جست‌وجوی استاد و پژوهشگر

• Enumerative combinatorics
• Permutations
• Bijections
• +۵ مورد دیگر

Sergi Elizalde is a Professor of Mathematics at Dartmouth College, part of the Department of Mathematics within the Faculty of Arts & Sciences. He holds a B.A. from Universitat Politècnica de Catalunya and a Ph.D. from MIT. His research focuses on enumerative and algebraic combinatorics, with applications to computational biology and dynamical systems. Key areas include permutations, lattice paths, bijections, and pattern avoidance. He has supervised numerous PhD students and contributed to over 100 publications, including works on combinatorial properties of triangular partitions and chromosomal instability in tumors. Elizalde's work bridges pure mathematics with interdisciplinary applications, such as modeling tumor evolution. He has received awards like the Karen E. Wetterhahn Memorial Award and holds grants from the Simons Foundation and NSF. His teaching spans courses in combinatorics, graph theory, and algebra, reflecting his commitment to both research and education.

• Probability Theory
• Random Processes
• Statistical Physics
• +۷ مورد دیگر

Cecile Mailler is a Professor in Probability at the University of Bath, affiliated with the Probability Group (Prob-L@B). She holds a PhD from the Laboratoire de Mathématiques de Versailles (2013), supervised by Brigitte Chauvin and Danièle Gardy. Previously, she was an EPSRC Postdoctoral Fellow (2018-2021) and a postdoc at Prob-L@B (2013-2016). Her research focuses on branching processes, random trees, reinforcement models, Pólya urns, random networks, and statistical physics. She serves as an Associate Editor for the Applied Probability Trust and Stochastic Processes and Their Applications . Research interests include: Branching processes and random trees Reinforcement mechanisms and stochastic approximation Random networks and preferential attachment Statistical physics models (zero-range processes, condensation) Satisfiability and analytic combinatorics Recent work emphasizes phase transitions in random trees, reinforcement learning algorithms, and applications to network dynamics. She has organized conferences on random walks and stochastic processes, including a 2026 event at CIRM. Her teaching includes mini-courses on Pólya urns and probabilistic combinatorics at international summer schools. Grants and fellowships include the EPSRC Postdoctoral Fellowship (2018-2021) and contributions to projects on stochastic networks and condensation phenomena. She advises PhD students in areas like multi-city ants processes and Pólya urn dynamics. Her work is supported by collaborations with institutions worldwide, including ETH Zurich, LMU Munich, and the Banff International Research Station.

Richard Montgomeryمشاهده پروفایل

دانشیار

• Extremal Combinatorics
• Probabilistic Combinatorics
• Graph Theory
• +۴ مورد دیگر

Richard Montgomery is an Associate Professor (Reader) at the School of Mathematics, University of Warwick, specializing in Extremal and Probabilistic Combinatorics. He holds an ERC Starting Grant for research on spanning subgraphs in graphs. His academic journey includes a PhD from the University of Cambridge (2015), supervised by Andrew Thomason, and postdoctoral roles at Trinity College, Cambridge, and the University of Birmingham. Research interests focus on combinatorial structures like graphs, hypergraphs, and their probabilistic properties. Key awards include the EMS Prize (2024), Philip Leverhulme Prize (2020), and European Prize in Combinatorics (2019). He currently supervises PhD students Jun Yan, Daniel Iľkovič (jointly with Daniel Kráľ), and Teo Petrov, while mentoring postdoctoral researchers Debsoumya Chakraborti and Natalie Behague. Montgomery's work bridges theoretical combinatorics with applications, publishing extensively in top journals (e.g., Journal of the London Mathematical Society, Geometric and Functional Analysis). His recent research explores Ramsey theory, extremal graph conjectures, and random graph properties. He organizes the Warwick Combinatorics Seminar and has delivered keynote lectures globally, including at the British Combinatorics Conference and European Congress of Mathematics. Grants: ERC Starting Grant (2020), Birmingham Fellowship (2018–2020) Labs/Teams: Leads combinatorics research group at Warwick, collaborates internationally on graph theory projects

Daniel Ueltschiمشاهده پروفایل

استاد

• Statistical Mechanics
• Mathematical Physics
• Probability Theory
• +۳ مورد دیگر

Professor Daniel Ueltschi is a faculty member in the Department of Mathematics at the University of Warwick. His research focuses on statistical mechanics, mathematical physics, probability theory, and analysis. He teaches courses such as MA4L2 Introduction to Statistical Mechanics and MA152 Mathematical Analysis II. He is actively involved in organizing the Warwick Statistical Mechanics Seminar and has contributed to events like the TCC module on Quantum Spin Systems (2022). His work explores topics including quantum Bose gases, random permutations, cluster expansions, and phase transitions. He collaborates with researchers globally and has authored numerous papers on topics ranging from Bose-Einstein condensation to quantum spin systems. His research often bridges theoretical physics and rigorous mathematical analysis. Professor Ueltschi has held positions at the University of Warwick since at least 2009, contributing to both teaching and cutting-edge research. His academic contributions include foundational work on spatial random permutations and rigorous bounds on critical temperatures for dilute Bose gases. He maintains an active role in the academic community, hosting seminars and workshops that foster interdisciplinary discussions in statistical mechanics and related fields.

• Graph Grammars
• Artificial Intelligence
• Algorithms and Data Structures
• +۳ مورد دیگر

Dr. Anna Paszyńska is a Lecturer at Jagiellonian University, specializing in computational science and graph-based algorithms. Her research focuses on graph grammars, artificial intelligence, and numerical methods for solving complex engineering problems. She has contributed to advanced simulations in environmental modeling, tsunami prediction, and adaptive finite element methods. Her work integrates graph theory with computational techniques to optimize solver performance and address real-world challenges like pollution tracking and disaster simulations. Affiliation: Jagiellonian University Key Research Areas: Graph grammars, AI-driven simulations, computational fluid dynamics, and numerical algorithms. Her recent publications highlight innovations in physics-informed neural networks (PINNs) for solving PDEs and optimizing tsunami and pollution models. She actively collaborates on interdisciplinary projects involving environmental science, engineering, and computer science.

Jeffrey A. Bilmesمشاهده پروفایل

استاد

• Machine Learning
• Optimization
• Graphical Models
• +۶ مورد دیگر

Jeffrey A. Bilmes is a Professor in the Department of Electrical and Computer Engineering at the University of Washington, Seattle, with adjunct roles in Computer Science & Engineering and Linguistics. He founded the MELODI Lab, focusing on machine learning, optimization, and data interpretation. Bilmes holds a Ph.D. from UC Berkeley and a Master's from MIT. His research spans graphical models, speech recognition, bioinformatics, and submodular optimization, with notable contributions like the GMTK toolkit and pioneering work in submodularity. He has received prestigious awards including the NSF Career Award (2001), NAE Gilbreth Lectureship (2008), and best paper awards at ICML/NIPS (2013). Bilmes has held leadership roles in UAI and NeurIPS conferences, and his work bridges theoretical foundations with practical applications in computational systems and human-computer interaction. Education: Ph.D., Computer Science, UC Berkeley; M.S., MIT. Research Interests: Machine learning, temporal graphical models, submodularity, speech interfaces, and algorithmic optimization. His work on submodular functions has been recognized with multiple awards, including the 25-year ICS award for his 1997 matrix optimization research. He actively contributes to academic service through conference organization and editorial roles at JMLR. The MELODI Lab develops cutting-edge tools like GMTK, PhiPAC, and Vocal Joystick for real-world applications. Recent Activities: Invited lectures at Yale (2016), Harvard (2015), and IIT Bombay. Co-organized NIPS workshops on discrete optimization (2013–2016). Authored influential papers on submodular optimization, semi-supervised learning, and parallel computing. Current research emphasizes submodular applications in large-scale data management and distributed systems.

Geir Dahlمشاهده پروفایل

استاد

• Combinatorial Matrix Theory
• Discrete Mathematics
• Convexity and Polyhedral Theory
• +۵ مورد دیگر

Geir Dahl is a Professor in the Department of Mathematics at the University of Oslo, specializing in Differential Equations and Computational Mathematics. His research focuses on combinatorial matrix theory, including majorization order, polytopes, and (0,1)-matrices. He teaches courses such as Linear Algebra, Linear Optimization, and Mathematical Optimization. Dahl's work spans topics like doubly stochastic matrices, spectral graph theory, and applications in tennis rankings. His extensive publications include studies on permutation polytopes, matrix classes, and combinatorial optimization. He is actively involved in the Computational Mathematics research group and has collaborated extensively with researchers like Richard Brualdi.

Xiaodong Liمشاهده پروفایل

دانشیار

• High-Dimensional Statistics
• Statistical Learning
• Matrix Completion
• +۷ مورد دیگر

Xiaodong Li is an Associate Professor in the Department of Statistics at the University of California, Davis, within the College of Letters and Science. His research lies at the intersection of high-dimensional statistics, statistical learning, and nonconvex optimization, with applications in matrix completion, phase retrieval, and network analysis. His research interests focus on developing and analyzing statistical methodologies for modern data science challenges. He investigates high-dimensional inference , nonconvex optimization algorithms , and robust statistical methods for complex data structures. His work often bridges theoretical guarantees with practical algorithmic implementation, particularly in unsupervised learning and signal processing. The recent publications highlight a strong trend toward theoretical analysis of nonconvex methods in matrix estimation, community detection in networks , and high-dimensional signal recovery . His work frequently appears in top-tier journals such as the Annals of Statistics , IEEE Transactions on Information Theory , and Journal of Machine Learning Research , reflecting consistent contributions to both statistical theory and machine learning. Scientific Awards: No scientific awards mentioned in the provided text. Xiaodong Li has actively advised multiple PhD students, including Ji Chen (2020), Xingmei Lou (2022), Yucheng Liu (2022), and Xiaohan Hu (2024), with Zhentao Li currently under supervision. His research is supported by publications rather than explicitly mentioned grants, indicating a strong publication-driven academic profile. He teaches a wide range of courses from undergraduate probability and regression to graduate-level mathematical statistics and longitudinal data analysis. While no specific lab or research group name is mentioned, his collaborative work with researchers such as Emmanuel J. Candes, T. Tony Cai, and Yudong Chen suggests active participation in the broader statistical learning and high-dimensional data analysis community. His recent work on polytree models and community detection indicates ongoing exploration into structured high-dimensional models and network inference.

Dominik Mehlemمشاهده پروفایل

پژوهشگر

• Point Cloud Compression
• Data Compression
• 3D Graphics
• +۴ مورد دیگر

Dominik Mehlem is a Researcher at the Institute of Communications Engineering (RWTH Aachen University), specializing in point cloud compression and video coding . He contributes to the ISO/IEC MPEG standardization process for 3D data compression. Research Focus : Development of compression algorithms for point cloud data, analysis of Versatile Video Coding (VVC) vs. HEVC in video-based point cloud compression (V-PCC), and inter-frame prediction techniques like 3D motion compensation in geometry-based point cloud compression (G-PCC). Email : mehlem@ient.rwth-aachen.de Office Hours : Mon-Thu, 10:00–12:00 and 14:00–16:00 (by appointment) Key Publications : Recent works in Visual Communications and Image Processing (VCIP) and International Picture Coding Symposium (PCS) , focusing on entropy coding, graph-based methods, and VVC optimization for point cloud compression.

Sidney Ira Resnickمشاهده پروفایل

استاد

• Applied Probability
• Extreme Value Theory
• Heavy-Tailed Distributions
• +۵ مورد دیگر

Sidney Ira Resnick is the Lee Teng-Hui Professor in Engineering and a distinguished faculty member in the School of Operations Research and Information Engineering at Cornell University. He has held visiting appointments at institutions such as the University of Amsterdam, Erasmus University Rotterdam, and Technical University Munich (as John Von Neuman Visiting Professor). His academic career spans roles including Director of Cornell’s School of Operations Research and Information Engineering (2004). Education: B.S. in Mathematics from Queens College (1966), M.S. and Ph.D. in Statistics from Purdue University (1968, 1970). Research interests focus on applied probability, extreme value theory, heavy-tailed distributions, and their applications in data networks, finance, and risk analysis. His work emphasizes the influence of heavy tails in modeling systems where large values have significant impacts. Recent projects include studying preferential attachment networks, reciprocity in directed networks, and hidden regular variation. Publications highlight contributions to stochastic models, data network analysis, and extreme value theory. Notable works include *Heavy-Tail Phenomena: Probabilistic and Statistical Modeling* (2007) and *Extreme Values, Regular Variation and Point Processes* (1987, reprinted 2008). He has authored over 170 papers and four books. Awards include Fellow of the Institute of Mathematical Statistics, TUM Distinguished Affiliated Professor (2012), and the Marcel Neuts Award for Best Paper in Stochastic Models (2003). Teaching spans courses like Engineering Stochastic Processes, quantitative risk analysis, and time series analysis. He serves as an editor for journals such as *Stochastic Models* and *Extremes*, and on editorial boards for Springer and Birkhauser series.

• Applied Statistics
• Machine Learning
• Sequential Decision-Making
• +۶ مورد دیگر

Amitis Shidani is a DPhil in Statistics student at the Department of Statistics, University of Oxford, supervised by Prof. Arnaud Doucet and Prof. George Deligiannidis. She is broadly interested in Applied Statistics and Machine Learning, with a focus on Sequential Decision-Making, Bandit Learning, and the integration of optimization, robust statistics, fairness, and causal inference for real-world applications. Education: Bachelor’s in Electrical Engineering and Computer Science (double major) from Sharif University of Technology, where she worked on Causal Inference in Computational Genomics with Babak Khalaj. Professional Background: Lead data scientist at CafeBazaar (Iranian Android app-store with 40M+ users) for two years prior to Oxford. Her research interests span Sequential Decision-Making, Game Theory, Optimization, Robust Statistics, Conformal Prediction, and Causal Inference. She is affiliated with the Computational Statistics and Machine Learning and Statistical Theory and Methodology research groups. Recent publications highlight her work in diverse areas, including martingale concentration inequalities, AI deployment in healthcare, transformer architectures, and theoretical machine learning. Her office is located at G.01, and she uses she/her pronouns.

Michele Peruzziمشاهده پروفایل

استادیار

• Spatial Statistics
• Bayesian Methods
• Environmental Health
• +۴ مورد دیگر

Overview Michele Peruzzi is an Assistant Professor of Biostatistics at the University of Michigan–Ann Arbor's School of Public Health. His research focuses on Bayesian methods for spatial and multivariate data, particularly in environmental health applications, high-resolution sensor data, and scalable statistical computing. He develops software tools like the meshed and spamtree R packages to address computational challenges in large-scale analyses. Education PhD in Statistics, Università Bocconi (2019) MSc in Economic and Social Sciences, Università Bocconi (2013) BSc in Economic and Social Sciences, Università Bocconi (2010) Research Focus Peruzzi's work addresses three core areas: (1) scalable Bayesian geostatistical methods for environmental sensors (e.g., satellite imaging, air quality), (2) response surface modeling for large health datasets, and (3) climate change impacts on ecosystems and human health. His methods emphasize computational efficiency and reproducibility. Key Contributions Developed meshed Gaussian processes for partitioned domain analysis Pioneered spatial multivariate trees for big data regression Advanced phenology modeling via satellite data for continental-scale vegetation analysis Affiliations Based in Ann Arbor, MI, Peruzzi holds office in SPH II M4531. His work intersects statistics, environmental science, and public health, with applications in climate change mitigation and precision medicine.

Ian Luddenمشاهده پروفایل

استادیار

• Combinatorial Optimization
• Algorithmic Game Theory
• Graph Theory
• +۵ مورد دیگر

Ian Ludden is an Assistant Professor of Computer Science and Software Engineering at Rose-Hulman Institute of Technology. He holds a PhD in Computer Science (Theory and Algorithms) from the University of Illinois Urbana-Champaign (2023) and a BS in Computer Engineering and Mathematics from Rose-Hulman Institute of Technology (2016). His research focuses on combinatorial optimization, algorithmic game theory, graph theory, and their applications in political redistricting, healthcare analytics, and sports analytics. Education: PhD in Computer Science, University of Illinois Urbana-Champaign, 2023 Bachelor of Science in Computer Engineering and Mathematics, Rose-Hulman Institute of Technology, 2016 Research Interests: Optimization in political redistricting (e.g., fairness metrics, compromise protocols) Algorithmic game theory applications in public policy Healthcare analytics (e.g., pandemic mortality modeling) Graph theory and 3D zoning optimization Sports bracket analysis and prediction Recent Work Trends: Recent publications highlight optimization frameworks for redistricting in Missouri and Arizona, transfer distance midpoints for compromise plans, and aerosol risk models for airplane seating. His work bridges theoretical computer science with practical policy applications. Awards: NSF Graduate Research Fellowship Illinois CS Outstanding Teaching Assistant (Lifetime) Grants & Projects: His NSF-funded research on redistricting protocols and frameworks (e.g., Optimap) demonstrates contributions to both theoretical and applied domains. Collaborations include work with the Missouri League of Women Voters on redistricting impact analysis. Labs/Teams: Active in computational policy research and optimization methodologies, with GitHub projects focusing on redistricting games and bracket analytics.

Zhenqin Wuمشاهده پروفایل

استادیار

• AI/ML for Computational Biology
• Spatial Omics
• Multi-modal Spatial Biology
• +۳ مورد دیگر

Zhenqin Wu is an Assistant Professor at the Department of Computer Science, University of Hong Kong. He holds a PhD from Stanford University (2016–2022) under advisors Prof. James Zou and Dr. Vijay Pande, with research focused on applying AI/ML to computational biology and spatial omics. His work bridges machine learning and spatial biology, developing tools for analyzing spatial proteomics, transcriptomics, and histopathological imaging. Education: PhD in Computer Science, Stanford University (2016–2022) BSc in Chemistry, Peking University Research Interests: AI-driven spatial omics analysis Multimodal data integration in biology Machine learning for medical imaging Computational modeling of cellular microenvironments His recent work highlights include developing the ROSIE AI framework for immunofluorescence generation and the CORAL method for spatial multiomics integration. Articles focus on immunotherapy response prediction, disease biomarker discovery, and interpretable models for tissue structure analysis. Professional Affiliations: Scientific Advisor, Enable Medicine (biotechnology startup) AI Fellow, Genesis Therapeutics He actively mentors PhD/postdoc researchers and collaborates on AI-driven drug discovery projects. His lab focuses on translating spatial omics insights into clinical applications.

Huijuan Wangمشاهده پروفایل

دانشیار

• Temporal Network Analysis
• Higher-Order Network Modeling
• Information Diffusion
• +۳ مورد دیگر

Huijuan Wang serves as Associate Professor in the Multimedia Computing Group within the Department of Intelligent Systems at Delft University of Technology's Faculty of Electrical Engineering, Mathematics and Computer Science. With over 20 years of affiliation at TU Delft, she completed both her Master's degree and PhD at this institution before rising to her current faculty position. Her research focuses on temporal network analysis, higher-order network modeling, and information diffusion dynamics. Professor Wang investigates epidemic spreading models, network prediction algorithms, and complex system behaviors through both theoretical frameworks and practical applications. Her work bridges computer science, mathematics, and real-world network phenomena including urban systems and social dynamics. Analysis of her recent publications reveals a strong emphasis on temporal network properties, higher-order dependencies, and predictive modeling. Her research demonstrates consistent innovation in extracting diffusion backbones, measuring network dissimilarity, and developing memory-based prediction techniques for evolving networks. Co-founded Dutch Network Science Society (2018) Established Young Talent Prize recognizing emerging researchers Developed free community events fostering industry-academia collaboration Professor Wang actively mentors PhD candidates, emphasizing genuine interest in students' development as she experienced during her own doctoral studies. She champions initiatives promoting social safety, trust-building, and work-life balance within academic environments while leading efforts to establish processes addressing social safety concerns through cultural transformation.