جست‌وجوی استاد و پژوهشگر

Roger Mongمشاهده پروفایل

دانشیار

• Topological Phases
• Quantum Simulation
• Condensed Matter Physics
• +۳ مورد دیگر

Roger Mong is an Associate Professor in the Department of Physics & Astronomy at the University of Pittsburgh's Dietrich School of Arts and Sciences. His research focuses on topological phases of matter, quantum simulation techniques, and quantum computing. He holds an NSF CAREER Award (2019) and has contributed to advancements in understanding topological insulators, fractional quantum Hall systems, and tensor network methods for simulating quantum systems. His work bridges theoretical condensed matter physics and quantum information science, with key contributions to topological order characterization, error-correcting quantum codes, and the development of novel quantum simulation platforms. Mong has also explored the interplay between symmetry, topology, and quantum dynamics in systems such as superconducting heterostructures and Rydberg atom arrays. Key Research Themes: Topological phases and their nonlocal invariants Tensor networks for higher-dimensional quantum simulations Quantum error correction via topological codes Quantum Hall edge states and thermalization dynamics Awards: NSF CAREER Award (2019) Advising: Mentor for graduate students in theoretical condensed matter physics. His research group actively collaborates on experimental efforts to realize topological materials and scalable quantum architectures, with a focus on advancing both fundamental theory and applied technologies.

• Quantum Information Science
• Quantum Computing
• Quantum Information Theory
• +۲ مورد دیگر

Hlér Kristjánsson is an Adjunct Professor at the Faculty of Arts and Science within the Department of Computer Science and Operations Research at Université de Montréal. His research focuses on quantum information science, quantum computing, and theoretical physics, with affiliations at Mila - Quebec Artificial Intelligence Institute. He teaches IFT6760C – Seminar in Machine Learning and oversees research projects funded by the Natural Sciences and Engineering Research Council of Canada (NSERC). Key projects include exploring causal structures and functional programming in quantum computation (2025–2031), supported by NSERC Discovery Grants and Tremplin programs. His work bridges quantum mechanics with computational models, addressing topics like quantum switch simulation, indefinite causal order, and resource-theoretic frameworks. Collaborations involve experimental quantum communication enhancements and second-quantized Shannon theory. Teaching responsibilities span master’s (e.g., Machine Learning), doctoral programs in computer science, and specialized DESS programs. Funding milestones include a 7-year NSERC grant (PVX20965/RGP) and a PVXXXXXX/DGECR initiative. No scientific awards are explicitly listed, though his contributions to quantum gravity as a communication resource and routed quantum circuits highlight innovative directions. Research is anchored at Mila, emphasizing interdisciplinary applications in AI and quantum systems.

Maria Polukarovمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Algorithmic Game Theory
• Multi-Agent Systems
• +۴ مورد دیگر

Dr. Maria Polukarov is an Associate Professor in Computer Science at King's College London, serving as Deputy Senior Tutor and Director of the Distributed Artificial Intelligence Research Group. Her affiliations include the Faculty of Natural, Mathematical & Engineering Sciences and the Department of Informatics. She is a Fellow of the Higher Education Academy and an Editorial Board Member of the Electronic Commerce Research and Applications . Her research focuses on algorithmic game theory, multi-agent systems, computational social choice, and strategic voting. She explores applications in financial markets, AI-driven systems, and behavioral models. Recent work includes analyzing carbon allowance auctions, green disclosure policies, and systemic risks in recommendation systems. Key contributions span robust market-making strategies, thermodynamic financial market analysis, and error modeling in voting systems. Her work frequently bridges theoretical foundations with practical applications in economics, finance, and societal systems. Supervised 5 research works Leading the Driving BMW project (EPSRC-funded, 2024–2026) on multi-winner election dynamics Active in hubs like the Trusted Autonomous Systems Hub and Finance Hub

Georges Dionneمشاهده پروفایل

استاد

• Risk Management
• Insurance
• Contract Theory
• +۳ مورد دیگر

Georges Dionne is a Full Professor of Finance and Holder of the Canada Research Chair in Risk Management at HEC Montréal. He is affiliated with the Department of Finance and a member of CIRRELT (Interuniversity Research Center on Enterprise Networks, Logistics, and Transportation). His research focuses on risk management, insurance, contract theory, portfolio selection, and applied econometrics. Education: M.A. from the University of Ottawa and Ph.D. in Economics from the Université de Montréal. He has held visiting positions at Georgia State University and École Polytechnique in Paris. His work bridges academia and industry, contributing to risk modeling for financial institutions and regulatory frameworks. Research highlights include developing the Credit VaR model for a Canadian bank, influencing Basel regulations, and pioneering auto insurance pricing models in Quebec. His recent studies address climate risk, inflation impacts on insurance, and high-frequency trading dynamics. Publications span over 175 articles in journals like the Journal of Risk and Insurance and co-authored books such as the Handbook of Insurance. He has supervised 36 Ph.D. and 138 Master’s students, mentoring research in fraud detection, corporate hedging, and financial risk analysis. Awards include the Kulp-Wright Book Award (2021), Harris Schlesinger Prize (2022), and Fellow of the Canadian Economics Association (2019). He serves on editorial boards of leading journals and advises SCOR Canada’s Risk Committee.

• Numerical Linear Algebra
• Differential-Algebraic Equations
• Control Theory
• +۳ مورد دیگر

Prof. Tatjana Stykel is a Professor of Computational Mathematics at the University of Augsburg, affiliated with the Faculty of Mathematics, Natural Sciences, and Materials Engineering. She holds positions in the Institute of Mathematics and leads the Computational Mathematics research group. Her academic journey includes a PhD from Technische Universität Berlin (2002) and a Habilitation (2009), both in numerical mathematics. Prior to her current role, she led a junior research group at TU Berlin’s MATHEON Research Center and held visiting professorships at TU Berlin and Humboldt-Universität zu Berlin. Stykel’s research focuses on Numerical Linear Algebra (e.g., generalized eigenvalue problems, matrix equations), Differential-Algebraic Equations (numerical methods and stability), and Control Theory (descriptor systems, model reduction). She has pioneered energy-adaptive optimization methods and Riemannian geometry techniques for complex systems, including applications in quantum chemistry and electromagnetism. Her awards include the Richard von Mises Prize (2007), Leslie Fox Prize (2003), and multiple fellowships from the Pacific Institute for the Mathematical Sciences and Soros programs. She has led major projects like the EU-funded EU-MORNET (2014–2018) and contributed to BMBF networks in model reduction for coupled systems. Stykel teaches advanced numerical methods, including courses on differential equations, optimization, and model reduction. Her work emphasizes interdisciplinary collaboration, with partnerships in robotics, semiconductor technology, and fluid dynamics. She advises several students, including alumni who have become prominent in applied mathematics and engineering.

Alicia Sempere Marinمشاهده پروفایل

پژوهشگر

• Art History
• Renaissance Studies
• Gender Studies
• +۴ مورد دیگر

Alicia Sempere Marin is a predoctoral researcher at the Department of History of Art, University of Murcia, Spain. She is affiliated with the Faculty of Arts and Humanities and participates in research groups such as Art, Power and Gender and Visual Studies: Images, Texts, Contexts . Her work focuses on the intersection of art history, gender studies, and cultural heritage, particularly exploring Habsburg patronage, gender representation in art, and material culture. She has contributed to over 30 peer-reviewed publications and book chapters since 2020. Her research examines topics like imperial symbolism in medals, cross-cultural exchanges in the Spanish Empire, and the role of women as cultural agents in Renaissance Europe. She actively integrates pedagogical innovations such as gamification and digital tools (e.g., Wooclap) to enhance art history education. Recently, she has also conducted studies on industrial heritage in Murcia, advocating for the preservation of cultural landscapes like the Cabezo de Torres paprika mill. Ms. Sempere Marin's work bridges historical analysis with contemporary educational practices, emphasizing the relevance of art history in understanding gender dynamics, power structures, and material culture across different eras.

Megan Deeمشاهده پروفایل

مدرس ارشد

• International Politics
• Global Governance
• Nuclear Non-Proliferation
• +۵ مورد دیگر

Dr. Megan Dee is a Senior Lecturer in International Politics at the University of Stirling, affiliated with the Faculty of Arts and Humanities. Her research focuses on global nuclear governance, EU diplomacy, and multilateral trade politics. She holds leadership roles including Director of Learning and Teaching in History Heritage and Politics and Programme Director for International Politics. Her research explores minilateral group dynamics in nuclear disarmament, effective multilateralism in global nuclear governance, and the EU's role as a security actor. She is a member of the P5 Young Professionals Network on Strategic Affairs and contributes to the Brexit Centre for Policy, Conflict and Co-operation Research. Awards include the 2015 Best Paper Prize (European Foreign Affairs Review) and 2019 Teaching Excellence Awards. Professional Qualifications: Senior Fellow of the UK Higher Education Academy External Roles: External Examiner for European and International Politics MSc at University of Edinburgh (2023) Committees: Chair of Faculty of Arts and Humanities Ethics Committee (2018), Athena Swan Working Group (2021) Teaching innovations include active learning simulations for international negotiations, earning her recognition for pedagogical excellence. Her work bridges academic research with real-world policy challenges in nuclear diplomacy and global trade systems.

• Cognitive Science
• Psychology
• Computational Modeling
• +۵ مورد دیگر

Andrew Perfors is a Professor of Psychology at the University of Melbourne, leading the Complex Human Data Hub and the Computational Cognitive Science Lab within the Melbourne School of Psychological Sciences . His research focuses on applying quantitative methods to understand higher-order cognition, including concepts, language, decision-making, and misinformation dynamics. He employs computational models and experimental approaches to investigate how cognitive constraints and social environments shape human behavior at individual and group levels. Education: PhD in Brain & Cognitive Sciences (MIT, 2008), MA in Linguistics (Stanford, 2000), BSc in Symbolic Systems (Stanford, 1999). Research Themes: Cognitive modeling of decision-making, cultural evolution, language acquisition, and misinformation spread. His work bridges computational methods with psychological experimentation, exploring topics like sampling assumptions in reasoning, trust in information sources, and the cognitive basis of gender categorization. Key Projects: Active grants include Understanding Information and Trust (2018–2025), Bridging the Meaning Gap (2023–2027), and Anti-trans Disinformation Campaigns analysis. Recent work addresses misinformation mitigation strategies and the cognitive naturalness of trans-inclusive gender categories. Grants & Collaborations: Funded by ARC and NHMRC. Collaborations include work on contact-tracing technologies during the pandemic and cross-cultural studies on privacy calculus. Labs & Teams: Directs the Complex Human Data Hub, focusing on societal challenges like misinformation and cultural dynamics. Leads interdisciplinary teams in the Computational Cognitive Science Lab, integrating psychology, computer science, and linguistics.

• Computational Mechanics
• Stochastic Finite Elements
• Multiscale Modeling
• +۳ مورد دیگر

Professor Chenfeng Li FLSW is Chair in Civil Engineering at Swansea University and Co-Director of the Zienkiewicz Institute for Modelling, Data and AI. An internationally recognized expert in computational engineering, he develops innovative solutions for civil infrastructure, materials science, and energy sectors. Professor Li's research focuses on computational mechanics, uncertainty quantification, and risk assessment. His methods address technical challenges in water-energy-food systems, geotechnical engineering, and manufacturing. Collaborations with industry leaders like ARUP, Costain, and Bauer translate research into practical applications for infrastructure and construction. Key research areas include: Stochastic finite element methods Multiscale modeling of materials Fluid-structure interaction Uncertainty quantification in complex systems Digital reconstruction of microstructures He leads the module 'Design and Analysis for Temporary Works' for MSc Civil Engineering students, providing continuing professional development for industry practitioners. Honors include Fellowship in the Learned Society of Wales. Professor Li serves as Editor-in-Chief of Engineering Computers and contributes to international research networks addressing global infrastructure challenges.

• Computer Science
• Machine Learning
• Artificial Intelligence
• +۷ مورد دیگر

Hao Tian is a professor at Georgia State University's Department of Computer Science, with affiliations at institutions including Hubei University of Economics, University of Alberta, and Shanghai Key Laboratory of Trustworthy Computing. His research spans interdisciplinary domains at the intersection of computer science, applied mathematics, and engineering. Key research areas include: Machine learning architectures for image and signal processing Graph-theoretical methods for pattern recognition Federated learning and edge computing frameworks Fuzzy systems for industrial process control Numerical methods in computational mechanics Recent publications demonstrate trends in: Higher-order network representation and learning Physics-informed neural networks for financial modeling Peridynamic models for crack propagation analysis Transformer-based approaches in point cloud processing His work has been applied to municipal solid waste incineration, biomedical imaging, and smart city infrastructure, reflecting a strong engineering focus.

Mehmet Özgür Oktelمشاهده پروفایل

استاد

• Condensed Matter Physics
• Quantum Physics
• Quasicrystals
• +۳ مورد دیگر

Mehmet Özgür Oktel is a Professor of Physics at Bilkent University in Ankara, Turkey. His research focuses on condensed matter theory, particularly quasicrystals, artificial gauge fields in cold atoms, and ultradilute quantum droplets. He has held visiting appointments at Cornell University and Ohio State University. Oktel earned his Ph.D. from MIT (2001) and B.Sc. from Middle East Technical University (1996), with notable achievements including a Bronze Medal at the International Physics Olympiad (1992). Education: Ph.D. in Physics (1996-2001), Massachusetts Institute of Technology B.Sc. in Physics & Electrical Engineering (1992-1996), Middle East Technical University High School Diploma (1985-1992), Eskişehir Anadolu Lisesi Research Themes: Quasicrystals: Studies electronic and wave dynamics in non-periodic structures, including localization and higher-dimensional projections. Artificial Gauge Fields: Explores quantum phases of cold atoms under synthetic electromagnetic fields. Ultracold Droplets: Investigates self-bound Bose-Einstein condensates and their stability under quantum fluctuations. Awards: American Physical Society Outstanding Referee (2025) Bilkent Distinguished Teaching Award (2018) TUBITAK Encouragement Award (2009) TUBA GEBIP Young Investigator Award (2005-2008) Advising & Grants: Directed 19 Ph.D. and M.S. students, many now in top institutions like Max Planck Institute and Harvard. Recipient of TUBITAK Kariyer Grant (2005-2010) and Ohio State Fellowship (2001-2003). Labs/Teams: Leads a theoretical condensed matter group at Bilkent, collaborating on cold atom experiments and quasicrystal simulations.

• Cognitive Neuroscience
• Neurogenetics
• Parkinson's Disease
• +۷ مورد دیگر

Nathan Beu is a cognitive neuroscientist and experimental psychologist at the University of Adelaide's School of Psychology within the Faculty of Health and Medical Sciences. He conducts research at the intersection of cognitive neuroscience, neurogenetics, and clinical applications, with particular focus on Parkinson's disease, traumatic brain injuries, and cognitive aging. His work spans multiple laboratories including the Cognitive Neural Sciences Lab and the Cognition, Ageing, and Neurodegenerative Disease laboratory. Dr. Beu's research interests encompass the neurobiological substrate of higher-order cognition, intelligence, and motor control, with special emphasis on neurogenetics and frontal cortico-basal ganglia pathways. He employs a model-based systems neuroscience approach that integrates individual differences methods, computational modeling, genotyping, EEG, neuroimaging, neurostimulation techniques, and behavioral testing. His work has four broad themes: (1) psychonomics and psychometrics of individual differences in intelligence; (2) philosophical foundations of experimental cognitive science; (3) interventions to enhance cognitive and motor functions; and (4) formal models of pathology for improved medical diagnostics. His publication record demonstrates significant contributions to understanding response inhibition, cognitive control, and evidence accumulation models. Recent work has focused on neural correlates of cognitive decline, genetic markers of executive function, and computational approaches to decision-making. His research employs decomposition models to capture distinct cognitive, neural, and motor processes involved in decisions and responses, mapping these onto imaging and genetic information to improve early detection of cognitive decline. Dr. Beu is deeply committed to open science practices, sharing research code and data, preregistering studies, and supporting replication efforts. His work emphasizes justice, equity, and diversity in both education and scientific research, aiming to create a more transparent and reliable scientific community. He collaborates with researchers including Dr. Irina Baetu, Prof. Nicholas Burns, and others in the Cognitive Neural Sciences Lab, investigating how learning from experiences guides choices and how cognitive control abilities change during aging and in Parkinson's disease. Their work focuses on discovering genetic variations that may help preserve cognitive abilities and inform new treatments for cognitive decline.

• Complexity Science
• Non-Equilibrium Systems
• Theoretical Physics
• +۳ مورد دیگر

Professor Kim Christensen is a Professor of Theoretical Physics at the Department of Physics within the Faculty of Natural Sciences at Imperial College London. His research focuses on theoretical and numerical aspects of complex systems, particularly the spontaneous emergence of patterns and organization in non-equilibrium systems. He holds affiliations with the Centre for Cardiac Engineering, Centre for Complexity Science, Condensed Matter Theory Group, and other interdisciplinary initiatives. Christensen earned a Ph.D. in Science (1993) and a Master of Science (1990) from the University of Århus, Denmark. His work integrates theoretical, computational, and experimental studies across disciplines including geology, atmospheric physics, biology, sociology, medicine, and fire safety. A key contribution is the textbook Complexity and Criticality (2005), co-authored with Nicholas Moloney, which is widely used globally. His research interests emphasize interdisciplinary complexity science, with a focus on applying transferable tools to real-world systems. Recent work includes studies on atmospheric rivers, cardiac electrophysiology modeling, quantum networks, and venture capital network dynamics. He has over 8,150 citations (Google Scholar, June 2025), reflecting his impactful contributions to identifying universal research questions and knowledge transfer. Labs/Teams: Condensed Matter Theory Group, Physics of Life, ElectroCardioMaths Programme Grants/Awards: Not explicitly listed but implied by citation impact and textbook影响力

Nata Stulovaمشاهده پروفایل

پژوهشگر

• Software Engineering
• Formal Methods
• Natural Language Processing
• +۳ مورد دیگر

Nata Stulova is a Researcher currently working as a staff research scientist at MacPaw's Technological R&D Center, focusing on software engineering research involving formal and informal program specifications. Formerly, she held academic roles including senior postdoctoral researcher at the University of Bern (SCG), researcher at EPFL's LARA Lab, and PhD work at the Technical University of Madrid (UPM). Her research spans software documentation consistency, NLP applications, runtime verification, and low-code tools. She has led multiple projects addressing code comment quality, empirical studies on documentation practices, and tool development for requirements engineering. Despite transitioning to industry in 2022, she maintains active collaboration with academia, publishing in top conferences like ICSE and IEEE Transactions. She holds a PhD in Computer Science from UPM and has authored numerous peer-reviewed articles and conference papers. Her work emphasizes bridging academic research with industry applications, particularly in software analysis and developer productivity. Education: PhD in Computer Science, Technical University of Madrid (2018) MSc in Artificial Intelligence, Technical University of Madrid (2013) BSc in Applied System Analysis, NTUU "KPI" (2012) Research Interests: Her work focuses on improving software quality through automated documentation tools, runtime verification techniques, and low-code solutions for requirements engineering. She explores NLP methods for detecting inconsistent or outdated comments and optimizing static analysis efficiency. Recent projects include SwiftEval (LLM-generated code evaluation) and RepliComment (cloned comment detection). She also emphasizes practical applications of formal methods in dynamic languages like Prolog and Python. Grants & Funding: ASA: Agile Software Assistance TRACES: Resource-Aware Software Tools N-GREENS: Energy-Efficient Software Labs/Teams: Core member of the Ciao system development team at IMDEA Software Institute, contributor to the Software Composition Group (SCG) at UniBe, and collaborator with MacPaw's TR&D team on industry-focused R&D.

Maciej Bestaمشاهده پروفایل

پژوهشگر

• Sparse Graph Computations
• Large Language Models
• Graph Neural Networks
• +۷ مورد دیگر

Maciej Besta is a leading researcher at ETH Zurich's Institute for Computing Platforms, where he heads research initiatives at the Scalable Parallel Computing Lab (SPCL) and contributes to the ETH Future Computing Laboratory (EFCL). Working under the mentorship of Professor Torsten Hoefler, he has established himself as a prominent figure in high-performance computing, graph processing, and large language models. Position: Researcher at Institute for Computing Platforms, ETH Zurich Research Leadership: Head of Sparse Graph Computations and Large Language Models Research at SPCL Collaboration: Leads project management for SPCL's contributions to ETH Future Computing Laboratory Besta's research spans multiple abstraction levels, from hardware and network topologies to middleware, algorithms, and programming models. His primary focus areas include graph-enhanced language models, graph neural networks, graph databases, and sparse models, with applications across various computational settings. He approaches these problems through rigorous performance modeling and formal reasoning, emphasizing both scalability and practical implementation. His recent publications reveal a clear trend toward integrating graph structures with language models and AI systems. Besta has pioneered work on graph databases, knowledge graphs of thoughts, and higher-order graph neural networks, while maintaining his strong foundation in high-performance computing and network topology design. His research bridges traditional HPC with cutting-edge AI, creating novel approaches for efficient large-scale computation. IEEE TCSC Award for Excellence in Scalable Computing (Early Career, 2023) Multiple Best Paper Awards at Supercomputing conferences (2022, 2023) ACM SIGHPC Doctoral Dissertation Award (2022) ETH Medal for outstanding doctoral thesis (2021) Fellow of The Explorers Club (2022) Besta actively mentors ETH Zurich students through semester projects, Bachelor's, and Master's theses, focusing on graph processing and related computer science challenges. His mentorship extends beyond technical guidance, incorporating lessons from his extensive polar and mountaineering expeditions that emphasize mental resilience, efficient risk management, and leadership. He has supervised numerous student projects that have resulted in high-impact publications at top-tier conferences. As a core member of the Scalable Parallel Computing Lab, Besta collaborates with researchers across ETH Zurich and international institutions. His unique approach integrates insights from extreme environment expeditions into research methodology, creating a distinctive framework for tackling complex computational problems. The lab's work under his leadership spans theoretical modeling, practical implementation, and real-world deployment of high-performance systems.