Find Academic Supervisors

Max RussoView profile

Assistant Professor

• Computational Statistics
• Bayesian Statistics
• Decision Theory
• +5 more

Max Russo is an Assistant Professor of Statistics at the Ohio State University, where he focuses on developing advanced statistical methodologies for biomedical research and healthcare data analysis. PhD from the University of Padova (2019) Research spans computational statistics, Bayesian modeling, and clinical trial design Key applications: drug safety profiling, high-dimensional data analysis, electronic health records (EHR) Russo's research combines Bayesian statistics with computational innovation , particularly for challenges in clinical trial design and real-world evidence generation . He specializes in: Variational Bayes techniques for scalable inference Signal detection in high-dimensional healthcare databases Bayesian decision theory for adaptive clinical trials Electronic health records (EHR) analysis Pharmacoepidemiology and medication safety His recent publications highlight cross-disciplinary impacts in biostatistics and public health , with a focus on: Drug safety surveillance using claims data Comparative effectiveness of pharmaceutical interventions Algorithmic approaches for complex healthcare datasets Policy implications of clinical trial design Contact: Russo.325@osu.edu

Fred ParkView profile

Assistant Professor

• Mathematical Image Processing
• Computer Vision
• Numerical Partial Differential Equations
• +3 more

Fred Park serves as Assistant Professor of Mathematics in the Department of Mathematics & Computer Science at Whittier College. His research bridges mathematical theory with practical applications in image processing and computer vision, maintaining active collaborations across computational disciplines. Education: Ph.D. in Mathematics, University of California, Los Angeles M.A. in Mathematics, University of California, Los Angeles Professor Park's research focuses on mathematical image processing with emphasis on total variation methods, blind deconvolution, and shape modeling. His work spans computer vision applications including 3D surface reconstruction, texture extraction, and medical image analysis. Current projects explore non-convex optimization for deep learning compression and scientific computing implementations for real-world problem solving. His teaching philosophy integrates real-world mathematical problems to inspire undergraduate learning. Recent publications demonstrate strong trends in image segmentation frameworks combining anisotropic-isotropic total variation, network slimming for CNN efficiency, and medical imaging applications such as COVID-19 lung scan clustering. His work consistently bridges theoretical mathematics with computational implementation. Professor Park actively seeks to incorporate research into undergraduate education, developing methodologies to motivate learning through practical applications. His teaching experience spans undergraduate lower and upper division courses at UCLA, UC Irvine, and University of Michigan during postdoctoral positions. Research facilities include computational resources at Whittier College's Science & Learning Center (SLC 218), supporting work in image processing, shape modeling, and 3D reconstruction from point cloud data.

• Geometric Measure Theory
• Calculus of Variations
• Partial Differential Equations
• +3 more

Francesco Maggi is a Full Professor in the Department of Mathematics at the University of Texas at Austin. His research focuses on geometric measure theory, calculus of variations, and partial differential equations, with an emphasis on isoperimetric problems, capillarity theory, and mean curvature flow. His work explores stability properties of geometric configurations, regularity of minimal surfaces, and applications to physical phenomena like soap films and crystal structures. Key research areas include: geometric variational problems, quantitative isoperimetric inequalities, nonlocal surface tensions, and singular limits in free boundary problems. His contributions have advanced understanding of minimal surfaces, rigidity in geometric inequalities, and energy-driven pattern formation. Recent work addresses topics such as almost-honeycomb structures in low-energy clusters, mesoscale flatness criteria for hypersurfaces, and the approximation of Plateau’s laws via phase-field models. Collaborations span analysis, geometry, and mathematical physics, with applications to materials science and continuum mechanics.

LAMBERT ZoéView profile

Associate Professor

• Signal and image processing
• Medical imaging
• Deep learning
• +7 more

Zoé Lambert is an Associate Professor at the University of Rouen Normandy and a member of the LITIS laboratory. She completed her PhD in Applied Mathematics (2019–2022) at INSA Rouen Normandie, funded by the Normandy Region, with a thesis on hybrid variational and deep learning approaches for medical image segmentation. Prior to her PhD, she worked as a Research Engineer at INSA Rouen (2018–2019) on weakly supervised learning for medical imaging and as a Data Scientist Intern at AID in Paris (2018). Her current research focuses on integrating geometric and topological constraints into deep learning models for medical imaging and coastal cliff crack detection. Research Interests Zoé develops advanced image segmentation methods combining variational models and deep learning, with emphasis on: Topological prescriptions in CNNs Geometric constraints for anatomical accuracy Registration techniques via nonlinear elasticity Public dataset creation for crack segmentation Directional total variation decomposition models AI applications in energy transition (Prioreno tool) Scientific Contributions She co-organized the SegTHOR challenge at IEEE ISBI’19, which automated segmentation of thoracic organs-at-risk for radiotherapy. Her work on weighted total variation regularization and Mumford-Shah terms has improved segmentation reliability in scenarios with limited data. She also contributed to the DEPHY3GEO project at CEREMA (2023–2024), focusing on cliff crack detection using thermal infrared imagery. Honors and Collaborations Zoé won first place in the Normandy Datathon (2023) for her energy vulnerability decision support tool. She served on INSA Rouen’s Scientific Council (2020–2022) and laboratory council (LMI). Her collaborations span LITIS, LMI, CEREMA, INRIA, and international institutions like INSA Lyon and the University of Bordeaux.

• Machine Learning
• Probabilistic Models
• Bayesian Inference
• +5 more

Michael Hughes is an Assistant Professor of Computer Science at Tufts University's School of Engineering. His research focuses on statistical machine learning and its applications in healthcare, including probabilistic models, Bayesian inference, and semi-supervised learning. He leads projects such as automated cardiovascular disease diagnosis via echocardiograms and mortality risk prediction from electronic health records. Education: PhD, Computer Science, Brown University (2016) MS, Computer Science, Brown University (2012) BS, Computer Science, Franklin W. Olin College of Engineering (2010) Research Interests: Bayesian hierarchical models, variational inference, clinical informatics, and interpretable AI. His work bridges theoretical advancements with high-impact applications in healthcare. Awards & Activities: Recipient of multiple reviewer awards at top conferences (NeurIPS, AISTATS) Organizer of workshops on Bayesian methods and health informatics Principal investigator on grants from NSF, NIH, and the US Army Teaching: Courses include Intro to Machine Learning, Bayesian Deep Learning, and Statistical Pattern Recognition. Labs & Teams: Leads the Tufts Machine Learning research group and collaborates with clinicians at Tufts Medical Center on healthcare AI projects.

Bin LiView profile

Researcher

• Machine Learning
• Biomedical Engineering
• Bioimage Informatics
• +5 more

Dr. Bin Li is a postdoctoral researcher in the Institute of Biomedical Engineering at the University of Oxford . His work bridges machine learning , bioimage informatics , and computational pathology , with a focus on advancing AI applications in healthcare. Education: PhD in Biomedical Engineering (2020) from the University of Wisconsin-Madison under Professor Kevin Eliceiri. Dr. Li's research centers on developing deep learning techniques for histopathology , emphasizing weakly supervised and semi-supervised learning to analyze unlabeled/coarsely-labelled data. He also explores multimodal learning for image-based drug screening, integrating data from microscopy, genomics, and proteomics. His publication history reveals expertise in stochastic PDEs , singular SPDEs , and nonlinear dynamics , indicating cross-disciplinary foundations in mathematics and physics. Dr. Li contributes to the Professor Jens Rittscher's group , focusing on innovative computational methods for biomedical image analysis.

Sarah FisherView profile

Lecturer

• Philosophy of Language
• Digital Speech Ethics
• Generative AI
• +4 more

Dr. Sarah Fisher is a Lecturer in Philosophy at Cardiff University, specializing in socially and empirically engaged philosophy of language. She holds a Research Fellow position at University College London (UCL), contributing to interdisciplinary projects on content moderation ethics and free speech online. Fisher's academic journey includes a PhD from the University of Reading, an MSc from the University of Edinburgh, and a BA from the University of Oxford. Her research spans philosophy of language, digital speech ethics, and the implications of AI on communication. Her research focuses on linguistic framing, contextual effects on meaning, and the challenges posed by generative AI in public discourse. She has published widely in journals such as Philosophy Compass , Synthese , and Review of Philosophy and Psychology . Fisher actively engages in public debates on content moderation, AI governance, and the ethics of online communication. Recent talks include discussions on synthetic content, AI accountability, and the role of philosophy in addressing digital challenges. She is a regular speaker at conferences and collaborates with institutions like Yale Law School and the University of Oxford. Fisher’s work bridges theoretical philosophy with applied issues in technology, policy, and social media governance.

Hanye ZhuView profile

Research Professor

• Partial Differential Equations
• Mathematical Analysis
• Nonlinear Analysis
• +3 more

Hanye Zhu is a Phillip Griffiths Assistant Research Professor of Mathematics at Duke University, affiliated with the Department of Mathematics and Trinity College of Arts & Sciences. Their research focuses on partial differential equations, particularly p-Laplacian equations, conductivity problems, and gradient estimates in nonlinear and singular contexts. Education: Ph.D. in Mathematics from Brown University (2024). Research interests include mathematical analysis of elliptic and parabolic PDEs with measure data, asymptotic behavior in conductivity problems, and regularity theory for quasilinear equations. Recent work emphasizes the interplay between material science and advanced PDE techniques. Publications highlight contributions to conductivity problem asymptotics, gradient estimates for singular equations, and analysis of insulated systems using p-Laplacian frameworks. No awards are explicitly listed in the provided materials.

Russell SchwabView profile

Associate Professor

• Nonlocal Equations
• Integro-Differential Equations
• Free Boundary Problems
• +3 more

Russell Schwab is an Associate Professor in the Department of Mathematics at Michigan State University. His research focuses on nonlocal equations, integro-differential operators, free boundary problems, and homogenization theory. He has contributed to the analysis of fluid dynamics phenomena, stochastic processes, and applied mathematical frameworks. His work bridges theoretical and applied mathematics, with notable contributions to viscosity solutions for Muskat problems and Hele-Shaw flows. Recent studies emphasize Neumann homogenization and min-max formulas for elliptic operators. Russell has explored collaborative research on nonlocal operator structures, advancing applications in partial differential equations and calculus of variations. His publications reflect a deep engagement with both classical and emerging areas of mathematical analysis.

Layan El HajjView profile

Academic

• Partial Differential Equations
• Complex Analysis
• Free Boundary Problems
• +4 more

Layan El Hajj is an Associate Teaching Professor in the Department of Mathematics at Carnegie Mellon University Qatar, part of the Mellon College of Science. She holds a Ph.D. from McGill University and specializes in complex analysis, partial differential equations, and geometric function theory. Her research focuses on free boundary problems, logharmonic mappings, and univalent functions, with contributions to convexity, symmetry, and stability in mathematical analysis. Her work bridges theoretical advancements with applications in nonlinear PDEs, geometric mappings, and harmonic analysis. Notable areas include radial symmetry in elliptic PDEs, convexity of free boundaries, and properties of poly-analytic functions. She maintains an active Google Scholar profile and contributes to undergraduate and graduate education in mathematics.

Qian-Yong ChenView profile

Associate Professor

• Numerical Analysis
• Scientific Computing
• Computational Fluid Dynamics
• +2 more

Qian-Yong Chen serves as an Associate Professor in the Department of Mathematics and Statistics at the University of Massachusetts Amherst, maintaining an office in LGRT 1521 with regular office hours (Tuesdays and Thursdays 11:15AM-12:45PM) and contactable via cqy@umass.edu or 413-545-9611. His academic credentials include: Ph.D. from Brown University (2004) M.S. from Chinese Academy of Sciences (1999) B.S. from University of Science & Technology of China (1996) Chen's research centers on Numerical Analysis and Scientific Computing , with specialized expertise in Computational Fluid Dynamics , Traffic Flow Modeling , and Nonlinear Partial Differential Equations . His work bridges theoretical mathematics and practical engineering applications, developing advanced numerical techniques for complex physical systems including turbulence, traffic networks, and quantum phenomena. Analysis of his 2001-2016 publications reveals consistent innovation in numerical methodologies: coarse-graining for turbulence, spectral/finite-volume reconstructions for hyperbolic PDEs, and uncertainty quantification in transportation models. Key trends include cross-disciplinary adaptation of optimization techniques and hybrid approaches combining dynamical systems theory with computational physics. No scientific awards are documented in the provided materials. Information regarding student advisement or research funding remains unspecified in the source text. The scraped content contains no references to laboratory facilities or collaborative research teams.

Florian DörflerView profile

Professor

• Distributed Control Systems
• Optimization
• Cyber-Physical Systems
• +7 more

Florian Dörfler is a Full Professor at ETH Zurich's Department of Information Technology and Electrical Engineering and Deputy Head of the Automatic Control Laboratory. He holds a Ph.D. in Mechanical Engineering from the University of California, Santa Barbara (2013), and a Diplom in Engineering Cybernetics from the University of Stuttgart (2008). Prior to ETH, he was an Assistant Professor at UCLA (2013–2014). His research focuses on distributed control and optimization in complex systems, including smart grids, robotic coordination, and social networks. Key areas include power grid stability, online feedback optimization, and data-driven control. He has advised students who received or were finalists for prestigious awards at major conferences such as the European Control Conference and American Control Conference. Notable awards include the IFAC Manfred Thoma Medal (2020), European Control Award (2020), and IEEE Circuits and Systems Best Paper Award (2016). His work spans theoretical advancements and practical applications, with contributions to stability analysis, decentralized control, and energy systems. Led the Automatic Control Laboratory and contributes to ETH Zurich’s broader efforts in cyber-physical systems. Active in academic service, including editorial roles and conference organization. His lab develops cutting-edge solutions for grid resilience, distributed optimization, and networked systems.

• Medical Imaging
• Quantitative MRI
• Magnetic Resonance Imaging (MRI) Techniques
• +5 more

Rudolf Stollberger is a Professor at TU Graz's Institute of Biomedical Imaging. His research focuses on advanced MRI techniques, including CEST-MRI, arterial spin labeling, quantitative MRI parameter quantification, and optimal control-based RF pulse design. He holds academic appointments in medical technology and has expertise in high-field MRI (7T), image reconstruction algorithms, and deep learning applications in medical imaging. Education and affiliations are not explicitly detailed, but his work spans interdisciplinary areas such as neuroimaging, perfusion quantification, and biomedical signal processing. His research portal is accessible via http://www.imt.tugraz.at/ . Key research interests include developing robust RF pulses for inhomogeneous fields, improving CEST MRI contrast via optimal control, and enhancing real-time fMRI neurofeedback systems. He has contributed to model-based reconstructions for IVIM and DTI, as well as automated segmentation methods using deep learning. Notable articles from 2025–2023 highlight innovations in MRI sequence optimization, artifact reduction, and explainable AI for medical imaging datasets. His work addresses challenges in high-resolution ASL perfusion imaging, T1 quantification, and lung vessel morphology analysis.

Marie-Claude ArnaudView profile

Professor

• Dynamical Systems
• Hamiltonian Dynamics
• Weak KAM Theory
• +3 more

Marie-Claude Arnaud is a Full Professor at the University of Paris and member of the Jussieu-Paris Rive Gauche Institute of Mathematics. She is an honorary senior member (2013-2018) of the Institut Universitaire de France and holds memberships in the Société Mathématique de France and European Mathematical Society. Previously, she served as Full Professor at Avignon University (2001-2019) and held academic positions at Université Paris 11, Paris 7, and Louis Pasteur University. Education: 1983-1987: Ecole Normale Supérieure de Paris (Bachelor in Mathematics 1984, Agrégation 1985, DEA/M2 1986) 1986-1990: PhD in Mathematics, Université Paris 7 1999: Habilitation, Université Paris 11 Research Interests: Her work focuses on dynamical systems with emphasis on Hamiltonian dynamics, weak KAM theory, and conformally symplectic systems. She investigates relationships between invariant sets' geometric properties (regularity, isotropy) and dynamic behaviors (Lyapunov exponents, entropy), with applications to celestial mechanics and conservative systems. Awards and Honors: Bourbaki Seminar Invitation (2019) Senior Member, Institut Universitaire de France (2013) Exceptional Class Professor promotion (2013) French Academy of Sciences Prize (2011) ICM Invited Speaker (2010) Academic Service: She directs the ANR CoSyDy project (2021-2026), co-organizes Oberwolfach workshops, serves on editorial boards (Annales de Toulouse), and has supervised 5 doctoral students. Current doctoral advisees include Elisa Dalgalarrondo and Victor Maeght.

Manuel FriedrichView profile

Professor

• Calculus of Variations
• Multiscale Methods
• Free-discontinuity Problems
• +7 more

Manuel Friedrich is a Professor for Applied Analysis at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) since 2021. Previously, he was a Junior Professor at the University of Münster (2018–2021) and held a postdoctoral position at the University of Vienna (2015–2018) supported by the Feodor Lynen Fellowship. His research focuses on applied analysis, calculus of variations, nonlinear PDEs, and mathematical modeling of materials science problems, including fracture mechanics, multiscale methods, and atomistic models. His academic journey includes a PhD in the TopMath program at Universität Augsburg (2012–2015), a Master’s degree at TU Munich (2010–2012), and a Bachelor’s degree at TU Munich (2007–2010). He has been actively involved in teaching, including courses on analysis, partial differential equations, and variational methods at FAU and Münster. Key research directions include variational fracture models, geometric rigidity, and the analysis of atomistic systems. His work has been supported by grants such as the DFG (German Research Foundation) and the GRK (Graduiertenkolleg) programs. Notable contributions include studies on Griffith fracture models, linearization of fracture evolution, and crystallization in materials. He leads a research group with members including Marco Bresciani, Antonio Donnarumma, and Joscha Seutter. Former members include Rufat Badal, Leonard Kreutz, and others. His scientific achievements include the Feodor Lynen Fellowship and publications in top journals like Archive for Rational Mechanics and Analysis and SIAM Journal on Mathematical Analysis.