جستجوی استاد راهنمای دانشگاهی

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.

• Macroeconomics
• International Economics
• Financial Mathematics
• +۳ مورد دیگر

Dr. Dirk Veestraeten is a Visiting Professor at the University of Amsterdam's Faculty of Economics and Business, specifically within the Macro & International Economics section. His research focuses on advanced mathematical techniques applied to economic and financial contexts, including stochastic processes, Laplace transforms, and option pricing models. He has contributed significantly to understanding first passage time problems in diffusions and special function applications in finance. His work frequently bridges pure mathematics and applied economics, with notable publications on parabolic cylinder functions, Marcum Q function properties, and Ornstein-Uhlenbeck processes. Recent research explores currency option pricing under target zones and multiplicity in CEV models. Dr. Veestraeten maintains active engagement with theoretical developments in financial mathematics and econometric modeling, producing foundational contributions to special function analysis and integral transforms.

Jesse Sharpمشاهده پروفایل

پژوهشگر ارشد

• Mathematical Biology
• Computational Statistics
• Ecosystem Modelling
• +۲ مورد دیگر

Dr. Jesse Sharp is a Research Fellow and Co-leader of the Environment, Agriculture & Natural Systems Theme in QUT's Centre for Data Science. His research fuses applied mathematics, statistics, and data science to model ecological and agricultural systems, emphasizing sustainability and resilience. Projects involve collaborative industry partnerships to optimize environmental decision-making. Research domains include: Ecological dynamics under environmental change High-performance computational methods for biological systems Uncertainty quantification in complex models Publications demonstrate strong cross-disciplinary integration, with applications spanning coral reef recovery, cancer treatment optimization, and sustainable fisheries. Methodological innovations appear in parameter estimation techniques and optimal control frameworks. No awards are documented.

Gregor Gantnerمشاهده پروفایل

استاد

• Numerical Analysis
• Partial Differential Equations
• Finite Element Methods
• +۴ مورد دیگر

Gregor Gantner is a Professor for Mathematics at the University of Bonn (Bonn Junior Fellow) since November 2023. Previously, he held a tenured Inria Starting Faculty Position in Paris (2022-2023) and postdoctoral roles at TU Wien, the University of Amsterdam, and TU Wien again. He completed his PhD in Technical Mathematics at TU Wien in 2017, supervised by Dirk Praetorius, and received a Diploma in Technical Mathematics from TU Wien in 2014. His research focuses on numerical methods for partial differential equations, including finite element methods (FEM), boundary element methods (BEM), isogeometric analysis (IGA), and adaptive algorithms for optimal convergence. Key areas include space-time methods, a posteriori error analysis, and adaptive mesh-refining strategies. Notable awards include the 2019 Austrian Mathematical Society Dissertation Prize, Promotio sub auspiciis Praesidentis rei publicae (2018), and the Dr.-Klaus-Körper Prize (2018). He has led projects such as the French-German ANR-DFG project on robust adaptivity for nonlinear PDEs and the FWF Erwin Schrödinger Fellowship on optimal adaptivity for space-time methods. Teaching activities include advanced courses on numerical analysis and scientific computing, such as 'Optimality of Adaptive Finite Element Methods' (2025) and 'Boundary Integral Equations and BEM' (2024). His software contributions include the IGABEM2D package.

• Biophysics
• Neuroscience
• Cell Biology
• +۳ مورد دیگر

Victor Matveev is a Professor in the Department of Mathematical Sciences at New Jersey Institute of Technology (NJIT). His research focuses on biophysical modeling of calcium-dependent processes in cellular systems, particularly presynaptic calcium signaling and synaptic plasticity. He has led multiple NSF-funded projects exploring calcium dynamics, including studies on cell calcium spatiotemporal dynamics and mechanisms of synaptic facilitation. Key research areas include calcium nanodomains, synaptic vesicle fusion mechanisms, and mathematical approaches to modeling neural systems. His work bridges applied mathematics, neuroscience, and biophysics, with applications in understanding cellular communication pathways. Recent projects include the 2022 FACM conference on mathematical biology and a 2015 NSF grant studying cell calcium dynamics. He collaborates with researchers in computational neuroscience, as evidenced by contributions to the Encyclopedia of Computational Neuroscience (2022). Grants and funding history spans over two decades, with notable projects on presynaptic calcium dynamics (2004-2008), spatiotemporal calcium dynamics (2015-2018), and applied mathematics conferences (2022-2023). His research outputs combine theoretical modeling with experimental insights, yielding 39 publications and over 1,100 citations.

• Probability Theory
• Statistics
• Stochastic Processes
• +۳ مورد دیگر

Adrian Röllin is a Professor at the Department of Statistics and Data Science, National University of Singapore. He leads research in distributional approximations, Stein’s method, and infectious disease modeling. The department is actively recruiting tenure-track faculty and postdocs in statistics and data science. Current affiliation: National University of Singapore Department: Statistics and Data Science Research interests span probability theory, stochastic modeling of epidemics, and machine learning applications to active matter systems. His recent work involves Lévy-type processes and transition path theory. Key methodologies: Stein’s method, Score-based algorithms, Dynamical network modeling Application areas: Neuroscience, Epidemiology, Statistical physics Scientific contributions include serving as Chair of the Scientific Programme Committee for the 65th ISI World Statistics Congress (2025). He co-supervises PhD students and visiting researchers, with recent graduates working on SIR epidemic models and Lévy noise dynamics. PhD advisees: Ryo Imai, Shang Li, Wai Hoh Tang Postdocs: Francesca Cottini, Yuanfei Huang, Tianshu Cong His GitHub projects include csgc for subgraph statistics, RSTISim for STI simulations, and a residual convolutional neural network for Reversi game AI. He also co-authored a humorous whiskey tasting study.

Christophette Blanchet-Scallietمشاهده پروفایل

مدرس

• Applied Probability
• Statistics
• Stochastic Processes
• +۴ مورد دیگر

Christophette Blanchet-Scalliet is a Lecturer in the Department of Mathematics and Computer Science at École Centrale de Lyon, affiliated with the Camille Jordan Institute (UMR CNRS 5208). She obtained her doctorate in 2001 and her Habilitation to Supervise Research in 2016. Currently serving as Director of the Mathematics and Computer Science Department and Head of the Dual Diploma program at Centrale Lyon-ENSAE, she has been active in academia since 2002, with positions at both École Centrale de Lyon (since 2007) and the University of Nice Sophia-Antipolis (2002-2007). Her research spans applied probability , statistics , stochastic processes , stochastic control , Kriging , and robust optimization . She has developed significant expertise in Ornstein-Uhlenbeck processes, backward stochastic differential equations, Hamilton-Jacobi-Bellman equations, and applications in financial mathematics. Her work bridges theoretical probability with practical applications in risk assessment, insurance, and optimization under uncertainty. Analysis of her recent publications reveals a consistent focus on stochastic processes and their applications, with increasing emphasis on computational methods, sensitivity analysis, and optimization techniques. Her research shows strong connections between theoretical probability and practical applications in finance, insurance, and environmental risk assessment, with growing interdisciplinary collaboration across mathematical fields. Dr. Blanchet-Scalliet has supervised five doctoral students since 2015: Mélina Ribaud (2015-2018), Laura Gay (2016-2019), Thierry Gonon (2019-2022), Benoit Nieto (2021-2024), and Noé Fellmann (2021-2024), typically in co-supervision with colleagues from related research groups. She has secured significant research funding through multiple projects including the CIROQUO Consortium (2020-2028), ANR DREAMES (2021-2025), ANR Oquaido Chair (2015-2020), and others. She is actively involved in the CIROQUO research consortium as co-leader, which brings together multiple academic institutions and industry partners including École Centrale de Lyon, Mines Saint-Etienne, University of Toulouse 3, Stellantis France, BRGM, CEA, IFP Energies Nouvelles, and others to advance research in uncertainty quantification and optimization.