Micheal Babatunde Oguntola is a Postdoctoral Fellow at the Department of Mathematics, University of Bergen (UiB), Norway. He has previously collaborated with the Department of Energy Resources at the University of Stavanger and NORCE Norwegian Research Center AS. His research focuses on computational modeling, optimization, and decision-making frameworks in energy recovery systems. Current Affiliation: Department of Mathematics, UiB Past Affiliations: University of Stavanger, NORCE Norwegian Research Center AS Research Interests : Oguntola specializes in enhanced oil recovery (EOR) optimization, multiphysics simulations of porous media, and decision analysis in petroleum engineering. His work integrates applied mathematics, computational science, and geothermal energy modeling. Publication Trends : His recent publications (2019–2024) emphasize ensemble-based optimization techniques, polymer flooding applications, and multiphysics simulations for geothermal and oil recovery systems. Key subfields include stochastic gradient methods, heterogeneous reservoir modeling, and surrogate modeling. Collaborations & Grants : Oguntola has collaborated with institutions like OPM and the IOR Center, supported by grants under reference 230303.
Akil Narayan is a Professor in the Department of Mathematics and a member of the Scientific Computing and Imaging (SCI) Institute at the University of Utah. His office is located in WEB 4666 (SCI) and LCB 116 (Math). He has previously held positions as Assistant Professor at the University of Massachusetts Dartmouth (2012-2015) and Visiting Assistant Professor at Purdue University (2009-2012). His educational background includes: Ph.D. in Applied Mathematics from Brown University (2009) M.Sc. in Applied Mathematics from Brown University (2004) B.S. in Engineering Sciences and Applied Mathematics from Northwestern University (2003) B.S. in Electrical Engineering from Northwestern University (2003) Akil Narayan's primary research interests lie in numerical analysis, scientific computing, and approximation algorithms. His work spans multiple domains including uncertainty quantification, multifidelity modeling, optimization, and computational methods for partial differential equations. He has made significant contributions to the development of numerical methods for solving complex computational problems across various scientific and engineering disciplines. His research often bridges theoretical mathematics with practical applications in fields such as biomedical engineering, ecology, and power systems. Analysis of his recent publications reveals a strong focus on uncertainty quantification, multifidelity methods, and scientific machine learning. His work increasingly integrates traditional numerical methods with modern machine learning techniques, particularly in the development of physics-informed neural networks. There's also a notable emphasis on structure-preserving numerical methods and optimization techniques for computational models. His research has significant applications in biomedical imaging, particularly in electrocardiographic imaging and cardiac modeling. While specific scientific awards are not detailed in the available information, his extensive publication record in top-tier journals demonstrates recognition in his field. His work appears regularly in prestigious journals such as SIAM Journal on Scientific Computing, Journal of Computational Physics, and SIAM Review. Professor Narayan has advised numerous graduate students through the Department of Mathematics and the School of Computing at the University of Utah. His current advisees include Filip Belik, Haoyu Chen, John Turnage, and Yinqian Yu, working on topics ranging from numerical methods for PDEs to operator learning and uncertainty quantification. His former students have gone on to positions at institutions including General Motors, Amazon, Intel Corporation, and various academic institutions. He has also secured research funding supporting his work in computational mathematics and scientific computing, though specific grant details are not provided in the available text. He is actively involved with the Scientific Computing and Imaging (SCI) Institute at the University of Utah, where he collaborates with researchers across disciplines. His work through the UncertainSCI project focuses on uncertainty quantification for computational models in biomedicine and bioengineering, particularly in cardiac applications. He frequently collaborates with researchers in the Department of Mathematics, School of Computing, and the SCI Institute on interdisciplinary projects that combine mathematical theory with practical computational applications.
Olga Mula is a researcher at Eindhoven University of Technology (TU Eindhoven) in the Netherlands specializing in optimal transport theory, Wasserstein spaces, and model reduction techniques for partial differential equations. Her work bridges theoretical mathematics with practical applications in state estimation and inverse problems. Her research interests focus on Optimal Transport , Wasserstein Spaces , Model Reduction , and Numerical Analysis of PDEs . She develops algorithms for state estimation in metric spaces, particularly focusing on the Wasserstein space of probability measures. Her work includes developing reduced models using barycentric approximation, analyzing convergence properties, and addressing challenges in sensor placement for optimal data acquisition. Her recent publications demonstrate significant contributions to understanding how to build efficient reduced models in Wasserstein spaces for both forward and inverse problems. She has developed theoretical frameworks for state estimation algorithms, analyzed their performance in terms of Kolmogorov widths, and created practical implementations using sparse Wasserstein barycenters. Her work spans pure mathematical theory to applications in image processing and PDE-constrained optimization. Her scientific contributions include: Development of piecewise-affine algorithms for state estimation Nonlinear model reduction on metric spaces for conservative PDEs Sparse approximation using Wasserstein barycenters Applications to shape reconstruction and line completion Theoretical analysis of approximation rates in Wasserstein spaces She collaborates extensively with leading researchers in the field including Cohen, Dahmen, Feydy, and Rai. Her work demonstrates both theoretical depth and practical relevance, connecting abstract mathematical concepts to real-world problems in data assimilation and inverse modeling.
Prof. Dr. rer. nat. Malte Prieß is a Professor for Cloud Technologies at Kiel University of Applied Sciences since October 20224, following eight years as Dean and Professor of Applied Computer Science at Schleswig-Holstein Cooperative State University (DHSH) . He teaches modules including Cloud Computing , Web Applications , and Advanced Cloud Computing , with additional involvement in Agile Development Methods and Software Engineering . Academic Background: Diploma in Physics (with distinction) from Leibniz University Hanover and Max Planck Institute for Gravitational Physics (2002-2007) Dr. rer. nat. (magna cum laude) from Kiel University in Algorithmic Optimal Control (2008-2012) Research Interests focus on the intersection of cloud computing, artificial intelligence, and modern software engineering . His work includes surrogate-based optimization for climate models, AI-driven document capture systems , and ethical considerations in AI deployment within project work. Recent Publications demonstrate expertise in AI vulnerability assessment , deep learning training optimization , and document search algorithms for governmental agencies. Scientific Recognition: Best Paper Award at CLOUD COMPUTING 2025 for "Graph of Effort" vulnerability assessment Accepted fellowship at AI Campus (Stifterverband) for "Teaching AI, learning AI at DHSH" (2022) Research Projects: Central Innovation Programme for SMEs (ZIM): "AI MODULES for the skilled trades" (2024/25) HR dashboard for DRK Schwesternschaft, Lübeck Scalable Data Analytics project under BMBF FHprofUnt program (2018)
Dr. Lennart Johnsson is a Professor of Computer Science at the University of Houston, with affiliations at the Royal Institute of Technology (KTH) in Sweden. He has held faculty positions at Caltech, Yale University, Harvard University, and KTH, and industry roles at ABB Research and Thinking Machines Corp. His research focuses on High-Performance Computing (HPC), energy-efficient systems, parallel algorithms, and grid computing. He has collaborated with institutions like PRACE, and companies including AMD, Intel, and Texas Instruments, leading to innovations such as energy-efficient HPC servers and DSP-based architectures. Research interests include optimizing HPC for energy efficiency, embedded processors (e.g., DSPs), and novel interconnection networks. He has pioneered software libraries like CMSSL and contributed to standards such as MPI and High-Performance Fortran. Awards include the Machtey Best Student Paper Award and recognition in the Gordon Bell Prize competition. Dr. Johnsson has supervised numerous students, including those working on adaptive scheduling, grid computing, and bioinformatics. He founded the Texas Learning and Computation Center and led initiatives like the Texas GigaPoP and RENoH network. Current projects explore energy-efficient HPC using DSP architectures. Key contributions include the first No. 1 system on the Top500 list (1993), grid computing frameworks, and infrastructure for distributed applications. He has served on boards for PRACE, NSF, and Swedish research councils, and advises on national HPC strategies.
Prof. Dr.-Ing. Robert Seifried is a Full Professor and Chair for Structural Mechanics at the Institute of Mechanics and Ocean Engineering , Hamburg University of Technology (TUHH) . He also serves as the Dean of the School of Multidisciplinary Engineering Science and Technologies since 2019. His leadership roles include Head of FIT, Head of AIW, Head of Engineering Science, and Head of Theoretical Mechanical Engineering. He is actively involved in research, teaching, and academic administration. PhD in Mechanical Engineering, University of Stuttgart (2005) Habilitation in Applied Mechanics, University of Stuttgart (2012) Postdoctoral research at UC Berkeley (2006–2007) Assistant Professor at University of Stuttgart (2008–2013) Full Professor at University of Siegen (2013–2014) Full Professor at TUHH (since 2014) Prof. Seifried’s research centers on multibody system dynamics , with a strong emphasis on underactuated and flexible systems . His work spans soft robotics , particle dampers , topology optimization , and ocean engineering . He investigates advanced control strategies such as funnel control, servo constraints, and stable inversion, particularly for systems with nonlinearities and impacts. His research integrates computational modeling (e.g., isogeometric analysis, adjoint sensitivity) with experimental validation in areas like wave energy converters and underwater robotics. The recent publications highlight a trend toward interdisciplinary robotics and smart ocean technologies . Key themes include soft robot control and sensing , particle damping for vibration suppression , topology optimization under uncertainty , and modeling of nonlinear ocean waves . These works demonstrate a consistent focus on enhancing system performance, robustness, and energy efficiency through advanced simulation and control. Lead investigator in multiple research projects on multibody dynamics and ocean systems Advisor to numerous PhD and Master’s students in robotics and mechanics Developer of simulation tools like Dynmanto for multibody systems Recipient of research funding for projects in soft robotics, wave energy, and vehicle dynamics Prof. Seifried leads a dynamic research group focused on mechanics and marine engineering . His team develops novel solutions for soft and underwater robots , hybrid particle dampers , and optimized marine structures . The group combines theoretical modeling, numerical simulation, and experimental testing, often in collaboration with industry and international institutions. Their work supports future advancements in autonomous marine systems, resilient infrastructure, and energy harvesting technologies.
Elisa Davoli is a Professor at TU Wien, affiliated with the Multiscale Calculus of Variations Research Group (E101-01-3). Her work focuses on calculus of variations, micromagnetics, and material science, with applications in phase transitions, nonlocal models, and stochastic homogenization. She collaborates extensively with researchers such as Irene Fonseca, Manuel Friedrich, and Ulisse Stefanelli. Her recent research includes studies on fractional Cahn-Hilliard systems, sharp-interface limits, and optimal control in nonlocal frameworks. She also investigates stochastic homogenization in micromagnetics and structural changes in nonlocal denoising models through bilevel learning. Key contributions include existence results in large-strain magnetoelasticity, two-scale convergence methods for composite materials, and the derivation of linearized fracture models under non-interpenetration constraints. Her work bridges mathematical analysis with applications in solid mechanics and image processing.
George Vossen serves as Professor of Applied Mathematics and Computer-Aided Simulation at the Department of Engineering and Computer Science, Niederrhein University of Applied Sciences. He holds the administrative position of Chairman of the University Examination Board and teaches core mathematics courses including Mathematik 1 and 2 for bachelor programs, Numerische Methoden for master programs, and Angewandte Mathematik - Optimierung as an elective. His office is located in room B 410 with consultation hours by email appointment. Professor Vossen's research focuses on optimal control theory for partial differential equations with industrial applications. His work develops mathematical models for laser cutting processes to minimize surface roughness and stabilize melt flow boundaries, optimizes charging protocols for lithium-ion batteries using electro-chemical models, and advances model reduction techniques for complex systems. He has made significant contributions to switching time optimization in bang-bang control and proper orthogonal decomposition methods for parabolic control problems. His publication record from 2010-2018 demonstrates consistent interdisciplinary work bridging mathematics with engineering challenges. The research shows strong thematic continuity in developing numerical methods for real-world optimal control problems, with applications spanning laser technology, energy systems, and biomedical engineering. His collaborative approach is evident through numerous co-authored publications addressing practical industrial constraints.
Dr. Constantin Christof is a researcher at the University of Duisburg-Essen, Germany, leading the AG Optimal Control of Partial Differential Equations research group. His academic activities include teaching Mathematical Imaging (lectures/exercises), Practical Course in Numerical Mathematics (case studies), and Bachelor Seminar Mathematics for the Summer Semester 2025. His research spans Variational Inequalities , Optimal Control , Numerical Analysis , PDE-Constrained Optimization , and Nonsmooth Optimization . Key contributions focus on theoretical foundations of obstacle problems, directional differentiability, and stability analysis for variational inequalities. Recent work extends to machine learning applications like physics-guided neural networks for gas source localization and neural network optimization landscapes. Christof's publication trend (2021-2025) reveals deep specialization in nonsmooth optimization for PDE-constrained problems, with 15+ high-impact journal articles in SIAM, ESAIM, and IEEE venues. His work bridges theoretical analysis (e.g., Lipschitz stability, strong stationarity) and computational methods (semismooth Newton techniques), addressing challenges in rate-independent systems and non-Lipschitzian nonlinearities. Scientific Awards: No awards documented in available records. Advising and Grants: Current information does not specify student supervision or grant funding details. His research group structure suggests active mentorship of junior researchers through collaborative publications. Labs and Teams: Heads the AG Optimal Control of Partial Differential Equations research group, driving interdisciplinary projects connecting mathematical optimization with environmental monitoring and machine learning applications.
Luis Ammann is a Researcher at the University of Duisburg-Essen, Faculty of Mathematics, working in the research group 'Optimal Control of Partial Differential Equations' led by Prof. Dr. Irwin Yousept. He is based in Room WSC-W-4.19 at Thea-Leymann-Straße 9, D-45127 Essen, and can be contacted at luis.ammann@uni-due.de. His primary research interests include Analysis and Optimization of Wave Phenomena, Algorithms for Nonlinear Problems, Numerical Analysis of PDEs, Full Waveform Inversion, and Sequential Quadratic Programming. His work focuses on mathematical and numerical methods for inverse problems in wave propagation, particularly using PDE-constrained optimization techniques for acoustic imaging applications. Ammann's recent publications (2023-2024) demonstrate expertise in hyperbolic PDE-constrained optimization, with emphasis on full waveform inversion and sequential quadratic programming methods. His research bridges theoretical analysis with computational implementation for seismic imaging problems, showing strong trends in second-order optimization techniques and numerical analysis of wave equations. He has taught multiple courses at the University of Duisburg-Essen since Winter Term 2020/2021, including Numerical Mathematics Practical, Acoustic and Electromagnetic Wave Phenomena, Optimization Practical, and Optimal Control of Partial Differential Equations across various semesters through Summer Term 2024. Luis Ammann is an active member of the 'Optimal Control of Partial Differential Equations' research group, collaborating with Prof. Dr. Irwin Yousept and colleagues on projects involving wave phenomena, inverse problems, and numerical optimization techniques for partial differential equations.
Dr. Peter Gangl is a Senior Research Scientist at the Johann Radon Institute for Computational and Applied Mathematics (RICAM), Austrian Academy of Sciences, where he leads research in optimization and computational mathematics. He holds a Ph.D. (2017) and M.Sc. (2012) from Johannes Kepler University Linz. His research focuses on topology/shape optimization, topological derivatives, and multiphysics simulation of electric machines. Research Interests: Gangl specializes in developing mathematical methods for PDE-constrained optimization, with emphasis on computational electromagnetics, automated sensitivity analysis, and multi-objective design. His work bridges applied mathematics and industrial applications, particularly in electric motor optimization. Research Projects: Principal Investigator for SFB F90 'CREATOR' (2022–2026) on topology optimization under electro-thermal coupling Led FWF project 'Multiphysical Shape Optimization of Electrical Machines' (2020–2024) Awards & Honors: Richard C. DiPrima Prize (SIAM, 2018) Erwin Wenzl Preis (2018) Anile Prize (ECMI, 2018) Promotio sub auspiciis (Austria's highest academic honor, 2017) Students & Collaboration: Supervises PhD candidates (Nepomuk Krenn, Michael Winkler) and collaborates with institutions including TU Graz, TU Darmstadt, and international partners. Organizes workshops/seminars on optimization and computational mathematics.
Noemi Petra is an Associate Professor in the Department of Applied Mathematics at the University of California, Merced , with research focusing on large-scale inverse problems , PDE-constrained optimization , uncertainty quantification , and optimal experimental design . Her work bridges mathematical theory with practical applications in geophysics and energy systems. Current Role: Associate Professor at UC Merced Research Interests: Large-scale inverse problems, PDE-constrained optimization, uncertainty quantification Her research has produced significant contributions in Bayesian inference for ice sheet modeling, power system estimation , and scalable optimization algorithms . She has developed open-source software frameworks like hIPPYlib-MUQ for data-model integration. Key research trends include: Bayesian Methods for geophysical inverse problems Optimization Techniques for power systems and ice dynamics Scalable Algorithms for high-dimensional uncertainty quantification Software Development for large-scale inverse problems Scientific awards: NSF CAREER award (2017) for large-scale Bayesian inverse problems She actively collaborates on ice sheet dynamics and power grid modeling , with software tools adopted in academic and industrial applications.
William M. McEneaney is a Professor in the Department of Mechanical and Aerospace Engineering at the University of California, San Diego, within the Jacobs School of Engineering. He maintains an active research program while teaching advanced courses including MAE289C (Spring 2025), MAE142 (Fall 2024), MAE180 (Fall 2024), and MAE288A (Spring 2024). His office is located in 1809 EBU I, and he has been recognized with prestigious fellowships from both SIAM and IEEE. Professor McEneaney's research spans several interconnected areas at the intersection of control theory, applied mathematics, and physics. His primary interests include nonlinear control theory, numerical methods for Hamilton-Jacobi equations, and Max-Plus/Idempotent Analysis. His work extends to astrodynamics and the n-body problem, where he applies principles of stationary action to solve complex orbital mechanics problems. He has made significant contributions to understanding the relationships between stochastic and deterministic control systems, with applications ranging from risk-sensitive filtering to quantum systems. His recent publications demonstrate a strong focus on staticization techniques, which provide computational reductions for systems with low-dimensional nonlinearities. His work bridges theoretical developments in Hamilton-Jacobi PDEs with practical applications in control systems, wave equations, and quantum mechanics. A notable trend is his exploration of connections between optimal control theory and fundamental physics principles, particularly in developing representations for Schrödinger equations using stationary action principles. Fellow of SIAM (Society for Industrial and Applied Mathematics) Fellow of IEEE (Institute of Electrical and Electronics Engineers) Professor McEneaney has supervised numerous graduate students and collaborated extensively with researchers including P.M. Dower, R. Zhao, and Y. Zheng. His research has been supported by various funding sources that enable his work on computational methods for control problems and fundamental solutions. His book "Max-Plus Methods for Nonlinear Control and Estimation" (Birkhauser, 2006) is a significant contribution to the field. He has also co-edited several influential volumes including "Numerical Methods for Optimal Control Problems" (Springer, 2018) and "Adversarial Reasoning: Computational Approaches to Reading the Opponent's Mind" (CRC Press, 2007). His research group focuses on developing novel computational approaches for solving complex control problems, particularly those involving Hamilton-Jacobi equations. They maintain strong connections with the broader applied mathematics community through participation in workshops like the 2017 Numerical Methods in Optimal Control workshop. The group's work often intersects with physics applications, particularly in quantum systems and orbital mechanics, where they develop efficient numerical methods for solving two-point boundary value problems.
Jon Wilkening is a Professor in the Department of Mathematics at the University of California, Berkeley, with affiliations to Lawrence Berkeley National Laboratory (LBNL). He specializes in applied mathematics and numerical analysis, focusing on partial differential equations, water waves, and computational methods. Education: Not explicitly stated in provided texts Appointed in 2005 Collaborations: LBNL Research interests include: Water waves (3D standing waves, vortex sheets, mode-locked lasers) Boundary value problems with adjoint methods and shape optimization Dirichlet-to-Neumann operators in solid/fluid mechanics Numerical optimization, optimal transportation, and spectral deferred correction Least squares finite elements and interface problems in elasticity Perturbation methods and lubrication theory Publication trends show strong focus on computational fluid dynamics (water waves, vortex sheets), numerical methods (spectral, multigrid), and mathematical modeling of physical systems. His work spans partial differential equations, fluid mechanics, and applied optimization. Awards : NSF CAREER Award (2010-2015) Frederick A. Howes Scholar (2003) DOE Computational Science Fellowship (1997-2001) Flinn Foundation Scholarship (1992-1996) Teaching history includes courses like Math 156 (Numerical Analysis), Math 228B (Differential Equations), Math 118 (Fourier Analysis), and others from 2005-2025.
Giovanni Fantuzzi serves as a W1 Professor (equivalent to Assistant Professor) in the Department of Mathematics at Friedrich-Alexander University Erlangen-Nuremberg. He leads research within the FAU DCN-AvH Chair for Dynamics, Control, Machine Learning and Numerics under the Alexander von Humboldt Professorship framework, holding office in Room 03.318 with contact details including giovanni.fantuzzi@fau.de and +49 9131 85-67134. His educational background includes a PhD and Master of Engineering in Aeronautics from Imperial College London, supplemented by a research position in Engineering Science at the University of Oxford during his doctoral studies. Key academic milestones are documented through his ORCID, Google Scholar, and LinkedIn profiles. Fantuzzi's research program integrates mathematical analysis with computational optimization to solve nonlinear differential equations, focusing on deriving a priori scaling laws for heat transport and developing provable numerical schemes for PDE-constrained optimization. His methodology bridges convex optimization, polynomial optimization, and dynamical systems theory, with recent applications extending to transformer neural networks and sentiment analysis through hardmax mechanisms. Current teaching includes Data-driven methods for dynamical systems and Polynomial optimization and applications for WS 24/25. Analysis of his 15 most recent publications reveals dominant trends in fluid mechanics (particularly convection and heat transfer), polynomial optimization techniques, and data-driven dynamical systems analysis. His work consistently applies convex optimization frameworks to derive rigorous bounds in physical systems while expanding into machine learning applications like transformer model analysis. Geophysical Fluid Dynamics Fellowship at WHOI (2015) EPSRC Doctoral Prize Fellowship (2018) Imperial College Research Fellowship Fantuzzi's research program is supported by prestigious fellowships including the Imperial College Research Fellowship and EPSRC Doctoral Prize. His academic service includes organizing the FAU MoD Lecture & Workshop on AI for maths and maths for AI (June 2025) and co-hosting the #MLPDES25 Machine Learning and PDEs Workshop. He actively supervises research within the FAU DCN-AvH group, focusing on polynomial optimization applications in dynamical systems and PDEs. As core faculty in the FAU DCN-AvH Chair, Fantuzzi collaborates within a multidisciplinary team specializing in dynamics, control, machine learning, and numerical methods. The group maintains strong international connections through workshops like the Oberwolfach Seminar on Polynomial Optimization for Nonlinear Dynamics and participates in conferences including CIN-PDE and Nečas Seminar on Continuum Mechanics, driving innovation at the intersection of mathematics and computational physics.
![](https://speakerdeck.com/jjzhu/"https://files.speakerdeck.com/presentations/61119698c86b4253ba7cb578443ec001/slide_0.jpg?29489597")
