Find Academic Supervisors

• Dynamic Fracture Mechanics
• Penetration Mechanics
• Aeroelasticity
• +5 more

Yuri Antipov is a Professor in the Department of Mathematics at Louisiana State University (LSU). His work focuses on applied mathematics, particularly in dynamic fracture mechanics, fluid dynamics, and scattering of electromagnetic waves. He has held academic positions at LSU since 2002, including roles as Associate Professor (2002–2004) and Professor (2004–present). Prior roles include Lecturer/Research Officer at the University of Bath (1997–2002), Humboldt Foundation Fellow at the University of Stuttgart (1996–1997), and academic roles at Odessa State University (1982–1995). Education: Ph.D. (Doctor in Sciences) in Solid Mechanics from Moscow State University (1993), Ph.D. (Candidate in Sciences) in Differential Equations from Odessa State University (1983), and a Diploma in Applied Mathematics (1979). Research Interests include mathematical modeling of crack propagation, supercavitating flows, and Riemann-Hilbert boundary-value problems. His work integrates advanced analytical methods like automorphic functions and Riemann surfaces to solve complex problems in elasticity and fluid dynamics. Recent publications span topics like graded materials, vortex dynamics, and coupled thermoelasticity models. Notable contributions include studies on fracture mechanics in heterogeneous media, scattering theory for anisotropic materials, and the development of exact solutions for functional-difference equations using Riemann surfaces.

Stephen P. ShipmanView profile

Professor

• Spectral Theory
• Electromagnetic Resonance
• Meta-Materials
• +5 more

Stephen P. Shipman is a Professor and Director of Graduate Studies in the Department of Mathematics at Louisiana State University (LSU), within the College of Science. His research focuses on the mathematics of wave phenomena, particularly in composite media, with emphasis on spectral theory, electromagnetic resonance, meta-materials, and nonlinear dispersive systems. He has held academic roles at LSU since 2002, including Visiting positions at institutions like the Institute for Mathematics and its Applications (IMA) and the Institut Élie Cartan. His education includes a Ph.D. in Mathematics from the University of Arizona under Nicholas Ercolani. Shipman’s research spans theoretical and applied mathematics, addressing problems in wave scattering, quantum graphs, and periodic media. His recent work includes studies on Fermi surfaces in multi-layer quantum graphs and defect states in bilayer graphene. He has advised numerous graduate and undergraduate students, leading projects on topics such as resonance in waveguides and metamaterials. His funding includes five NSF grants and support from the LSU Board of Regents. Shipman has received multiple teaching awards, including the 2024 LSU Distinguished Faculty Teaching Award and the 2012 N. A. Khizhnyak Award for contributions to electromagnetic theory. He serves as an Associate Editor for SIAM Journal on Applied Mathematics and has organized conferences like the UH-TAMU-LSU Undergraduate Math Conference. His lab collaborates on projects such as the Math Consultation Clinic , engaging undergraduate students in applied research.

Alexander G. RammView profile

Professor

• Wave Scattering
• Inverse Problems
• Mathematical Physics
• +3 more

Dr. Alexander G. Ramm is Professor of Mathematics at University of Kansas, specializing in mathematical physics and scattering theory. With a PhD from Moscow State University (1964) and Dr.Sci from Mathematics Institute, Minsk (1972), his career includes positions at Leningrad Institute of Precision Mechanics and Optics. Research: Develops asymptotic solutions for wave scattering by small particles, creating materials with engineered refraction properties. His work enables negative refraction materials and customized wave-focusing capabilities through particle distribution design.

• Numerical Methods for Partial Differential Equations
• Fractional Partial Differential Equations
• Inverse Problems
• +3 more

Abdallah BRADJI is a Full Professor of Mathematics at the University of Annaba-Algeria (since January 2015), affiliated with the Department of Mathematics in the Faculty of Sciences. He holds a PhD from Aix-Marseille University (2005) and a French Habilitation (HDR) from Aix-Marseille University (2016). His research focuses on numerical methods for partial/fractional PDEs, inverse problems, and computational modeling with COMSOL Multiphysics. He has held postdoctoral positions at the Weierstrass Institute (Berlin, Germany) and the Nečas Center (Prague, Czech Republic). Teaching roles include lecturing in undergraduate and graduate courses (e.g., Numerical Analysis, Finite Elements, Operational Research) since 1994, with recent responsibilities in Master’s programs in Metallurgy and Process Engineering. He has been invited as a Visiting Professor at Sorbonne University Paris Nord (2024) and LAGA Paris 13 (multiple visits), and has reviewed for prestigious journals like Comptes Rendus (CRAS) and ESAIM:M2AN. Research interests span numerical methods for fluid mechanics, fractional differential equations, and convergence analysis of finite volume/element schemes. His work emphasizes high-order accuracy and stability in complex applications.

Aras BachoView profile

Research Fellow

• Partial Differential Equations
• Machine Learning
• Deep Learning
• +4 more

Dr. Aras Bacho is a Postdoctoral Research Associate in the Computing and Mathematical Sciences Department within the Division of Engineering and Applied Science at the California Institute of Technology (Caltech), where he works with Professor Houman Owhadi. His research spans mathematical analysis, partial differential equations, and machine learning applications. Dr. Bacho completed his PhD in Partial Differential Equations at the Technical University of Berlin with a thesis titled "On the Nonsmooth Analysis of Doubly Evolution Inclusions of First and Second Order with Applications" under the supervision of Prof. Dr. Etienne Emmrich. He also holds M.Sc. degrees in Mathematics from both the Technical University Berlin and Université Pierre et Marie Curie in Paris (through an ERASMUS+ scholarship), as well as a B.Sc. in Technomathematics from the Technical University Berlin. Dr. Bacho's research interests focus on the intersection of mathematical analysis and machine learning. His work explores partial differential equations, nonsmooth analysis, physics-informed neural networks, and applications in robotics and computational physics. His research bridges theoretical mathematics with practical applications in artificial intelligence and engineering systems. He has developed novel approaches to physics-informed machine learning and has made contributions to the mathematical foundations of scientific machine learning. Dr. Bacho's publications demonstrate a strong trajectory from theoretical mathematical analysis toward increasingly applied work in scientific machine learning. His recent work shows particular interest in physics-informed neural networks, equation discovery, and the mathematical foundations of AI reliability. He has published on topics ranging from classical PDE theory to cutting-edge applications of deep learning in scientific computing, with a focus on mathematical rigor and practical applicability. 2008: First prize in the Nationwide Mathematics Competition in Germany (Bundeswettbewerb Mathematik) 2009: DPG Prize for special achievements in physics issued by the German Physical Society 2012: Perfect score in the Math Advent Calendar organized by the DFG Research Center MATHEON 2014-2015: Erasmus+ Scholarship for studies at Université Pierre et Marie Curie 2017: PhD-Scholarship issued by TU Berlin (4 Months) 2021-present: Mentoring Program for young researchers Dr. Bacho has presented his work at numerous international conferences including the Workshop on Control of Self-Organizing Nonlinear Systems, the Hausdorffschool on Modeling and analysis of evolutionary problems, and the International Council for Industrial and Applied Mathematics (ICIAM) 2023. His research has been supported by various grants including his PhD scholarship from TU Berlin and participation in mentoring programs for young researchers. He collaborates with researchers across mathematics, computer science, and engineering disciplines. Dr. Bacho is actively involved in the scientific machine learning community, collaborating with researchers at Caltech and beyond. His work at the intersection of mathematical analysis and AI places him at the forefront of scientific machine learning research, with particular emphasis on the mathematical foundations of physics-informed neural networks and the reliability of AI systems for scientific applications.

• Microwave Sensing
• Dielectric Measurements
• Structural Health Monitoring
• +6 more

Antonio Masciullo is a researcher in the Department of Electrical and Information Technologies Engineering at the University of Campania Luigi Vanvitelli, specializing in microwave sensing, dielectric measurements, and biomedical instrumentation. His work spans multiple domains including structural health monitoring, neurological disorder detection, and water infrastructure diagnostics. His primary research interests encompass Microwave Sensing , Dielectric Measurements , Structural Health Monitoring , Biomedical Instrumentation , Radar Systems , and Signal Processing . Masciullo's work demonstrates a strong interdisciplinary approach, bridging electrical engineering principles with practical applications in healthcare, civil infrastructure, and environmental monitoring. Analysis of his recent publications (2021-2024) reveals a significant focus on EEG-based Alzheimer's disease detection, microwave-based skin diagnostics, and structural monitoring systems. His research shows consistent innovation in developing non-invasive sensing technologies with practical applications in medical diagnostics and infrastructure monitoring. The work often involves collaboration with multidisciplinary teams to address complex engineering challenges. Masciullo has made significant contributions to the fields of microwave sensing technology and biomedical instrumentation through his extensive publication record in high-impact journals and conference proceedings. His collaborative work spans multiple research domains, indicating strong interdisciplinary connections across engineering disciplines with applications in healthcare, civil infrastructure, and environmental monitoring systems.

• Machine Learning
• Computer Vision
• Data Science
• +4 more

Xiaojun Li is a Professor affiliated with the Department of Mechanical Engineering at the University of Maryland, with additional research ties to Texas A&M University and institutions in China. His work spans interdisciplinary fields including machine learning, computer vision, and engineering systems. He has contributed to over 130 publications since 1985, focusing on topics like deep learning applications in healthcare, seismic modeling, and intelligent systems for infrastructure. His research emphasizes practical solutions in domains such as medical diagnostics, tunnel construction optimization, and energy forecasting. Key contributions include the development of JaunENet for jaundice detection, advanced seismic wave modeling techniques, and intelligent systems for automated tunnel construction. He collaborates extensively with experts in data science, environmental engineering, and computer science. His work bridges theoretical advancements with real-world applications, addressing challenges in healthcare technology, sustainable infrastructure, and energy management. Recent projects highlight innovation in multimodal hate speech detection, smart evacuation systems using VR, and the application of graph-based methods for EEG emotion recognition. His research often integrates big data analytics and AI-driven approaches to solve complex problems in both technical and societal contexts.

Sara LombardoView profile

Professor

• Integrable Systems
• Automorphic Lie Algebras
• Nonlinear Waves
• +4 more

Sara Lombardo is a Professor of Mathematics and Executive Dean of the School of Mathematical & Computer Sciences at Heriot-Watt University, UK. She holds visiting roles at Loughborough University, Sapienza University of Rome, and the Vrije Universiteit Amsterdam. A graduate of the University of Rome La Sapienza (MSc in Mathematical Physics) and Leeds University (PhD in Mathematics), her career includes positions at Leeds, Manchester, Rome, and Amsterdam before her current role. Her research focuses on integrable systems, automorphic Lie algebras, nonlinear waves, and rogue waves, supported by fellowships from EPSRC, NWO, and IRCSET. Research interests include infinite-dimensional algebras, nonlinear PDEs, and mathematical modeling. She advocates for interdisciplinary research and diversity in science. Awards include the Suffrage Science Award (2020) and Blanceflor Boncompagni Ludovisi Young Scientists Award (2003). Her work addresses fundamental problems in mathematical physics, with contributions to stability analysis of nonlinear waves, soliton theory, and spectral methods. Recent articles explore rogue wave dynamics, automorphic Lie algebra classification, and integrable wave models. She is a member of the Maxwell Institute for Mathematical Sciences and actively contributes to sustainable development goals through her research.

Jörn BehrensView profile

Professor

• Numerical Methods in Geosciences
• Climate Modeling
• Tsunami Simulation
• +5 more

Prof. Jörn Behrens is a Professor of Numerical Methods in Geosciences at the University of Hamburg's Department of Mathematics. His work focuses on computational geoscience, including adaptive numerical methods, tsunami modeling, and multiscale simulations. He leads the Numerical Methods in Geosciences research group and is affiliated with the Climate, Climatic Change, and Society (CLICCS) cluster of excellence. Education & Positions: Since 2009: Full Professor at University of Hamburg 2012: Visiting Scientist at Isaac Newton Institute, Cambridge 2007–2009: Privatdozent at University of Bremen Extensive experience at Alfred-Wegener-Institut and TU Munich Research Interests: Behrens develops numerical algorithms for geophysical flows, emphasizing adaptive mesh refinement, discontinuous Galerkin methods, and tsunami hazard modeling. His work bridges computational mathematics and geoscience applications, with a focus on environmental challenges like climate modeling and disaster risk assessment. Recent efforts include cloud computing for instant hazard simulations and interdisciplinary projects like the Global Tsunami Model (GTM). Key Publications: Recent work includes bathymetry reconstruction via PDE-constrained optimization, low-rank preconditioning for fluid flow, and tsunami hazard frameworks within EPOS. These reflect his expertise in computational methods and geophysical problem-solving. Grants & Projects: Lead on EU-funded DT-GEO (Digital Twin for Geophysical Extremes) Co-chair of COST Action AGITHAR (Tsunami Hazard Research) Contributions to CLICCS and EPOS infrastructure development Labs & Teams: Leads the Numerical Methods in Geosciences group, collaborating internationally on tsunami modeling, climate systems, and high-performance computing.

Litian WangView profile

Professor

• Solid State Physics
• Material Sciences
• Elastodynamics
• +3 more

Litian Wang is a Professor at the Department of Engineering, Oslo Metropolitan University. His research focuses on solid state physics, material sciences, elastodynamics, and acoustics. He holds a PhD in Physics from the University of Oslo (1993) and has collaborated with companies like Fireco AS and Biobe AS in Fredrikstad. His work spans theoretical and applied physics, including studies on phononic crystals, elastic wave propagation in anisotropic materials, and phonon focusing patterns. Notable contributions include analyses of surface wave behavior, degeneracy spaces in elastic media, and computational modeling of material properties. Wang has also explored practical applications such as soundproofing materials and material selection strategies for industrial contexts. Career highlights include over 30 peer-reviewed articles in journals like Wave Motion and Journal of the Acoustical Society of America , as well as contributions to scientific conferences and media outlets. His research emphasizes bridging theoretical physics with industrial innovation, particularly in Norway's manufacturing sector.

• Inverse Problems
• Numerical Analysis
• Partial Differential Equations
• +4 more

Burhan PEKTAŞ is a Professor of Computer Engineering at the Faculty of Engineering and Natural Sciences, Üsküdar Üniversitesi. He holds a Ph.D. from Kocaeli University (2006) and has held academic positions including Vice Dean of the Faculty of Engineering and Natural Sciences since 2021. His research focuses on inverse problems, numerical analysis, and mathematical modeling with applications in engineering and environmental systems. Education: BSc in Mathematics, Kocaeli University (1998) MSc in Mathematics, Kocaeli University (2002) PhD in Mathematics, Kocaeli University (2006) Research Interests: His work emphasizes computational methods for solving inverse problems in partial differential equations, numerical simulations of heat transfer and diffusion processes, and cybersecurity applications in telehealth systems. He has organized sessions at international conferences such as Inverse Problems: Modeling and Simulation (since 2002). Academic Contributions: With 17 publications (h-index 6), his articles span journals like Journal of Inverse and Ill-posed Problems and Journal of Mathematical Chemistry. His 2020 study on wave equation boundary measurements exemplifies his expertise in inverse problem techniques. Administrative Roles: Head of Computer Engineering Department (2023–present) Vice Dean, Faculty of Engineering and Natural Sciences (2021–present) Teaching: Currently instructs courses in Calculus, Numerical Analysis, and Python Programming for undergraduates.

Lee SobotkaView profile

Professor

• Nuclear and Radiochemistry
• Nuclear Structure
• Nuclear Reactions
• +7 more

Lee Sobotka is a Professor of Chemistry and Physics at Washington University in St. Louis , with affiliations to the Department of Chemistry and the McDonnell Center for the Space Sciences. His research spans fundamental nuclear physics and applied nuclear science, focusing on topics such as nuclear structure, nuclear reactions, radiation detection technology, and medical imaging. Education: PhD in Chemistry from the University of California-Berkeley; BS in Chemistry from the University of Michigan. Research Interests include the de-excitation modes of highly-excited nuclei, continuum structure of exotic nuclei, nucleosynthesis processes, nuclear fusion and fission dynamics, asymmetry-dependent nucleon correlations, equation of state of nuclear matter, and advanced radiation detectors with applications in medicine. Collaborative efforts also explore plant physiology using 11C positron imaging. Recent Publications highlight discoveries of new isotopes (e.g., 11O, 17Na), investigations into proton decay mechanisms, development of radiation detection systems, and interdisciplinary applications of nuclear science in astrophysics and plant biology. Key keywords across these works include nuclear structure, detector technology, nucleosynthesis, and radiation therapy. Scientific Awards : Awarded a three-year $1,365,000 Department of Energy grant (2023–2026) to study nuclear reactions and structure linked to element formation and neutron-rich isotopes. Applied Work extends to radiation health and safety, including a technical forum at St. Louis University (2023) addressing concerns at Jana Elementary School. He emphasizes public communication of complex nuclear science issues.

Lucas ChesnelView profile

Research Fellow

• Applied Mathematics
• Physics
• Acoustics
• +8 more

Lucas Chesnel is a research fellow at Inria, affiliated with the Idefix team (Inria/Institut Polytechnique de Paris/EDF). He works within the Applied Mathematics Unit (UMA) at Ensta Paris, focusing on mathematical analysis of wave propagation in physics-related problems. Research Interests : Wave propagation in metamaterials and plasmonic structures Partial differential equations and spectral theory Asymptotic analysis and inverse problems Applications in acoustics and electromagnetism Contact : Email: Lucas.Chesnel@inria.fr Office: UMA, Ensta Paris, 2.3.27, 828, Bd des Maréchaux, 91762 Palaiseau Cedex, France Phone: +33 (0)1 81 87 21 39

Lucas ChesnelView profile

Researcher

• Applied Mathematics
• Wave Propagation
• Metamaterials
• +8 more

Lucas Chesnel is a Researcher at Inria Saclay, working within the Idefix Team (Inria/Institut Polytechnique de Paris/EDF). He is affiliated with the Applied Mathematics Unit (UMA) at ENSTA Paris, a member institution of Institut Polytechnique de Paris. Role: Inria Research Fellow Affiliated Institutions: Inria Saclay, ENSTA Paris (Institut Polytechnique de Paris) His research focuses on mathematical analysis of physical problems in acoustics and electromagnetism, with emphasis on wave propagation in metamaterials, plasmonic structures, and theoretical frameworks for invisibility with finite measurement sets. Methodologically, he works across partial differential equations, numerical analysis, spectral theory, asymptotic analysis, and inverse problems. Key Research Themes: Metamaterials, Plasmonics, Wave Dynamics, Applied Mathematics Foundations Contact details: Email: lucas.chesnel@inria.fr Office: 2.3.27, UMA, ENSTA Paris Address: 828, Bd des Maréchaux, 91762 Palaiseau Cedex, France

Arnaud KnippelView profile

Lecturer

• Network Design and Optimization
• Mathematical Programming
• Graph Theory
• +8 more

Arnaud Knippel is a Lecturer in the Department of Mathematical Engineering at INSA. He has been responsible for internships in the GM department since 2015 and is a member of the GM Department Council. His research focuses on network design and dimensioning using mathematical programming and graph theory, with applications in telecommunications, transport, logistics, electricity, physics, and chemistry. He also explores the intersection of Operations Research (RO), Artificial Intelligence (AI), and Data Science, particularly in partitioning/classification problems and subgraph isomorphism for pattern recognition. Knippel has coordinated major research projects such as XTerM (2016-2019, as INSA’s scientific coordinator) and Fractal Grid (2016-2019), both funded by ANR and FEDER. He contributed to CLASSE2 (2016-2018) and PLURADYS (2015), a health-focused classification project. His doctoral supervision includes Zacharie Ales (co-advised with LITIS & C. Gout), S. Benhida (co-supervised with ENSA Agadir), and I. Khames (with JG Caputo). His research interests emphasize theoretical and applied aspects of graph theory, optimization, and AI. Projects like XTerM and Fractal Grid highlight his work on network oscillations and electrical grid design, respectively. Despite no explicit mention of scientific awards, his extensive publication record and project involvement underscore his academic contributions.