Find Academic Supervisors

• Dissipative Partial Differential Equations
• Chaos and Turbulence
• Applied Functional Analysis
• +12 more

Dr. Michele Bartuccelli is a Reader in the School of Mathematics and Physics at the University of Surrey. He holds a Laurea in Theoretical Physics Cum Laude from Italy and a PhD from Denmark. His academic career is centered on nonlinear dynamics, dissipative systems, and mathematical modelling. Laurea in Theoretical Physics Cum Laude, Italy PhD, Denmark Dr. Bartuccelli's research focuses on dissipative partial differential equations , chaos and turbulence , applied functional analysis , and mathematical modelling in population dynamics and ecosystems. He investigates time-dependent nonlinear oscillators and Hamiltonian dynamics , with significant contributions to KAM theory and stability analysis . His work often involves sharp estimates in functional inequalities and the use of the crest factor as a tool for detecting turbulent behavior in PDE solutions. His recent publications (2019–2025) reveal a strong focus on nonlinear Hill systems , spin-orbit dynamics (especially Mercury’s 3:2 resonance), quasi-periodic attractors , and explicit norm estimates for solutions of equations like the Swift–Hohenberg and Kuramoto–Sivashinsky. He frequently collaborates with Guido Gentile and others, combining analytical rigor with numerical validation. Dr. Bartuccelli has developed fast numerical algorithms, such as the High-order Euler Method , for long-term integration of dissipative systems, enabling Monte Carlo simulations over astronomical timescales. His analytical work often employs sharp constants in Sobolev embeddings and Borel summability techniques for strongly dissipative systems. While no explicit awards or grants are listed, his consistent publication record in high-impact journals such as the Proceedings of the Royal Society A , Journal of Mathematical Analysis and Applications , and Monthly Notices of the Royal Astronomical Society underscores his significant contributions to applied mathematics and mathematical physics. He has supervised or collaborated with researchers such as Jonathan Deane, Guido Gentile, and Yuliya Kyrychko. His work on basins of attraction and time-varying dissipation in forced systems reveals deep insights into nonlinear attractor dynamics. He is actively involved in both analytical and computational aspects of dynamical systems, maintaining a strong presence in the mathematical community through his personal website and publications.

• Applied Mathematics
• Nonlinear Dynamics
• Mathematical Physics
• +12 more

Dr Jonathan Deane is a Senior Lecturer in the School of Mathematics and Physics at the University of Surrey, affiliated with the Department of Mathematics and Statistics. His research lies at the intersection of applied mathematics, nonlinear dynamics, and mathematical physics, with strong applications in engineering and planetary science. Undergraduate Degree: Physics, Merton College, Oxford (1979–1982) PhD and Postdoctoral Research: Department of Electrical and Electronic Engineering, University of Surrey (1986–1994) Lecturer, University of Surrey (1994–2000) Senior Lecturer, Department of Mathematics and Statistics, University of Surrey (2000–present) Jonathan Deane's research interests span a wide range of applied mathematical topics, including Poincaré inequalities , nonlinear elasticity , chaos in power electronics , triboelectric energy harvesting , and celestial mechanics . His work often combines rigorous analytical methods with numerical simulations to model complex physical phenomena. He has made significant contributions to the understanding of energy minimization in geometric mappings, cavity formation in elastic materials, and the long-term behavior of dissipative dynamical systems. The 15 most recent publications demonstrate a consistent focus on mathematical analysis of physical systems , particularly in the areas of inequalities , variational problems , dynamical systems , and energy conversion devices . The keywords across these works reflect deep engagement with functional analysis, geometric modeling, and nonlinear dynamics. Subfields such as spin-orbit resonance, triboelectric nanogenerators, and hydrostatic pumps indicate a strong interdisciplinary orientation linking mathematics with physics and engineering. His scientific contributions have been published in high-impact journals including: Proceedings of the Royal Society A SIAM Journal on Mathematical Analysis Nonlinear Differential Equations and Applications (NoDEA) Journal of Dynamics and Differential Equations IEEE Transactions on Circuits and Systems Dr Deane has supervised and collaborated extensively, particularly with researchers such as Jonathan Bevan, Michele Bartuccelli, and Guido Gentile. His work on chaotic DC-DC converters, sigma-delta modulators, and parametric amplifiers highlights applications in power electronics and signal processing. He has also contributed to fundamental modeling in energy harvesting, providing a comprehensive framework for triboelectric nanogenerators beyond simple geometries. He is actively involved in research groups focusing on: Nonlinear dynamics and chaos Mathematical modeling of physical systems Energy harvesting technologies Planetary spin-orbit dynamics

Su ChenView profile

Associate Professor

• Non-parametric Statistics
• Applied Statistics
• Biostatistics
• +3 more

Su Chen is an Associate Professor in the Department of Mathematical Sciences at the University of Memphis, with affiliations in the School of Public Health and the Institute for Intelligent Systems. She holds a Ph.D. in Statistics from Oklahoma State University (2013), following M.S. degrees in Quantitative Financial Economics and Statistics, and a B.S. in Statistics from Wuhan University of Technology, China (2007). Education Ph.D. Statistics, Oklahoma State University (2013) M.S. Quantitative Financial Economics, Oklahoma State University (2012) M.S. Statistics, Oklahoma State University (2009) B.S. Statistics, Wuhan University of Technology, China (2007) Her research focuses on Non- and Semi-parametric Statistics , Applied Statistics , and their applications in biostatistics and epidemiology. Recent work includes epigenetic studies on DNA methylation and health outcomes, statistical methods in financial derivatives, and educational data mining. Her articles explore topics such as disengagement detection in tutoring systems, longitudinal epigenetic trajectories during pregnancy, and distribution-free statistical tests. Award highlights include the Carl Marshall Award for Outstanding Ph.D. Graduate (2013) and interdisciplinary grants from NIH and IES. She advises graduate students in statistical theory and applications, co-leads departmental colloquia, and serves on editorial boards for journals like Journal of Lifestyle Diseases and Management . Teaching spans courses in probability, time series analysis, and statistical learning. She has secured over $3M in external funding for projects on eczema prevention and lung function development. Collaborative work extends to health disparities research and suicide prevention initiatives.

Viktor LarssonView profile

Assistant Professor

• Computer Vision
• Machine Learning
• 3D Geometry Estimation
• +4 more

Viktor Larsson is an Assistant Professor at the Centre for Mathematical Sciences, Lund University, specializing in Computer Vision and Machine Learning. Previously, he worked as a PostDoc and senior researcher at ETH Zurich's Computer Vision and Geometry group under Marc Pollefeys. His research focuses on geometric estimation problems in 3D computer vision, including Structure-from-Motion, visual localization, SLAM, and integrating machine learning into classical pipelines. Key research interests include robust estimation techniques, hybrid feature usage, and algorithmic efficiency. He actively supervises PhD/MSc students and has secured grants for impactful projects. Recent work explores data-driven alternatives to hand-crafted heuristics in vision tasks. Notable achievements include a docent qualification (2024), Best Paper Honorable Mention (CVPR 2024), and Best Student Paper (ICCV 2021). His articles span topics like RANSAC optimization, neural scene encoding, and eigenvalue-based localization methods. He also serves on conference committees for CVPR, 3DV, and ECCV, contributing to academic discourse through tutorials and plenary talks.

• Experimental Psychology
• Cognitive Science
• Artificial Intelligence
• +5 more

Fernando Marmolejo-Ramos is an academic affiliated with Flinders University's College of Education, Psychology, and Social Work. He holds a PhD in Experimental Psychology from the University of Adelaide and has held roles including Research Fellow at the University of South Australia, Visiting Research Fellow at the University of Adelaide, and Lecturer positions across multiple institutions. His expertise spans experimental psychology, cognitive science, and statistical methodologies. Education: BA in Psychology (Universidad del Valle, Colombia, 2003), MAppSc in Psychology (University of Ballarat, Australia, 2007), PhD in Experimental Psychology (University of Adelaide, Australia, 2011). He completed a postdoctoral fellowship at Stockholm University (2014–2016). Research focuses on embodied and artificial cognition, statistical modeling, and robust research methods. Key interests include language comprehension, cross-modal perception, machine behavior, and statistical cognition. Over 120 peer-reviewed articles and numerous grants highlight his contributions to fields like AI ethics, health informatics, and educational technology. His work has been featured in outlets like Nature Human Behavior and Science . Awards include the IEPRS Postgraduate Research Scholarship and the Young and Innovators Researchers Program Fellowship. He serves on editorial boards for journals such as Cognitive Processing and Frontiers in Applied Mathematics . Notable grants include projects on AI in cardiac care, machine learning for health diagnostics, and AI ethics in governance. Current roles include leading projects on AI in education and collaborative reasoning tools for defense applications.

Jandro L. AbotView profile

Associate Professor

• Composite Materials
• Structural Health Monitoring
• Carbon Nanotube Fibers
• +3 more

Jandro L. Abot is an Associate Professor at the Department of Mechanical Engineering within the School of Engineering at Catholic University . His research focuses on composite materials , structural health monitoring , and the development of carbon nanotube fiber-based sensors for aerospace, naval, energy, and biochemical applications. Ph.D. in Theoretical and Applied Mechanics, Northwestern University (2001) M.S. in Theoretical and Applied Mechanics, Northwestern University (1998) B.E. in Structural Engineering, Universidad de la Republica (Uruguay) (1993) Prof. Abot’s research integrates mechanics , materials engineering , and sensor fabrication to explore the electrical/mechanical/thermal characterization of composite materials . His work has received funding from NASA , the Department of State , and AFOSR . Key findings include the development of self-sensing composite materials using carbon nanotube yarns for applications in damage detection and strain monitoring . His publications (over 150 technical papers) emphasize piezoresistive sensors , thermoresistive behavior , and multifunctional composites . He serves on editorial boards of journals like Journal of Carbon Research , Sensors , and Composites Science and Technology , and collaborates with researchers in the USA , Brazil , Mexico , and Denmark . Prof. Abot contributes to academic governance in areas such as mentoring , recruitment , and international programs .

• Fluid Dynamics
• Energy Systems
• Winter Road Maintenance
• +5 more

Jeanette Cleotilde Polanco Pinerez is an Associate Professor at the Department of Industrial Technology, UiT The Arctic University of Norway, based in Narvik. She specializes in fluid dynamics, energy systems, and industrial applications of machine learning, with significant contributions to winter road maintenance technologies and Arctic infrastructure solutions. Her research focuses on: Advanced fluid mechanics and multiphase systems in industrial contexts Machine learning applications for Arctic transportation challenges Energy efficiency and renewable energy systems analysis Sensor technologies for industrial safety and environmental monitoring Thermodynamic modeling and heat transfer optimization Publication analysis reveals strong emphasis on predictive maintenance systems, alternative energy solutions, and Arctic-specific engineering challenges, with recent work increasingly incorporating AI/ML methodologies. She is an active member of the Materials Science research group and contributes to developing practical engineering tools for extreme climate conditions.

Bryan ChangalaView profile

Assistant Professor

• Quantum Molecular Science
• High-Precision Spectroscopy
• Cold Molecules
• +7 more

Bryan Changala is an Assistant Professor and Adjoint Associate Fellow at JILA, a joint institute of the University of Colorado Boulder and the National Institute of Standards and Technology (NIST). He is affiliated with the Department of Physics within the College of Arts and Sciences at the University of Colorado Boulder, where he leads an active research group focused on quantum molecular science and high-precision spectroscopy. His research centers on the development and application of advanced spectroscopic techniques, particularly using microwave and optical frequency combs, cavity-enhanced spectroscopy, and cold-molecule methods. His group investigates complex molecular systems such as ions, radicals, metal-ligand complexes, and nanoscale platforms. The research is organized into four main themes: astrochemistry and molecular growth in extreme environments, chemically tuning molecular optical and spin properties, large molecules with intramolecular spin networks, and quantum chemistry and nuclear motion theory. This integrated experimental and theoretical approach enables rigorous exploration of quantum phenomena in molecular systems. The publication record shows a strong focus on high-impact research in quantum and physical chemistry, particularly involving C60 fullerenes, reactive intermediates like DOCO, and medical breath analysis. His work frequently appears in top-tier journals such as Science , Nature , and PRX Quantum , demonstrating a consistent trend toward probing quantum dynamics, reaction kinetics, and molecular structure with unprecedented precision. The keywords across his articles reflect deep engagement with quantum control, spectroscopic innovation, and interdisciplinary applications in materials, health, and fundamental physics. While no formal scientific awards are listed in the provided text, his publications and affiliations with JILA and NIST indicate recognition within the physics and chemistry communities. He is involved in mentoring and advising through his research group, though specific student names are not listed. His work is supported by institutional affiliations with JILA and likely involves federal or institutional grants, though specific grants are not detailed. Bryan Changala's research is conducted through the Changala Group at JILA, located in Boulder, Colorado. The group leverages state-of-the-art facilities for frequency comb spectroscopy and cold molecule experiments, contributing to the broader JILA mission of advancing quantum science and technology. The lab emphasizes close integration of experimental data with in-house quantum chemical modeling, forming a synergistic team approach to molecular discovery.

• Energy Economics
• Econometrics
• Environmental Economics
• +3 more

Vitor Manuel Ferreira Moutinho is an Assistant Professor at Universidade da Beira Interior , with extensive research expertise in Energy Economics , Econometrics , and Environmental Economics . His work bridges macroeconomic theory with empirical analysis of energy systems, climate change, and sustainable development. PhD in Energy Systems and Climate Change (Universidade de Aveiro, 2015) 46+ peer-reviewed publications Specializes in stochastic frontier analysis , ARDL modeling , and decomposition methods Research trends reveal consistent focus on: Energy-environmental efficiency metrics Economic policy uncertainty impacts Electricity market dynamics Decarbonization strategies Ageing population effects on energy demand

Ravikant SinghView profile

Research Fellow

• Structural Fire Engineering
• Cold-Formed Steel Structures
• Finite Element Analysis
• +4 more

Dr. Ravikant Singh is a Post Doctoral Fellow Level 1 at University College Dublin's School of Civil Engineering, working on the STANDARDIZE project with the Modern Methods in Construction Research Group (MMCRG). Previously, he served as a Guest Lecturer at the National Institute of Technology (NIT), Arunachal Pradesh, India from August to December 2024, where he taught Construction Planning and Management, Engineering Geology, and Engineering Hazard Analysis and Control with a focus on Structural Fire Hazards. Dr. Singh's educational background includes: Ph.D. in Structural Engineering from Indian Institute of Technology (IIT) Patna M.Eng. in Structural Engineering from National Institute Of Technical Teachers Training and Research Institute (NITTTR), Bhopal B.Eng. in Civil Engineering from Rajiv Gandhi Proudyogiki Vishwavidyalaya (RGPV), Bhopal Dr. Singh's research focuses on structural fire engineering, particularly on developing standardized solutions for innovative construction techniques in Ireland. His work combines experimental fire testing and advanced numerical finite element simulations to improve understanding of failure mechanisms in structural elements exposed to fire. He specializes in cold-formed steel members exposed to elevated temperatures, investigating thermal gradients, critical temperatures, buckling behaviors, and structural performance under various fire scenarios. His publication record demonstrates a consistent focus on advancing fire safety standards for cold-formed steel structures. Dr. Singh's research has explored various aspects including thermal gradients in flexural members, reliability assessment of design proposals, critical temperature prediction, local-distortional coupled buckling, and the effects of different loading patterns on structural performance during fire exposure. His work bridges theoretical research with practical engineering applications, contributing significantly to the field of structural fire engineering. Dr. Singh has received several prestigious awards and honors: Best Poster Award (2021, IIT Patna) Best Poster Award (2022, IIT Patna) My Research in 3 Minutes Award (2022, IIT Patna) International Travel Grant from Department of Science and Technology (DST), Government of India (2023) International travel support from IIT Patna (2022) Full funding for both PhD and M.Tech through Ministry of Education (MoE), Government of India During his time at IIT Patna, Dr. Singh actively mentored Postgraduate and PhD Scholars while serving as the Department Academic Program Coordinator (2022-2024) and Postgraduate Training & Placement Head (2022-2024). He has secured significant research funding including the International Travel Grant from DST, Government of India, and full funding for his doctoral and master's studies through the Ministry of Education. His collaborative approach is evident in his extensive publication record and professional activities, which include peer reviewing for prominent journals in structural engineering. Dr. Singh is part of the Modern Methods in Construction Research Group (MMCRG) at University College Dublin, where he contributes to the STANDARDIZE project. His work involves close collaboration with industry partners to develop practical fire safety solutions for innovative construction techniques in Ireland. He maintains active professional engagement through memberships in the American Society of Civil Engineers, Institution of Structural Engineers (IStructE), and Structural Stability Research Council (SSRC).

Alessandro GabbanaView profile

Researcher

• Computational Fluid Dynamics
• Lattice-Boltzmann Method
• Turbulence Modeling
• +3 more

Alessandro Gabbana is a University Researcher at Eindhoven University of Technology, affiliated with the Applied Physics and Science Education school and the Computational Multiscale Transport Phenomena group. His research focuses on computational physics, particularly lattice-Boltzmann methods and fluid dynamics. He co-leads the HTCrowd project (2020–2026), developing high-tech platforms for human crowd flow monitoring and modeling. Research interests span: Advanced computational fluid dynamics (CFD) Turbulence subgrid modeling using data-driven approaches Pedestrian dynamics and probabilistic flow analysis Neural network integration with kinetic schemes His publications demonstrate strong emphasis on improving lattice-Boltzmann boundary conditions, turbulence closures, and large-scale pedestrian dynamics simulations.

Kerstin Lux-GottschalkView profile

Assistant Professor

• Uncertainty Quantification
• Stochastic Differential Equations
• Optimal Control Theory
• +4 more

Kerstin Lux-Gottschalk is an Assistant Professor at the Department of Mathematics and Computer Science , Eindhoven University of Technology, affiliated with the Centre for Analysis, Scientific Computing and Applications (CASA) . She specializes in uncertainty quantification, stochastic differential equations, and optimal control, with applications to climate science, epidemiology, and ecology. Education : B.Sc. and M.Sc. from University of Mannheim, Germany; semester abroad at Université Nice Sophia Antipolis, France; Ph.D. from University of Mannheim (2020) under Prof. Dr. Simone Göttlich. Postdoctoral Research : Technical University of Munich (2020–2023) in Multiscale and Stochastic Dynamics group. Her research focuses on quantifying uncertainty in tipping points of complex systems, including: Analysis of random ordinary differential equations Climate modeling of Atlantic meridional overturning circulation Non-Markovian bifurcation detection Reinforcement learning for control systems Recent publications explore uncertainty quantification of tipping thresholds, stochastic control in transport systems, and numerical methods for SDEs. She contributes to UN Sustainable Development Goals related to climate action and sustainable infrastructure.

• Econometrics
• Operations Research
• Household Economic Analysis
• +4 more

Dr. Marta Dziechciarz is a faculty member at Wrocław University of Economics, affiliated with the Department of Econometrics and Operations Research. She teaches courses in econometrics, financial data modeling, and programming in R. Current position: Lecturer Department: Econometrics and Operations Research University: Wrocław University of Economics Contact: marta.dziechciarz@ue.wroc.pl Her research focuses on: Subjective well-being and income expectations in households Fuzzy set theory applications in economic measurement Market segmentation techniques for durable goods Non-monetary poverty factors and housing economics Multivariate analysis of consumer behavior Econometric modeling of household welfare Key publication trends include: Panel data analysis of European well-being (2024) Fuzzy logic applications for perception measurement (2020) Comparative studies across Central European countries (2021) Consumer durables and housing market analysis (2017-2018) Human capital and training needs identification (2016) She utilizes both traditional econometric methods and unconventional fuzzy number coding for complex economic analyses. Teaching activities include: Applied econometrics Dynamic and financial econometrics Computer tools for data analysis Financial data modeling and forecasting Programming in R

Debarghya GhoshdastidarView profile

Assistant Professor

• Machine Learning
• Artificial Intelligence
• Network Science
• +3 more

Prof. Debarghya Ghoshdastidar is an Assistant Professor of Theoretical Foundations of Artificial Intelligence at Technische Universität München (TUM) since 2019. He holds a Tenure Track position within the TUM School of Computation, Information and Technology. His research focuses on the statistical foundations of machine learning, network science, and their applications in neuroscience, crowdsourcing, and computer vision. Education: B.E. in Electrical Engineering from Jadavpur University (2010), M.S. and Ph.D. in Computer Science from Indian Institute of Science (2016). Following his PhD, he conducted postdoctoral research at the University of Tübingen, leading a Baden-Württemberg Foundation-funded junior research group. Research Interests: Theoretical machine learning, network analysis, statistical learning theory, and algorithmic guarantees for AI systems. His work bridges abstract theoretical frameworks with real-world applications, particularly in understanding the robustness and generalization of learning algorithms. Awards: Recipient of the Baden-Württemberg Elite Program for Postdocs (2017) and Google Ph.D. Fellowship (2013). His contributions include novel algorithms for graph hypothesis testing and spectral hypergraph partitioning. Grants and Advising: Led a junior research group in Tübingen; no explicit student lists provided here. Active in theoretical contributions to AI, with a focus on interpretable and robust machine learning.

Phaedon-Stelios KoutsourelakisView profile

Professor

• Uncertainty Quantification
• Multiscale Modeling
• Computational Mechanics
• +3 more

Prof. Phaedon-Stelios Koutsourelakis is a Professor of Continuum Mechanics at the Technical University of Munich (TUM), affiliated with the TUM School of Engineering and Design. His research focuses on computational strategies for modeling and analyzing physical systems with a strong emphasis on uncertainty quantification, multiscale phenomena, and stochastic systems. He holds a PhD from Princeton University and has held academic positions at institutions including Cornell University, Heriot-Watt University, and the Lawrence Livermore National Laboratory. His educational background includes a degree from the National Technical University of Athens and a PhD from Princeton University (1998). Key research areas include data-driven coarse-graining techniques, Bayesian inference in engineering systems, and physics-aware machine learning models for high-dimensional problems. He has received notable awards such as the Top Teaching Trophy from the Munich School of Engineering (2014–2016) and the Dean’s First Year Merit Prize at Princeton (1998). His work bridges computational mechanics with statistical methods, addressing challenges in materials design, structural health monitoring, and predictive modeling under uncertainty. Recent projects include developing physics-constrained deep learning frameworks for surrogate modeling and quantifying model bias in digital twins of infrastructure systems. Notable contributions include the Predictive Coarse-Graining framework and probabilistic methods for uncertainty quantification in heterogeneous media. His research also extends to optimization under uncertainty and interdisciplinary applications in biomedical engineering and materials science.