جست‌وجوی استاد و پژوهشگر

• Agricultural Economics
• Agricultural Policy
• Economic Sustainability
• +۵ مورد دیگر

Ivana Janeska Stamenkovska is a Professor at the Faculty of Agricultural Sciences and Food , Ss. Cyril and Methodius University in Skopje , affiliated with the Institute of Agroeconomics and Department of Agrarian Policy . Her work focuses on agricultural economics, policy design, and rural development. Role: Full Professor Institution: Ss. Cyril and Methodius University in Skopje Institute: Institute of Agroeconomics Department: Department of Agrarian Policy Email: ijaneska@fznh.ukim.edu.mk Her research spans agricultural policy development, socio-economic analysis of agri-food sectors, and sustainability frameworks across pre-accession countries, with a focus on EU integration processes, youth employment in agribusiness, and groundwater governance. Recent projects include optimizing vegetable production through linear programming and assessing organic agriculture in North Macedonia. The 2025–2022 articles highlight her expertise in policy modeling, resource management, and competitiveness analysis, particularly in developing economies. Key themes include youth engagement in rural economies, precision agriculture adoption, and mathematical approaches to farm financing. As a leader in agro-economic research, she contributes to regional development frameworks and collaborates on EU-funded initiatives like IPARD II and METROFOOD-MK. Her work bridges theoretical policy analysis with practical implementation in transitional and post-transitional agricultural systems.

Pedro Pintado Jorge Gonçalvesمشاهده پروفایل

مدرس ارشد

• Computational neuroscience
• Machine Learning
• Simulation-based inference
• +۴ مورد دیگر

Pedro Pintado Jorge Gonçalves is a Senior Lecturer at the Faculty of Engineering Sciences at KU Leuven, with primary affiliation in the Department of Electrical Engineering and strong ties to the Department of Computer Science. He leads the Gonçalves lab at the VIB-KU Leuven Center for Neuro Electronics Research Flanders (NERF), and is also a member of the KU Leuven Brain Institute and Leuven.AI Institute for Artificial Intelligence. His educational background includes training as a physicist with a focus on neuroscience applications. His research integrates machine learning with computational neuroscience to develop methods for extracting mechanistic insights from complex neural data. His work specializes in simulation-based inference techniques that bridge data-driven and theory-driven approaches in neuroscience. The publication trends reveal a strong focus on developing and applying machine learning methods to neuroscience problems, with particular emphasis on neural circuit modeling, biophysical simulations, and the development of computational tools like the sbi toolkit. His research spans multiple scales from single neurons to population dynamics and behavior. As an educator, he teaches courses including Calculus and Mathematical Engineering, and mentors numerous students through his lab. His collaborative approach is evident in his extensive co-authorship network across computational neuroscience and machine learning fields. Dr. Gonçalves actively supervises multiple PhD and master's students in his lab, fostering the next generation of computational neuroscientists. His research is supported by multiple ongoing projects including 'Simulation-based inference for mechanistic models of neural dynamics' (2025-2029) and 'Computational research into the principles of robustness in neural systems' (2023-2027). His laboratory focuses on combining theoretical models with advanced machine learning methods to design more accurate neural models, quantitatively test mechanistic hypotheses, and derive experimentally-testable predictions about neural systems in health and disease.

• Machine Learning
• Mathematical Image Analysis
• Scientific Computing
• +۶ مورد دیگر

Rihuan Ke serves as a Lecturer within the School of Mathematics at the University of Bristol, United Kingdom. With expertise spanning mathematical theory and computational applications, Dr. Ke actively contributes to interdisciplinary research that bridges abstract mathematics with real-world imaging challenges across scientific domains. Education: BSc MSc PhD Research Interests: Dr. Ke's work centers on developing mathematical frameworks integrated with data-driven methodologies for large-scale imaging analysis. Their research pioneers hybrid approaches combining variational methods, tensor algebra, and deep learning architectures to solve complex inverse problems. Key application areas include medical diagnostics (MRI/CT enhancement), materials science (microscopy segmentation), environmental monitoring (satellite imagery), and astronomical observations (telescope data processing). The research emphasizes creating interpretable models that maintain mathematical rigor while leveraging modern machine learning techniques. Publication Trends: Analysis of 15 recent publications (2016-2024) reveals a strong trajectory from foundational tensor mathematics toward applied deep learning for imaging. The 2020-2024 period shows concentrated innovation in semi-supervised segmentation frameworks, invertible neural architectures for inverse problems, and unsupervised denoising methods. A consistent theme involves developing mathematically grounded solutions for data-scarce scenarios, with growing emphasis on real-world deployment in traffic monitoring, medical imaging, and astronomical systems. Scientific Awards: No scientific awards or honors were documented in the provided materials. Advising and Grants: The available information does not specify any doctoral students, postdoctoral researchers, or research funding sources. Dr. Ke's collaborative network includes prominent figures in mathematical imaging such as Carola-Bibiane Schönlieb, indicating active participation in interdisciplinary research consortia. Laboratory Affiliations: While specific lab assignments aren't detailed, Dr. Ke's work aligns with computational imaging groups at Bristol, particularly those focused on inverse problems and machine learning applications in scientific imaging domains.

• Quantum Chaos
• Random Matrix Theory
• Condensed Matter Physics
• +۳ مورد دیگر

Dr. Sebastian Muller is a Senior Lecturer at the School of Mathematics, University of Bristol, specializing in Applied Mathematics and Mathematical Physics. His research bridges quantum chaos, semiclassical methods, and condensed matter physics, with a focus on universal quantum behavior in chaotic systems. Academic Rank: Senior Lecturer Institution: University of Bristol, School of Mathematics Research Interests : Quantum Chaos and its connection to classical chaotic dynamics Semiclassical approximations linking quantum properties to classical trajectories Random Matrix Theory applications in spectral statistics and wave function analysis Condensed matter systems like mesoscopic conductors and many-body quantum systems His recent work explores interference effects in chaotic systems, Anderson localization in clean wires, and symmetry-dependent quantum mechanical behavior. Publications emphasize trajectory-based semiclassical methods and embedded ensembles for many-particle systems.

• Numerical Methods for Partial Differential Equations
• Fluid Mechanics
• Combustion Modeling
• +۵ مورد دیگر

Laura Saavedra Lago is an Associate Professor at the Department of Applied Mathematics for Aerospace Engineering, Universidad Politécnica de Madrid (UPM), Spain. She earned her PhD in Mathematics from Universidade de Santiago de Compostela in 2012, receiving the Extraordinary Doctorate Award. Her research focuses on numerical simulations of fluid flows, particularly turbulent multiphase flows, combustion modeling, and compressible dynamics, with applications in aerospace engineering and energy systems. Affiliated with the "Fluid Mechanics and Combustion Analysis and Experimentation" research group Member of the Spanish Section of the Combustion Institute since 2009 President of the Women and Mathematics Commission (Real Sociedad Matemática Española) Her research interests include: Numerical methods for partial differential equations Compressible and multiphase flow simulation Stabilized finite element techniques Applications to aerospace and energy systems Recent projects: MSOCasi (2022–2026): Mathematical Modeling, Simulation, Optimization, and Control Hydrogen Combustion & SAF (2023–2026): Simulation tools for sustainable aviation fuels ENCODING (2023–2026): Hybrid physics-based and data-driven combustion modeling Scientific awards: Extraordinary Doctorate Award (2012) Excellent DOCENTIA-UPM Teaching Evaluation (2017–2021) Very Favorable DOCENTIA-UPM Teaching Evaluation (2011–2016) She actively supervises academic projects, including: Compressible Navier-Stokes solvers 3D electric-thermal models for Li-ion batteries Airflow analysis in nasal cavities

• Network Traffic Modeling
• Performance Evaluation
• Queueing Theory
• +۳ مورد دیگر

Kentaro Hoshi is an Assistant Professor at the Global Education Center, focusing on information science and programming education. He has taught courses such as Fundamentals of Information Science , Basic AI Programming , and Introduction to Python/Java/C++ Programming across multiple quarters in 2025. His research interests include network traffic modeling, performance evaluation of web systems, and applying queueing theory to analyze non-Poisson traffic processes. He has worked on advanced traffic models with general arrival and service time distributions, including Round-Robin processing systems, validated through simulation. The Scopus API data (as of 2025) indicates 6 publications, 8 citations, and an h-index of 2. Key projects involved modeling timeout traffic in web networks and evaluating system performance under generalized distributions and feedback mechanisms.

Aimé LACHALمشاهده پروفایل

مدرس

• Probability Theory
• Stochastic Processes
• Applied Mathematics
• +۴ مورد دیگر

Aimé LACHAL is a Lecturer in Mathematics at the National Institute of Applied Sciences (INSA) Lyon, affiliated with the Camille Jordan Institute. His career spans over three decades, with prior roles at the University of Lyon and the School of Military Applications of Atomic Energy in Cherbourg (EAMEA). 1984: DEUG A in Structural and Material Sciences 1985: B.Sc. in Mathematics (summa cum laude) 1986: M.Sc. in Mathematics (summa cum laude) 1987: Mathematics Aggregation (14th rank) 1988: M.Sc. Research in Mathematics (DEA) 1992: Ph.D. in Probability (highest honors) 1995: Habilitation to Supervise Research in Mathematics His research focuses on Probability Theory and Stochastic Processes , particularly integrated Brownian motion, pseudo-processes governed by higher-order PDEs, and their applications to statistical testing, DNA replication, and quantum entropy. Key contributions include explicit distributions for sojourn times, first passage problems for non-Markov processes, and interdisciplinary work in Mathematical Biology and Random Walks . Publications cover advanced topics in Annals of Probability , Stochastic Processes and their Applications , and other high-impact journals.

• Optimization
• Scheduling Theory
• Game Theory
• +۵ مورد دیگر

Eddie Anderson is a Professor of Decision Sciences at the University of Sydney Business School. He previously held the position of Associate Dean (Research) and Associate Dean (Research Education) at the same institution. Before joining the University of Sydney in 2008, he was at the Australian Graduate School of Management, University of New South Wales. He is also a Member of the Sydney Institute of Agriculture. His educational background includes an honours degree in Mathematics from Cambridge and a PhD in Operations Management, also from the University of Cambridge. Professor Anderson's research spans several interconnected areas in decision sciences and operations research. His work focuses on optimization , scheduling theory , and game theory , with applications in supply chains (particularly contracts and risk), energy markets , and more recently, agriculture supply chains . He has made significant contributions to understanding competitive interactions in markets, especially in wholesale electricity markets where he has analyzed supply function equilibria. His research often incorporates stochastic elements to model uncertainty and risk, recognizing that firms make decisions considering both expected profits and risk exposure. Analysis of Professor Anderson's recent publications reveals a strong focus on supply chain management, risk analysis, and game-theoretic approaches to market competition. His work spans theoretical developments in optimization and game theory while maintaining strong connections to practical applications in energy markets, procurement, and agricultural supply chains. A notable trend is the increasing integration of risk considerations into traditional optimization frameworks, reflecting the growing importance of risk management in business decision-making. Professor Anderson has published 69 papers in international academic journals, with Google Scholar showing 4300 career citations and an h-index of 33. He served as the Area Editor for Energy at the journal Operations Research from 2012-2017. His 2014 book "Business Risk Management: Models and Analysis" has become a significant reference in the field. Professor Anderson has successfully supervised 14 PhD students to completion. His academic leadership roles have been extensive, including Associate Dean Research at AGSM, UNSW (2002-2004), Acting Dean AGSM (2006), Postgraduate Associate Dean Faculty of Business, UNSW (2006-2008), and Chair of the Discipline of Business Analytics at Sydney University (2008-2013). He has secured multiple research grants, including projects on "Digitally Decommoditised Agriculture" (2017), "Robust optimization under uncertainty with limited data" (2016), and "Competitive supplier bidding in supply chains" (2011).

• Machine Learning
• Big Data Analytics
• Pattern Recognition
• +۷ مورد دیگر

Professor Junbin Gao is Professor of Big Data Analytics at the University of Sydney Business School. He previously served as Professor in Computing (2010-2016) and Associate Professor (2005-2010) at Charles Sturt University, and held academic positions at University of New England and University of Southampton. He is also a Member of the Sydney Southeast Asia Centre, demonstrating his regional academic engagement. Professor Gao earned his BSc and MSc from Huazhong University of Science and Technology (HUST) and PhD from Dalian University of Technology (DUT). His academic journey includes Professor at HUST (1997), Postdoctoral Research Fellow at Wuhan University (1991-1993), and multiple guest professorships at leading Chinese institutions including Wuhan University, HUST, and Beijing University of Technology. Professor Gao's research spans from approximation theory and multivariate spline functions to wavelet applications, and now focuses on machine learning and big data analytics. His recent work explores matrix neural networks, tensorial recurrent neural networks, and innovative approaches to data subspace clustering on manifolds. He integrates Riemannian geometry with machine learning to address challenges with manifold-valued data, finding applications in international relations, financial panel data, and computer vision. His publication record shows a clear progression toward increasingly sophisticated applications of machine learning in business analytics. Professor Gao has secured significant research funding including two Discovery Project grants from the Australian Research Council (ARC). His 260 academic papers and two books demonstrate substantial scholarly impact across multiple domains of data science and machine learning. Professor Gao supervises numerous research students working on cutting-edge topics including multivariate volatility forecasting, interpretable uncertainty systems, graph machine learning for consumer behavior, and deep learning for stock market prediction. His research group bridges theoretical advances with practical applications in business analytics, with particular interest in international relations research and financial panel data analysis.

Andreas Pavlogiannisمشاهده پروفایل

دانشیار

• Formal Methods
• Algorithmic Verification
• Automata Theory
• +۶ مورد دیگر

Andreas Pavlogiannis is an Associate Professor in the Department of Computer Science at Aarhus University, Denmark, where he leads research in the Programming Languages group. His work focuses on formal methods, algorithmic verification, and concurrency analysis, with additional interests in network diffusion and evolutionary graph theory. He holds a PhD from IST Austria and actively recruits PhD students and PostDocs. Research Interests: Pavlogiannis investigates foundational and applied aspects of concurrent systems, including static/dynamic program analysis, automata theory, and weak memory models. His interdisciplinary work bridges computer science with evolutionary biology through network diffusion models and game theory. Awards: Distinguished Paper Award (PLDI 2025) Best Paper Award (ASPLOS 2024) Best Presentation Award (SOAP 2024) Best Paper Award (ASPLOS 2022) Teaching: He teaches courses on Programming Languages (Bachelor), Algorithmic Model Checking (Master), and Program Analysis (Master). Service: Serves on program committees for top conferences including POPL, PLDI, CAV, and IJCAI. Organizer of CONFEST 2025. Funding: Research supported by Villum Foundation, Danish Free Research Foundation (DFF), Stibo Foundation, and Austrian Science Fund (FWF).

Omar Montasserمشاهده پروفایل

استادیار

• Machine Learning Theory
• Adversarial Robustness
• Distribution Shifts
• +۲ مورد دیگر

Omar Montasser is an Assistant Professor in the Department of Statistics and Data Science at Yale University. His research focuses on theoretical foundations of machine learning with emphasis on adversarial robustness and distribution shifts, addressing critical challenges in modern AI reliability. His educational background includes: PhD in Computer Science from Toyota Technological Institute at Chicago (advised by Nathan Srebro) Combined BS/MS in Computer Science and Engineering from Penn State University (worked with Daniel Kifer and Sean Hallgren) Montasser's research program centers on developing theoretical frameworks for robust machine learning. He investigates PAC learnability under adversarial perturbations, transformation invariances, and distribution shifts, bridging gaps between theoretical guarantees and practical robustness. His work provides foundational insights into why deep learning models fail under distributional changes and how to design provably robust systems. Analysis of his publications from 2017-2025 reveals consistent focus on adversarial robustness theory. Key themes include minimax optimal learners for adversarial settings, OOD generalization guarantees, and computational aspects of robust learning. His most influential contributions characterize conditions for adversarially robust learnability and develop efficient algorithms for robust classification. Scientific recognition includes: FODSI-Simons Postdoctoral Fellowship COLT 2019 Best Student Paper Award Multiple oral presentations at NeurIPS (2022) Spotlight presentation at NeurIPS 2020 He actively recruits PhD students for his research group, emphasizing theoretical machine learning. Teaching experience includes Information and Coding Theory (Fall 2022) and Learning Theory (TA, Fall 2020). While specific grants aren't detailed, his publication record in top venues (NeurIPS, COLT, ICML) suggests substantial research funding. He maintains active collaborations with leading researchers including Avrim Blum and Nathan Srebro. Montasser leads a research group at Yale focused on theoretical machine learning, with current projects examining relaxed benchmarks in online classification and score matching in diffusion models. The group operates within Yale's Department of Statistics and Data Science, leveraging university resources for theoretical and computational research.

• Electrocatalysis
• PEM fuel cells
• Degradation mechanisms of fuel cell catalysts
• +۳ مورد دیگر

Matthias Arenz serves as an Affiliate Professor in the Department of Chemistry at the University of Copenhagen, where he has maintained a prominent research position since 2010. His work centers on electrocatalysis and fuel cell technology, with particular focus on understanding catalyst degradation mechanisms and developing more durable electrocatalytic materials. With over 158 research publications and an H-index of 29, Professor Arenz leads an active research group that has secured substantial external funding and maintains extensive international collaborations. Arenz completed his academic training in Germany, earning a Diploma in Physics from the University of Bonn in 1999, followed by a PhD in Physical Chemistry from the same institution in 2002. His early career included prestigious postdoctoral appointments: a DAAD stipend at Lawrence Berkeley National Laboratory (LBNL) in 2001 and a Feodor Lynen Postdoctoral scholarship at LBNL from 2002-2004. In 2006, he established himself as an independent researcher through an Emmy Noether Research group leadership position at TU-Munich before joining the University of Copenhagen faculty in 2010. Professor Arenz's research program focuses on electrocatalysis for proton exchange membrane (PEM) fuel cells, specifically investigating the activity and stability of platinum-alloy nanoparticles, degradation mechanisms of fuel cell catalysts, and utilizing size-selected metal clusters as model catalysts. His work bridges fundamental surface science with practical energy applications, employing advanced characterization techniques to understand catalyst behavior at the atomic level under operating conditions. This research directly addresses critical challenges in fuel cell durability and efficiency. Analysis of Arenz's recent publication record reveals a strong emphasis on operando and in situ characterization methods to study catalyst behavior during actual operation. His research spans from fundamental studies of single-crystal models to applied research on commercial catalyst systems. Key emerging themes include the development of high-entropy alloys for improved stability, advanced characterization of degradation mechanisms, and innovative cell designs for electrochemical studies. Professor Arenz has successfully secured substantial external research funding, with over 20 million DKK from Danish and European sources since 2010, and approximately 1.5 million euros from German sources between 2004-2009. He has supervised 5 postdoctoral researchers, 9 PhD students, 3 master's students, and 5 bachelor's students either currently or to completion. His research group operates within the Nanocatalysis facility at the University of Copenhagen, maintaining state-of-the-art equipment for electrocatalyst synthesis and characterization.

Harrison Bo Hua Zhuمشاهده پروفایل

استادیار

• Probabilistic Machine Learning
• Deep Learning
• Infectious Disease Modeling
• +۷ مورد دیگر

Harrison Bo Hua Zhu is an Assistant Professor at the Section for Health Data Science and AI within the Department of Public Health at the University of Copenhagen. He joined the university in November 2024 and is also a member of the Machine Learning and Global Health Network and part of the research group led by Samir Bhatt and David Duchêne. His educational background includes: PhD in Modern Statistics and Statistical Machine Learning from Imperial College London (2019-2023), supervised by Seth Flaxman and Yingzhen Li MSci in Mathematics from Imperial College London (2015-2019), with an exchange year at École Polytechnique Fédérale de Lausanne Dr. Zhu's research focuses on developing probabilistic machine learning methods for public health applications, particularly in infectious disease modeling and phylogenetics. His work bridges theoretical advances in Gaussian processes and Bayesian deep learning with practical epidemiological challenges. He specializes in creating scalable models that can handle multimodal data sources including time series, satellite imagery, and genomic sequences. His publication record shows a clear progression from foundational machine learning methodology to impactful public health applications, with significant contributions during the COVID-19 pandemic as part of the Imperial College London Response Team. Recent work demonstrates expertise in Markovian Gaussian Process Variational Autoencoders and multimodal learning approaches for health data science. Dr. Zhu actively supervises students at multiple levels: Current PhD students: Mathilde Marie Brünnich Sloth (life-course epidemiology) and Mariya Pavlova (Climate Change AI) Previous students: Alexander Pondaven (MEng thesis on diffusion models), Qing Pan (MSc on meta-learning), and Gengjian Hu (MSc on climate data compression) He maintains strong industry connections through past work at Fano (2023-2024) and an internship at Amazon, demonstrating the practical applicability of his research. His GitHub activity shows active development of machine learning tools, including implementations of neural processes and Gaussian process models.

Giannis Nikolentzosمشاهده پروفایل

استادیار

• Learning on Graphs
• Graph Mining
• Machine Learning
• +۱ مورد دیگر

Giannis Nikolentzos is an Assistant Professor at the Department of Informatics and Telecommunications, University of Peloponnese. His research focuses on graph representation learning, machine learning, and artificial intelligence. Prior to this role, he was a postdoctoral researcher at Ecole Polytechnique and completed his PhD in Computer Science at Athens University of Economics and Business, advised by Michalis Vazirgiannis. He holds an MSc in Artificial Intelligence from the University of Southampton and a Diploma in Electrical and Computer Engineering from the University of Patras. His research interests include learning on graphs, graph mining, and machine learning applications in healthcare and social networks. He has contributed to advancements in graph neural networks (GNNs), adversarial attacks on GNNs, and synthetic data generation using variational graph autoencoders. Recent Publications highlight trends in understanding GNN representations, adversarial robustness, and graph reasoning with large language models. His work has been presented at top conferences like NeurIPS, ICML, and ICLR. Nikolentzos has developed the Grakel Python library for graph kernels and contributed to open-source tools like the repository for 'What Do GNNs Actually Learn?' which includes implementation details, datasets (e.g., IMDB-BINARY), and dependencies such as PyTorch and NetworkX.

James McCoyمشاهده پروفایل

استاد

• Differential Geometry
• Partial Differential Equations
• Geometric Analysis
• +۳ مورد دیگر

Professor James McCoy is a mathematician specializing in geometric analysis, particularly curvature-driven flows of hypersurfaces. He holds a professorship at the University of Newcastle's School of Mathematical and Physical Sciences and serves as Deputy Director of the Australian Mathematical Sciences Institute (AMSI). His research focuses on geometric evolution equations, including curvature flows, differential geometry, and partial differential equations. McCoy earned his PhD from Monash University in 2002 under Klaus Ecker and has held roles such as Head of Mathematics at Newcastle and ARC Postdoctoral Fellow at the University of Wollongong. Education: PhD in Mathematics (Monash University, 2002), Bachelor of Science (Honours) in Mathematics (Monash University). Professional roles include Visiting Professorships at Okinawa Institute of Science and Technology and Honorary Fellowships at the University of Wollongong. He has reviewed for over 30 journals and served as Associate Editor of the Journal of the Australian Mathematical Society. Research Interests: Long-time existence of curvature flows, singularity formation, geometric PDEs, and applications to biomembranes and nanotechnology. His work bridges geometric analysis with applied problems like fire front modeling and carbon nanostructure joining via calculus of variations. Grants: Over $1.8M in funding, including ARC Discovery Projects on curvature flows and geometric evolution equations. Recent grants include support for lockdown research and course development initiatives. Advising & Collaboration: Supervised multiple PhD students in geometric flows and nanotechnology modeling. Collaborates internationally on projects such as fire dynamics and red blood cell shape analysis. Member of University committees including Academic Senate and Athena SWAN initiatives. Labs/Teams: Active in geometric analysis research groups, contributing to AMSI's national mathematical sciences advocacy. Leads projects on curvature flow representation formulas and higher-order geometric equations.