Find Academic Supervisors

• Tectonics
• Structural Geology
• 3D Geological Modelling
• +3 more

Laurent Ailleres is a Senior Lecturer at Monash University's School of Earth Atmosphere and Environment. He holds a role as a Senior Research Fellow in the School of Geosciences. His research focuses on tectonics, structural geology, 3D geological modeling, and integration of geophysical datasets. Key projects include three-dimensional Bayesian modeling of geological/geophysical data (ARC-funded), ore body modeling (AMIRA P1249), and improving usability of Loop3D software (Auscope). Research Interests: Laurent's work spans tectonic boundaries, structural analysis using aeromagnetic data, 3D multi-scale modeling, and geophysical inversion techniques. He emphasizes combining geological and geophysical datasets to advance mineral exploration and crustal architecture understanding. Recent Articles: His 2022 work on Paleoproterozoic gold events in West Africa and 2021 studies on automated geological mapping and LoopStructural software showcase his contributions to 3D modeling and geophysical integration. Articles often address fault modeling, structural overprinting, and igneous intrusion analysis. Grants & Collaborations: Leads multiple ARC, CRC, and industry-funded projects. Collaborations span institutions globally, focusing on mineral systems, crustal dynamics, and open-source geoscience tools. Labs/Teams: Central to the Loop Project team developing LoopStructural software. Engaged with international research networks for 3D geological modeling applications.

• Geometric Shape Analysis
• Deep Learning
• 3D Shape Matching
• +2 more

Maks Ovsjanikov is Professor of Computer Science at École Polytechnique and Visiting Research Scientist at Google DeepMind. Leading the GeomeriX team, his ERC-funded research develops deep learning methods for 3D shape analysis with applications in protein modeling and computer vision. Recent breakthroughs include CrAI for antibody detection in cryo-EM data and surface-based protein representations. His group pioneers neural approaches for non-rigid shape matching and geometric deep learning fundamentals. Collaborations span structural biology, with work featured at top venues including SIGGRAPH, CVPR, and Nature journals.

Krishnan ShankarView profile

Professor

• Riemannian Geometry
• Lie Groups
• Non-negatively Curved Manifolds
• +5 more

Krishnan Shankar is a Professor of Mathematics at the University of Oklahoma since 2002. He holds a B.A. from Reed College and a Ph.D. from the University of Maryland under Karsten Grove. His research focuses on Riemannian Geometry, Lie groups, non-negatively curved manifolds, Topological Data Analysis, and Cryptography. He teaches courses in mathematical statistics and advanced geometry topics. His work explores geometric structures with non-negative curvature, symmetry properties of manifolds, and applications of topological methods. Recent research includes studies on Eschenburg spaces, spherical rank rigidity, and isoperimetric spectra in geometric group theory. He has collaborated extensively with researchers like Martin Kerin and Ralf Spatzier. Teaching highlights include Math 4743/5743 on mathematical statistics and a literacy course on comparison theorems in Riemannian geometry. His publications span leading journals including Duke Mathematical Journal and Journal of Differential Geometry. Beyond academia, he engages in educational technology and environmental stewardship.

• Air Vehicle Design (Derivative and Unconventional)
• Computational Aerodynamics
• Aeroelasticity
• +2 more

Professor Andrea Da Ronch is a faculty member at the University of Southampton's School of Engineering, holding the academic rank of Professor and serving as Director of the Boeing Flight Simulators Laboratory. She maintains active research leadership and teaching responsibilities within the Aeronautics and Astronautics program, with a demonstrated commitment to high-quality education evidenced by two nominations for "Outstanding Lecturer" and Fellowship of the Higher Education Academy. Her research spans Air Vehicle Design (Derivative and Unconventional), Computational Aerodynamics, Aeroelasticity, Weather Modeling, and Multidisciplinary Optimisation. She develops scalable computational methods at the intersection of aerospace engineering, mathematics, and computer science to support next-generation sustainable air transportation, targeting aviation decarbonization, Advanced Air Mobility (AAM) connectivity, and accelerated industry product development through physics-informed machine learning approaches. Recent publications (2024-2025) reveal a pronounced trend toward geometric deep learning and graph neural networks for transonic flow prediction, morphing wing analysis, and propeller-wing interaction. These works emphasize computational efficiency in unsteady aerodynamics while addressing critical challenges in aircraft design validation and dynamic maneuver simulation. Scientific awards include: Rotary International Award (2012) Aerospace Speakers Travel Grant (2010) T.I.M.E. Double Degree Award (2008) AIAA Atmospheric Flight Mechanics Best Paper Award (2019) Fellow of the Higher Education Academy (2014) Senior Member of AIAA (2017) She currently supervises three PhD students (Declan Salazar Clifford, Gabriele Immordino, Giuseppe Morichetti) with funding secured from EPSRC, European Union H2020, US Air Force Office of Scientific Research, and Royal Academy of Engineering projects including IMPACT anti-ice coatings optimization, Sparsified Reduced Modeling, and transonic buffet analysis. As Director of the Boeing Flight Simulators Laboratory, she provides hands-on curriculum enhancement for engineering students. Her active membership in the Aerodynamics and Flight Mechanics Research Group facilitates collaboration on sustainable aviation initiatives while supporting her leadership in major projects like HOMER- with Professor Bharath Ganapathisubramani.

Yakov NekrichView profile

Associate Professor

• String Algorithms
• Compressed Data Structures
• Computational Geometry
• +3 more

Yakov Nekrich is an Associate Professor of Computer Science at Michigan Technological University with expertise in algorithms and data structures. His research spans geometric data structures, string algorithms, and compressed data representations, bridging theoretical and applied computer science. His research interests include: Algorithm Design : Focus on efficient solutions for computational geometry and string processing. Data Structures : Optimization for dynamic and multidimensional data, including external memory implementations. Compressed Data Structures : Balancing space efficiency with fast query performance for massive datasets. Recent publications highlight advancements in: 4D dominance range reporting (SODA 2023, SoCG 2020) Colored range searching (SoCG 2020, SODA 2020) Dynamic planar point location (STOC 2021, SIAM J. Comp. 2018) Compressed index construction (SODA 2017) These works emphasize geometric algorithms, database optimization, and algorithmic complexity. Teaching at Michigan Tech since 2019, he has delivered courses in Advanced Algorithms and Introduction to Algorithms . Previously, he taught Algorithms (CS341) at University of Waterloo (2014) and Data Structures and Data Management (CS240) (2015-2016). As an active conference organizer, he has served on program committees for SPIRE (2013-2020), PODS (2015), LATIN (2016), and DCC (2020-2023), among others.

André NusserView profile

Researcher

• Algorithms and Complexity
• Computational Geometry
• Dynamic Time Warping
• +4 more

André Nusser is a CNRS Researcher affiliated with the COATI group at Inria Center, Université Côte d’Azur. His work focuses on algorithms, computational geometry, and metric space analysis, particularly around the Fréchet distance, dynamic time warping, and graph hyperbolicity. Current Position: CNRS Researcher at Inria Université Côte d’Azur Primary Research Areas: Algorithms & Complexity, Computational Geometry, Metric Space Analysis Research Focus: André's research explores geometric optimization problems, trajectory analysis, and fine-grained complexity. Key contributions include advancements in dynamic time warping under translation, diameter computation for intersection graphs, and efficient algorithms for polygon partitioning. Awards & Recognition: Recipient of the Best Paper Award at VLDB 2014, Honorable Mention at AAAI 2014, and Best Poster Runner-up at SIGSPATIAL 2019. His work on Hausdorff distance and graph hyperbolicity has been invited to special issues. Academic Collaborations: Co-author on over 20 publications across conferences like STOC, SoCG, SODA, ESA, and journals including ACM Transactions on Algorithms and Discrete & Computational Geometry. Collaborates with leading researchers in computational geometry and algorithms. Software Advocacy: Actively promotes free software in research, highlighting tools like Ipe, Xournal++, Zotero, and Zulip for scientific productivity and collaboration.

William L. PardonView profile

Professor

• Algebraic Geometry
• Topology
• Hodge Theory
• +3 more

William L. Pardon is a Professor in the Department of Mathematics at Duke University. He holds a B.A. from the University of Michigan and a Ph.D. from Princeton University. His research focuses on the Algebra and Geometry of Varieties, with particular emphasis on topics such as Hodge structures, Chern classes, and cohomological studies of singular varieties. His recent work includes studies on modular varieties, Hodge theory in singular spaces, and filtered resolutions in algebraic geometry. Though no specific awards or grants are listed, his contributions to geometric and algebraic research are evident through his publications. Teaching includes advanced mathematics courses such as Math 221. No current students or labs are mentioned in the provided data.

Hao ZhangView profile

Professor

• Computer Graphics
• Visual Computing
• Geometric Modeling
• +5 more

Hao Zhang is a Distinguished Professor and IEEE Fellow at Simon Fraser University's School of Computing Science, leading the GrUVi (Graphics & Vision) Lab. He holds a Ph.D. from the University of Toronto's Dynamic Graphics Project and degrees from the University of Waterloo. His research focuses on computer graphics, geometric modeling, and visual computing, with over 200 publications, including 70+ in SIGGRAPH/TOG. He directs research in 3D representation learning, generative models, and fabrication-aware design. Awards include the ACM SIGGRAPH Academy induction (2025) and IEEE Fellow (2024). He advises numerous students, many of whom have become professors or industry leaders. His work spans theoretical contributions (e.g., Test of Time Award for 2013 paper) and applied innovations like Slice3D and ArcPro. He is Technical Papers Chair for SIGGRAPH 2025 and has held roles at Amazon as an Amazon Scholar. Education: Ph.D., Dynamic Graphics Project, University of Toronto MMath and BMath, University of Waterloo Research Interests: Geometric deep learning, 3D vision, CAD representation, spatial AI, computational fabrication. Key projects include neural implicit fields (IM-Net), structured 3D synthesis (BSP-Net), and generative models (LOGAN). Collaborations include Adobe, Autodesk, and Amazon.

Kjell Gunnar RobbersmyrView profile

Professor

• Mechatronics
• Machine Design
• Condition Monitoring
• +5 more

Kjell Gunnar Robbersmyr is a Professor at the University of Agder's Department of Engineering Sciences and director of the Top Research Center in Mechatronics. With a Ph.D. in mechanical engineering from NTNU (1992), his career spans academic leadership, research management at Agder Research, and active contributions to IEEE. His work focuses on mechatronics, machine design, and condition monitoring, with special emphasis on fault diagnosis in electric motors and vehicle crash modeling. Senior Member of IEEE Member of Norwegian Academy of Technical Sciences Member of Agder Academy of Sciences Research interests include: Advanced fault diagnosis in electric drives using AI and signal processing Vehicle crashworthiness modeling with lumped parameter and finite element methods Optical measurement technology for machine monitoring Digital twin applications for infrastructure and wind energy systems Condition monitoring of low-speed bearings and rotating machinery Recent publications demonstrate expertise in: Deep learning for imbalanced motor fault datasets Transformer networks in power electronics diagnostics 3D reconstruction techniques for mechanical systems Multi-classifier decision fusion in power systems Dynamic operations modeling for electric vehicles Scientific contributions include: Over 50 peer-reviewed articles Leadership in the Intelligent Monitoring research group Development of novel inverter topologies Innovations in wind turbine condition monitoring Advancements in laser-based mechanical diagnostics

Gleb PogudinView profile

Assistant Professor

• Symbolic computation
• Algorithms for differential equations
• Algorithms for difference equations
• +5 more

Gleb Pogudin is an Assistant Professor at École Polytechnique, Institute Polytechnique de Paris, where he is a member of the MAX team within the Laboratoire d'informatique. His research focuses on the intersection of symbolic computation, differential equations, and algebraic methods with applications across multiple scientific domains. Dr. Pogudin's primary research interests span several interconnected areas: Symbolic computation and computer algebra algorithms Theory and applications of differential and difference equations Nonlinear algebra and polynomial systems Structural identifiability of dynamical models Model reduction techniques for complex systems His recent publications (2024-2025) demonstrate a strong focus on developing theoretical foundations for differential elimination, structural identifiability analysis, and model reduction. These works span applications in systems biology, epidemiology, pharmacology, and optics. Notably, his research bridges pure mathematical theory with practical computational implementations, creating tools that address real-world scientific challenges. Dr. Pogudin has developed several significant software tools that implement his theoretical advances: StructuralIdentifiability.jl: A Julia package for assessing structural identifiability CLUE: Software for exact model reduction of ODE models via constrained lumping SIAN: Software for structural identifiability analysis of ODE models His GitHub repositories contain numerous implementations of algorithms from his papers on differential elimination and related topics, demonstrating his commitment to making theoretical advances practically accessible to researchers across disciplines.

Steve OudotView profile

Research Professor

• Topological Data Analysis
• Persistence Theory
• Homological Algebra
• +2 more

Steve Oudot is a Senior Researcher (Directeur de Recherche) at Inria where he leads the GeomeriX research group, and serves as an Adjunct Professor at École Polytechnique. His research focuses on topological and geometric approaches to data analysis. Research Interests: Primarily works on persistence theory and its connections to homological algebra and representation theory, topological data analysis with applications to statistics and machine learning, multimodal time series analysis, and manifold learning/sampling theory. His research bridges theoretical mathematics with computational applications in data science. Publication Trends: Recent works (2024-2025) focus on multiparameter persistence theory, stability analysis of topological descriptors, and computational methods for persistence module decomposition. His publications demonstrate strong emphasis on theoretical foundations with algorithmic implementations for geometric data analysis. Student Advising: Currently supervising 3 PhD students (Michel, Li, Mordacq) and has graduated 7 doctoral students since 2014. Alumni now hold positions in academia (Lacombe at LIGM, Carrière at Inria) and industry (Berkouk at CNIL, Solomon at Deep Detection). Teaching: At École Polytechnique, teaches courses on topological data analysis (INF556), algorithms for data analysis (INF442), and computational geometry/topology (MPRI). Also taught at international schools in Luxembourg (2018), TUM (2016), and La Marsa (2016).

Andreas HerzView profile

Professor

• Computational Neuroscience
• Theoretical Neuroscience
• Neural Coding
• +11 more

Andreas Herz is a Professor and Senior Scientist at the Division of Neurobiology, Faculty of Biology, Ludwig-Maximilians-Universität München (LMU). He is a Board Member and Speaker of the Bernstein Center Munich, a leading institution in computational neuroscience. His research integrates theoretical modeling with experimental data to explore neural dynamics, information processing, and spatial navigation. Institution: Ludwig-Maximilians-Universität München School: Faculty of Biology Department: Division of Neurobiology Leadership: Speaker, Bernstein Center Munich Email: herz@bio.lmu.de Herz’s research focuses on computational and theoretical neuroscience, particularly the neural basis of spatial navigation, population coding in grid cells, dendritic spine biophysics, and sensory information processing. He employs methods from dynamical systems, probability theory, and information theory to model single neurons and large-scale networks. His work spans model systems from grasshoppers to rodents and zebrafish, aiming to uncover general principles of neural computation. The recent publications highlight a strong trend in understanding grid cell dynamics, spatial memory, and dendritic computation. His work frequently involves decoding neural population activity, analyzing in vivo electrophysiological data, and developing theoretical frameworks for neural coding. Themes include burst firing, phase precession, remapping, and energy-efficient neural coding. He has mentored numerous PhD and MSc students through the Graduate School of Systemic Neurosciences (GSN), including Alexander Mathis, Johannes Nagele, and Michaela Poth. His group actively collaborates with experimental labs, contributing to interdisciplinary advances in neuroscience.

Melanie SchmidtView profile

Professor

• Geometric Data Analysis
• k-means clustering
• Combinatorial Optimization
• +3 more

Professor Melanie Schmidt is a faculty member in the Department of Computer Science at Heinrich-Heine-Universität Düsseldorf, where she leads the Algorithms and Data Structures research group. Previously, she was affiliated with the University of Bonn's Institute of Computer Science, where she completed her PhD under Prof. Dr. Heiko Röglin and headed a subgroup on "clustering for big data" within his research group. Current Position: Professor at Heinrich-Heine-Universität Düsseldorf Previous Position: Researcher and lecturer at University of Bonn PhD Advisor: Prof. Dr. Heiko Röglin Her research focuses on geometric data analysis, particularly k-means clustering in data streams, combinatorial optimization, and approximation algorithms. Her work bridges theoretical computer science with practical applications in big data processing. She has made significant contributions to understanding the theoretical foundations of clustering algorithms while developing efficient implementations for real-world applications. Professor Schmidt's publication record shows a consistent evolution from theoretical analysis of k-means to practical implementations for big data environments. Her recent work explores fairness in clustering, privacy-preserving techniques, and efficient algorithms for high-dimensional data. She has published in top-tier conferences including SODA, ICALP, ESA, and ITCS, demonstrating both theoretical rigor and practical relevance. Best Student Paper Award at ESA 2012 (joint work with Martin Groß, Jan-Philipp W. Kappmeier, and Daniel Schmidt) She actively supervises numerous Master's and Bachelor's students, with current advisees including Lena Carta, Lukas Drexler, and Anna Arutyunova. Her research group includes members such as Anja Rey, Julian Wargalla, and Annika Hennes. She teaches advanced courses in algorithms and data structures, with a focus on randomized algorithms and efficient algorithm design for big data problems. Professor Schmidt leads the Algorithms and Data Structures research group at Heinrich-Heine-Universität Düsseldorf, which focuses on developing and analyzing efficient algorithms for fundamental computational problems, with particular emphasis on clustering, geometric data analysis, and big data applications.

Richard Hepworth-YoungView profile

Academic

• Algebraic Topology
• Homological Stability
• Magnitude Homology
• +10 more

Dr Richard Hepworth-Young is a Reader in the School of Natural and Computing Sciences at the University of Aberdeen, where he has been a faculty member since 2011. His research is centered in algebraic topology, with significant contributions to homological stability and magnitude homology. He teaches advanced courses such as MX4540 Knots and MX4546 Algebraic Topology, employing a flipped classroom model. He is an active member of the mathematical community, organizing the Scottish Topology Seminar. His research interests include: Algebraic Topology Homological Stability Magnitude Homology String Topology Diagram Algebras (Temperley-Lieb, Brauer, Partition) Orbifolds and Stacks His recent publications demonstrate a strong trend in applying homological methods to algebraic and combinatorial structures, particularly diagram algebras and directed graphs. He has pioneered work on magnitude homology—a categorification of Tom Leinster's magnitude—with applications in metric geometry and curvature. His work often involves deep collaborations with researchers such as Simon Willerton, Rachael Boyd, and Emily Roff. Richard Hepworth-Young has not received any explicitly mentioned scientific awards in the provided text. He has supervised or collaborated with several researchers, though no formal list of PhD or Master’s students is provided. He has been involved in multiple research grants through his publications and collaborations, though specific grant details are not listed. He is not known to lead a formal lab or research team, but his collaborative network includes prominent figures in topology.

Helen WongView profile

Professor

• Quantum Topology
• Hyperbolic Geometry
• Applications of Topology in Molecular Biology
• +3 more

Helen Wong is a Professor of Mathematics in the Department of Mathematical Sciences. Her research focuses on quantum topology, hyperbolic geometry, and applications of topology to molecular biology, data analysis, and quantum computation. Education: BA from Pomona College; PhD from Yale University. She has held notable fellowships including the Simons Fellowship (2021-22), Joan and Joseph Birman Fellowship (2021-22), and von Neumann Fellowship (2017-18). She also received the Prize Teaching Award at Yale University and a Fulbright Research Award in Budapest. Her research explores quantum invariants' connections with hyperbolic geometry, applications in molecular biology (e.g., protein folding), and quantum computing. Recent work includes studies on skein algebras, knot theory in 3D manifolds, and topological models for biopolymers. Helen has secured multiple NSF grants: RUI: Pure and Applied Knot Theory (2023-2026) RUI: Knots in Three-Dimensional Manifolds (2019-2023) RUI: Skeins on Surfaces (2015-2019) Her work bridges pure mathematics with interdisciplinary applications, particularly in biology and quantum technologies.