جست‌وجوی استاد و پژوهشگر

John Stockieمشاهده پروفایل

استاد

• Fluid Dynamics
• Industrial Mathematics
• Scientific Computing
• +۷ مورد دیگر

John Stockie is a Professor in the Department of Mathematics at Simon Fraser University (SFU), part of the Faculty of Science. He earned his Ph.D. in Applied Mathematics from the University of British Columbia in 1997. His research focuses on fluid dynamics, industrial mathematics, and scientific computing, with an emphasis on developing numerical methods for solving complex physical systems governed by nonlinear PDEs. He combines asymptotic analysis, finite volume methods, and adaptive mesh techniques to address applications in fuel cells, biofluid dynamics, and porous media flow. Stockie’s work bridges theoretical and applied mathematics, addressing real-world challenges such as hydrogen fuel cell efficiency, tree sap exudation mechanisms, and glacier dynamics. His teaching includes courses like MACM 316 Numerical Analysis I. He has contributed to open-access educational resources, including a clicker question bank for numerical analysis. His research trends span environmental modeling (e.g., atmospheric dispersion, glacier simulation), biomedical applications (biofilm dynamics), and industrial problems (emission estimation). He emphasizes interdisciplinary collaboration, often involving computational tools like level set methods and Bayesian inversion approaches. Stockie’s lab focuses on developing robust numerical algorithms and their application to fluid-structure interactions, multiphase flows, and environmental systems. His work has implications for climate science, biomedical engineering, and sustainable energy technologies.

Huixia Judy Wangمشاهده پروفایل

استاد

• Quantile regression
• Extreme value theory
• Bioinformatics
• +۴ مورد دیگر

Huixia Judy Wang is a Professor in the Department of Statistics at The George Washington University since 2017. Previously, she held positions at North Carolina State University from 2006 to 2017, advancing from Assistant to Associate Professor. Her expertise spans quantile regression, extreme value theory, bioinformatics, and longitudinal data analysis with applications in environmental science, health, and finance. Education: Ph.D., Statistics, University of Illinois at Urbana-Champaign, 2006 M.S., Statistics, Fudan University, 2002 B.S., Statistics, Fudan University, 1999 Research focuses on developing robust statistical methods for analyzing extremes, high-dimensional data, and complex dependencies. Key themes include quantile-based approaches for spatial/temporal data, survival analysis with detection limits, and subgroup identification in clinical trials. She has contributed to methodological advancements in copula models, threshold regression, and Bayesian quantile inference. Her publications address applied problems such as climate variability impacts, biomedical data interpretation, and financial risk modeling. She maintains an active record of interdisciplinary collaboration across environmental science, genomics, and public health.

Henk Nordeمشاهده پروفایل

استاد

• Cooperative Game Theory
• Non-Cooperative Game Theory
• Operations Research
• +۴ مورد دیگر

Henk Norde is a Full Professor in the Department of Econometrics and Operations Research at Tilburg University’s Tilburg School of Economics and Management (TiSEM). His academic career includes roles as Head of Department (2013–2018), Academic Director of BSc/MSc programs, and Chair of Faculty Council (2010–2012). He specializes in cooperative and non-cooperative game theory, with notable contributions to cost allocation, risk capital distribution, and graph-based game models. Education and career milestones include: PhD supervision of seven scholars, including Tim Boonen and Stefano Moretti. Extensive teaching experience with awards for excellence in large-group instruction and course development (e.g., Mathematics for E&BI, Wiskunde 1/2). Administrative roles: Member of Faculty Assessment Committee, Educational Board, and Exam Committee. Research focuses on: Cost allocation in spanning tree problems and multi-glove games. Risk capital allocation using generalized Aumann-Shapley principles. Properties of weighted minimum coloring and generalized additive games. Publications span 30+ years, emphasizing coalitional stability, merge-proofness, and game-theoretic applications. Awards include multiple 'Best Lecturer' titles and Teaching Quality Awards for foundational courses. Key projects include collaborations on finance game theory, railway timetable optimization, and combinatorial problem solutions.

• Anthropology
• Cultural Anthropology
• Archaeology
• +۵ مورد دیگر

John Johnson is an Adjunct Professor in the Department of Anthropology at the University of California, Santa Barbara, and serves as Curator of Anthropology at the Santa Barbara Museum of Natural History. His affiliations include the College of Letters and Science and the Department of Anthropology. He holds the academic rank of Adjunct Professor, with no indication of part-time status. While explicit educational details are not provided in the text, his research interests span interdisciplinary domains. In Anthropology, he focuses on cultural and archaeological studies, museum curation, and heritage preservation. Simultaneously, his computational work involves algorithm design, graph theory, and parameterized complexity, as evidenced by his recent publications in computer science and mathematics. His research bridges traditional anthropological methods with modern computational techniques. Johnson's articles reflect a strong computational focus, addressing topics such as graph enumeration, kidney exchange optimization, and privacy-preserving algorithms. These works highlight contributions to theoretical computer science, operations research, and interdisciplinary applications in healthcare and data privacy. No scientific awards or grants are explicitly mentioned in the provided materials. He currently advises no listed students, though his role as a curator may involve mentorship in museum-related projects. His work is associated with the Santa Barbara Museum of Natural History and the Department of Anthropology’s research initiatives.

Jianer Chenمشاهده پروفایل

استاد

• Algorithms and complexity
• Computer graphics
• Bioinformatics
• +۶ مورد دیگر

Jianer Chen is a Professor in the Department of Computer Science and Engineering at Texas A&M University, part of the College of Engineering. He holds multiple academic positions and has contributed significantly to research in algorithms, computer graphics, bioinformatics, and computer networks. Educational Background: Ph.D. Mathematics, Columbia University, 1990 Ph.D. Computer Science, New York University, 1987 M.Phil. Mathematics, Columbia University, 1990 M.A. Mathematics, Columbia University, 1989 M.S. Computer Science, New York University, 1984 B.S. Computer Science, CSUT, P.R. China, 1982 Research Interests: His work focuses on Algorithms and Complexity , advancing computational efficiency and theoretical foundations. He explores Computer Graphics , including geometric modeling and Bézier patch subdivision. In Bioinformatics , he develops algorithms for phylogenetic trees and genetic data analysis. His Computer Networks research addresses data center optimization and sensor network reliability. Publications Trends: Recent work emphasizes parameterized algorithms, kernelization techniques, and their applications to big data, graph theory, and geometric problems. He also contributes to optimization in scheduling and network protocols, reflecting interdisciplinary engagement with both theoretical and applied computer science. Awards & Honors: AFS Distinguished Faculty Achievement Award (university level), 2007 E.D. Brockett Professorship Award, 2005 Janet Fabri Award (Best Ph.D. Dissertation), 1988 Multiple recognitions for teaching and research excellence Advising & Grants: Chen has secured grants such as the NSF Research Initiation Award (1991) and Amoco Faculty Award (1998). Though no advisees are listed, his work spans mentoring doctoral and master’s students in algorithm design and computational theory. His research initiatives often address practical challenges in high-performance computing and network reliability. Labs/Teams: No specific labs or teams are explicitly mentioned in the provided texts. However, his collaborations suggest involvement in interdisciplinary groups focused on bioinformatics, data center networks, and geometric modeling.

Sampath K. Kannanمشاهده پروفایل

استاد

• Algorithms
• Computational Biology
• Program Checking
• +۵ مورد دیگر

Professor Sampath K. Kannan is the Henry Salvatori Professor in the Department of Computer and Information Science at the School of Engineering and Applied Science, University of Pennsylvania. His research focuses on algorithms, computational biology, program checking, and network security. ACM Fellow (2013) ACM SIGACT Distinguished Service Award (2012) Outstanding Faculty Advising Award (2005) Ford Foundation 'Best Advisor' Award (2004) His research spans algorithms (massive data processing, shortest-path optimization), computational biology (evolutionary tree reconstruction, phylogenetic analysis), and program checking (runtime verification, trust management). Recent work involves data stream algorithms and healthcare scheduling optimization . He has taught advanced courses in algorithms and complexity theory since 1994, including CIS 677 (Complexity Theory) and GCB 537 (Computational Biology). His publications bridge theoretical computer science and applied systems research , with a focus on scalability, correctness, and security.

Eliza O’Reillyمشاهده پروفایل

استادیار

• Mathematics of Data Science
• Stochastic and Convex Geometry
• Point Processes
• +۴ مورد دیگر

Eliza O’Reilly is an Assistant Professor in the Department of Applied Mathematics & Statistics at Johns Hopkins University (JHU) and a member of the Data Science and AI Institute. Her research focuses on the mathematical foundations of data science, combining stochastic and convex geometry, high-dimensional probability, and statistical learning theory. She develops geometric models and algorithms for analyzing complex data structures, with applications in machine learning, optimization, and high-dimensional statistics. Dr. O’Reilly holds a B.S. in Mathematics from the University of Pittsburgh (2013) and a Ph.D. in Mathematics from the University of Texas at Austin (2019), where she was advised by François Baccelli. She completed a postdoctoral fellowship at the California Institute of Technology (2019–2022), sponsored by Venkat Chandrasekaran and Joel Tropp. Her work has been supported by NSF fellowships, including a NSF Graduate Research Fellowship and a NSF Postdoctoral Research Fellowship. Her research interests include: Randomized partitioning algorithms (e.g., Mondrian forests) Regularization in statistical inference and inverse problems Convex and nonconvex optimization methods Determinantal point processes and repulsive models High-dimensional geometry of random convex sets Her publications span theoretical guarantees for machine learning algorithms, optimization frameworks for data-driven problems, and geometric analysis of stochastic processes. Notable contributions include minimax analyses for random tessellation forests and spectral methods for convex regression. She actively collaborates with researchers in applied mathematics, statistics, and computer science.

• Management Science
• Operations Research
• Optimisation Algorithms
• +۷ مورد دیگر

Professor Celia Glass is a faculty member at Bayes Business School (formerly Cass), City, University of London, where she holds the title of Professor of Management Science within the Faculty of Actuarial Science and Insurance. She has been at the institution since 1999 and was awarded a personal Chair in 2003 for Operational Research and later in 2016 for Management Science. She earned her BSc and PhD from King's College London. She has held visiting appointments at Queen Mary University of London and has been actively involved in professional societies. Her research focuses on Management Science and Operations Research , particularly in scheduling, optimization, and logistics. Key areas include doctor and nurse rostering for the NHS, call centre tour scheduling, optimization in telecommunications (wireless mesh and optical networks), combinatorics of acyclic graphs, and production logistics in food safety laboratories. Her work is often supported by EPSRC grants and has practical industrial applications. Her recent publications demonstrate a consistent trend in combinatorial optimization and scheduling theory , with applications spanning healthcare , telecommunications , and manufacturing . Her work frequently involves developing and analyzing algorithms for complex scheduling problems, often with real-world constraints like perishability, employee preferences, and network limitations. Fellow, Operational Research Society Fellow, London Mathematical Society Fellow, Institute of Mathematics and its Applications Member, EPSRC College She has supervised numerous PhD students, including Robert Schumacher, Roger Knight, and Ian B. Davies, on topics related to scheduling and optimization. She has secured research funding and performed significant consultancy work for organizations such as the NHS (Whittington Hospital), OfCom, and the Registered Nursing Homes Association, translating her academic research into practical solutions. She has also co-organized workshops and served on editorial boards for journals like European Journal of Operational Research and Operations Research for Health Care . Professor Glass is a key member of research teams focused on applying operational research to healthcare planning and industrial optimization. Her spin-out company, Nightglass Medical Rostering Ltd., provides customized rostering software for the NHS, showcasing the direct impact of her research.

• Biological Invasions
• Conservation Biology
• Plant Ecology
• +۳ مورد دیگر

Prof. Franz Essl is a Professor in the Faculty of Life Sciences at the University of Vienna, leading the Department of Botany and Biodiversity Research. His work focuses on biological invasions, plant ecology, and conservation biology, with particular attention to climate change impacts and policy implications. He has published over 378 articles and led 10+ research projects, including the BioMonI initiative for island biodiversity monitoring and studies on plant naturalization under environmental change. Research interests include global patterns of invasive species, ecological consequences of climate change, and the interplay between human activities and biodiversity. Notable contributions address policy measures for managing biological invasions, cultural perceptions of non-native species, and the legacy effects of historical land use. Recent publications highlight topics like the cultural integration of invasive species, ethical dilemmas in conservation, and the global biodiversity footprint of urban consumption. His work bridges ecological research with socio-economic factors, emphasizing the need for interdisciplinary solutions to ecological challenges. Prof. Essl collaborates internationally, with projects spanning Europe, Asia, and Africa. His research has been featured in high-impact journals like Nature Ecology and Evolution and Global Change Biology , and his activities include conference presentations and policy briefs guiding invasive species management.

• Artificial Intelligence
• Digital Medicine
• Medical Image Analysis
• +۷ مورد دیگر

Zhen Yu is a Research Fellow in the Department of Data Science & AI at Monash University, actively contributing to cutting-edge research at the intersection of artificial intelligence and digital medicine. Their work focuses on developing AI models for medical image analysis, survival prediction, and disease screening, particularly in dermatology and ophthalmology. Their research interests span Artificial Intelligence , Medical Image Analysis , Digital Medicine , and Survival Analysis . They apply advanced machine learning techniques—including transformers and hierarchical models—to solve complex healthcare problems such as melanoma detection, retinal aging biomarker identification, and infectious disease differentiation. Recent publications demonstrate a strong trend toward explainable and clinically applicable AI systems, particularly in skin lesion classification and nonparametric survival modeling. Their work frequently appears in high-impact journals like npj Digital Medicine and Machine Learning , reflecting both technical rigor and clinical relevance. Zhen Yu is actively involved in significant research projects, including: Melanoma Research Victoria (MRV) Collaboration (2024–2029), where they serve as Chief Investigator AUTOMATE ME : Automated risk assessment and screening for melanoma and skin cancer (2026–2030), where they are an Associate Investigator Their collaborative network includes leading clinicians and data scientists across Australia, focusing on translating AI innovations into real-world healthcare solutions.

Luc Devroyeمشاهده پروفایل

استاد

• Computer Science
• Probability Theory
• Algorithms
• +۴ مورد دیگر

Luc Devroye is a Professor in the School of Computer Science at McGill University, Montreal. He joined McGill in 1977 after completing his PhD at the University of Texas in 1976. His research spans probabilistic analysis of algorithms, random structures, and typography. Notable contributions include work on random trees, data structures, and simulation methods. Affiliations : McGill University since 1977, School of Computer Science. Education : PhD in Computer Science from the University of Texas (1976), studies at the University of Leuven and Osaka University. Research Interests : Algorithms, probability theory, random graphs, data structures, and typography. Focus on average-case analysis, random tree structures, and simulation techniques. His work emphasizes theoretical foundations with practical applications, including contributions to random number generation and algorithmic efficiency. He advises students in theoretical computer science and maintains an active research agenda with over 500 publications. Teaching : Courses like COMP 252 (Algorithms), COMP 690 (Probabilistic Analysis of Algorithms). Students : Mentored numerous PhD and MSc students, many now in academia and industry.

Gillat Kolمشاهده پروفایل

دانشیار

• Theoretical Computer Science
• Communication Complexity
• Interactive Coding
• +۴ مورد دیگر

Gillat Kol is an Associate Professor in the Department of Computer Science at Princeton University's School of Engineering and Applied Science. She joined the faculty in 2016 after postdoctoral positions at the Institute for Advanced Study and the Technion - Israel Institute of Technology. Her research focuses on theoretical computer science, with emphasis on interactive coding, communication complexity, distributed computing, and algorithmic theory. She has been recognized with prestigious awards including an NSF CAREER Award and a Sloan Research Fellowship, as well as multiple teaching accolades from Princeton's School of Engineering and Applied Science. Education: Ph.D. in Computer Science from the Weizmann Institute (2013) Her recent work explores fundamental tradeoffs in communication efficiency for noisy channels, streaming algorithms for graph problems, and cryptographic verification protocols in distributed systems. The majority of her publications address interactive error resilience, multi-pass streaming complexity, and theoretical challenges in networked environments. NSF CAREER Award (2018) Sloan Research Fellowship Kol's research has been honored with multiple teaching awards, reflecting her commitment to academic mentorship. She actively contributes to theoretical advancements in communication models and has published extensively at top conferences in theoretical computer science.

Cristina Requejoمشاهده پروفایل

دانشیار

• Operational Research
• Integer Programming
• Combinatorial Optimization
• +۷ مورد دیگر

Cristina Requejo is an Associate Professor at ISEG – Lisbon School of Economics and Management, University of Lisbon, and a researcher at CEMAPRE/REM. Her academic career spans institutions including the University of Coimbra and the University of Aveiro, where she held teaching and research roles in mathematics and operational research. Education: PhD in Mathematics, Operations Research – Faculty of Sciences, University of Lisbon (2004) Master's in Operational Research – University of Lisbon Degree in Mathematics – University of Coimbra Her research centers on optimization, particularly integer programming, combinatorial optimization, robust optimization, vehicle routing, and healthcare logistics. She applies advanced mathematical programming techniques to real-world problems in logistics, scheduling, and network design. Her work bridges theoretical algorithm development and practical applications in sectors like healthcare and transportation. The most recent publications reflect a strong focus on MIP-based heuristics, Lagrangian duality, and vehicle routing with skill and temporal constraints. Her research consistently appears in high-impact operations research journals, demonstrating sustained contributions to algorithmic and modeling advances in optimization. Scientific Awards: No awards explicitly mentioned in the text. She has supervised multiple graduate students, including doctoral candidates funded by FCT scholarships, and has contributed to active research projects promoting international collaboration. Her teaching includes advanced courses in decision making, optimization, and operational research at the doctoral and master’s levels. She is affiliated with research labs such as CEMAPRE/REM, contributing to a vibrant research environment in applied mathematics and economics.

Dag Hauglandمشاهده پروفایل

استاد

• Combinatorial Optimization
• Global Optimization
• Network Flow Models
• +۷ مورد دیگر

Dag Haugland is a Professor in the Department of Informatics at the University of Bergen, Norway, where he conducts research and teaches in optimization, particularly combinatorial and global optimization with applications in network flows, energy systems, and logistics. He is affiliated with the Optimization research group and maintains an active research profile with recent publications in top-tier journals. Research Interests: His work focuses on Combinatorial Optimization , Global Optimization , and Network Flow Models , applying mathematical programming techniques to real-world problems in offshore wind energy, gas pipeline transportation, wireless networks, and vehicle routing. His research emphasizes integer programming, polyhedral analysis, and algorithmic design. Recent Research Trends: His latest publications (2023–2024) address tighter bounds in broadcast time problems and hydropower scheduling, reflecting a continued focus on theoretical and applied optimization in communication and energy networks. Earlier works (2016–2020) explore pooling problems, offshore wind farm cable layouts, and portfolio optimization, demonstrating interdisciplinary reach. Scientific Contributions: Extensive publication record in optimization and operations research. Supervision of multiple PhD and master’s students. Active involvement in conference proceedings and technical reports. Advising and Grants: Dag Haugland has supervised numerous students, including Marika Ivanova and Arne Klein, on topics such as offshore wind farm optimization and multicast tree problems. His work has been supported by Norwegian research funding, including the Research Council of Norway (project reference 249994). He contributes to academic service through conference organization (e.g., Norsk Informatikkonferanse) and collaborative research. Labs and Teams: He is a key member of the Optimization research group at the Department of Informatics, University of Bergen, which focuses on algorithmic and mathematical approaches to complex decision problems in engineering and industry.

Jared Saiaمشاهده پروفایل

استاد

• Theoretical Computer Science
• Distributed Algorithms
• Computer Security
• +۸ مورد دیگر

Jared Saia is a Professor in the Department of Computer Science at the University of New Mexico, within the College of Engineering. His research spans theoretical computer science, with a focus on distributed algorithms, security, game theory, and spectral methods. He has taught numerous advanced courses in algorithms, data structures, blockchains, and game theory. University: University of New Mexico School: College of Engineering Department: Department of Computer Science Academic Rank: Professor Email: saia@cs.unm.edu Education: PhD in Computer Science, University of Washington, 2002 BS in Computer Science, Stanford University, 1993 His research interests lie at the intersection of theory and practical systems, particularly in enabling large-scale groups to function effectively without centralized control. He has made significant contributions to distributed consensus, blockchain technologies, and algorithmic game theory. The recent publications and course topics reflect a strong trend toward interdisciplinary work combining algorithms with economics, security, and machine learning. His work often employs probabilistic and geometric methods to solve complex distributed computing problems. Scientific Awards: NSF CAREER Award School of Engineering Junior Faculty Research Excellence Award School of Engineering Senior Faculty Research Excellence Award Several best paper awards Prof. Saia has been actively involved in mentoring through graduate courses and research supervision. He has led projects on secure distributed systems, blockchain applications, and algorithmic resilience. His teaching includes core graduate courses such as CS 561 (Algorithms and Data Structures), CS 506 (Advanced Geometric and Probabilistic Methods), and specialized seminars on Bitcoin and game theory. Labs and Research Groups: While not explicitly named, his course websites and research themes suggest leadership in a theoretical computer science and distributed systems research group at UNM, focusing on algorithm design for secure and decentralized environments.