Find Academic Supervisors

• Hydrology
• Catchment Management
• Sustainable Water Management
• +4 more

Willem Vervoort is a Professor in Hydrology and Catchment Management and Director of the ARC Training Centre in Data Analytics for Resources and Environments (DARE) at the University of Sydney. He holds academic affiliations with the School of Life and Environmental Sciences and is a member of the Sydney Environment Institute, Sydney Institute of Agriculture, Sydney Southeast Asia Centre, and The Net Zero Institute. His expertise lies in quantitative hydrology, sustainable water management, and climate change impacts. Vervoort earned a PhD in field hydrology from the University of Georgia and an Agricultural Engineering degree from Wageningen University in Soil Conservation. His research focuses on developing sustainable water management strategies, particularly in agricultural contexts, leveraging interdisciplinary approaches. Key areas include groundwater dynamics, catchment functionality, and the integration of remote sensing and machine learning for hydrological modeling. Recent work emphasizes climate adaptation, afforestation impacts, and water market functionality in Australia and globally. Grants : Includes leadership in Horizon 2020-funded projects on remote sensing for agricultural water management and GRDC-funded initiatives on frost and heat management analytics. Labs/Teams : Directs the DARE Centre and collaborates with international teams in Uruguay, Fiji, and Italy, advancing SWAT model applications and hydrological forecasting. Publications span hydrological processes, climate extremes, and eco-hydrological interactions, with a focus on scaling models for policy and decision-making. His work bridges academic research with practical solutions for environmental sustainability and resource management.

Peter SarnakView profile

Professor

• Number Theory
• Automorphic Forms
• Spectral Theory
• +3 more

Peter Sarnak is a Professor of Mathematics at Princeton University, affiliated with the School of Mathematics. His primary research field is Number Theory , with significant contributions to automorphic forms, spectral theory, and quantum chaos. Email: sarnak@math.princeton.edu Recent publications highlight his work on L-functions, Möbius disjointness, and nodal sets of eigenfunctions. His research intersects arithmetic geometry, dynamical systems, and harmonic analysis, often addressing deep connections between randomness and arithmetic structures. Key subfields include automorphic forms, spectral gaps, thin matrix groups, and applications of number theory to geometric packings. Collaborations span prominent mathematicians like Jean Bourgain and Sug Woo Shin.

Alice ThorneyworkView profile

Associate Professor

• Soft Matter Physics
• Nanotechnology
• Colloidal Science
• +5 more

Alice Thorneywork is an Associate Professor of Physical Chemistry and Royal Society University Research Fellow at the University of Oxford, where she also serves as a Tutorial Fellow in Physical Chemistry at Lincoln College. Her research focuses on experimental soft matter and nanoscale systems, utilizing colloids, microfluidics, solid-state nanopores, and DNA nanotechnology to investigate fluctuations and transport in driven, out-of-equilibrium systems. Her educational background includes an undergraduate degree in Chemistry and a DPhil in Physical and Theoretical Chemistry, both completed at Christ Church, University of Oxford. Her doctoral research under Prof. Roel Dullens examined colloidal model systems for two-dimensional hard sphere fluids, earning her the 2019 RSC Statistical Mechanics and Thermodynamics Group Young Scientist Award. Thorneywork's research spans soft matter physics , nanotechnology , and non-equilibrium statistical mechanics , with emphasis on ionic transport in nanopores and microfluidic colloidal transport. She develops novel methods to decode noise signatures in nanofluidic systems and measures collective diffusion without particle trajectories, bridging single-particle to continuum-scale phenomena. Her work has direct applications in DNA sequencing, molecular sensing, and understanding biological transport mechanisms. Recent publications reveal a strong trend toward methodological innovation in analyzing fluctuations and transport, particularly through power spectral density interpretation and the development of the 'Countoscope' technique. Her research consistently connects experimental observations with theoretical frameworks to uncover universal behavior in non-equilibrium systems across diverse length and time scales. Notable scientific awards include: 2019 RSC Statistical Mechanics and Thermodynamics Group Young Scientist Award Oppenheimer Research Fellow (2018-2021) Royal Society University Research Fellowship ERC Starting Grant (now UKRI-funded) Thorneywork leads an active research group funded by the Royal Society and UKRI, collaborating closely with theoretical and computational physicists. Her group employs holographic optical tweezers and DNA origami to engineer nanoscale transport processes, with recent projects focusing on polymer adsorption in nanopores and colloidal capture dynamics in microfluidic channels. She actively mentors PhD students and postdoctoral researchers in advanced experimental techniques. Her laboratory within the Physical and Theoretical Chemistry Laboratory integrates colloidal model systems, microfluidics, and solid-state nanopores to probe fundamental physical phenomena. Current initiatives include functionalizing nanopores with DNA origami for precise transport control and applying mesoscale analysis methods to single-molecule biological experiments, positioning her group at the forefront of soft matter and nanoscale transport research.

Andrew MuglerView profile

Associate Professor

• Theoretical Physics
• Cell Biology
• Biophysics
• +5 more

Andrew Mugler is an Associate Professor in the Department of Physics & Astronomy at the University of Pittsburgh, part of the Dietrich School of Arts and Sciences. His research group investigates cell behavior through theoretical physics, employing statistical physics, stochastic modeling, and information theory. Key projects include collective sensing, metastatic invasion in cancer, long-range cellular signaling, and criticality in biological systems. Mugler’s work spans molecular to multicellular scales, often collaborating with experimental teams. Current focuses include understanding how communication enhances sensing in cell ensembles, the role of feedback in biochemical networks, and the application of percolation theory to biological signaling. His interdisciplinary approach bridges physics and biology, addressing fundamental questions in systems biology. Labs/Teams: The Mugler Group. No scientific awards explicitly listed, though his contributions are highlighted through collaborative research. No advisees listed in available texts, but his lab likely engages students in theoretical and computational projects.

Franco RuzzenentiView profile

Assistant Professor

• Energy & Fuels
• Environmental Studies
• Complex Systems
• +7 more

Franco Ruzzenenti is an Assistant Professor (tenure track) at the University of Groningen, affiliated with the Faculty of Science and Engineering and the Integrated Research on Energy, Environment & Society (IREES). He holds additional roles as Programme Director of the MSc 'Energy & Environmental Sciences' and Director of Education at the Energy & Sustainability Institute Groningen (ESRIG). His research focuses on complex systems, network theory, and energy sustainability, with particular emphasis on topics like energy efficiency, rebound effects, and carbon implications of global systems. Affiliations: University of Groningen, Faculty of Science and Engineering, IREES, ESRIG Expertise: Energy Modelling, Industrial Ecology, Climate Change, Sustainable Development Goals Ruzzenenti’s work bridges environmental and socio-economic systems, addressing challenges such as fossil carbon accumulation in the technosphere, sovereign wealth funds’ carbon footprints, and the dynamics of grid-scale battery storage in the EU. He employs interdisciplinary methods, including agent-based modeling, econometrics, and network theory, to analyze energy transitions and policy impacts. His research outputs span high-impact journals like Cell Reports Sustainability , Nature Climate Change , and Energy Policy , reflecting contributions to global sustainability science. Collaborations include projects funded by the EU and international partnerships on energy systems and environmental policy.

• Optimal Control
• Partial Differential Equations
• Numerical Analysis
• +3 more

Prof. Michael Hintermüller is a Professor at Humboldt University of Berlin, affiliated with the Faculty of Mathematics and Natural Sciences and the Institute of Mathematics, specializing in Applied Mathematics. He also holds an affiliation with the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) in Berlin. His research focuses on optimal control of partial differential equations (PDEs), variational inequalities, numerical analysis, and their applications in imaging and energy systems. Notable contributions include work on machine learning-informed PDEs, stochastic optimization, and mathematical modeling of coupled systems like hydrogen-electric markets. His recent publications emphasize data-driven methods in imaging, regularization techniques, and optimization algorithms for complex systems. He explores interdisciplinary topics such as quantum systems simulation, gas network control, and energy market modeling. His work often bridges theoretical analysis and computational implementation, with applications in engineering and medicine. Prof. Hintermüller’s research aligns with initiatives like MaRDI, aiming to build infrastructure for mathematical data sciences. His academic profile reflects a strong commitment to advancing applied mathematics through rigorous analysis and innovative numerical methods.

Vladimir KolmogorovView profile

Professor

• Discrete Optimization
• Combinatorial Optimization
• Global Optimization
• +3 more

Vladimir Kolmogorov is a Professor at the Institute of Science and Technology Austria (IST Austria), leading the Kolmogorov Group focused on Discrete Optimization. He previously held positions as Assistant Professor at IST Austria (2011–2014), Lecturer at University College London (2005–2011), and Associate Researcher at Microsoft Research (2003–2005). His research spans algorithm development for graphical models, combinatorial optimization, and applications in computer vision. Education: M.S. in Applied Mathematics and Physics from the Moscow Institute of Physics and Technology, and Ph.D. in Computer Science from Cornell University (2003). Research interests include complexity classifications, algorithm design for discrete optimization problems (e.g., max-flow, min-cost matching), and applications in computer vision. Notable contributions include the 'Boykov-Kolmogorov' max-flow algorithm and 'Blossom V' for minimum cost perfect matching. Honors include the ERC Consolidator Grant (2014–2019), Koenderink Prize (2012), and best paper awards at CVPR and ECCV. His work on optimization algorithms and theoretical complexity has significantly impacted computer vision and combinatorial optimization fields. Advising and grants: Supervised multiple PhD students and postdocs, with current advisees including Martin Dvořák and Pavel Arkhipov. His team explores inference in graphical models, combinatorial optimization, and discrete optimization theory.

Fei LuView profile

Associate Professor

• Malliavin Calculus
• Stochastic Partial Differential Equations
• Stochastic Modeling
• +4 more

Fei Lu is an Associate Professor in the Department of Mathematics at Johns Hopkins University, affiliated with the Krieger School of Arts & Sciences. His research focuses on probability, statistics, and their applications in scientific computing, including stochastic modeling, data assimilation, and statistical inference. He holds a Ph.D. from the University of Kansas (2013), advised by David Nualart and Yaozhong Hu. Prior to JHU, he was a postdoctoral fellow at UC Berkeley and the Lawrence Berkeley National Laboratory under Alexandre Chorin. Education: PhD in Mathematics, University of Kansas, 2013 Postdoctoral Fellowship: UC Berkeley & Lawrence Berkeley National Laboratory Research Interests: Malliavin Calculus and Stochastic PDEs Data-driven Stochastic Model Reduction Monte Carlo Methods and Data Assimilation Nonparametric Kernel Learning His work bridges theoretical probability with computational methods, addressing challenges in complex systems modeling and uncertainty quantification. Advising & Grants: While specific student names are not listed, his research group focuses on interdisciplinary projects in stochastic systems. Funding sources and grants are not explicitly detailed in the provided text. Labs/Teams: Collaborations span applied mathematics, computational physics, and machine learning, though specific lab affiliations are not mentioned.

Mehmet CanerView profile

Professor

• Econometrics
• Empirical International Finance
• High-Dimensional Econometrics
• +2 more

Dr. Mehmet Caner is a Professor in the Department of Agricultural and Resource Economics at North Carolina State University's College of Agriculture and Life Sciences. Previously at Ohio State University (2015-2019), he specializes in econometrics with research emphasis on empirical international finance and high-dimensional econometric testing. He serves as associate editor for multiple leading journals including Journal of Econometrics and Journal of Business and Economic Statistics. His methodological innovations focus on high-dimensional econometrics, particularly developing testing frameworks for models with large parameter spaces. Research extends to applications in financial portfolio optimization, market integration analysis, and machine learning incentive structures. Recent publications (2020-2024) demonstrate strong emphasis on LASSO-based inference, deep learning integration in factor models, and constrained portfolio analysis under high-dimensional complexity. Education: Ph.D. Economics, Brown University (1996) A.M. Economics, Brown University (1993) Bachelor of Science Business Administration, Middle East Technical University (1988) Scientific Awards: Research Award, NC State University Research Award, University of Pittsburgh

• Neuroscience
• Systems Biology
• Computational Biology
• +4 more

Stig William Omholt is a Research Professor and Research Director at the Kavli Institute for Systems Neuroscience at NTNU. His work bridges systems biology, neuroscience, and computational modeling. Key research interests include Alzheimer’s disease mechanisms, population dynamics modeling, and cardiovascular physiology. He has contributed to major initiatives like the Functional Annotation of All Salmonid Genomes (FAASG) and pandemic response strategies during the COVID-19 crisis. Publications span topics such as amyloid beta-reelin interactions in Alzheimer’s, household-targeted vaccination strategies, and mitochondrial genetics. His interdisciplinary approach integrates mathematical modeling with experimental data to address complex biological systems. Recent work emphasizes translational applications in personalized medicine and ecological conservation. Key Contributions: Alzheimer’s pathology modeling, wildlife population dynamics, and pandemic intervention strategies Collaborations: International teams in genomics, cardiology, and epidemiology Expertise: Systems-level analysis of physiological and ecological systems

Brian C. HallView profile

Professor

• Mathematical Physics
• Random Matrix Theory
• Lie Groups
• +4 more

Brian C. Hall is a Professor in the Department of Mathematics at the University of Notre Dame, affiliated with the College of Science. He holds a Ph.D. from Cornell University (1993). His research focuses on mathematical physics, with specialties in random matrix theory, Lie groups, geometric quantization, and free probability. Notably, he has contributed to the study of the Brown measure of free multiplicative Brownian motions and PDE methods in random matrix theory. His work often bridges mathematical analysis and theoretical physics, including connections to quantum chaos and Yang-Mills theory. Hall has authored influential graduate texts on Lie groups and quantum theory for mathematicians. His research has been published in prestigious journals such as Probability Theory and Related Fields and Communications in Mathematical Physics . Education: Ph.D., Cornell University, 1993. Research interests include mathematical physics, random matrix theory, free probability, and geometric quantization. His recent work explores the eigenvalue dynamics of random matrices and heat flow effects on polynomial zeros. He has collaborated extensively on projects involving PDE approaches to eigenvalue distributions and Brown measure calculations. Hall’s publications also cover topics like Segal-Bargmann transforms and two-dimensional Yang-Mills theory. His research trends emphasize interdisciplinary methods, combining probability theory, operator algebras, and differential equations to address problems in quantum mechanics and statistical physics. While no specific awards are listed, his contributions have been recognized through invited lectures at institutions including Ohio State University and the Banff International Research Station. His academic profile includes roles as a faculty member and researcher, with ongoing engagement in mathematical physics communities.

• Partial Differential Equations
• Approximation Theory
• Nonlinear Approximation
• +3 more

Guergana Petrova is a Professor in the Department of Mathematics at Texas A&M University, affiliated with the College of Arts & Sciences. She holds a Ph.D. from the University of South Carolina (1999) and earlier degrees from Sofia University, Bulgaria. Her research focuses on Partial Differential Equations, Approximation Theory, Nonlinear Approximation, and Numerical Methods, with notable contributions to hyperbolic PDEs, conservation laws, and numerical quadrature. Her academic career includes roles as Assistant Professor at the University of Michigan (1999–2001) and Texas A&M University (2001–2006), followed by promotion to Associate Professor (2006–2011) and Full Professor (2011–present). Her work bridges theoretical analysis and computational methods, emphasizing nonlinear approximation, neural network approximation, and reduced modeling techniques. Key research trends in her publications include advancing numerical schemes for PDEs, analyzing approximation theory in high-dimensional spaces, and exploring machine learning applications. She has collaborated extensively with researchers like DeVore, Kurganov, and Wojtaszczyk on topics ranging from greedy algorithms to fluid dynamics. Petrova’s contributions include foundational work on central-upwind schemes for hyperbolic systems, optimal recovery methods, and entropy number analysis. Her teaching includes advanced courses in numerical analysis and applied mathematics.

• Traffic Conflict Analysis
• Connected and Automated Vehicles
• Human Factors and Driving Behaviour
• +5 more

Professor Md. Mazharul Haque is a distinguished academic and researcher in transportation engineering at Queensland University of Technology (QUT), Australia. He serves as Head of the School of Civil & Environmental Engineering and holds a Professorial rank. His expertise spans econometrics, artificial intelligence, and traffic safety. He has secured over $3 million in research funding, published over 110 peer-reviewed articles, and achieved an h-index of 27 (Scopus) and 33 (Google Scholar). His work is recognized through prestigious roles like Associate Editor of Accident Analysis & Prevention and ASCE Journal of Transportation Engineering . Research focuses include Traffic Conflict Analysis, Connected and Automated Vehicles (CAVs), Human Factors in Driving, and Black Spot Identification. He co-founded the startup Advanced Mobility Analytics Group (AMAG), developing AI-driven road safety tools. Awards include the TRB Outstanding Paper Award (2020, 2016) and the John Kirby Award (2014). He has advised multiple PhD students on topics like CAV integration, safety modeling, and traffic flow optimization. His grants address real-time risk assessment, safety evaluation of traffic signals, and CAV behavioral impacts. Education includes a PhD from the National University of Singapore (2005–2009) and degrees from Bangladesh University of Engineering and Technology (BUET). Professional memberships include TRB Committees, ASCE, and editorial roles in leading journals. Key contributions include pioneering AI-based video analytics for crash risk forecasting and advancing econometric models for transportation systems.

Ryan ChiechiView profile

Associate Professor

• Molecular Electronics
• Self-assembled Monolayers
• Conjugated Polymers
• +5 more

Ryan Chiechi is an Associate Professor in the Department of Chemistry at North Carolina State University, affiliated with the College of Sciences. His research focuses on organic-materials chemistry, particularly the design, synthesis, and implementation of organic molecules for thin-film and interface applications. He leads interdisciplinary projects in molecular electronics, self-assembled monolayers, and unconventional nanofabrication. Education: Ph.D. in Chemistry, University of California - Los Angeles (2009) B.S. in Chemistry, University of Oregon (2001) His research explores molecular electronics , thermoelectrics , and organic photovoltaics , with a focus on optimizing charge transport and energy conversion in organic materials. Recent work highlights advancements in n-type organic semiconductors, self-assembled monolayers, and biohybrid systems like photosystem I-based devices. Publications emphasize novel nanofabrication techniques , thermoelectric material design , and stable organic electrochemical transistors . His work bridges fundamental chemistry with applications in energy and electronics, leveraging quantum mechanical effects and environmental stability studies. Dr. Chiechi collaborates across disciplines, contributing to the development of sustainable materials and bio-inspired technologies. His lab focuses on scalable, air-stable systems with applications in sensors, energy harvesting, and neuromorphic computing.

Richard SharpView profile

Professor

• Ergodic Theory
• Dynamical Systems
• Geometry
• +3 more

Richard Sharp is a Professor of Mathematics at the University of Warwick, specializing in Ergodic Theory and Dynamical Systems with applications to geometry, group theory, and noncommutative geometry. He holds a PhD from the University of Warwick (1990), following undergraduate studies at Imperial College London (BSc 1987) and postgraduate work at the same institution (MSc 1988). His research explores topics such as Anosov flows, closed geodesics, and zeta functions, with a focus on geometric and statistical properties of dynamical systems. Sharp's work includes significant contributions to the understanding of orbit counting, growth rates in hyperbolic groups, and large deviations in dynamical systems. He has secured major research grants, including EPSRC funding for projects like 'Critical Exponents and Thermodynamic Formalism for Geometrically Infinite Spaces' (2017–2020) and 'Hyperbolic Dynamics and Noncommutative Geometry' (2012–2015). He collaborates extensively with institutions across the UK and internationally, contributing to networks like the LMS-funded Ergodic Theory Meetings. His teaching responsibilities include modules like MA424 Dynamical Systems. He maintains an active seminar culture at Warwick and participates in collaborative workshops on ergodic theory and geometric dynamics. Sharp’s publications span high-impact journals such as Inventiones mathematicae , Communications in Mathematical Physics , and Mathematische Annalen , reflecting his deep engagement with both pure and applied aspects of dynamical systems and geometry.