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Hala GhousseiniView profile

Professor

• Mathematics Education
• Teacher Development
• Educational Research
• +3 more

Hala Ghousseini is the John Grover Harvey Professor of Math Education and Professor in the Department of Curriculum and Instruction at the University of Wisconsin-Madison. Her research focuses on teacher learning, particularly how professional education designs can support equitable and responsive teaching practices. She emphasizes the role of mathematical representations in children’s learning and teacher collaboration. Education: PhD in Mathematics Education, University of Michigan (2008) MA in Mathematics Education, American University of Beirut (1996) BS in Mathematics, American University of Beirut (1989) Teaching Diploma in Secondary Mathematics Education, American University of Beirut (1989) Her research interests include teacher professional development, classroom discourse, and the intersection of mathematics education with equity. Recent work explores how collaborative learning environments and surgical coaching models can improve educational and medical practices. Awards: John Grover Harvey Professorship (2019–2024) Spencer Postdoctoral Fellowship (2012–2014) 2014 Outstanding JTE Article Award (AACTE) Grants/Advising: Ghousseini’s work is supported by grants focusing on teacher education and interdisciplinary collaboration. She advises on projects integrating surgical coaching principles into educational settings. Media Contributions: Featured in the Big Ten Network’s Out of the Blue series and the Journal of Teacher Education Insider for her insights on teacher education.

• Ergodic Theory
• Dynamical Systems
• Pure Mathematics

Charles Walkden is a Lecturer in Pure Mathematics at the School of Mathematics, University of Manchester, where he has been employed since 1997 after completing his PhD at the University of Warwick under Bill Parry. His academic career spans over two decades with continuous contributions to mathematical research and education. Education: PhD in Mathematics, University of Warwick (1997) Dr. Walkden's research specializes in Ergodic Theory and Dynamical Systems, with significant work on iterated function systems, skew products, and hyperbolic systems. His investigations frequently integrate probability theory to analyze limit laws, invariant measures, and spectral properties within dynamical frameworks, advancing theoretical understanding of complex systems. His publication record reveals a sustained focus on contractive dynamical systems, particularly examining limit theorems for iterated maps, structural properties of skew products, and topological dynamics. This body of work demonstrates methodological consistency in applying operator theory and stochastic analysis to solve problems in pure mathematics. Scientific Awards: No awards documented in available sources. Dr. Walkden has supervised three PhD students (Sara Santos 2005, Ian Morris 2006, and current student Andrew Moss) and serves as Programme Director for the MSc in Pure Mathematics. He actively seeks new PhD candidates interested in ergodic theory, reflecting his commitment to academic mentorship and program development. He contributes to the Analysis and Dynamical Systems research group within the University of Manchester's Data Science Institute, collaborating on interdisciplinary projects that connect pure mathematics with computational applications.

• Continuous Optimization
• Machine Learning
• Design and Analysis of Optimization Algorithms
• +3 more

Jiajin Li is an Assistant Professor in the Operations and Logistics Division at the UBC Sauder School of Business, concurrently affiliated with the Institute of Applied Mathematics (IAM) at the University of British Columbia. He holds a BSc in Statistics from Chongqing University and a PhD in Systems Engineering and Engineering Management from the Chinese University of Hong Kong (CUHK). Prior to joining UBC, he was a postdoctoral researcher at Stanford University's Department of Management Science and Engineering. His research focuses on continuous optimization , design and analysis of optimization algorithms , and machine learning , with emphasis on nonsmooth/nonconvex optimization, minimax problems, and distributionally robust optimization. His work bridges theoretical foundations with applications in graph data analysis and algorithmic stability. He teaches courses including Logistics and Operations Management (BCom), Seminar on Theoretical Developments in Management (PhD), and Optimization Theory and Applications (PhD). He actively seeks PhD students with strong mathematical or coding backgrounds through UBC Sauder or IAM programs. Key research contributions include advancements in primal-dual balancing for minimax optimization, stability evaluation via distributional perturbation analysis, and outlier-robust algorithms for graph data. His methodologies often integrate optimal transport theory and subdifferential analysis. He is affiliated with multiple research communities, including the IEEE and conferences like NeurIPS, ICML, and ICLR. His work emphasizes algorithmic robustness and scalability in complex optimization landscapes.

Neil BramleyView profile

Senior Lecturer

• Computational Cognitive Science
• Causal Cognition
• Active Learning
• +3 more

Neil Bramley is a Senior Lecturer in Psychology at the University of Edinburgh, affiliated with the School of Philosophy, Psychology and Language Sciences within the College of Arts, Humanities & Social Sciences. Currently serving as Director of Data and Open Research (2023–2026), he previously held roles such as Marketing Officer for the Psychology Department (2019–2022). His research focuses on computational cognitive science, exploring algorithms underlying human intelligence through computational modeling and behavioral experiments. He teaches courses in causal cognition and cognitive modeling, and actively supervises MSc and PhD students in cognitive science topics. Education: PhD in Experimental Psychology MRes in Computer Science MSc in Cognitive & Decision Sciences MA (Hons) in Philosophy Research Interests: Causal cognition, active learning, hypothesis generation, decision-making, resource rationality, and philosophy of mind. His work bridges computational models with human behavior, emphasizing how people learn and control complex systems through interaction. Grants: EPSRC New Investigator Grant (2021–2024) for 'Computational Constructivism: The Algorithmic Basis of Discovery.' Labs/Teams: Leads the Bramley Computational Cognitive Science Lab, investigating causal learning, temporal dynamics, and human-like generalization in AI.

Stuart MackenzieView profile

Professor

• Gas-phase metal clusters
• Magnetic field effects in proteins
• Animal magnetoreception
• +7 more

Stuart Mackenzie is Professor of Chemistry at the University of Oxford and a Tutorial Fellow of Magdalen College. His research spans from gas-phase metal cluster chemistry to the photochemical origins of animal magnetoreception, with a focus on developing novel spectroscopic techniques to address fundamental questions at the intersection of physical chemistry, quantum biology, and materials science. His research interests fall into two main categories: Structure and reactivity of small gas-phase metal and metal-ligand clusters, where his group employs techniques including infrared photodissociation, velocity map imaging and molecular beam reaction dynamics to probe key interactions as a function of cluster size and isomeric form Magnetic field effects in cryptochrome proteins, where he collaborates with Professors Peter Hore and Chris Timmel to study how photoexcitation of blue-light receptor proteins generates spin-correlated radical pairs influenced by Earth's magnetic field—a phenomenon central to animal magnetoreception in migratory birds His recent publications (2022-2025) demonstrate continued leadership in both research areas, with work spanning infrared spectroscopy of metal clusters, magnetic field effects in cryptochrome proteins, radical pair photochemistry, and the development of sensitive spectroscopic methods based on optical cavities. His group's work is highly interdisciplinary, combining experimental techniques with theoretical modeling to address complex problems in quantum biology and cluster science. Stuart has developed unique and highly sensitive spectroscopic methods based on optical cavities that have enabled his group to move beyond model chemical systems to probe field effects in real protein samples. His work on animal magnetoreception combines quantum mechanics, photochemistry, protein expression and unfolding, and zoology to address fundamental questions about how birds navigate using Earth's magnetic field.

Juha SiitonenView profile

Assistant Professor

• Organic Chemistry
• Natural Product Synthesis
• Molecular Interactions
• +3 more

Juha Siitonen is an Assistant Professor of Organic Chemistry at Aalto University, based in the Department of Chemistry and Materials Science within the School of Chemical Engineering. His research focuses on organic synthesis, particularly the total synthesis of natural products and the development of innovative chemical methodologies. He leads the Experimental and Theoretical Organic Synthesis (ETOS) research group, continuing Aalto's tradition in natural product synthesis dating back to Professor Gustaf Komppa's 1903 camphor synthesis. Siitonen holds a PhD from the University of Jyväskylä (2018) and previously worked as a postdoctoral researcher and university lecturer at Rice University. His interdisciplinary approach integrates quantum mechanics, thermodynamics, and computational modeling to understand molecular interactions. He emphasizes problem-solving skills in education, advocating for students to view complex challenges as opportunities for innovation. Research interests include synthesizing complex molecules efficiently, exploring bioactive natural products, and applying physics-based tools to chemical design. His work aims to reduce resource consumption in synthesis through inventive reaction pathways. Beyond academia, he pursues visual arts, electronics design, and running, reflecting his curiosity-driven mindset.

Peter WakkerView profile

Professor

• Behavioral Economics
• Decision Under Uncertainty
• Risk and Ambiguity
• +3 more

Professor Peter Wakker is a leading academic in behavioral economics and decision theory at the Erasmus School of Economics (ESE), Erasmus University Rotterdam. He specializes in decision-making under uncertainty, particularly focusing on ambiguity, risk, and prospect theory. His work bridges economics, psychology, statistics, and mathematics, with publications in top journals such as the Journal of Economic Theory and Nature Physics . Wakker has collaborated with Nobel laureates and holds prestigious awards, including the Frank P. Ramsey Medal (2013) and an honorary doctorate from the University of St. Gallen (2016). His research addresses foundational aspects of decision theory, including axiomatic frameworks for ambiguity, probability weighting, and utility functions. He has developed innovative methods like the Prince incentive system for measuring preferences and contributed to resolving paradoxes like Rabin’s calibration problem. Wakker’s work emphasizes practical applications in insurance, medical decision-making, and policy analysis, often advising media on risk communication. Wakker directs the Behavioral Economics research group at ESE and teaches advanced courses on risk and ambiguity, as well as behavioral economics. His interdisciplinary approach reflects collaborations with scholars in psychology, health sciences, and finance. He maintains an active role in academic leadership, including editorial work and conference organization. Key achievements include being ranked among the top 2% of global researchers (Stanford University list), one of the most influential economists (Ioannidis et al., 2020), and the co-author of a seminal paper in Journal of Economic Theory (Köbberling & Wakker, 2005), recognized as one of the 50 most influential papers in its history.

Xilu WangView profile

Research Fellow

• Data-driven Optimization
• Artificial Intelligence
• Machine Learning
• +5 more

Dr. Xilu Wang is a Surrey Future Fellow at the University of Surrey, affiliated with the School of Computer Science and Electronic Engineering. They are part of the Nature Inspired Computing and Engineering Research Group and the Surrey Institute for People-Centred Artificial Intelligence (PAI). Their research focuses on data-driven optimization, trustworthy AI, and machine learning, with specialties in multi-objective optimization, Bayesian optimization, evolutionary machine learning, and federated optimization. Dr. Wang holds a PhD and can be contacted via email . Research interests include developing algorithms for fair, privacy-preserving machine learning and optimization. Key topics span neural architecture search, federated optimization, and transfer learning. Dr. Wang has supervised multiple postgraduate students, including Abel Alexander (University of Surrey), Adrian Kruse (Bielefeld University), and Scheu Louis Lamar (Bielefeld University), focusing on Bayesian optimization and federated learning applications. Publications emphasize large-scale optimization, federated systems, and secure evolutionary algorithms. Notable works include surrogate-assisted optimization methods and privacy-preserving frameworks. Their contributions bridge theoretical advancements with practical applications in autonomous driving, SAR imagery, and resource-constrained computing. Dr. Wang’s work has been featured in IEEE Transactions on Evolutionary Computation and other top-tier journals. They actively explore interdisciplinary collaborations, particularly in trustworthy AI and federated learning security. Current projects aim to enhance algorithmic fairness, scalability, and real-world deployment in complex systems.

Juan PeypouquetView profile

Professor

• Optimization
• Mathematical Analysis
• Variational Inequalities
• +4 more

Juan Peypouquet is a Professor of Optimization at the University of Groningen, affiliated with the Faculty of Science and Engineering and the Bernoulli Institute for Mathematics, Computer Science, and Artificial Intelligence. His research focuses on theoretical aspects of optimization, dynamical systems, and their applications in healthcare, mining, and other fields. He holds a Ph.D. in Applied Mathematics and has contributed extensively to the field of variational analysis and numerical optimization. His research interests include optimization algorithms, asymptotic analysis of dynamical systems, and machine learning applications in healthcare management. Notable projects include predicting no-show appointments in pediatric hospitals using machine learning and studying inertial iterations for convex minimization problems. Peypouquet has published over 35 peer-reviewed articles, with recent work emphasizing inertial Krasnoselskii-Mann iterations, Hessian-driven damping dynamics, and penalty schemes for constrained variational inequalities. His contributions bridge theoretical mathematics and practical applications, such as optimization in mining operations and image processing. Affiliations: Bernoulli Institute, Systems, Control and Applied Analysis group. Ancillary Activities: Yoga instructor at the Sports Center RUG & HG, and member of the Huis voor de Sport Groningen foundation.

Edon KelmendiView profile

Lecturer

• Algorithmic Verification
• Dynamical Systems
• Logics in Computer Science
• +3 more

Dr. Edon Kelmendi is a Lecturer in Computer Science at Queen Mary University of London, affiliated with the School of Electronic Engineering and Computer Science and the Centre for Fundamental Computer Science. His research focuses on algorithmic verification, dynamical systems, logics in computer science, game theory, and automata theory. He holds a position combining teaching and research responsibilities (T&R). His research interests revolve around theoretical computer science, particularly in the algorithmic analysis of dynamical systems, decision procedures for ω-regular properties, and stochastic games. He explores topics such as linear recurrence sequences, reachability problems in hybrid systems, and submixing properties in stochastic environments. Dr. Kelmendi’s recent work includes advancements in decidability questions for discrete and continuous linear dynamical systems, the design of learning algorithms for stochastic games, and formal verification techniques for automata and logical systems. His publications span venues like the ACM/IEEE Symposium on Logic in Computer Science (LICS) and the International Journal of Game Theory. He is actively involved in the Centre for Fundamental Computer Science, contributing to research themes such as formal methods, theoretical computer science, and algorithmic foundations. No specific grants or advising roles are explicitly listed in the provided materials.

Manfred TrummerView profile

Professor

• Applied Mathematics
• Computational Methods in Medical Imaging
• Numerical Methods for Differential Equations
• +3 more

Manfred Trummer is a Professor in the Department of Mathematics at Simon Fraser University (SFU), part of the Faculty of Science. His research focuses on computational methods in medical imaging, particularly dynamic single photon emission computed tomography (SPECT), and numerical methods for differential equations, including spectral collocation and radial basis function techniques. He emphasizes solving ill-posed problems and improving stability, accuracy, and adaptivity in numerical solutions. Trummer holds a Ph.D. in Mathematics from ETH Zurich (1983). His work bridges applied mathematics and computational science, addressing challenges in medical imaging reconstruction and high-order numerical methods for differential equations. Notable contributions include advancements in spectral methods, iterative reconstruction techniques for SPECT, and preconditioning strategies for ill-conditioned systems. His research spans dynamic imaging applications, such as kidney imaging and 4D SPECT reconstruction, alongside foundational numerical analysis topics like matrix factorization, boundary layer resolution, and RBF stability. Recent publications highlight innovations in spectral collocation for mixed functional differential equations and conformal mapping using Szegő kernels. Trummer teaches graduate courses such as APMA 923 (Numerical Methods in Continuous Optimization). While no specific grants or labs are detailed in the text, his work reflects sustained engagement with interdisciplinary computational challenges in mathematics and medical imaging.

Allysa LumleyView profile

Assistant Professor

• Analytic Number Theory
• Distribution of Prime Numbers
• L-functions
• +3 more

Dr. Allysa Lumley is an Assistant Professor in the Department of Mathematics and Statistics at York University's Faculty of Science. Her research focuses on analytic number theory, particularly the distribution of prime numbers and values of L-functions over number and function fields. She holds a Doctorate in Pure Mathematics from York University (2019), a Master's from the University of Lethbridge (2014), and a Bachelor of Science in Mathematics (2010). As a faculty member, she teaches undergraduate mathematics courses and currently serves as Faculty Sponsor for the York University student chapter of the Association for Women in Mathematics. Recent teaching includes Math 1013/1014 courses before her 2023 maternity leave. Her research explores explicit bounds for L-functions, Chebyshev theta functions, and primes in short intervals, with publications in journals like the Journal of the London Mathematical Society and Acta Arithmetica. Education: PhD, Pure Mathematics (Analytic Number Theory), York University, 2019 MSc, Pure Mathematics, University of Lethbridge, 2014 BSc, Mathematics, University of Lethbridge, 2010 Research Interests: Analytic number theory methods applied to distribution problems of primes and L-functions. Specializes in explicit results, zero density estimates, and distribution of values in both number and function field settings. Current work emphasizes asymmetrical distributions in cubic L-functions and Selberg-style central limit theorems in function field contexts. Teaching & Service: Teaches calculus and introductory mathematics courses. Active in promoting gender equity in STEM through her role with the Association for Women in Mathematics. Recent teaching leaves accommodate family commitments while maintaining research output.

• Neuroscience
• Multisensory Integration
• Cognitive Neuroscience
• +3 more

Aaron Nidiffer, Ph.D., is a Research Assistant Professor in the Department of Neuroscience at the University of Rochester's School of Medicine and Dentistry (SMD). His research focuses on understanding multisensory integration, speech processing, and neurophysiological mechanisms underlying social cognition. He employs EEG and computational modeling to investigate how the brain processes naturalistic stimuli, with clinical applications in schizophrenia research and neurodevelopmental disorders. Dr. Nidiffer's work emphasizes the interplay between sensory modalities, attentional processes, and neural plasticity. His recent studies explore how gaze direction and temporal synchrony influence multisensory perception, as well as the role of predictive coding in speech comprehension. He advocates for open science practices, contributing to standardized frameworks for neural data sharing. Key research themes include: Multisensory integration in complex environments EEG biomarkers for schizophrenia spectrum disorders Cross-modal speech processing (lipreading, audiovisual speech) Computational modeling of subcortical-cortical interactions His publications span 2011–2025, demonstrating sustained focus on temporal dynamics, attentional modulation, and clinical neuroscience applications. No academic awards are explicitly listed in the provided materials.

• Mathematics Education
• Teacher Training
• Lesson Study
• +3 more

Fay Baldry is an Associate Professor in the School of Education at the University of Leicester, where she serves as Programme Leader for the Master of Education (M.Ed.) degree and Co-Leader for the Secondary PGCE course. Previously, she taught mathematics in secondary schools and held roles including Head of Department before joining the University of Warwick in 2011, where she contributed to PGCE and MSc courses in Mathematics Education. Her work focuses on teacher professional development, lesson study, and classroom interaction dynamics. Her research interests include mathematics education, teacher training methodologies, and the implementation of lesson study in initial teacher education. She explores challenges in classroom observation, error handling during instruction, and the impact of educational policies like Maths Hubs. Her studies often bridge theoretical frameworks with practical classroom applications, emphasizing collaborative learning and equity. Recent publications highlight trends in teacher collaboration, the role of board work in problem-solving lessons, and the complexities of analyzing classroom interactions. Her work challenges conventional performance metrics in education, particularly regarding disadvantaged students' outcomes. While no specific grants or awards are noted, her contributions to PGCE and M.Ed. programs reflect her commitment to shaping future educators. She supervises trainee teachers but no student names are listed here. Her research often involves cross-institutional partnerships, though specific labs or teams are not detailed in the provided information.

Semyon V. TsynkovView profile

Professor

• Partial differential equations
• Numerical analysis
• Scientific computation
• +7 more

Semyon V. Tsynkov is a Professor and Associate Director for CRSC at the Department of Mathematics, North Carolina State University. His research focuses on numerical methods for partial differential equations with applications to wave propagation, fluid dynamics, and inverse problems. He has made significant contributions to the development of artificial boundary conditions and difference potentials methods. MSc in Engineering Physics, Moscow Institute of Physics and Technology, 1989 PhD in Computational Mathematics, Russian Academy of Sciences, 1992 DSc in Computational Mathematics, Russian Academy of Sciences, 2004 Tsynkov's research spans multiple areas of computational mathematics and physics. His work on numerical methods for problems on unbounded domains, particularly artificial boundary conditions using difference potentials, has been influential in computational fluid dynamics and wave propagation problems. He has extensively studied non-deteriorating methods for long-time numerical simulation of large-scale 3D unsteady problems, with applications to acoustics, electromagnetism, optics, and plasma physics. His more recent work focuses on radar imaging, inverse problems in remote sensing, and active control of sound. His publication record shows a strong focus on computational methods for wave propagation problems, particularly in the areas of SAR imaging through the Earth's ionosphere, nonlinear Helmholtz equation, and computational electromagnetics. His work often bridges theoretical numerical analysis with practical applications in physics and engineering, demonstrating both mathematical rigor and practical relevance. Sackler Visiting Chair, Tel Aviv University, 2006 Invited Project Director, Russian Academy of Sciences, 2008-2010 Professor Tsynkov has supervised numerous PhD students and postdoctoral researchers, though specific names aren't provided in the available information. He has been involved in multiple research grants, particularly in computational mathematics and its applications to physical problems. His work on the difference potentials method has led to practical implementations in various computational fluid dynamics and wave propagation codes. He maintains an active research group at North Carolina State University focused on numerical analysis and scientific computing, with particular emphasis on partial differential equations and analysis. His group collaborates with researchers at the Keldysh Institute of Applied Mathematics and other international institutions, continuing the legacy of his mentor, V.S. Ryaben'kii.