Find Academic Supervisors

Naratip SantitissadeekornView profile

Senior Lecturer

• Data Assimilation
• Inverse Problems
• Geophysical Fluid Dynamics
• +5 more

Naratip Santitissadeekorn is a Senior Lecturer in Data Assimilation at the School of Mathematics and Physics, University of Surrey, where he is affiliated with the Mathematics at the Interface Group. His work bridges mathematics, data science, and real-world applications in urban planning, crime analysis, and geophysical fluid dynamics. Dr. Santitissadeekorn received his PhD from Clarkson University in 2008, with a dissertation titled "Transport Analysis and Motion Estimation of Dynamical Systems of Time-Series data." His doctoral research was supervised by Professor Erik Bollt. Following his PhD, he completed two significant postdoctoral positions: from 2008-2011 at the University of New South Wales, Sydney, Australia, working with Professor Gary Froyland on numerical techniques for finite-time Lagrangian coherent set identification, with applications to delimiting the polar vortex and Agulhas rings; and from 2011-2014 at the University of North Carolina-Chapel Hill, working with Professor Chris Jones on data assimilation projects. Dr. Santitissadeekorn's research focuses on inverse problems and data assimilation in geophysical fluid dynamics, the applications of Lagrangian Coherent Structures (LCS), and computational ergodic theory. His work combines theoretical mathematics with practical applications, particularly in urban growth modeling and crime analysis. He has developed innovative methods for identifying coherent structures in fluid flows, estimating transition probabilities from spatiotemporal data, and creating data-driven frameworks for urban expansion scenarios. His research demonstrates how mathematical techniques can be applied to solve real-world problems in environmental science, urban planning, and public safety. An analysis of Dr. Santitissadeekorn's recent publications (2020-2023) reveals a strong focus on urban expansion modeling and network analysis. His work on urban growth has evolved from basic cellular automata models to sophisticated frameworks that manage uncertainty through parameter clustering and growth mode identification. His research on Hawkes processes has advanced ensemble-based filtering techniques for analyzing count data in large networks. These publications demonstrate a consistent pattern of applying mathematical rigor to complex spatiotemporal phenomena, with increasing emphasis on data-driven approaches and practical applications. Dr. Santitissadeekorn has made significant contributions to data assimilation methods, particularly through the development of the extended Poisson-Kalman filter (ExPKF) for urban crime modeling. His teaching includes courses in Algebra and Bayesian Statistics, reflecting his expertise in both theoretical and applied mathematics. While specific awards are not mentioned in the available information, his extensive publication record in high-impact journals demonstrates recognition within his field. Dr. Santitissadeekorn's research has practical implications for urban planning and law enforcement. His work on urban expansion models helps planners understand different growth trajectories, while his crime modeling research contributes to improved police patrolling strategies. His interdisciplinary approach, combining mathematics, computer science, and domain-specific knowledge, positions him at the forefront of applying data science to societal challenges.

Werner BauerView profile

Lecturer

• Numerical Analysis
• Scientific Computing
• Fluid Dynamics
• +5 more

Dr. Werner Bauer is a Lecturer in Mathematics at the University of Surrey, affiliated with the Mathematics at the Interface Group within the School of Mathematics and Physics. His research focuses on numerical analysis and scientific computing, particularly in the Mathematics of Planet Earth. Key areas include parallel-in-time methods for oscillatory PDEs, structure-preserving discretizations for fluid dynamics, stochastic flow models for ensemble prediction, and geometric formulations of fluid and magnetohydrodynamic systems. He also explores finite difference and finite element methods, with prior work on grid adaptation in weather and climate models. His research interests span numerical methods for geophysical flows, stochastic modeling of oceanic and atmospheric dynamics, and energy-conserving computational frameworks. Bauer’s recent work emphasizes uncertainty quantification, ensemble forecasting, and the development of compatible finite element schemes to ensure physical conservation laws in simulations. Bauer’s publications highlight advancements in structure-preserving discretizations, stochastic parameterization of mesoscale eddies, and variational integrators for geophysical equations. His work bridges applied mathematics and computational science with applications in climate modeling and environmental fluid dynamics.

Emily WhitingView profile

Associate Professor

• Computer Graphics
• Computational Fabrication
• Architectural Geometry
• +4 more

Emily Whiting is an Associate Professor of Computer Science at Boston University and Director of the Shape Design & Computation Lab. She also serves as Director of PhD Admissions and Co-Director of the BU Computer Graphics Lab. Her research focuses on computational fabrication, architectural geometry, and computer-aided design, bridging digital geometry processing, engineering mechanics, and rapid prototyping. She holds a PhD from MIT (2012), an SM in Design & Computation from MIT (2006), and a BASc in Engineering Science from the University of Toronto (2004). Previously, she was faculty at Dartmouth and a Marie Curie Postdoctoral Fellow at ETH Zurich. Her research interests include 3D printing optimization, structural design for fabrication, and tools for functionally-valid object creation. Notable projects include work on elastic garments, climbing experience replication, and print-wind instrument design. Her work has been featured on TEDx and PBS NOVA, and she has received awards such as the NSF CAREER Award and Sloan Research Fellowship. Education: PhD (MIT), SM (MIT), BASc (University of Toronto) Labs: Shape Design & Computation Lab, BU Computer Graphics Lab Key Projects: Knitting 4D garments, Environment-Scale Fabrication, Thermal-comfort casts Recent professional activities include program committee roles at SIGGRAPH 2025 and UIST 2024, and serving as Program Co-Chair for Pacific Graphics 2024. She advises a team of PhD and MS students, with alumni now in academia and tech industries.

• Stochastic Analysis
• Rough Path Theory
• Mathematical Finance
• +3 more

Peter K. Friz is an Einstein Professor in Mathematics at TU-Berlin, affiliated with the Institute of Mathematics, and associated with the Weierstrass Institute for Applied Analysis and Stochastics. His research focuses on stochastic analysis, rough path theory, and mathematical finance, with particular emphasis on volatility modeling and applications to quantitative finance. He has held prestigious grants, including ERC Starting and Consolidator Grants, and coordinates the DFG research unit 'Rough paths, stochastic partial differential equations, and related topics.' Friz's work bridges theoretical stochastic analysis and practical financial applications, emphasizing rough path theory and its implications for differential equations and stochastic processes. His collaborations include organizing international conferences and courses on rough paths, with invited lectures at institutions like Cambridge, Paris, and Bonn. Supported by DFG, the European Research Council, and the Einstein Foundation, his research explores geometric aspects of pathwise analysis and stochastic volatility dynamics. He has advised numerous PhD students and maintains active roles in academic administration, including coordinating Berlin Mathematical School programs and teaching advanced topics in stochastic calculus. His contributions to rough path theory and stochastic finance are recognized through his academic leadership and influential publications, including co-authoring the seminal book Multidimensional Stochastic Processes as Rough Paths .

David FouheyView profile

Assistant Professor

• Computer Vision
• Machine Learning
• AI for Science
• +6 more

David Fouhey is an Assistant Professor at New York University, jointly appointed between the Courant Institute of Mathematical Sciences (Computer Science) and the Tandon School of Engineering (Electrical and Computer Engineering). He previously held positions at the University of Michigan and was a postdoctoral researcher at UC Berkeley. His research focuses on learning-based computer vision, particularly in 3D reconstruction, AI for science, and human-object interaction. Education: PhD in Robotics from Carnegie Mellon University (2013-2018) Bachelor of Arts in Computer Science from Middlebury College (2007-2011) Research Interests: His work spans 3D reconstruction from images , AI-driven scientific measurement (e.g., solar physics, evolutionary ecology), and human interaction modeling . Notable projects include Stereo4D for 3D motion analysis and SyntheticIA for solar magnetogram fusion. Recent Articles: Recent work emphasizes interdisciplinary applications of vision (e.g., bird morphology analysis) and robust 3D techniques like Perspective Fields for camera calibration. His 2025 Nature Scientific Data paper on bird skeletal traits highlights his AI-for-science focus. Grants & Collaborations: Secured a NASA grant for heliophysics tools and collaborates with institutions like NASA’s SDO mission and the Astrophysical Journal. Labs & Teams: Leads a NYU research group focused on vision and robotics, with active collaborations in astrophysics and ecology.

Samson ZhouView profile

Assistant Professor

• Algorithms and theory
• Data science
• Machine learning
• +7 more

Samson Zhou is an Assistant Professor in the Department of Computer Science & Engineering at Texas A&M University, part of the College of Engineering. He holds a Ph.D. from Purdue University and dual B.S. and M.Eng. degrees from MIT in Computer Science and Mathematics. His research focuses on theoretical computer science, data science, and machine learning, with specializations in numerical linear algebra, streaming algorithms, and differential privacy. He has held postdoctoral positions at institutions including Carnegie Mellon University and Rice University. Education: Ph.D., Computer Science, Purdue University (2018) M.Eng., Computer Science, MIT (2011) B.S., Computer Science & Mathematics, MIT (2010-2011) Research Interests: Dr. Zhou explores intersections of algorithms, data science, and machine learning, emphasizing numerical linear algebra, streaming algorithms, differential privacy, and adversarial robustness. He designs efficient algorithms for large-scale data processing with provable guarantees. Key Contributions: His work includes advancements in sliding window clustering, adversarially robust streaming algorithms, and privacy-preserving techniques. Notable achievements include the Silver Best Paper Award at ICML 2021 and spotlight presentations at ICLR 2025. Service & Leadership: He organizes workshops (e.g., TTIC 2024 on Learning-Augmented Algorithms) and serves on program committees for SOSA, NeurIPS, and COLT. He also co-leads the TAMU Math Circle's problem-solving sessions and the Algorithms & Data Science Reading Group. Labs/Teams: He collaborates on projects involving co-hosted postdocs (e.g., Chen Wang) and masters students (e.g., Shenghao Xie), focusing on topics like coresets, streaming algorithms, and fair clustering.

Chen Wei WayneView profile

Assistant Professor

• Generative Design AI
• Machine Learning
• Uncertainty Quantification
• +3 more

Chen Wei Wayne is an Assistant Professor in the Department of Mechanical Engineering at Texas A&M University. His research focuses on generative design AI, machine learning, uncertainty quantification, and advanced manufacturing. He leads the DIGIT Lab, which develops AI methods for design innovation, automation, and manufacturing integration. Education: Ph.D., Mechanical Engineering, University of Maryland, College Park (2019) M.S., Mechanical Engineering, Chongqing University, China (2015) B.S., Mechanical Engineering, Chongqing University, China (2012) Research Interests: Generative adversarial networks (GANs) for design synthesis Data-driven metamaterials and multiscale systems Uncertainty quantification in engineering design AI-driven design automation Awards & Honors: ASME Journal of Mechanical Design Reviewer of the Year Award (2023) ASME DAC Best Paper Award (2022) Journal of Mechanical Design Editors’ Choice Honorable Mention (2021) Lab Activities: Recent lab milestones include successful completion of TAMUQ Summer Research Programs (2024) Hosts undergraduate researchers like Wisam Gadam and Eddie Guerrero

Carmen Galaz GarcíaView profile

Academic

• Environmental Data Science
• Remote Sensing
• Machine Learning in Ecology
• +3 more

Carmen Galaz García is an Assistant Teaching Professor at the Bren School of Environmental Science & Management at UC Santa Barbara. She teaches data science courses including EDS 220 and capstone projects, emphasizing accessible technical education and DEIJ initiatives. Previously, she worked at the National Center for Ecological Analysis and Synthesis (NCEAS), analyzing remote sensing data and developing educational resources. Carmen holds a Ph.D. in Mathematics (UCSB) and a B.Sc. in Mathematics from the University of Guanajuato/CIMAT, Mexico. Her research focuses on environmental data science applications such as invasive species mapping using machine learning and geospatial analysis. She actively contributes to reproducible workflows in Python and collaborates on topological data analysis in environmental contexts. Professional affiliations include NCEAS and Bren School's environmental data programs. Education: Ph.D. in Mathematics, UC Santa Barbara (2021) B.Sc. in Mathematics, Universidad de Guanajuato/CIMAT (2015) Research emphasizes interdisciplinary approaches combining mathematics, ecology, and data science to address environmental challenges. She leads capstone projects integrating real-world environmental datasets and mentors students through collaborative data science workflows.

Jonathan PakianathanView profile

Professor

• Algebraic Topology
• Cohomology of Groups
• Lie Algebras
• +3 more

Jonathan Pakianathan is a Professor of Mathematics at the University of Rochester's School of Arts & Sciences, Department of Mathematics. He holds a PhD from Princeton University (1997) and a BS in Mathematics and Physics from Caltech (1992). His research focuses on algebraic topology, cohomology of groups and Lie algebras, geometric combinatorics, and finite fields. He has held leadership roles, including Director of Graduate Studies (2014–2020) and Director of Undergraduate Studies (2003–2010). Notable awards include the Goergen Award for Excellence in Teaching (2014) and the Sloan Foundation Doctoral Dissertation Fellowship (1996). His research explores applications of topology and algebra in discrete geometry, often collaborating with Alex Iosevich and students. Recent work addresses topics like Fuglede's conjecture over finite fields, geometric configurations, and probabilistic methods in combinatorics. His articles frequently intersect harmonic analysis, group theory, and number theory, reflecting interdisciplinary strengths. Education : PhD, Princeton University, 1997 BS in Mathematics and Physics, Caltech, 1992 Awards : Goergen Award for Excellence in Undergraduate Teaching, 2014 Sloan Foundation Doctoral Dissertation Fellowship, 1996 H. J. Ryser Scholarship, 1991 Grants include an NSA Mathematical Sciences Grant (2016–2017, $110,000 total) with A. Iosevich. He advises numerous PhD students, many of whom now hold academic or research positions. His teaching spans undergraduate to graduate courses, including algebra, topology, and financial mathematics. Research groups and collaborations extend to geometric combinatorics, algebraic topology, and applications in physics and data science. Ongoing projects explore topological methods in discrete geometry and probabilistic structures over finite fields.

• Partial Differential Equations
• Numerical Analysis
• Mathematical Biology
• +3 more

Professor Charles Elliott of Warwick's Mathematics Institute specializes in the numerical analysis of partial differential equations with applications in biology, materials science, and geometric evolution. His research develops finite element methods for surface PDEs, free boundary problems, and phase transitions, with recent publications on Cahn-Hilliard equations and information propagation models. Honors include the Royal Society Wolfson Research Merit Award, Humboldt Prize, and honorary doctorate from Sussex University. As Editor-in-Chief of the IMA Journal of Numerical Analysis and editor for Interfaces and Free Boundaries, he shapes research dissemination in computational mathematics. Teaching covers topics in PDEs and mathematical modeling. His theoretical work on surface evolution equations informs applications in cell motility studies and biomembrane physics, connecting mathematical rigor with biological and physical phenomena.

Katrin LeschkeView profile

Associate Professor

• Surface Theory
• Integrable Systems
• Discrete Geometry
• +3 more

Dr. Katrin Leschke is an Associate Professor in Mathematics at the University of Leicester and Deputy Director for Postgraduate Research (PGR) in the College of Science and Engineering. She holds a Royal Society Short Industry Fellowship (2022–2023). Her research focuses on surface theory, integrable systems, and applications of discrete geometry to machine learning, engineering, and chemistry. She has held research positions at TU Berlin, University of Massachusetts Amherst, and University of Augsburg, where she earned her habilitation. She serves as an external examiner at the University of Manchester and participates in committees like European Women in Mathematics and the London Mathematical Society's SLAM committee. Education: Diplom and PhD in Geometry from TU Berlin Habilitation from University of Augsburg Her work bridges pure mathematics with applied fields, emphasizing geometric visualization and collaborations across disciplines. Key research areas include minimal surfaces, Darboux transformations, and non-axisymmetric turbine design optimization. She is affiliated with the Creative Computing Research Group and AIDAM, fostering interdisciplinary innovation. Notable contributions include studies on integrable systems linking CMC surfaces, isothermic surfaces, and constrained Willmore surfaces, alongside applications in engineering fluid dynamics. Awards highlight her industry-academia collaboration excellence.

Francesco BulloView profile

Professor

• Control Theory
• Network Systems
• Distributed Control
• +5 more

Francesco Bullo is a Distinguished Professor of Mechanical Engineering at the University of California, Santa Barbara (UCSB), affiliated with the College of Engineering. He holds joint appointments in Electrical and Computer Engineering, Computer Science, and the Center for Control, Dynamical Systems, and Computation. His research focuses on distributed control, network systems, and neural networks, with notable contributions to contraction theory and social dynamics analysis. Education: Laurea (1994, University of Padova), PhD (1998, Caltech). Leadership roles: Former IEEE CSS President, SIAG CST Chair. Research interests include biological/artificial neural networks, distributed control of robotic networks, and synchronization in power grids. He authored books like Lectures on Network Systems and Contraction Theory for Dynamical Systems . His work spans 300+ publications, including impactful articles on Hopfield networks, power grid stability, and optimization. Awards include IEEE Fellow, ASME Fellow, and SIAM Fellow. Advising and grants: Mentored over 30 PhD students and led major projects like the NSF MURI on team behavior modeling. Current research includes neural synchronization and AI-driven control. Labs/teams: Directs the UCSB Center for Control, Dynamical Systems, and Computation, and collaborates on interdisciplinary initiatives like the Network Science for Medicine white paper.

Rocco ServedioView profile

Professor

• Computational Complexity Theory
• Computational Learning Theory
• Property Testing
• +3 more

Rocco Servedio is a Professor in the Department of Computer Science at Columbia University, where he leads research in theoretical computer science with a focus on computational complexity theory, learning theory, and the role of randomness in computation. He previously served as Chair of the Computer Science Department from 2018 to 2021. He holds a Ph.D., MS, and AB in Mathematics from Harvard University. His research interests include property testing, computational learning theory, and algorithmic lower bounds. He has contributed to foundational work in areas like junta testing, trace reconstruction, and convexity testing. Servedio has held leadership roles in major conferences such as STOC, CCC, and COLT, and has mentored students through courses like Unconditional Lower Bounds and Derandomization . Education: Ph.D. in Computer Science, Harvard University MS in Computer Science, Harvard University AB in Mathematics, Harvard University His research bridges theoretical computer science and applied mathematics, with recent work exploring the intersection of Gaussian processes, convex geometry, and algorithmic efficiency. Servedio's contributions to the field are exemplified through his involvement in high-impact conferences and his leadership in advancing fundamental computational theories.

• Explainable AI
• Human-AI Interaction
• Machine Learning
• +7 more

Andreas Holzinger is a Professor at Graz University of Technology, with additional affiliations at Medical University Graz and University of Natural Resources and Life Sciences Vienna in Austria. He is recognized as an IFIP Fellow (2021) for his significant contributions to information processing and computer science. His work spans multiple institutions across Europe, with notable collaborations extending to the University of Alberta in Canada. Professor Holzinger's research focuses on Human-Centered AI, Explainable AI (XAI), and their practical applications across diverse domains. His work bridges theoretical AI advancements with real-world implementations in healthcare, forestry, and human-robot interaction. He has pioneered approaches in counterfactual explanations, graph neural networks, and human-in-the-loop systems that emphasize transparency and trustworthiness in AI decision-making processes. His recent publications demonstrate a strong trend toward integrating large language models with traditional AI systems while maintaining explainability. Holzinger's work consistently emphasizes the human element in AI systems, ensuring that technological advancements serve human needs rather than obscuring decision processes. His research in medical AI, smart forestry, and agricultural applications shows a commitment to solving practical problems with human-centered technological solutions. Scientific Awards: IFIP Fellow (2021) Professor Holzinger has been instrumental in establishing design guidelines for explainable AI systems, particularly through his work on post-hoc versus ante-hoc explanations. His research on Kandinsky Patterns has provided valuable experimental frameworks for pattern analysis and machine intelligence. He has secured significant research funding for projects bridging AI with practical applications in healthcare and environmental monitoring. His leadership extends to the organization of major conferences and workshops, including the CD-MAKE conference series, where he has fostered interdisciplinary collaboration between AI researchers and domain experts. His work on the CLARUS platform demonstrates practical implementations of interactive explainable AI for medical applications.

Chris RogersView profile

Professor

• Financial mathematics
• probability theory
• stochastic analysis
• +2 more

Chris Rogers is a Professor of Statistical Science within the Department of Pure Mathematics and Mathematical Statistics (DPMMS) at the University of Cambridge, actively contributing to research at the intersection of probability theory, stochastic analysis, and financial applications. His academic profile reflects deep engagement with mathematical finance and theoretical probability through publications and departmental affiliations. His research spans financial mathematics, probability theory, stochastic analysis, statistics, and mathematical economics, with emphasis on rigorous mathematical frameworks for financial markets. Key themes include option pricing mechanisms, stochastic process modeling, and geometric probability applications, often addressing real-world financial instruments like Asian options and S&P500 index behaviors through advanced probabilistic techniques. Analysis of his 15 most recent publications (2016-2018) reveals consistent focus on stochastic calculus applications in finance, particularly Lévy processes, diffusion models, and optimal stopping problems. His work bridges theoretical probability with quantitative finance, demonstrating expertise in translating complex stochastic phenomena into financial modeling solutions across asset pricing, risk assessment, and market analysis domains. No scientific awards were documented in the provided source material. Information regarding PhD/Master's student supervision, research grants, or collaborative teams was not specified in the available texts, indicating absence of such details in the source documentation.