates with global modelers to link aerosol composition to climate forcing and health outcomes like oxidative stress. Recent publications (2025-2024) reveal three dominant trends: (1) rigorous molecular-scale analysis of secondary aerosol formation under varying humidity/temperature, (2) source apportionment breakthroughs in diverse regions (India, Europe, Arctic) using 14C and AMS data, and (3) quantification of health-relevant aerosol properties such as oxidative potential through DTT assays. High-resolution mass spectrometry is a consistent methodological thread across these studies. His scientific awards include: MENRT research fellowship from French ministry of research (2007-2010) Excellence Scholarship (top 1% student, University of Saint Joseph, 2005) Distinction Prize (best student, University of Saint Joseph, 2005) As Group Head, El Haddad oversees the Molecular Cluster and Particle Processes group's research direction and mentorship of junior scientists. While specific grant details are absent from the text, his leadership in multi-institutional publications (e.g., CERN CLOUD, iCUPE) implies active grant management and international collaboration. The group's work bridges laboratory simulations, field deployments, and health/climate modeling to address air pollution complexities. The Molecular Cluster and Particle Processes group develops cutting-edge online/offline mass spectrometers for 1 Hz-resolution atmospheric analysis. They deploy instruments in laboratory smog chamber experiments and global field studies, focusing on molecular marker identification, emission source characterization, and aging process quantification. Collaborations with biochemists and climate modelers extend their impact beyond pure aerosol physics into health risk assessment and policy-relevant climate science.

Chuanhai Liuمشاهده پروفایل

استاد

• Statistical Inference
• Computational Methods
• Bayesian Analysis
• +۴ مورد دیگر

Chuanhai Liu is an Associate Head and Professor of Statistics at Purdue University’s Department of Statistics. He specializes in computational methods for statistical inference, Bayesian analysis, and big data computing systems. His research emphasizes robust algorithms, inferential models, and probabilistic methods. Education: M.S., Wuhan University, Probability and Statistics (1987) M.A., Harvard University, Statistics (1990) Ph.D., Harvard University, Statistics (1994) Research Interests: Computational methods (EM algorithms, MCMC), statistical software systems for big data, theoretical foundations of inference, and prior-free probabilistic modeling. His work bridges computational efficiency and statistical validity, with applications in network analysis, environmental data, and machine learning. Awards: Fellow of the American Statistical Association (2007) Elected Member of the International Statistical Institute (2006) Frank Wilcoxon Prize (2000) Advising & Grants: Supervised 9 Ph.D. students, including interdisciplinary collaborations. His grants focus on distributed statistical computing and inferential frameworks. Active in software development for large-scale data analysis. Labs/Teams: Leads statistical computing initiatives at Purdue, contributing to scalable algorithms and probabilistic inference systems.

V.S. Subrahmanianمشاهده پروفایل

استاد

• Artificial Intelligence
• Cybersecurity
• Machine Learning
• +۳ مورد دیگر

V.S. Subrahmanian is the Walter P. Murphy Professor of Computer Science at Northwestern University and a Faculty Fellow at the Northwestern Buffett Institute for Global Affairs. His research focuses on AI-driven solutions for security challenges, including forecasting terror attacks, preventing poaching, and analyzing social media threats. Education includes a PhD and MS in Computer Science from Syracuse University (NY) and an MSc (Tech.) from Birla Institute of Technology and Science (India). Research interests span AI applications in cybersecurity, predictive modeling for security policy, and machine learning for geospatial/social network analysis. Recent work addresses deepfakes, banking crisis forecasts, and airline profit optimization. Notable publications include works on Android malware detection, adversarial attack defenses, and geospatial conflict analysis. His research has influenced international security policies and industry practices. No scientific awards are listed in the provided text. Advising/grants information appears incomplete in the source material. Active involvement in multidisciplinary teams tackling global security challenges is implied through his institutional affiliations.

Lyudmila Mihaylovaمشاهده پروفایل

استاد

• Signal Processing
• Bayesian Methods
• Monte Carlo Methods
• +۹ مورد دیگر

Professor Lyudmila Mihaylova is a distinguished academic at the University of Sheffield's School of Electrical and Electronic Engineering, where she holds the position of Professor of Signal Processing and Control. She has established herself as a leading researcher in the fields of signal processing, Bayesian methods, and autonomous systems, with significant contributions to particle filtering techniques for intelligent transportation systems. Her work bridges theoretical developments with practical applications across multiple domains including transportation, healthcare, and industrial automation. Prof. Mihaylova's research interests center on nonlinear filtering, sequential Monte Carlo methods, statistical signal processing, and sensor data fusion. Her work spans both theoretical advancements and practical implementations, with particular focus on high-dimensional problems including vehicular traffic flow estimation, image processing, and localization in sensor networks. She has extensive experience with various image modalities such as optical, thermal, LIDAR, SAR, and hyperspectral imaging. Her group actively develops novel methods for autonomous intelligent systems focusing on sensing, tracking, decision making, and machine learning applications. Analysis of Prof. Mihaylova's recent publications reveals a strong trend toward uncertainty quantification in machine learning models, particularly for safety-critical applications. Her work increasingly integrates traditional signal processing techniques with modern deep learning approaches, with applications spanning sewer inspection robotics, medical diagnostics (particularly sleep apnea detection), UAV swarm tracking, industrial manufacturing, and autonomous vehicle systems. A significant portion of her recent research focuses on developing robust methods that can handle incomplete or outlier-corrupted data while providing reliable uncertainty estimates. Among her notable professional achievements: President of the International Society of Information Fusion (ISIF) Senior member of the IEEE Signal Processing Society Associate Editor for IEEE Transactions on Aerospace and Electronic Systems Associate Editor for Elsevier Signal Processing Journal Prof. Mihaylova has successfully mentored numerous PhD students and postdoctoral researchers, many of whom have gone on to prominent academic and industry positions. Her research has been supported by major funding bodies including EPSRC, EU, MOD/DSTL, and industry partners, with recent projects including 'Protecting Environments with UAV Swarms' (InnovateUK, 2022-2024), 'ShiRAS: Towards Safe and Reliable Autonomy in Sensor Driven Systems' (NSF-EPSRC, 2019-2023), and 'Confident safety integration for Cobots' (Lloyd's Register Foundation, 2019-2020). Her research group follows a collaborative approach with the philosophy 'We share knowledge, we grow.' Prof. Mihaylova maintains active research collaborations with institutions worldwide and has held previous academic positions at Lancaster University (2006-2013) and University of Bristol (2004-2006), along with research visiting positions at the University of Ghent, Katholic University of Leuven, and the Bulgarian Academy of Sciences.

• Quantum Photonics
• Quantum Technologies
• Quantum Optics
• +۲ مورد دیگر

Ana Predojevic is a University Lecturer at the Department of Physics, Stockholm University, focusing on quantum photonics and quantum technologies. Her research explores quantum optics, quantum information, and the generation and characterization of entangled light states using semiconductor devices and nonlinear processes. She completed her Habilitation at the University of Innsbruck (2016) and earned a PhD in Quantum Optics from the Institute of Photonic Sciences (ICFO) in Barcelona (2009). Her research career includes prestigious fellowships such as the Elise Richter and Lise Meitner awards from the Austrian Science Fund. Her recent work emphasizes two-photon interference, phonon-induced dephasing, photon indistinguishability, and multipartite entanglement engineering, leveraging cavity quantum electrodynamics and deep learning techniques for quantum state analysis. She has contributed to advancements in micropillar cavity devices for efficient photon pair generation and polarization entanglement studies in quantum dot systems. Scientific Awards: Elise Richter Fellowship (2014) Kanada Prize, University of Innsbruck (2014) Nachwuchsförderung Young Researcher Award (2013) Lise Meitner Fellowship (2010) Generalitat de Catalunya PhD Fellowship (2005) Her current role involves developing quantum light sources for real-world applications in communication, sensing, and simulation, working with the Quantum Photonics group at Stockholm University.

Garvesh Raskuttiمشاهده پروفایل

دانشیار

• Statistical Machine Learning
• Optimization Theory
• Graphical and Network Modeling
• +۵ مورد دیگر

Garvesh Raskutti is an Associate Professor in the Department of Statistics at the University of Wisconsin–Madison, affiliated with the School of Computer, Data & Information Sciences. He holds joint affiliations with the Departments of Computer Science, Electrical and Computer Engineering, and the Wisconsin Institute of Discovery Optimization Group. His research focuses on statistical machine learning, optimization, graphical/network modeling, and information theory, with applications to systems biology and neuroscience. Education: MEng from the University of Melbourne (2008), PhD from UC Berkeley (2012, advised by Martin Wainwright and Bin Yu), and postdoctoral work at SAMSI. Teaching awards include Honored Instructor (2014, 2017) and Madison Teaching and Learning Excellence Fellow (2015). He advises multiple PhD and undergraduate students, including Yuan Li, Hyebin Song, and Lili Zheng. His grants include NGA HM0476-17-1-2003 (Co-PI), ARO W911NF-17-1-0357 (Co-PI), and NSF-DMS 1407028 (Sole PI). Research interests span large-scale statistical inference, computational-statistical trade-offs, and applications in systems biology and neuroscience. His work bridges optimization (e.g., gradient descent, convex regularization), high-dimensional regression, and network analysis. Recent trends in publications emphasize methods for non-convex optimization, sparse models, and network structure learning. Awards include teaching recognition and grants in statistical methodology. Advising spans theoretical and applied projects, with former students like Gunwoong Park (now at University of Korea). Collaborations include interdisciplinary teams in machine learning and signal processing.

Dmitriy Traytelمشاهده پروفایل

دانشیار

• Logic
• Automata Theory
• Runtime Verification
• +۷ مورد دیگر

Dmitriy Traytel is an Associate Professor at the University of Copenhagen's Department of Computer Science since August 2020, where he currently heads the Software, Data, People & Society (SDPS) section. Prior to this position, he worked as a senior researcher (Oberassistent) in the Information Security Group led by David Basin at ETH Zürich. He completed his PhD at TU München under Tobias Nipkow's supervision in 2015. His research focuses on formal methods, particularly logic, automata theory, runtime verification and monitoring, decision procedures, (co)induction and (co)recursion, and interactive theorem proving. Traytel develops formally verified tools for runtime monitoring including VeriMon, TimelyMon, and WhyMon, emphasizing correctness and efficiency in monitoring complex temporal properties. His recent publications (2021-2025) demonstrate a strong focus on first-order temporal logic monitoring, with particular attention to explainable verdicts, scalable parallel implementations, and formal verification of monitoring algorithms. The work spans theoretical foundations in logic and category theory while maintaining practical applications in runtime verification systems. Distinguished Paper Award at POPL 2025 for 'Barendregt Convenes with Knaster and Tarski' Best Student Paper Award at FSCD 2016 Distinguished Paper Award at ATVA 2018 Traytel has supervised numerous PhD, Master's, and Bachelor's students in areas spanning formal verification, runtime monitoring, and theorem proving. His research has been supported through collaborations with major institutions including ETH Zürich and TU München. He actively contributes to the academic community by serving on program committees for major conferences including ITP 2025 and RV 2025. He leads the Software, Data, People & Society section at the University of Copenhagen, focusing on developing formally verified tools for runtime verification that bridge theoretical computer science with practical applications in security and system monitoring.