Find Academic Supervisors

Rafael de Andrade MoralView profile

Professor

• Statistical Modelling
• Ecology
• Wildlife Management
• +3 more

Rafael de Andrade Moral is a Professor of Statistics at Maynooth University's Faculty of Science & Engineering (since 2025), with prior roles as Associate Professor (2023-2025) and Assistant Professor (2018-2023). He holds a PhD in Statistics (University of São Paulo, 2014-2017) and dual bachelor's degrees in Biology and Education. His work bridges Statistical Ecology , Computational Biology , and Data Science , focusing on modeling ecological systems, agricultural pest dynamics, and biodiversity-ecosystem function relationships. Key research themes include Bayesian modeling , multivariate ecological forecasting , and machine learning applications . He founded the Theoretical and Statistical Ecology Research Group and serves on committees like the Young-ISA Chair . His recent articles span topics like insect abundance forecasting , weed-crop competition under climate change , and neuroinformatics-based learning analysis , reflecting interdisciplinary engagement. Scientific accolades include the Young Statistician Showcase Prize (2018), A-mu-sing Competition First Place (2021), and Maths Week Award (2022). He has advised three PhD students and contributed to over 50 peer-reviewed publications. Active in teaching innovation (e.g., Teaching Statistics through Music ), he also provides statistical consultancy to organizations like NIBIO and Jomakol .

Antti KnowlesView profile

Professor

• Probability Theory
• Mathematical Physics
• Random Matrices
• +6 more

Antti Knowles is a Full Professor at the University of Geneva , Section of Mathematics. His research lies at the intersection of probability theory, mathematical physics, and analysis, with a strong focus on random matrices, random graphs, and quantum dynamics. He leads the research group Analysis, Mathematical Physics and Probability and mentors postdocs and doctoral students. Research Interests: Prof. Knowles's work spans a wide range of topics including random matrices , random graphs , statistical mechanics , stochastic processes , high-dimensional statistics , quantum field theory , and quantum dynamics . His contributions are foundational in understanding spectral properties of complex systems and their physical implications. Publications: His recent publications demonstrate a consistent focus on spectral theory of random graphs and matrices, delocalization phenomena, and quantum statistical mechanics. These works often appear in top-tier journals such as Communications in Mathematical Physics , Annals of Probability , and Journal of the European Mathematical Society . Grants & Support: He has received significant funding from the European Research Council (ERC) and the Swiss State Secretariat for Education, Research and Innovation through projects RandMat (2017–2022) and ProbQuant (2022–2027). Editorial Work: Prof. Knowles serves on the editorial boards of Annales de l’Institut Fourier , Annals of Applied Probability , L'Enseignement Mathématique , and Journal of Statistical Physics .

Tomer MarkovichView profile

Researcher

• Soft Active Matter
• Condensed Matter Physics
• Thermodynamics
• +5 more

Dr. Tomer Markovich is a Senior Academic Staff member at the School of Mechanical Engineering , part of the Fleischman Faculty of Engineering at Tel Aviv University . His research focuses on the physics of soft active matter, particularly the development of thermodynamic frameworks for non-equilibrium systems and the study of chiral active materials with unique properties such as odd viscosity. He also explores biological contexts like active gels and active polar liquid crystals. Education B.Sc. in Physics and Computer Science (Magna Cum Laude), Tel Aviv University, 2010 Ph.D. in Condensed Matter Physics, Tel Aviv University, 2016 Dr. Markovich's research spans the study of active field theories , mechanical criticality in disordered systems , and non-linear dynamics in biopolymer networks . His work also addresses chiral active fluids , entropy production , and nonreciprocal interactions , with applications in synthetic and biological active materials. Recent Publications Trends: His recent works focus on non-equilibrium thermodynamics, chiral active matter, and the mechanical properties of disordered and biopolymer networks. Topics include odd elasticity , minimal dissipation control , temporal correlations in chromosome dynamics , and nonaffine deformation mechanisms . Scientific Awards CTBP Postdoctoral Fellowship, Rice University (2018–2022) Blavatnik Postdoctoral Fellowships Program for Israeli Scientists (2016–2018) Dothan Scholarship for Academic Achievements (2016) Getti Scholarship for Excellence in Research (2014) Haya Rozet Scholarship for Academic Excellence (2013) Academic Appointments include postdoctoral research at the University of Cambridge (2016–2018) and Rice University (2018–2022). His current role involves theoretical research in soft active matter, with a focus on thermodynamic frameworks and chiral properties, contributing to both fundamental and applied aspects of mechanical engineering and condensed matter physics.

Anne Marie KirkegaardView profile

Research Fellow

• Public Health
• Indoor Climate
• Environmental Epidemiology
• +5 more

Anne Marie Kirkegaard is a PhD Fellow at Aalborg University, affiliated with the Department of Construction, Urban and Environmental Engineering within the Faculty of Engineering and Science. She works in the Section for Building Technology, Processes & Indoor Climate, specifically in the Research Group for Indoor Climate Quality and Building Systems. Her research bridges the fields of building science and public health, focusing on how indoor environmental conditions impact human health outcomes across Denmark's population registries from 2000-2021. Dr. Kirkegaard's research interests center on the relationship between housing conditions, indoor climate quality, and public health outcomes. Her work primarily employs cohort analysis of Danish population data to investigate how factors like housing type, indoor annoyances, and building systems affect mental health (particularly depression risk) and respiratory conditions (including Chronic Obstructive Pulmonary Disease). She has developed specialized expertise in analyzing large-scale register-based studies to identify mediating pathways between physical building characteristics and health outcomes, with particular attention to how perceived annoyances and social factors like loneliness influence depression risk. Her publication record demonstrates a clear research trajectory connecting building science with epidemiological methods. Starting with clinical studies on hospital outcomes, her work has evolved to focus on population-level housing and health relationships. Her most impactful recent work examines how housing type influences depression risk through perceived indoor annoyances and loneliness, revealing important indirect pathways between building design and mental health. This systems approach to understanding the building-health relationship represents a significant contribution to interdisciplinary environmental health research. Dr. Kirkegaard has been actively involved in three major research projects: her PhD project on housing conditions and indoor climate for public health (2021-2024) supervised by L. Gunnarsen; a broader research project on the same topic (2021-2025); and a project tracking development in housing conditions and indoor climate in Denmark from 2000-2021 (2020-2023). These projects involved extensive collaboration with researchers from public health, epidemiology, and building science disciplines. Based at Aalborg University's Copenhagen campus (AC Meyers Vænge 15, 2450 Copenhagen SW), Dr. Kirkegaard works within a research environment that combines engineering approaches with health science methodologies. Her position in the Research Group for Indoor Climate Quality and Building Systems places her at the critical intersection of technical building systems and human health outcomes, allowing her to contribute valuable insights that inform both building design practices and public health policy.

Filip LindskogView profile

Professor

• Actuarial Mathematics
• Insurance Mathematics
• Financial Mathematics
• +5 more

Filip Lindskog is a Professor of Insurance Mathematics at Stockholm University (SU) , where he heads the Mathematical Statistics division within the Department of Mathematics . With a background in financial mathematics and actuarial science, his research focuses on quantitative risk management, non-life insurance pricing, and applications of biostochastics and biostatistics. He has co-authored the textbook Risk and Portfolio Analysis: Principles and Methods (Springer, 2012) and supervises PhD students in actuarial mathematics. Education \n \n MSc in Engineering Physics, KTH Royal Institute of Technology (2000) \n PhD in Mathematical Statistics, ETH Zürich (2004) \n Research Interests Filip's work spans actuarial mathematics , financial risk modeling , and insurance analytics . He investigates stochastic processes in regime-switching environments, capital requirements for insurers, and mathematical frameworks for claims reserving. His recent publications emphasize machine learning applications in risk adjustment, asymptotic analysis of Poisson models, and regulatory compliance under IFRS 17.\n Scientific Contributions \n \n Editor, Scandinavian Actuarial Journal (2018–present) \n Director of SU's Master's Program in Actuarial Mathematics (2016–present) \n Head of SU's Mathematical Statistics Division (2018–present) \n \n Students and Collaborations Current and former PhD students include Nils Engler , Lina Palmborg , Jonas Alm , and Johan Nykvist . Former postdocs include Julie Thøgersen , Abhishek Pal Majumder , and Kristoffer Lindensjö . His research group explores discrete random structures, financial applications of biostatistics, and insurance modeling under capacity constraints.\n

• Applied Data Science
• Network Analysis
• Longitudinal Data Analysis
• +6 more

Dr. Mahdi Shafiee Kamalabad is an Assistant Professor in the Department of Methodology and Statistics at Utrecht University's Faculty of Social and Behavioural Sciences. His research focuses on developing advanced statistical and machine learning methods for complex data analysis, particularly in social and behavioral sciences, life sciences, and bioinformatics. Applied Data Science Network Analysis Bayesian Statistics Longitudinal Data Analysis He specializes in Dynamic Bayesian Network Models, Relational Event Models, and Change Point Detection algorithms. His work spans interdisciplinary collaborations, combining educational psychology, applied linguistics, and data science to improve understanding of multilingual classroom interactions and epidemic prediction models. He has contributed to R software packages like remify, remstats, and remstimate for relational event history data analysis. Notable projects include "Better Together: A Social Network Analysis of Multilingual Interactions in the Classroom" (2022) and methodological developments for malaria dynamics analysis in Cameroon. His teaching includes Data Wrangling and Data Analysis courses. Funding sources include Utrecht University's Faculty of Social and Behavioural Sciences.

• Network Modeling
• Teletraffic Engineering
• Internet Mathematics
• +3 more

Edward Chlebus is an Adjunct Associate Professor of Computer Science at the Illinois Institute of Technology , where he serves as director of the Network Modeling and Teletraffic Analysis Lab (NEMTAL) . He holds an M.S. and Ph.D. in electrical engineering from Cracow University, Poland. Ph.D., Electrical Engineering, Cracow University (1990) M.S., Electrical Engineering, Cracow University (1985) His research focuses on network modeling, teletraffic engineering, and performance evaluation of computer and communications systems. He pioneered the statistical analysis of handoff traffic in wireless systems through his 1995 ICUPC paper. Recent publications highlight his work on Wi-Fi network performance, traffic modeling with heavy-tailed distributions, and email traffic characterization. His research spans wireless infrastructure, commercial network analysis, and statistical methods for internet traffic. Scientific Awards : 1985 Best MS Thesis Award (SEP, Poland) 1990 & 1991 Best Paper Awards (Polish Symposium on Communications) Advisor of 1992 SEP Best MS Thesis Award Dr. Chlebus has received research funding from France Telecom, Telecom Australia Research Laboratories, and Motorola. He contributes to academic discourse as an editor of The Open Information Systems Journal and frequent reviewer for IEEE publications.

• Bioinformatics
• Genome Informatics
• Computational Biology
• +3 more

Dr. Kassian Kobert is a researcher at the University of Bielefeld, affiliated with the Faculty of Engineering, the Institute for Bioinformatics Infrastructure (BIBI), and the Center for Biotechnology (CeBiTec). His primary research base is within the Genome Informatics Group, where he focuses on computational approaches to genomic analysis and biological data processing. He maintains an active research presence with recent publications extending to 2022. Dr. Kobert's research interests center on bioinformatics and computational biology, with particular expertise in algorithm development for genomic data analysis. His work bridges computer science and biological applications, focusing on creating efficient computational methods for handling large-scale genomic datasets. His research spans phylogenetic inference, sequence alignment, viral evolution, and the development of specialized software tools for next-generation sequencing data processing. The interdisciplinary nature of his work connects computer science theory with practical biological applications, particularly in evolutionary biology and genomics. Analysis of Dr. Kobert's publication record reveals a consistent focus on developing computational methods for biological data analysis. His work demonstrates a trajectory from fundamental algorithm development to practical software implementation, with notable contributions including the PEAR read merger and ExaBayes phylogenetic analysis tool. His research shows strong emphasis on computational efficiency, particularly for handling the massive datasets generated by modern genomic technologies. The publications indicate expertise in both theoretical computer science aspects (algorithm design, computational complexity) and practical biological applications (viral evolution, phylogenetic analysis). Dr. Kobert appears to have significant involvement in software development for bioinformatics applications, with several publications describing tools that have become standard in genomic analysis workflows. His work on parallel computing approaches suggests engagement with high-performance computing environments necessary for modern genomic research.

• Computational Methods
• Numerical Analysis
• Control Theory
• +9 more

Boris Kramer is an Associate Professor in the Department of Mechanical and Aerospace Engineering at the University of California San Diego , affiliated with the Jacobs School of Engineering . He leads research in computational methods for control , optimization , and uncertainty quantification of complex systems, with applications in space weather modeling , systems biology , and soft robotics . Center for Extreme Events Research (CEER) Center for Computational Mathematics (CCoM) Air Force Center of Excellence Multi-Fidelity Modeling His work focuses on reduced-order modeling (ROM) and data-driven methods that preserve physical structures (e.g., energy conservation in Hamiltonian systems). Recent projects include collaborations with Samsung Electronics for semiconductor manufacturing optimization and leadership in DOE's PSAAP IV program for radiation resilience modeling. Kramer's research has been featured in Nature Computational Science and Science News , with grants from NSF , DoD , and AFOSR . His group has advised students like Opal Issan (published in Journal of Computational Physics) and Nate Linden (Nature Communications). Scientific awards include: NSF CAREER Award (2022) DoD Newton Award for Transformative Ideas (2020) Outstanding PhD Student Award (2024-2025, UCSD MAE) MURI funding for Digital Twins (2023) Outreach efforts include participation in the Barrio Logan Science & Art Expo and the Southeast San Diego STEM Ecosystem , emphasizing science communication for K-12 audiences.

Paul DellarView profile

Lecturer

• Lattice Boltzmann methods
• Complex fluids
• Magnetohydrodynamics
• +4 more

Paul Dellar is a Governing Body Fellow and Tutor in Applied Mathematics at Corpus Christi College, University of Oxford, where he also serves as a University Lecturer in Applied Mathematics. He returned to Oxford in 2007 after previous academic positions including a lectureship at Imperial College London and a Junior Research Fellowship at Corpus Christi College (2001–2004). His educational background includes undergraduate and graduate studies at the University of Cambridge, supplemented by participation in the Woods Hole Summer Study Programme in Geophysical Fluid Dynamics. Dr. Dellar's research spans several interconnected domains: Lattice Boltzmann methods : Developing computational frameworks for fluid dynamics, electromagnetic systems, and quantum applications, with industrial uses in automotive and nuclear engineering. Geophysical fluid dynamics : Deriving improved shallow water equations to model ocean currents (e.g., Antarctic Bottom Water crossing the equator) and atmospheric phenomena on planets like Jupiter. Multiscale modeling : Bridging kinetic theory, magnetohydrodynamics, and active matter systems through novel algorithms. His publications predominantly explore computational innovations in fluid dynamics and plasma physics, with recurring themes of lattice Boltzmann optimizations, conservation properties in geophysical models, and turbulence mechanisms in exotic systems. Recent work shows increasing focus on quantum lattice algorithms and high-precision magnetohydrodynamics solvers. Notable scientific recognition includes: Glasstone Research Fellowship (University of Oxford) He has supervised doctoral candidates such as Andrew Stewart, with whom he co-authored research on Coriolis force modeling in oceanography. Industrial collaborations include study groups addressing challenges like blood flow simulation for surgical stents and wave energy conversion systems.

Filippo BracciView profile

Professor

• Geometric Function Theory
• Iteration Theory and Commuting Holomorphic Maps
• Holomorphic Foliations
• +5 more

Filippo Bracci is a Full Professor at the Department of Mathematics , University of Rome Tor Vergata . His work focuses on geometric function theory, holomorphic dynamics, and complex analysis in higher dimensions, with applications to semigroups of holomorphic maps and Loewner equations. Research Interests include geometric function theory, iteration theory, holomorphic foliations, complex Monge-Ampère equations, and visibility properties in convex domains. He has contributed to understanding holomorphic evolution equations and their connections to dynamical systems. Grants : Principal Investigator for ERC Starting Grant 277691 (HEVO) and multiple PRIN projects (2007–2022) on complex manifolds and dynamics. Editorial Roles : Member of editorial boards for journals such as Computational Methods and Function Theory , Bulletin des Sciences Mathématiques , and Complex Analysis and Operator Theory . Leadership : Vice-President of INdAM (Istituto Nazionale di Alta Matematica) and involved in organizing seminars and academic programs.

• Fluid Mechanics
• Turbomachinery
• Computational Fluid Dynamics
• +4 more

Koen Hillewaert is a Lecturer at the University of Liège (ULiège), working within the Faculty of Applied Sciences, specifically in the Department of Aerospace and Mechanics. He is affiliated with the Design of Turbomachines and Propulsors (DoTP) research group, located in Building B52/3 at Quartier Polytech 1. His work focuses on advanced computational methods for fluid dynamics and turbomachinery applications. His educational background includes: Doctor of Engineering Sciences from Catholic University of Louvain (2013) Electromechanical Engineer from Ghent University (1995) Hillewaert's research spans multiple areas of fluid dynamics and turbomachinery, with particular expertise in computational fluid dynamics using high-order methods. His work focuses on turbomachinery design and analysis, including gas turbines, hydraulic turbines, pumps, and compressors. He has developed expertise in discontinuous Galerkin methods for fluid flow simulations, with applications ranging from aerospace propulsion to wind energy systems. His research integrates numerical analysis, machine learning techniques for flow modeling, and advanced computational techniques for plasma physics applications. His recent publications demonstrate a strong focus on high-fidelity simulation methods for turbomachinery flows, with particular attention to wall-resolved simulations, turbulence modeling, and geometric variability effects. There's a clear trend toward integrating machine learning techniques with traditional computational fluid dynamics approaches, particularly for wall modeling in separated flows. His work spans both fundamental numerical method development and practical engineering applications in aerospace and energy systems. Hillewaert holds significant institutional roles including Vice-president of KNC in the European Research Community on Flow Turbulence and Combustion (ERCOFTAC) and membership on the steering committee of the CFD General Notation System (CGNS), indicating his standing in the international CFD community. He teaches several advanced courses including Aerospace Propulsion, Aerothermodynamics of High-Speed Flows, Wind Energy, Flow in Turbomachines, Practical Fluid Mechanics for the Process Industry, and Turbomachines, demonstrating his broad expertise across fluid dynamics and turbomachinery applications.

Stefano LucidiView profile

Professor

• Nonlinear Optimization
• Derivative-Free Optimization
• Mixed Integer Programming
• +3 more

Stefano Lucidi is a Full Professor of Operations Research at Sapienza University of Rome, where he is affiliated with the Department of Computer, Automatic and Management Engineering within the Faculty of Information Engineering, Computer Science and Statistics. He has held this position since November 1, 2000, after serving as Associate Professor from November 1, 1992 to October 31, 2000. He was coordinator of the PhD program in Operations Research from 2010 to 2012 and was a shareholder of the university spin-off ACTOR SRL until July 2024. His research interests span Nonlinear Optimization, Derivative-Free Optimization, Mixed Integer Programming, and Global Optimization. His methodological work focuses on unconstrained optimization methods, constrained optimization methods, non-differentiable optimization methods, derivative-free methods, and global optimization techniques. His applied research includes mathematical modeling of biological phenomena in cell kinetics, identification of astrophysical parameters, optimal management of bookable seats in rail transport, and optimal design of electromagnetic devices and industrial electric motors. His recent publications demonstrate a strong focus on derivative-free optimization methods, complexity analysis of algorithms, multi-objective optimization, and applications in diverse fields including healthcare, transportation, and neuroscience. His work bridges theoretical advances in optimization with practical applications across multiple domains. His significant contributions to the field include developing algorithms for simulation-driven design optimization, addressing nonsmooth optimization problems, and creating methods for multi-fidelity computations. His research has been published in top journals including Optimization Methods & Software, Journal of Optimization Theory and Applications, and Optimization Letters. Full Professor of Operations Research since 2000 PhD program coordinator (2010-2012) Shareholder of ACTOR SRL spin-off (2011-2024) Active contributor to optimization theory and applications Professor Lucidi has been instrumental in advancing optimization methodologies while maintaining strong connections to practical applications across various industries. His work continues to influence both theoretical developments and real-world implementations of optimization techniques.

Benedikt WirthView profile

Professor

• Optimisation and calculus of variations
• image processing
• scientific computing
• +9 more

Prof. Dr. Benedikt Wirth is a Professor of Mathematics at the University of Münster, Germany, affiliated with the Institute for Analysis and Numerics within the Department of Mathematics and Computer Science. He is an active researcher and educator specializing in optimization and calculus of variations, with significant contributions to mathematical imaging and shape analysis. His research interests include image processing, scientific computing, numerical analysis, optimization, shape spaces, geodesics in shape space, variational methods, elastic deformation, and optimal transport. Wirth has developed innovative mathematical frameworks for shape analysis, particularly focusing on Riemannian metrics for shape spaces and variational approaches to shape comparison and optimization. His recent publications (2023-2025) demonstrate continued leadership in mathematical optimization, with particular focus on PET reconstruction, dimension reduction techniques, manifold embeddings, and branched transport theory. His work bridges theoretical mathematics with practical applications in medical imaging and computer vision, showing particular strength in connecting geometric analysis with computational methods. CRC 1450 - A05: Targeting immune cell dynamics by longitudinal whole-body imaging and mathematical modelling CRC 1450 - A06: Improving intravital microscopy of inflammatory cell response by active motion compensation EXC 2044 - C1: Evolution and asymptotics EXC 2044 - C2: Multi-scale phenomena and macroscopic structures EXC 2044 - C3: Interacting particle systems and phase transitions EXC 2044 - C4: Geometry-based modelling, approximation, and reduction Prof. Wirth actively supervises numerous bachelor's and master's students, with over 40 theses completed under his guidance since 2015. His teaching portfolio includes courses on inverse problems, numerical methods for partial differential equations, shape spaces, optimization, and optimal transport. He has consistently maintained an active research program while contributing significantly to the education of the next generation of mathematicians.

• Space Physics
• Atmospheric Physics
• Cosmic Dust
• +7 more

Ingrid Brigitte Mann is a Professor at the Department of Physics and Technology, UiT The Arctic University of Norway. Her research spans space physics, atmospheric physics, and dusty plasma phenomena, with a focus on cosmic dust, meteoroids, and polar atmospheric interactions. Current Projects: Cosmic dust injection into the upper Earth atmosphere MXD2 rocket project to study the mesosphere EISCAT Research infrastructure project Completed Projects: Mesospheric Dust Project Dust in the Heliosphere Project EISCAT_3D (Research Infrastructure) Her recent publications highlight cosmic dust dynamics, PMSE analysis, and Solar Orbiter/Parker Solar Probe missions. Techniques include deep learning, radar measurements, and computational modeling. She teaches courses such as FYS-2000 Quantum Mechanics and FYS-2019 Introduction to Space Physics, and leads student rocket projects like G-Chaser.