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Zenon Medina-CetinaView profile

Associate Professor

• Stochastic Geomechanics
• Risk and Reliability Analysis
• Bayesian Networks
• +7 more

Zenon Medina-Cetina is an Associate Professor in the Zachry Department of Civil & Environmental Engineering at Texas A&M University (TAMU), with affiliations to the Mosbacher Institute for Trade, Economics, and Public Policy at the Bush School of Government and Public Service. He holds courtesy joint appointments in Petroleum Engineering, Ocean Engineering, Multidisciplinary Engineering, and Geography (College of Arts and Sciences). Dr. Medina-Cetina specializes in probabilistic risk assessment of geo-systems, stochastic geomechanics, and integrated site characterization. His work bridges engineering, geoscience, and computational methods to address complex challenges in infrastructure resilience, offshore geotechnics, and environmental systems. Education: Ph.D. (2006), M.Sc. (2005) in Civil Engineering from Johns Hopkins University; M.E. (1996) in Geostatistics from Universidad Nacional Autónoma de México (UNAM); B.Sc. (1994) in Soil Dynamics from Universidad Autónoma de Yucatán (UADY). He completed postdoctoral research at SIMULA and the Norwegian Geotechnical Institute (NGI). Research Interests: Stochastic processes and spatio-temporal simulation (3D-T) Risk, reliability, and sensitivity analysis Uncertainty quantification and inverse problems Bayesian networks and machine learning applications Optimal modeling of geomechanical systems Arctic and Gulf of Mexico geohazard mitigation Awards & Recognition: 2024: Mosbacher Research Fellow, College of Engineering Excellence Award 2022: Zachry Career Development Professorship II, Excellence in Teaching Award 2012: Fellow, Society for Underwater Technology 2001: Fulbright Scholarship Grants & Projects: Lead investigator of $7.4M+ in research funding from federal agencies (e.g., DHS, NSF) and industry Director of the Stochastic Geomechanics Laboratory (SGL) Principal on initiatives like the Arctic X-Grant and Yucatán Initiative Co-lead of the Network-to-Network for the Gulf of Mexico Teaching & Mentoring: Developed TAMU’s Introduction to Research Abroad Program (IRAP) Advised 14 doctoral and 13 master’s students, plus postdoctoral researchers Course instructor in Bayesian Risk Networks, Stochastic Mechanics, and Geomechanics Labs & Teams: Stochastic Geomechanics Laboratory (SGL): Focuses on data-driven risk modeling and computational geomechanics International collaborations with institutions in Mexico, Norway, and the Arctic region

Nicholas PerezView profile

Associate Professor

• Tectonics
• Basin Dynamics
• Fold-thrust Belts
• +1 more

Nicholas Perez is an Associate Professor at Texas A&M University, affiliated with the Basin Tectonic Group. His research focuses on tectonic processes such as basin dynamics, fold-thrust belts, and continental rifting. He investigates these phenomena through field and lab-based techniques, working in regions like the central Andes, Cascade Range, and Atlas Mountains. Perez holds a Ph.D. in Geological Sciences from the University of Texas at Austin (2015) and a B.S. in Geological Sciences (Special Honors) from the same university (2009). He has received notable awards including the NSF CAREER Award (2019) and the Dean's Distinguished Achievement Faculty Teaching Award (2020). His research emphasizes integrating sedimentology, structural geology, and thermochronology to understand mountain-building processes and crustal deformation. Key projects include studying the impact of flat-slab subduction on Andean tectonics and the role of structural inheritance in guiding deformation. Perez has advised multiple graduate students, including Pesek M. E., Gao Z., and Findlay C. P., and has led NSF-funded research initiatives. His work has been presented at major conferences like AGU and GSA, and he contributes to educational initiatives through the Center for Teaching Excellence. Perez is actively involved in the Basin Tectonic Group, which explores tectonic problems through multi-disciplinary approaches. His publications span over 30 peer-reviewed articles, focusing on topics like exhumation mechanisms, basin evolution, and Andean geodynamic processes. He has also contributed to invited presentations on cordilleran margin development and structural inheritance effects on deformation patterns.

• Porous Media
• Computational Mechanics
• Bayesian Inference
• +3 more

Mina Karimi is a Postdoctoral Scholar Research Associate in the Department of Mechanical and Civil Engineering at California Institute of Technology (Caltech). She is part of the Bhattacharya group, advised by Professor Kaushik Bhattacharya. Her research focuses on computational mechanics, poromechanics, and Bayesian inference applied to porous media systems. Key areas include reactive flow modeling, multiscale simulations, and data-driven approaches for subsurface engineering challenges. Her work integrates advanced computational methods with geomechanical and materials science problems, emphasizing energy systems and environmental applications. Recent studies explore carbon sequestration mechanisms, chemo-poro-mechanical coupling, and high-dimensional parameter estimation using Bayesian frameworks. She also investigates crack-healing phenomena in shape memory alloy composites and develops accelerated micromechanical models for solute transport. Publications span topics from machine learning-enhanced groundwater modeling to Hessian-informed sampling techniques for high-dimensional inverse problems. Her research bridges theoretical developments with practical applications in subsurface energy storage, geological carbon sequestration, and material behavior under extreme conditions. Advising is conducted under the mentorship of Prof. Bhattacharya, with affiliations to the Resnick Sustainability Institute at Caltech. Current efforts emphasize computational tools for poromechanics and uncertainty quantification in complex multiphase systems.

Lasse Riis ØstergaardView profile

Associate Professor

• Medical Imaging
• Biomedical Engineering
• Image Processing and Computer Vision
• +4 more

Lasse Riis Østergaard is an Associate Professor at the Department of Health Science and Technology, Aalborg University, affiliated with The Faculty of Medicine. His research focuses on medical imaging, AI applications in healthcare, and computational methods for analyzing medical images, particularly in CT and MRI. He leads projects such as 'Modelling and quantification of the cerebral cortex' and contributes to initiatives like the Danish Centre for Health Informatics and AI for the People. Key research areas include: Medical Image Analysis: Specializing in segmentation, artifact reduction, and quantitative measurements in CT/MRI AI in Surgery: Developing deep learning models for surgical action recognition and robotic skill assessment Clinical Applications: Investigating body composition via CT, lung function correlations, and placental vascular imaging He has authored over 120 publications and supervised 5 PhD students. Notable collaborations include work with the Virtual Brain project and NemoMed. Recent studies explore ethnic differences in body composition and AI-driven surgical training. Awards: None explicitly listed, but his work has received media attention including coverage in Surgical Endoscopy and Advances in Respiratory Medicine .

• Inverse Problems
• Partial Differential Equations
• Mathematics
• +3 more

Janne Nurminen is a Postdoctoral Researcher at the Department of Mathematics and Statistics, University of Jyväskylä, affiliated with the Faculty of Mathematics and Science. His research focuses on inverse problems, particularly in the context of partial differential equations (PDEs), including semilinear elliptic equations, minimal surface equations, and nonlinear elliptic operators. He explores theoretical aspects such as uniqueness, stability, and reconstruction methods in inverse problems, often involving geometric and differential geometric considerations. His work spans mathematical analysis of inverse boundary value problems, with applications to Riemannian geometry and medical imaging (e.g., electrical impedance tomography). Nurminen has contributed to advancing understanding of inverse problems with low regularity assumptions, nonlinearities, and unbounded potentials. His research bridges pure mathematics and applied sciences, emphasizing rigorous proofs and interdisciplinary relevance. Nurminen’s publications (2020–2025) highlight his expertise in inverse problems for various PDEs, including biharmonic operators and semilinear equations. His recent work (2024–2025) addresses minimal surface equations in Riemannian settings and low-regularity scenarios. He is part of the Inverse Problems research group at the University of Jyväskylä, which investigates indirect measurement methodologies and their mathematical foundations. No scientific awards are explicitly mentioned in the provided materials.

Uğurcan EroğluView profile

Assistant Professor

• Structural Engineering
• Vibration Analysis
• Damage Identification
• +3 more

Uğurcan Eroğlu serves as an Assistant Professor in the Department of Mechanical Engineering at Istanbul Technical University (İTÜ Ayazağa Kampüsü, 34000 Istanbul). His research focuses on structural mechanics with emphasis on vibration analysis, damage identification techniques, and nano-structure mechanics. He maintains active collaborations with international researchers including G. Ruta, E. Tüfekci, and A. Pau. His research interests encompass Structural Engineering, Vibration Analysis, Damage Identification, Nano-mechanics, Functionally Graded Materials, and Curved Beam Theory. Eroğlu develops advanced mathematical models using perturbation methods and non-local elasticity theories to solve complex structural problems. His work bridges theoretical mechanics with practical applications in structural health monitoring, particularly through modal curvature analysis and acoustic beamforming techniques for damage detection in arch structures and nano-beams. Analysis of his recent publications (2022-2025) reveals a consistent trend toward developing closed-form solutions for vibration problems in non-local structures. His research demonstrates increasing focus on nano-mechanics applications and damage detection methodologies, with notable contributions in parabolic arch analysis and beamforming techniques. The publications show interdisciplinary connections between structural mechanics, materials science, and signal processing. Dr. Eroğlu has secured two research projects as Principal Investigator: Hüzme Oluşturma Yöntemlerinin Yapısal Mekanikte Hasar Tespiti Problemlerinde Kullanılması (Beamforming Methods for Damage Detection in Structural Mechanics, 2023-2024, completed) and Reçine Baskı Yöntemi ile Parça Analizi (Resin Printing Method for Part Analysis, 2025, active). His scholarly impact is evidenced by an h-index of 10 and 287 citations across 25 research outputs from 2015-2025.

• Quantum chaos
• Quantum information theory
• Mathematical physics
• +6 more

Marek Kuś is a Full Professor at the Center for Theoretical Physics, Polish Academy of Sciences (PAS) in Warsaw, where he has held this position since 1995. He previously served as Full Professor at Cardinal Stefan Wyszyński University (2001-2012) and Director of the Center for Theoretical Physics, PAS (2003-2006). His international collaborations include visiting professorships in Germany, France, and the USA. Education: M.Sc. in Physics, Warsaw University (1979) Ph.D. in Physics, Warsaw University (1983) D.Sc. (Habilitation), Warsaw University (1988) Professor title conferred in 1995 His research spans quantum chaos, quantum information theory, and mathematical physics, with specialized interests in nonlinear phenomena, geometric methods, and quantum entanglement. Recent publications (2011-2015) focus on quantum correlations, entanglement detection frameworks, and symplectic geometry applications to quantum systems, reflecting sustained work in foundational quantum theory and complex systems. Scientific Awards and Leadership: Humboldt Fellowship (1987) President, Polish Society for Advancement of Arts and Sciences (2003-2005) Chairman, Scientific Council of National Center for Quantum Information (since 2008) Member, ERC Advanced Grants evaluation panel (2014) Editorial board member for International Journal of Quantum Information

Gino BiondiniView profile

Professor

• Applied Mathematics
• Nonlinear Waves
• Solitons
• +5 more

Gino Biondini is a Professor in the Department of Mathematics at the University at Buffalo. His research focuses on applied mathematics, nonlinear waves, solitons, integrable systems, inverse problems, applied probability, stochastic processes, and optics. He holds a PhD in Theoretical Physics from the University of Perugia, Italy. His work combines mathematical analysis with applications in diverse fields such as optical fiber communications, nonlinear optics, and water waves. Key contributions include studies on the inverse scattering transform for nonlinear equations with nonzero boundaries and the nonlinear stage of modulational instability. His research bridges pure mathematics and real-world phenomena, emphasizing both exact methods and numerical simulations. Recent publications explore topics like elliptic finite-band potentials, breather gas dynamics, and experimental realizations of solitons in Bose-Einstein condensates. Biondini collaborates with institutions globally, advancing understanding of dispersive shock waves, soliton interactions, and quantum fluid dynamics. His work has been cited extensively, notably in foundational papers on the focusing nonlinear Schrödinger equation and the Kadomtsev-Petviashvili system. Current research continues to address the universality of nonlinear wave behavior and applications in modern physics and engineering.

Lijun JiangView profile

Professor

• Electromagnetics
• Electromagnetic Compatibility (EMC)
• Signal Integrity
• +5 more

Dr. Lijun Jiang is the Kummer Endowed Professor at the Department of Electrical and Computer Engineering, Missouri University of Science and Technology. He holds a Ph.D. from the University of Illinois at Urbana-Champaign (2004) and has extensive industry and academic experience. He is an IEEE Fellow and ACES Fellow, recognized with awards including the IEEE Technical Achievement Award and IBM Research Technical Achievement Award. Education: Ph.D. in Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, USA M.S. in Electrical Engineering, Tsinghua University, China B.S. in Electrical Engineering, Beijing University of Aeronautics and Astronautics, China Research interests focus on electromagnetic modeling, high-speed electronic design, machine learning for signal/power integrity, microwave materials, and optics. His work bridges theoretical advancements with practical applications in antenna engineering and EDA solutions. Awards include IEEE Fellow (2019), ACES Fellow (2019), and the World’s Top 1% Scholar by ESI (2018). His recent research explores data-driven frameworks for electromagnetic near-field scanning and MIMO channel sounding. Key contributions include patents in antenna design and methodologies for electromagnetic compatibility. He has authored books on electromagnetic modeling and contributed chapters on metamaterials and angular momentum engineering.

Taufiquar KhanView profile

Professor

• Applied Mathematics
• Inverse Problems
• Computational Mathematics
• +3 more

Taufiquar Khan is a Professor and Chair of the Department of Mathematics and Statistics at the University of North Carolina at Charlotte (UNC Charlotte). Previously, he held professorial positions at Clemson University between 2000 and 2020, advancing from Assistant Professor to Professor of Mathematical Sciences. His academic leadership roles include Chair at UNC Charlotte since 2020 and various administrative roles at Clemson, including Associate Director for Graduate Studies and Director of Global Engagement Initiatives. He holds a Ph.D. in Applied Mathematics from the University of Southern California (2000) and degrees in Mathematics, Physics, and Aerospace Engineering from USC and Occidental College. His research focuses on inverse problems, computational mathematics, and their applications in biomedical imaging, engineering systems, and environmental science. Notable contributions include advancements in electrical impedance tomography, machine learning for inverse problems, and mathematical modeling of traffic systems. He has authored over 50 peer-reviewed publications and supervised multiple graduate students. Dr. Khan’s awards include a Humboldt Research Fellowship (2007) and Summer Faculty Fellowships at Brooks Air Force Laboratory. He has led international collaborations, including roles at Khalifa University and the University of Bremen. His work bridges theoretical mathematics with practical applications in healthcare, energy systems, and environmental sustainability.

Frauke LiersView profile

Professor

• Optimization under Uncertainty
• Distributionally Robust Optimization
• Quantum Computing
• +5 more

Prof. Dr. Frauke Liers holds the Professorship of Optimization under Uncertainty & Data Analysis at the Department of Data Science (DDS), Friedrich-Alexander-University Erlangen-Nürnberg. Her research focuses on robust and distributionally robust optimization, mathematical programming, and applications in energy systems, healthcare logistics, and quantum computing. Email: frauke.liers@fau.de ResearchGate: Frauke Liers Research Interests span optimization under uncertainty, data-driven mathematical programming, and interdisciplinary applications. Key areas include: Distributionally robust optimization with scenario reduction and chance constraints Quantum computing optimization for gate routing and noise suppression Energy system modeling (photovoltaics, gas networks, electricity networks) Healthcare logistics (patient transport scheduling under uncertainty) Nanoparticle technology and chemical process optimization Recent Publications emphasize: Advancements in quantum circuit optimization (2025) Explainable optimization methods (2024) Robust approaches for particle precipitation control (2024) Dynamic trajectory optimization (2023) Time-expanded models for network flows (2022)

Elise GrosjeanView profile

Assistant Professor

• Fluid Mechanics
• Partial Differential Equations (PDEs)
• Numerical Analysis
• +6 more

Elise Grosjean is an Assistant Professor in Applied Mathematics at ENSTA Paris. Her research focuses on fluid mechanics, partial differential equation analysis, numerical schemes, reduced order methods, inverse problems, and optimal transport. Education: PhD in Applied Mathematics (2022, Sorbonne Université), Master in Mathematics of Modeling (2018, UPMC), Engineer School in Applied Mathematics and Computer Science (2018, Polytech-Paris UPMC), Bachelor in Fundamental Mathematics (2015, UPMC) Her research integrates finite element/volume methods, programming languages, and numerical simulation of parameterized models from physics and biology. She actively develops reduced basis methods and applies them to tissue regeneration and fluid dynamics problems. Recent publications highlight her work on meniscus tissue regeneration modeling (2024), error estimates in two-grid reduced basis methods (2024), and numerical simulations using FreeFem++ for 3D bioreactor models and back-and-forth nudging algorithms for wave equations (2023-2024). These works span biomedical engineering, computational mathematics, and inverse problem solving. She has taught courses on differential-algebraic equations at TU Kaiserslautern and contributed to teaching at ENSTA Paris, Sorbonne Université, and ENSAE. Her technical expertise includes Python, C++, FreeFem++, and model order reduction frameworks. Elise is affiliated with INRIA-Saclay (current postdoc), RPTU Kaiserslautern (2022-2023), and the Jacques-Louis Lions Laboratory (2018-2022). She also explores intersections between mathematics and art in her creative practice.

Elina RobevaView profile

Assistant Professor

• Mathematical Statistics
• Machine Learning
• Causal Inference
• +6 more

Elina Robeva is an Assistant Professor in the Department of Mathematics at the University of British Columbia (UBC), effective until July 2025, when she will be promoted to Associate Professor. She is affiliated with the Institute of Applied Mathematics, CAIDA, and holds the titles of AMII Fellow and Canada CIFAR AI Chair. Her research focuses on the intersection of mathematical statistics, machine learning, combinatorics, and applied algebraic geometry, with an emphasis on causal inference, graphical models, and tensor decomposition. Education: She earned her PhD in Mathematics from UC Berkeley (2016), advised by Bernd Sturmfels, and a BS with Honors in Mathematics from Stanford University (2011). Prior to UBC, she was an NSF Postdoctoral Fellow and instructor at MIT (2016–2019). Research Interests: Her work bridges algebraic methods with statistical problems, including causal discovery in linear models, tensor decomposition applications in machine learning, and super-resolution imaging. She employs algebraic geometry and combinatorics to uncover structural insights in statistical problems. Awards: Recipient of the Andre Aisenstadt Prize (2024), Bernard Friedman Memorial Prize (2016), and Vanier Canada Graduate Scholarship (for student Pardis Semnani). She is also a Canada CIFAR AI Chair and AMII Fellow. Advising & Mentorship: She has mentored numerous students, including PhD candidates (e.g., Pardis Semnani), MSc students, and undergraduates through programs like NSERC USRA and MITACS. Notable collaborations include work on causal inference, log-concave density estimation, and tensor networks. Labs/Teams: Active in interdisciplinary research groups at UBC and collaborates internationally through programs like the Institute for Mathematical and Statistical Innovation (IMSI).

Franca HoffmannView profile

Assistant Professor

• Applied Mathematics
• Data Analysis
• Machine Learning
• +3 more

Franca Hoffmann is an Assistant Professor of Computing and Mathematical Sciences at the California Institute of Technology (Caltech), and an International Scientific Advisor at Quantum Leap Africa (QLA) at the African Institute for Mathematical Sciences (AIMS). Previously, she held positions including a Bonn Junior Fellow at the Hausdorff Center for Mathematics and the AIMS-Carnegie Research Chair in Data Science. She leads the Doctoral Training Program in Data Science at QLA in Rwanda. Education : B.S. (Imperial College London, 2010), M.S. (2013), Ph.D. (University of Cambridge, 2017). Her thesis focused on partial differential equations and their applications, honored with the 2016 Imperial College Outstanding Student Achievement Award. Research Interests : Interface of model-driven and data-driven approaches, including PDE analysis (nonlinear drift-diffusion equations, kinetic theory, optimal transport) and data analysis (inverse problems, Bayesian inference, clustering algorithms). Her work bridges theoretical foundations with applications in mechanics, social sciences, and machine learning. Teaching : Courses include Environmental Physical Organic Chemistry and Special Topics in Applied Mathematics at Caltech. She has organized workshops and conferences globally, emphasizing capacity-building in African mathematical sciences. Awards : 2016 Imperial College Outstanding Student Achievement Award. Labs/Teams : Leads the DTP-DS at QLA and co-organizes initiatives like the Young African Mathematicians Bonn Visitor Program. Active in promoting interdisciplinary education and outreach in Africa.

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.