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Dr. Xuhui Fan is a Lecturer in Artificial Intelligence at the School of Computing, Macquarie University. He holds a PhD in Computer Science from the University of Technology Sydney (Australia) and a bachelor's degree in Mathematical Statistics from China. Prior to his current role, he worked as a project engineer at Data61 (formerly NICTA), a postdoc fellow at the University of New South Wales, and a lecturer at the University of Newcastle. His research focuses on Bayesian methods, federated learning, temporal point processes, and neural network architectures. He is affiliated with the Data Horizons Research Centre and the Frontier AI Research Centre at Macquarie University. Key research interests include developing interpretable AI models, advancing federated learning for privacy-sensitive applications, and applying Bayesian techniques to complex data analysis. His work bridges theoretical advancements in machine learning with practical applications in areas such as anomaly detection, generative models, and spatio-temporal data analysis. Dr. Fan’s publications span top-tier conferences like NeurIPS, ICML, and IJCAI, covering topics such as diffusion models, nonstationary processes, and scalable relational models. He has contributed to surveys on Bayesian federated learning and developed novel frameworks for dynamic customer segmentation and network sustainability. His research collaborations span institutions in Australia and internationally, reflecting his expertise in interdisciplinary AI applications. Current projects emphasize ethical AI practices, efficient uncertainty quantification, and scalable inference techniques for large-scale datasets.

• Artificial Intelligence
• Machine Learning
• Bayesian Methods
• +5 more

Prof. Dan Olteanu is a full professor at the Department of Informatics, University of Zurich, leading the Data Systems and Theory (DaST) group. He holds visiting professorships at the University of Oxford and is an emeritus fellow of St Cross College. His academic journey includes a PhD from Ludwig Maximilian University (2005), postdoctoral roles at Saarland University and Cornell University, and prior faculty positions at Oxford (2007–2020). He has also worked in industry with companies like LogicBlox and RelationalAI, focusing on database systems and AI. Education: Bachelor’s in Computer Science, Politehnica University of Bucharest (2000) PhD in Computer Science, Ludwig Maximilian University (2005) Professional Roles: Full Professor, University of Zurich (since 2020) Visiting Professor, University of Oxford Emeritus Fellow, St Cross College Editorial Roles: ACM TODS, VLDBJ, SIGMOD Conference Chair: ICDT Council (since 2022) His research focuses on data systems theory, including query optimization, probabilistic databases, factorized databases, and in-database machine learning. He co-authored the seminal book Probabilistic Databases (2011) and has pioneered algorithms for efficient machine learning over relational data and incremental maintenance of analytical workloads. His work emphasizes scalable, theoretically grounded solutions for real-world data challenges. Awards: ICDT 2019 Best Paper Award ACM SIGMOD 2018 Distinguished PC Member Award ERC Consolidator Grant (2016) Oxford Outstanding Teaching Award (2009) Grants & Funding: Supported by Google, Microsoft Azure, Amazon AWS, EPSRC, and the European Commission. His research bridges academia and industry, with contributions to commercial systems like LogicBlox and RelationalAI. Labs & Teams: Heads the DaST group at Zurich, focusing on data systems theory and applications. Collaborates widely in the database and AI communities.

• Database Systems
• Database Theory
• Artificial Intelligence
• +7 more

Chen Wei Wayne is an Assistant Professor in the Department of Mechanical Engineering at Texas A&M University. His research focuses on generative design AI, machine learning, uncertainty quantification, and advanced manufacturing. He leads the DIGIT Lab, which develops AI methods for design innovation, automation, and manufacturing integration. Education: Ph.D., Mechanical Engineering, University of Maryland, College Park (2019) M.S., Mechanical Engineering, Chongqing University, China (2015) B.S., Mechanical Engineering, Chongqing University, China (2012) Research Interests: Generative adversarial networks (GANs) for design synthesis Data-driven metamaterials and multiscale systems Uncertainty quantification in engineering design AI-driven design automation Awards & Honors: ASME Journal of Mechanical Design Reviewer of the Year Award (2023) ASME DAC Best Paper Award (2022) Journal of Mechanical Design Editors’ Choice Honorable Mention (2021) Lab Activities: Recent lab milestones include successful completion of TAMUQ Summer Research Programs (2024) Hosts undergraduate researchers like Wisam Gadam and Eddie Guerrero

• Generative Design AI
• Machine Learning
• Uncertainty Quantification
• +3 more

Xianyang Zhang is a Professor in the Department of Statistics at Texas A&M University, affiliated with the College of Arts & Sciences. He holds a Ph.D. from the University of Illinois at Urbana-Champaign (2013) and a B.S. from the University of Science & Technology of China (2008). His research focuses on high-dimensional statistics, functional data analysis, kernel methods, and genomics, supported by grants from NIH, NSF, and Texas A&M. Education: Ph.D., Statistics, University of Illinois at Urbana-Champaign, 2013 B.S., Statistics, University of Science & Technology of China, 2008 Research Interests: Xianyang Zhang develops statistical theories and methodologies for complex data structures, including high-dimensional inference, kernel-based testing, change-point detection, and microbiome analysis. His work bridges computational and theoretical statistics, addressing challenges in genomics, omics-wide studies, and spatial statistics. Key Contributions: Developed KDist , a package for kernel and distance-based statistical inference Authored fastcpd for efficient change-point detection Advanced covariate-adaptive FDR control methods for omics studies Contributed to microbiome analysis tools like MicrobiomeStat and LinDA Advising & Grants: Advises multiple Ph.D. students in statistics and interdisciplinary projects Recipient of NIH and NSF grants for high-dimensional statistical research Collaborates with institutions like Mayo Clinic and Chinese University of Hong Kong Labs/Teams: Leads research groups focused on statistical methodology development, software implementation, and applications in computational biology and genomics.

• High-dimensional statistics
• Functional data analysis
• Time series analysis
• +8 more

Shuiwang Ji is a Professor and Truchard Family Endowed Chair in the Department of Computer Science & Engineering at Texas A&M University, where he also holds Presidential Impact Fellow and Chancellor EDGES Fellow titles. He specializes in machine learning, AI for science/engineering, and language models/agents. His research bridges theoretical advances and practical applications in materials science, quantum chemistry, and biomedical engineering. Education: Ph.D. in Computer Science from Arizona State University (2010). Research focuses on equivaraint neural networks for symmetry-aware learning, graph-based molecular modeling, and generative AI for scientific discovery. He develops algorithms that integrate physics principles with deep learning, addressing challenges in materials design, PDE solving, and biomolecular structure prediction. Publications emphasize symmetry-aware architectures (e.g., equivariant Fourier neural operators), efficient interatomic potential computations, and diffusion models for protein/DNA design. Recent work explores trustworthiness in LLMs and causal reasoning in graph neural networks. Awards include NSF CAREER Award (2014), IEEE Fellow (2023), and Texas A&M teaching excellence awards. His work has been recognized in top venues like NeurIPS, ICML, and ICLR. His research group collaborates on projects funded by NSF, NIH, and industry partners, advancing AI applications in healthcare, robotics, and environmental science.

• Machine Learning
• Artificial Intelligence for Science and Engineering
• Language Models and Agents
• +3 more

Jonathan Pakianathan is a Professor of Mathematics at the University of Rochester's School of Arts & Sciences, Department of Mathematics. He holds a PhD from Princeton University (1997) and a BS in Mathematics and Physics from Caltech (1992). His research focuses on algebraic topology, cohomology of groups and Lie algebras, geometric combinatorics, and finite fields. He has held leadership roles, including Director of Graduate Studies (2014–2020) and Director of Undergraduate Studies (2003–2010). Notable awards include the Goergen Award for Excellence in Teaching (2014) and the Sloan Foundation Doctoral Dissertation Fellowship (1996). His research explores applications of topology and algebra in discrete geometry, often collaborating with Alex Iosevich and students. Recent work addresses topics like Fuglede's conjecture over finite fields, geometric configurations, and probabilistic methods in combinatorics. His articles frequently intersect harmonic analysis, group theory, and number theory, reflecting interdisciplinary strengths. Education : PhD, Princeton University, 1997 BS in Mathematics and Physics, Caltech, 1992 Awards : Goergen Award for Excellence in Undergraduate Teaching, 2014 Sloan Foundation Doctoral Dissertation Fellowship, 1996 H. J. Ryser Scholarship, 1991 Grants include an NSA Mathematical Sciences Grant (2016–2017, $110,000 total) with A. Iosevich. He advises numerous PhD students, many of whom now hold academic or research positions. His teaching spans undergraduate to graduate courses, including algebra, topology, and financial mathematics. Research groups and collaborations extend to geometric combinatorics, algebraic topology, and applications in physics and data science. Ongoing projects explore topological methods in discrete geometry and probabilistic structures over finite fields.

• Algebraic Topology
• Cohomology of Groups
• Lie Algebras
• +3 more

Hazem U. Abdelhady is a Postdoctoral Research Fellow at the University of Michigan's School for Environment and Sustainability (SEAS), jointly appointed with the Cooperative Institute for Great Lakes Research (CIGLR). He will join Texas A&M University's Department of Geography as an Assistant Professor in Fall 2025. His research focuses on coastal and hydrodynamic processes, leveraging machine learning, remote sensing, and physics-based modeling to address climate-driven challenges in lakes and coastal systems. Dr. Abdelhady holds a PhD from Purdue University in Hydraulics and Hydrology Engineering and Computational Engineering (2024), an MS in Irrigation and Hydraulics Engineering from Cairo University (2020), and a BS in Civil Engineering (2018). His postgraduate certificate in Geospatial Information Sciences underscores his expertise in spatial data analysis. His research interests include coastal hydrodynamics, physical limnology, and AI-driven environmental modeling. Key projects involve understanding shoreline changes in Lake Michigan, predicting ice cover dynamics, and developing tools for climate adaptation. The Aggie CIS Lab , launching at Texas A&M in Fall 2025, will further advance these efforts, bridging disciplines to enhance coastal resilience. Recent work highlights include studies on climate impacts on lake temperatures, machine learning applications for wave modeling, and automated shoreline detection algorithms. He actively recruits PhD students for Fall/Spring 2026 to investigate Great Lakes dynamics and resilience strategies. Collaborations span academic and governmental institutions, emphasizing interdisciplinary approaches to environmental challenges. His publications span high-impact journals and conferences, reflecting contributions to both theoretical and applied aspects of water resources engineering.

• Coastal Processes
• Hydrodynamic Modeling
• Remote Sensing
• +4 more

Professor Tobias Nipkow is a leading researcher in formal methods and interactive theorem proving at the Technical University of Munich (TUM), affiliated with the School of Computation, Information and Technology and the Department of Computer Science. He is a core developer of the Isabelle proof assistant and leads the Theorem Proving Group. His work has profoundly influenced program verification, semantics, and formalized mathematics. University: Technical University of Munich School: School of Computation, Information and Technology Department: Department of Computer Science Research Group: Theorem Proving Group Key Projects: Isabelle, Archive of Formal Proofs, Concrete Semantics His research focuses on formal verification, higher-order logic, semantics of programming languages, and verified algorithms. He has pioneered the formalization of textbook algorithms, data structures like B+-trees and quadtrees, and logical systems. His work bridges theoretical foundations with practical tools for software correctness. The most recent publications show a strong trend in verifying classical algorithms (e.g., Gale-Shapley, Earley parser), data structures (B+-trees, deques), and decision procedures, primarily using Isabelle/HOL. His contributions span foundational logic, program analysis, and educational approaches to formal methods. Best Paper Award at CADE 28 (2021) Tobias Nipkow has made extensive contributions to advising and collaborative research, co-authoring with numerous researchers and students. He has secured support for large-scale formalization efforts and contributed to major projects like the Flyspeck proof of the Kepler conjecture. His work is supported by ongoing development of the Isabelle framework and the Archive of Formal Proofs. He leads the Theorem Proving Group at TUM, which is central to the development and application of Isabelle. The group fosters international collaboration, contributes to the Archive of Formal Proofs, and advances research in automated reasoning, semantics, and verified systems.

• Formal Methods
• Interactive Theorem Proving
• Program Verification
• +9 more

Kartik Sreenivasan is Associate Professor of Psychology and Associate Program Head for Undergraduate Studies in Psychology at New York University Abu Dhabi (NYUAD), with an affiliation in Biology. He is also a Global Network Associate Professor of Psychology, reflecting his role across NYU’s global campuses. His research is centered on the neurobiological basis of working memory and goal-directed cognition. His educational background includes a BA in Psychology from Yale University and a PhD in Neuroscience from the University of Pennsylvania under Dr. Amishi Jha. He completed postdoctoral training at UC Berkeley with Dr. Mark D’Esposito and joined NYUAD in 2014. Sreenivasan’s research focuses on how the brain maintains and manipulates information in working memory. Key interests include dynamic neural coding, feature binding, effective connectivity, and the transformation of memory into action. His lab employs fMRI, MEG, EEG, TMS, and behavioral paradigms to study both healthy and clinical populations. The most recent publications highlight trends in distributed neural systems, phase-coding mechanisms in working memory, and the role of subcortical structures. His work increasingly emphasizes network-level interactions, oscillatory dynamics, and the flexibility of memory representations under interference and attentional demands. Sreenivasan has received no explicitly mentioned scientific awards in the provided text. He actively mentors PhD students (Ying Zhou, Shanshan Li, Hannah Chu) and supervises undergraduate capstone projects. His lab has been supported by institutional funding, though specific grants are not listed. He teaches core courses such as Biopsychology, Cognitive Neuroscience, and Capstone Research in Psychology and Biology. The Sreenivasan Lab at NYUAD is a vibrant research group focused on understanding the neural underpinnings of cognition. It includes postdocs, research assistants, PhD students, and undergraduates, and has produced numerous publications in high-impact journals. The lab investigates topics such as working memory organization, neural connectivity, and the interplay between perception and memory.

• Working Memory
• Short-term Memory
• Cognitive Neuroscience
• +14 more

Anna R. Karlin is a Professor and the Bill & Melinda Gates Chair in Computer Science & Engineering at the University of Washington's Paul G. Allen School of Computer Science & Engineering. She serves as Associate Director of Graduate Studies and leads research in theoretical computer science within the Theory & Models of Computation focus area. Ph.D. from Stanford University (1987) Former researcher at Digital Equipment Corporation's Systems Research Center (5 years) Professor Karlin's research centers on theoretical computer science, with specific expertise in algorithm design and analysis, particularly probabilistic and online algorithms. Her work spans multiple interdisciplinary domains including algorithmic game theory, economics and computation, data mining, operating systems, networks, and distributed systems. Her research has evolved from foundational algorithmic work to impactful applications in market design, auction theory, and pricing mechanisms. Karlin's publication record demonstrates a consistent trajectory from classical theoretical computer science toward algorithmic game theory and mechanism design. Her recent work focuses on approximation algorithms for NP-hard problems, auction design, revenue maximization, and stable matching problems, with applications in online advertising, network economics, and resource allocation. She has developed influential algorithms for the Traveling Salesman Problem and made significant contributions to understanding interdependent valuations in combinatorial auctions. Bill & Melinda Gates Chair in Computer Science & Engineering Professor Karlin has advised numerous doctoral students throughout her career, with former students including prominent researchers like Jason Hartline, Frank McSherry, and Kira Goldner. Her collaborative research has been supported by various grants, including NSF funding (CCF-1813135 mentioned in her publications). She co-authored the influential textbook Game Theory, Alive with Yuval Peres, which serves as a rigorous introduction to game theory with applications across multiple disciplines. As a leader in theoretical computer science, Professor Karlin maintains active involvement in the Theory of Computation research group at the Allen School, fostering collaboration between theoretical foundations and practical applications in computer science.

• Theoretical Computer Science
• Algorithm Design and Analysis
• Probabilistic Algorithms
• +6 more

Chris Rogers is a Professor of Statistical Science within the Department of Pure Mathematics and Mathematical Statistics (DPMMS) at the University of Cambridge, actively contributing to research at the intersection of probability theory, stochastic analysis, and financial applications. His academic profile reflects deep engagement with mathematical finance and theoretical probability through publications and departmental affiliations. His research spans financial mathematics, probability theory, stochastic analysis, statistics, and mathematical economics, with emphasis on rigorous mathematical frameworks for financial markets. Key themes include option pricing mechanisms, stochastic process modeling, and geometric probability applications, often addressing real-world financial instruments like Asian options and S&P500 index behaviors through advanced probabilistic techniques. Analysis of his 15 most recent publications (2016-2018) reveals consistent focus on stochastic calculus applications in finance, particularly Lévy processes, diffusion models, and optimal stopping problems. His work bridges theoretical probability with quantitative finance, demonstrating expertise in translating complex stochastic phenomena into financial modeling solutions across asset pricing, risk assessment, and market analysis domains. No scientific awards were documented in the provided source material. Information regarding PhD/Master's student supervision, research grants, or collaborative teams was not specified in the available texts, indicating absence of such details in the source documentation.

• Financial mathematics
• probability theory
• stochastic analysis
• +2 more

Martin Rajman is a Senior Scientist at École Polytechnique Fédérale de Lausanne (EPFL) with multiple affiliations across the institution. He holds positions in the School of Computer and Communication Sciences (SIN - Teaching, SCI IC MR Group, SSC - Teaching) as well as in the Vice Presidency for Strategic Development (VPS Artificial Intelligence) and the Vice Presidency for Academic Affairs (SNAI Administration). He serves as the Executive Director of Nano-tera.ch, a large Swiss Research Program funding collaborative multi-disciplinary projects in Health and the Environment. Rajman's research spans the intersection of artificial intelligence, natural language processing, and information retrieval. His work demonstrates a consistent focus on developing practical applications of computational linguistics and machine learning techniques. Early in his career, he contributed significantly to syntactic parsing, stochastic language models, and vector space representations for text. More recently, his research has expanded into deep learning applications for 3D reconstruction, empathetic conversational agents, and distributed analytics systems. His publications reveal a trajectory from foundational NLP research toward increasingly applied and interdisciplinary work connecting AI with healthcare, environmental monitoring, and human-computer interaction. Analysis of his recent publications (2015-2024) shows a clear evolution toward more applied AI research with strong interdisciplinary connections. While maintaining his core expertise in natural language processing and information retrieval, his work has expanded into computer vision, healthcare applications, and sustainable computing. The publications demonstrate increasing collaboration across disciplines, with applications in medical imaging, mental health support systems, environmental monitoring, and human-centered AI. His leadership role in the Nano-tera.ch program reflects this interdisciplinary approach, connecting computing research with real-world challenges in health and environmental contexts. Rajman has mentored several PhD students including Ailomaa Marita, Eckard Emmanuel, Melichar Miroslav, and Veselý Martin. His research has been supported through the Nano-tera.ch program, which has funded more than 100 research projects with over 95 million CHF in public funding. He has also managed more than 20 European projects during his tenure as Director of the EPFL Global Computing Center. As Executive Director of Nano-tera.ch, Rajman leads a significant research initiative connecting EPFL with national and international partners. His work bridges academic research with industry applications, notably through collaborations with eBay on product ranking technology and with Elsevier on article recommendation systems. His leadership extends to managing large-scale research programs while maintaining an active research agenda and mentoring the next generation of computer scientists.

• Artificial Intelligence
• Computational Linguistics
• Natural Language Processing
• +7 more

Professor Ben Liang is a faculty member at the Department of Electrical and Computer Engineering , University of Toronto, holding the L. Lau Chair . He has served on editorial boards of IEEE Transactions on Mobile Computing , IEEE Transactions on Wireless Communications , and Wiley Security and Communication Networks . His research focuses on networked systems , mobile communications , and distributed machine learning , with applications in wireless network virtualization, edge computing, and resource optimization. He explores stochastic scheduling , computation-communication co-design , and multi-resource fair allocation . Key publication trends: Wireless federated learning (2024-2025) Network virtualization and MIMO systems (2022-2025) Online distributed optimization (2023-2025) Stochastic resource management (2020-2024) Scientific Awards: Fellow of IEEE Best Paper Award, ACM MSWiM 2013 INFOCOM 2010 Finalist Ontario ERA Award 2007 IFIP Networking 2005 Best Paper Intel Foundation Graduate Fellowship 2000 Polytechnic University valedictorian 1997 Affiliated with IEEE , ACM , and Tau Beta Pi , he teaches courses like ECE368: Probabilistic Reasoning and ECE421: Machine Learning , emphasizing stochastic networks and random processes .

• Networked Systems
• Mobile Communications
• Distributed Machine Learning
• +4 more

Jia-Jie Zhu is a machine learner and applied mathematician currently serving as head of an independent research group at the Weierstrass Institute for Applied Analysis and Stochastics in Berlin, with an upcoming appointment as tenured associate professor at KTH Royal Institute of Technology in Stockholm. Previously, he conducted postdoctoral research in machine learning at the Max Planck Institute for Intelligent Systems in Tübingen, Germany, following doctoral studies in optimization and numerical analysis at the University of Florida. His research focuses on the mathematical foundations of machine learning and optimization, particularly at the intersection of computational algorithms, dynamical systems, and probability theory. Key areas include: Robust probabilistic machine learning algorithms Kernel methods for distribution manipulation Variational methods for optimization over probability distributions Gradient flows and optimal transport theory Wasserstein and Fisher-Rao geometry Applications in generative modeling and causal inference Dr. Zhu's recent work reveals deep connections between partial differential equations, kernel methods, and machine learning, resulting in theoretically grounded algorithms for handling distribution shifts. His publications demonstrate a consistent trajectory from foundational optimization theory to cutting-edge applications in robust learning and generative modeling, with increasing emphasis on the mathematical structures underlying modern ML systems. He has secured significant research funding, including a DFG Project on 'Optimal Transport and Measure Optimization Foundation for Robust and Causal Machine Learning' within the Priority Program 'Theoretical Foundations of Deep Learning' (SPP 2298), and actively organizes academic events such as the Workshop on Optimal Transport from Theory to Applications (OT-DOM) and upcoming sessions at ICSP 2025 and SwissMAP. As an educator, Dr. Zhu teaches nonparametric statistics at Humboldt University of Berlin and serves as area chair for major conferences including AISTATS 2025. He maintains an active research group with opportunities for master's students, PhD candidates, and postdoctoral researchers interested in the mathematical frontiers of machine learning.

• Machine Learning
• Optimization
• Optimal Transport
• +5 more

Rodrigo González is an Assistant Professor at the Department of Mechanical Engineering, Eindhoven University of Technology, since 2022. His research focuses on data-driven modeling, estimation, and control methods for high-tech precision systems, with applications in motion control and continuous-time system identification. Education: Ph.D. in Electrical Engineering (KTH Royal Institute of Technology, 2022) M.Sc. in Electronic Engineering (Universidad Técnica Federico Santa María, 2016) His work emphasizes continuous-time system identification, state-space modeling, and Bayesian estimation techniques. Key research themes include motion control tuning, multivariable systems, and noise/disturbance modeling in precision engineering applications. Rodrigo has received the Best Electronic Engineering Student Award (2016) and Best Thesis Award from Universidad Técnica Federico Santa María. He has active collaborations with institutions like Universidad Técnica Federico Santa María through visiting researcher appointments. Scientific awards include: Best Electronic Engineering Student Award (2016) Best Thesis Award (Universidad Técnica Federico Santa María)

• Data-driven modeling
• System identification
• Control systems engineering
• +4 more