جست‌وجوی استاد و پژوهشگر

David Castañónمشاهده پروفایل

استاد

• Stochastic Control
• Estimation Theory
• Game Theory
• +۶ مورد دیگر

David Castañón is a Professor of Electrical and Computer Engineering (ECE) and Systems Engineering (SE) at Boston University. He holds a PhD from MIT (1976) and has held leadership roles including Department Chair of BU ECE (2010-2014) and President of the IEEE Control Systems Society (2008). His research focuses on stochastic control, optimization, game theory, and distributed computing, with applications in sensor management, inverse problems, and autonomous systems. Education: PhD, Massachusetts Institute of Technology (1976). Key affiliations include the Center for Information and Systems Engineering, the Rafik B. Hariri Institute for Computing, and the ALERT Department of Homeland Security Center of Excellence. He teaches courses such as EC702 Recursive Estimation and EC719 Statistical Learning Theory. Research interests span stochastic control, estimation theory, optimization algorithms, and multi-agent systems. Notable contributions include work on sensor management, cooperative operations, and inverse problem solutions for medical and security imaging. His work often integrates theoretical frameworks with practical applications in autonomous systems and distributed computing. Scientific achievements include IEEE Fellow status (2006), CSS Distinguished Member Award, and leadership roles in major conferences like the IEEE Conference on Decision and Control (2007 as General Chair). He has also served on the Air Force Advisory Board and the IEEE Society Review Committee. Grants and lab affiliations include the NSF Engineering Research Center for Subsurface Sensing (2001-2013) and the SENTRY DHS Center of Excellence (2021-present). His interdisciplinary collaborations bridge robotics, medical imaging, and security systems.

• Cognitive Neuroscience
• Perception
• Decision-Making
• +۳ مورد دیگر

Prof. Floris de Lange is a Professor at the Donders Institute for Brain, Cognition and Behaviour, Radboud University, and holds a part-time W3-Professorship in Cognitive Computational Neuroscience at the University of Bonn. His research focuses on understanding how top-down factors like goals, attention, expectations, and prior knowledge shape perception, cognition, and decision-making. He uses behavioral and neuroimaging techniques (MEG, fMRI, TMS) to study these processes in healthy and pathological brains. Key research themes include predictive perception, attention, and the neural mechanisms underlying decision-making. His work has been supported by prestigious grants such as the Vici and ERC Consolidator Grants. He teaches courses on Attention and Prediction, Cognitive Control, and Neurophysiology of Cognition and Behaviour. Education: Not explicitly stated in the provided text. Awards: Vici Grant (NWO), ERC Consolidator Grant, Ammodo Science Award, and others. Labs/Teams: Leads the Predictive Perception and Cognition group within the Donders Institute. His research highlights the brain’s predictive nature, demonstrating how expectations modulate sensory processing in early visual cortex and influence decision-making. He collaborates internationally, including an adversarial testing project on theories of consciousness.

Natalie Davisمشاهده پروفایل

استادیار

• Sustainability Transitions
• Food Systems
• Agent-Based Modelling
• +۳ مورد دیگر

Natalie Davis is an Assistant Professor in Environmental Geography at the Faculty of Science, Vrije Universiteit Amsterdam, and affiliated with the Amsterdam Sustainability Institute. Her research focuses on equity and agency in sustainability transitions, particularly within food systems, employing agent-based modelling and network analysis. She holds a PhD in Environmental Science (Lancaster University, 2021), an MSc in Environmental Science (Lancaster University, 2018), and a BSc in Information Science (University of North Carolina at Chapel Hill, 2015). Her work contributes to UN Sustainable Development Goals, emphasizing sustainable consumption and food systems. Her research explores consumer behavior, food system dynamics, and network structures, with recent projects including a NWO-funded study on the Dutch food system transition. She has published on topics such as dietary patterns, Indigenous food networks, and resource distribution inequalities. Her methodologies emphasize transdisciplinary approaches to address systemic challenges in sustainability transitions. Labs/Teams: Institute for Environmental Studies (IVM) and Amsterdam Sustainability Institute (ASI).

• Materials Science
• Computational Chemistry
• Density Functional Theory
• +۳ مورد دیگر

Michael Herbst is an Assistant Professor (tenure-track) at EPFL, holding a joint appointment in the School of Basic Sciences (SB) and the School of Engineering (STI). He leads the Mathematics for Materials Modelling (MatMat) research group, focusing on error control in atomistic simulations, density-functional theory (DFT), and interdisciplinary computational methods. His work bridges mathematics, materials science, and computer science, emphasizing robust algorithms and Julia-based software development. Herbst holds a PhD from Heidelberg University and has held postdoctoral positions at RWTH Aachen and Inria Paris. He is a core member of the MARVEL and CESMIX research centers. Education: 2018: Dr. rer. nat. (magna cum laude), Heidelberg University 2009–2013: BA and MSci (1st class) in Natural Sciences, University of Cambridge 2008–2009: Studies in Mathematics/Physics, TU Kaiserslautern Research Interests : Herbst's research centers on developing reliable computational methods for materials modeling, including error estimation in DFT, black-box SCF algorithms, and Julia-based tools like the Density-Functional Toolkit (DFTK). His work addresses challenges in high-throughput simulations, numerical stability, and interdisciplinary collaboration across mathematics, physics, and computer science. Grants & Projects : MARVEL Center for Computational Design (EPFL) CESMIX Center for Extreme-Scale Simulations (MIT) EMC² Project (Sorbonne/Inria/École des Ponts) Awards : HGS MathComp PostDoc Fellowship (2018–2021) DAAD Travel Funding (2018) Exploratory Research Space Fund (RWTH Aachen, 2022) Labs & Teams : Head of the MatMat group at EPFL, focusing on error-controlled simulations and open-source software development.

Stephen Lee-Urbanمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Game AI
• +۳ مورد دیگر

Stephen Lee-Urban is a Teaching Associate Professor in the Department of Computer Science & Engineering at Lehigh University, affiliated with the Rossin College of Engineering. He holds a Ph.D., M.S., and B.S. in Computer Science and Engineering from Lehigh University, all completed with summa cum laude distinction. His research focuses on fundamental and applied artificial intelligence, machine learning, game AI, cognitive systems, and automated planning. He has contributed to innovative projects such as HuManIC (human-machine interpretive control), CORA (cognitive systems framework), and crowdsourced narrative generation systems. His academic career includes significant work in cybersecurity through intelligent agent modeling of malware, as well as contributions to game AI for strategy games and military training simulations. Notable awards include summa cum laude honors for all three of his university degrees. Lee-Urban's scholarly output spans over 20 publications since 2000, with recent emphasis on AI applications in collaborative storytelling, adaptive planning systems, and human-computer interaction. His research integrates machine learning techniques with sociocultural analysis, crowd-powered content creation, and hierarchical task networks. Current work explores autonomous systems capable of leveraging crowd intelligence for generating interactive narratives and optimizing military training scenarios. While no specific grants or advising roles are listed, his interdisciplinary approach bridges computer science with game design, cybersecurity, and cognitive modeling.

• Artificial Intelligence
• Recommender Systems
• Information Systems
• +۲ مورد دیگر

Dietmar Jannach is a Full Professor at the University of Klagenfurt, Austria, affiliated with the Institute for Artificial Intelligence and Cybersecurity where he leads the Research Group for Information Systems. His academic roles include membership in the university's Senate and Curricular Commissions for Liberal Arts and Information Management. His research spans: Core Areas : Artificial Intelligence, Recommender Systems, and Software Engineering. Methodological Focus : Algorithm reproducibility, fairness in AI, sequential recommendations, and hybrid learning models. Emerging Interests : Generative AI for group decision support, ethical recommender systems, and foundation model applications. Jannach's recent publications critically evaluate reproducibility challenges in AI research, advocate for calibrated recommendations to mitigate bias, and explore agentic paradigms in group recommender systems. He emphasizes real-world validation, with studies on deployment challenges and developer experiences in software processes. He actively contributes to academic governance and mentors through research groups, though specific student advisees are not listed. Contact via Dietmar.Jannach@aau.at .

• Cognitive Psychology
• Neuroscience
• Visual Perception
• +۳ مورد دیگر

Yelda Semizer Kaplan is an Assistant Professor at the New Jersey Institute of Technology within the College of Humanities and Social Sciences . Her research intersects cognitive psychology and visual neuroscience, focusing on fundamental mechanisms of human perception and attention. Specializes in temporal integration and ensemble perception Investigates peripheral vision limitations in complex tasks Develops computational models of maze-solving cognition Key research themes analyze how visual clutter affects search performance, how temporal dynamics influence attentional blink, and how spatial configurations impact cognitive processing. Her work has been featured in Journal of Vision and Attention, Perception & Psychophysics , with collaborations across neuroscience and human factors domains. Media engagement highlights include contributions to NJIT's Research Institutes Showcase (2021) and Cyberpsychology Seminar Series (2021). Current work explores mental maze-solving mechanisms through novel behavioral paradigms.

Stefano Morettiمشاهده پروفایل

استاد

• High Energy Physics
• Particle Phenomenology
• Collider Physics
• +۵ مورد دیگر

Professor Stefano Moretti holds a Professorship at the University of Southampton's Department of Physics & Astronomy and is a Full Professor at Uppsala University's Theoretical Particle Physics group. He is a member of the Southampton High Energy Physics (SHEP) group and the Southampton Theory Astrophysics and Gravity (STAG) Research Centre. His research focuses on particle phenomenology, particularly collider physics, with over 550 publications and 34,000 citations. He is also a Visiting Staff at RAL PPD and part of the CMS experiment at CERN. Education: PhD from the University of Torino, followed by postdoctoral roles at the University of Cambridge, Rutherford Appleton Laboratory (RAL), and the University of Oxford. He joined Southampton in 2003 via an STFC Advanced Fellowship. Research Interests : Standard Model (QCD/EW interactions), Supersymmetry, Non-minimal Higgs Models, Monte Carlo Event Generators, and Higher Order Corrections. He authored textbooks on Supersymmetry and SM Phenomenology. Awards : 2024 Lilly and Sven Thuréus Prize (Sweden), 2018 Honorary Doctorate from Uppsala University, and 2025 Breakthrough Prize in Fundamental Physics (shared with LHC experiments). Teaching : Coordinates the UG module PHYS2006 (Classical Mechanics) and teaches the PG Phenomenology course within the NExT PhD School. Grants/Projects : Lead researcher on multiple STFC, Royal Society, and EU-funded projects. Active in Horizon Europe evaluations and UK REF2021 panel work. Labs/Teams : Director of the NExT Institute, member of SHEP and STAG, collaborates with CMS at CERN, and advises on policy for science assessment frameworks.

• Computer Science
• Machine Learning
• Data Science
• +۴ مورد دیگر

Yong Zhang is affiliated with Tsinghua University's Research Institute of Information Technology in Beijing, China. His research focuses on machine learning, optimization algorithms, edge computing, and their applications in areas like time series analysis, federated learning, and sensor networks. He has collaborated on projects involving neural networks, scheduling problems, and privacy-preserving techniques. Education: Yong Zhang earned a PhD in Computer Science and Engineering from Fudan University in 2007. His academic career includes roles at institutions like the Chinese Academy of Sciences and the University of Hong Kong, reflecting a strong interdisciplinary background. Research Contributions: His work spans theoretical computer science, algorithm design, and applied machine learning. Notable areas include developing efficient scheduling algorithms for energy systems, creating robust federated learning frameworks for industrial demand forecasting, and advancing methods for sentiment analysis using multimodal data. He has also contributed to biomedical engineering through smartphone-based health monitoring systems. Collaborations: He frequently collaborates with researchers at institutions like the University of Electronic Science and Technology of China, Nanyang Technological University, and The Hong Kong Polytechnic University. Key projects involve data caching optimization in edge computing, distributed algorithms for dynamic networks, and combinatorial optimization problems. Labs & Future Work: His team explores cutting-edge topics in AI-driven systems, including trust-aware machine learning, distributed resource allocation, and real-time data processing for IoT applications. Current research emphasizes scalable solutions for complex optimization challenges in both academic and industrial settings.

Franziska Pannachمشاهده پروفایل

استادیار

• Computational Linguistics
• Digital Humanities
• Semantic Web Technology
• +۴ مورد دیگر

Dr. Franziska Pannach is an Assistant Professor at the Faculty of Arts of the Rijksuniversiteit Groningen (Netherlands), affiliated with the Centre for Language and Cognition (CLCG) . She specializes in Computational Linguistics , Digital Humanities , and Semantic Web Technology , focusing on modeling narratives and mythological texts using NLP and ontologies. Her work emphasizes Open Science and cross-cultural comparisons of folktales and myths. Education : Dr. rer. nat. (PhD) in Applied Computer Science (Digital Humanities/Computational Linguistics), University of Göttingen, Germany (2024) MSc in Applied Computer Science (Digital Humanities), University of Göttingen (2019) BSc in Applied Computer Science (Computational Neuroscience), University of Göttingen (2013) Research Interests include computational approaches to mythological and folkloristic analysis, shallow ontologies for narrative comparison, and semantic web applications in cross-cultural studies. She leads the GOLEM (Graph Ontologies for Literary Evolution Models) project and contributed to the STRATA (Stratification Analyses for Mythic Narrative Materials) project. Teaching : Semantic Web Technology (MSc), Database-driven Web Technology (BSc), Digital Humanities: Tools and Methods (MSc), Analysing Data (MSc) Supervision : PhD students Pritha Majumdar (RUG) and Kristina Schneider (RUG/University of Mainz) Publications focus on narrative ontologies, mythological event annotation, and NLP for under-resourced languages. Her recent work includes the GOLEM Triple Store and shallow ontologies for myth comparison. Networks & Memberships : Member, Digital Humanities Association of Southern Africa (DHASA) since 2019 Co-founder and editor of the Journal of Digital Humanities Association of Southern Africa (JDHASA) Collaborations with institutions in Germany, South Africa, and Denmark

• Evolutionary Computation
• Genetic Programming
• Robotics
• +۵ مورد دیگر

Eric Medvet is a professor specializing in evolutionary computation, genetic programming, and robotics. He is actively involved in research areas such as neuroevolution, soft robotics, and modular robotics. His work bridges theoretical advancements in evolutionary algorithms with practical applications in robotics and AI. Roles: Conference chair for EuroGP (2020-2022), co-chair of multiple workshops and sessions. Key Research: Focus on genetic programming, embodied intelligence, and the design of adaptive robotic systems. Research Interests: His work emphasizes the development of scalable and interpretable AI systems, particularly through evolutionary methods applied to robotics. He explores topics like neuroevolution for soft robots, quality diversity algorithms, and the integration of machine learning with evolutionary computation. Publications: His recent work highlights trends in interpretable AI, modular robotics control, and evolutionary algorithms for complex systems. Notable contributions include studies on MAP-Elites, graph-based genetic programming, and the application of LLMs in automated testing. Grants & Labs: Developed frameworks like JGEA for evolutionary computation experiments. Collaborates on projects integrating evolutionary methods with real-world robotics applications.

• Natural Computing
• Financial Modelling
• Operations Research
• +۳ مورد دیگر

Professor Anthony Brabazon is the College Principal and Dean of the UCD College of Business at University College Dublin. He holds a prominent administrative role alongside his academic contributions in natural computing and business analytics. His research focuses on natural computing theory, financial modeling using evolutionary algorithms, and complex adaptive systems in business contexts. Brabazon co-founded the Natural Computing Research and Applications Group (NCRA) at UCD, which explores theoretical and practical applications of bio-inspired algorithms. Education: B Comm and Dip. Prof Accounting – University College Dublin DBA – Kingston University, London DPA and Dip Stat – University College Dublin and Trinity College Dublin MBA – Heriot-Watt University, Edinburgh MS – Stanford University Research Interests: Anthony's work bridges computational intelligence and finance, with contributions to genetic programming, foraging-inspired optimization, and business analytics. His research highlights the application of evolutionary algorithms to real-world problems in finance, operations research, and organizational strategy. Grants and Collaborations: HEA Support for COVID-19 Related Research (2020–2025) Financial Mathematics and Computation Cluster (FMC²) (2009–2016) Teaching: Coordinated MAcc Management Accounting courses at the postgraduate level (2018–2019). Professional Activities: Member of CIMA Ethics Board Editorial board roles in multiple journals Founder of EvoBAFin workshop on computational finance

J. Ian Munroمشاهده پروفایل

استاد

• Data Structures
• Space Efficient Data Structures
• Algorithm Design and Analysis
• +۷ مورد دیگر

J. Ian Munro is a University Professor and Canada Research Chair in Algorithm Design at the Cheriton School of Computer Science, University of Waterloo. He has been a faculty member at Waterloo since completing his PhD at the University of Toronto in 1971 and was appointed University Professor in 2006, one of the highest honors the university bestows on its faculty. Dr. Munro's research focuses on the design, analysis, and implementation of efficient algorithms and data structures, with particular emphasis on space-efficient solutions. His work spans several key areas including succinct data structures, tree algorithms, text search methods, priority queues, and memory management for data structures. His research has had significant impact on how we understand the theoretical limits of data representation while maintaining efficient query performance. The trajectory of his recent publications (2020-2024) reveals a continued focus on pushing the boundaries of space efficiency in data structures, particularly for graphs and trees. His work increasingly addresses dynamic scenarios where data structures must be updated efficiently while maintaining query performance. The research shows strong connections between information theory, combinatorics, and practical algorithm design, with applications spanning database systems, computational geometry, and text processing. Fellow of the Association for Computing Machinery (FACM) Elected Fellow of the Royal Society of Canada (FRSC) in 2003 Canada Research Chair in Algorithm Design Dr. Munro has supervised over 24 PhD students throughout his career and has been actively involved in teaching advanced courses on data structures and algorithms. His research has been consistently funded through competitive grants, including his Canada Research Chair position. He maintains active collaborations with researchers worldwide, as evidenced by his extensive co-authorship network across numerous publications. As a member of both the Algorithms and Complexity Group and the Data Systems Group at Waterloo, Dr. Munro contributes to interdisciplinary research that bridges theoretical computer science with practical systems challenges. His work continues to influence both theoretical understanding and practical implementations of efficient data management solutions.

• Machine Learning
• Theoretical Computer Science
• Data Science
• +۳ مورد دیگر

Lev Reyzin is a Professor of Mathematics, Statistics, and Computer Science at the University of Illinois Chicago (UIC) and Director of the IDEAL Institute. He specializes in the theory of machine learning, data science, and artificial intelligence, with affiliations to theoretical computer science and mathematical foundations. Prior roles include a Simons Postdoctoral Fellowship at Georgia Tech and an NSF Computing Innovation Fellowship at Yahoo! Research. He earned his Ph.D. from Yale University (NSF doctoral fellowship) and a bachelor’s degree from Princeton University. Research interests focus on algorithmic learning theory, computational complexity, and applications of machine learning to real-world problems. Notable work includes contributions to statistical learning algorithms, adversarial bandits, and graph theory. His research has been funded by NSF grants (e.g., ECCS-2217023, CCF-2307106), DOD programs, and others since 2015. He has received awards at leading conferences (ICML, COLT, AISTATS). Reyzin holds editorial roles, including Editor-in-Chief of Mathematics of Data, Learning, and Intelligence and Chair positions in major conferences like FOCS 2024 and ALT 2017. His leadership in academic organizations highlights his influence in shaping theoretical computer science and machine learning research.

Stephen W. Bradyمشاهده پروفایل

دانشیار

• Mathematics Education
• Pure Mathematics
• Criminal Profiling
• +۳ مورد دیگر

Stephen W. Brady is an Associate Professor of Mathematics, Statistics, and Physics at Wichita State University. He serves as Director of the General Education Algebra Program and Undergraduate Coordinator within the Fairmount College of Liberal Arts and Sciences. Since joining WSU in 1967, he has held roles including Search Committee Chair for over four decades and membership on numerous university committees, faculty senate, and the Athletic Board. He received his BA, MA, and PhD in Mathematics from Indiana University. His academic interests span mathematics education, pure mathematics, and interdisciplinary applications such as criminal profiling, demonstrated by his decade-long work on the BTK serial killer case. He has authored research papers and is writing the seventh volume of a mathematics textbook series (2,400+ pages as of 2021). Beyond academia, he has conducted 16+ expeditions to sub-Saharan Africa studying wildlife, often guiding others in these remote regions. Brady has maintained a 58-year unbroken teaching record and has been married to Phyllis (a retired elementary school principal) for 56 years. His efforts include curriculum development, graduate mentorship, and collaboration with law enforcement agencies.