Bojan Popov is a Professor in the Department of Mathematics at Texas A&M University, part of the College of Arts & Sciences. His research focuses on numerical analysis, nonlinear partial differential equations, and approximation theory, with a particular emphasis on invariant domain preserving schemes and hyperbolic conservation laws. He holds a Ph.D. from the University of South Carolina (1999) and an M.S. from the University of Sofia (1992). Popov has led or co-led numerous grants from agencies like NSF, DOD, and DOE, totaling over $30 million. He has advised four Ph.D. students and organized major conferences, including the 2007 'Approximation and Learning in High Dimensions' and the 2008 'Nonlinear Approximation Techniques Using L1'. His work bridges numerical methods with applications in fluid dynamics, materials science, and high-performance computing. Recent research includes invariant domain preserving techniques for hyperbolic systems, entropy viscosity methods, and robust finite element approximations. He teaches advanced courses such as Hyperbolic Conservation Laws (Math 638) and Linear Algebra (Math 304).
Satya Prakash Saraswat is a Postdoctoral Researcher at KTH Royal Institute of Technology's Nuclear Science and Engineering Unit in Stockholm, Sweden. He holds a Ph.D. from the Indian Institute of Technology Kanpur, with expertise in thermal-hydraulics, nuclear reactor safety, computational fluid dynamics (CFD), and system code development. His work spans fission and fusion reactor analysis, including contributions to the VALIDATIO project (University of Pisa) for fusion safety tools and the ATLAS project (Khalifa University) for advanced reactor safety enhancements. Research interests focus on computational modeling, AI integration in nuclear safety, and experimental validation of safety systems. He has developed skills in both experimental and numerical techniques, addressing challenges in multiphase flow, reactor core dynamics, and material compatibility. Key projects include validation of ASYST and SIMMER codes for condensation phenomena and lead-lithium interaction studies. Publications highlight advancements in burn-up wave characterization, code stability analysis (RELAP5/SIMMER), and thermal-hydraulic safety assessments for reactors like ESBWR and ITER systems. His work emphasizes enhancing safety tools through rigorous validation and innovative methodologies.
Stefano Scialo' is an Associate Professor in the Department of Mathematical Sciences "G.L. Lagrange" (DISMA) at the Polytechnic University of Turin. He is also a member of the Interdepartmental Center Ec-L - Energy Center Lab and serves as the contact person for the Bachelor's Degree Program in Mathematics for Engineering (L3). His academic journey began with a Master’s in Aerospace Engineering (2007), followed by a PhD in Mathematics for Engineering (2014), both from the same institution. After his PhD, he held postdoctoral and Assistant Professor positions at DISMA before being promoted to Associate Professor. Education: PhD in Mathematics for Engineering, Politecnico di Torino, 2014 Master in Aerospace Engineering, Politecnico di Torino, 2007 His research focuses on advanced numerical methods for partial differential equations, particularly in the context of complex multiscale and multiphysics systems. Key areas include the Virtual Element Method (VEM), domain decomposition techniques based on PDE-constrained optimization, and the simulation of flows in fractured porous media. He has made significant contributions to 3D-1D coupled problems, with applications in geosciences and biomedical modeling such as tumor-induced angiogenesis. His methodological work emphasizes robustness, scalability, and applicability to non-conforming and polygonal meshes, enabling high-performance computing solutions. The trend in his recent publications reveals a strong emphasis on developing and analyzing mixed virtual element methods, optimization-based coupling strategies, and their applications to engineering and biological systems. His work bridges theoretical numerical analysis with practical implementations in fluid dynamics and subsurface flow. Scientific Contributions: Principal Investigator of the FREYA project (2023–2026) on hybrid numerical approaches for fault reactivation. Coordinator of the INdAM-GNCS research project (2018–2019). Member of the research group "Numerical Analysis and Scientific Computing" at DISMA. Stefano Scialo' actively supervises doctoral students, including Matteo Trombini in the PhD program in Mathematical Sciences. He teaches a range of courses such as Advanced Scientific Programming in MATLAB, Numerical Methods and Scientific Computing, and specialized topics on Virtual Element Methods. He also contributes to curriculum development and academic governance through roles in doctoral colleges and degree program committees, including those for Mathematical, Mechanical, Aerospace, and Automotive Engineering. Laboratories and Research Groups: Member, Interdepartmental Center Ec-L - Energy Center Lab Research Group: Numerical Analysis and Scientific Computing (DISMA)
Professor Li Hua is a faculty member at the School of Mechanical & Aerospace Engineering, Nanyang Technological University (NTU), Singapore. He holds a Ph.D. in Mechanical Engineering from the National University of Singapore (1999) and has been recognized as a Fellow of the American Society of Mechanical Engineers (ASME) since 2019. His academic journey includes postdoctoral research at the University of Illinois at Urbana-Champaign (2000-2001), a visiting scientist role at Johns Hopkins University (2005), and research scientist positions at A*STAR’s Institute of High Performance Computing (2001-2006). Research Interests: Professor Li specializes in multiphysics modeling of soft matters (e.g., smart hydrogels in BioMEMS and biological cell microscale dynamics), machine learning-based prediction for 3D printing process-microstructure-property correlation, numerical computational methodologies (meshless and multiscale algorithms), sustainable energy simulations (building efficiency and fuel cells), and structural dynamics of high-speed rotating shells and composites. His work bridges computational mechanics, materials science, and biomedical applications. Publications & Collaborations: With over 200 peer-reviewed journal articles, he has authored/co-authored monographs such as Smart Hydrogel Modelling (Springer) and Rotating Shell Dynamics (Elsevier). His research is funded by agencies like NRF, EDB, SMI, and industry partners including Rolls-Royce, Emerson, and ABB. Scientific Awards: Fellow of ASME (2019) ICCM Investigator Award (2018) SMI Top Project Winner (2015) IBM & IHPC Silver Award (2003) Advising & Grants: He has advised Ph.D. students like Meng Zhang (awarded Best Student Paper at ICMFM XIX, 2018) and secured grants for computational BioMEMS, sustainable buildings, and advanced manufacturing projects. His industrial collaborations span aerospace, maritime, and energy sectors. Labs & Teams: Professor Li leads a research group at NTU, focusing on interdisciplinary projects with institutions like Imperial College, Technische Universität Dresden, and Johns Hopkins University. He contributes to academic governance as an NTU Senator and active member of professional societies.
Dr. Zhen Li is an Assistant Professor in the Department of Mechanical Engineering at Clemson University's College of Engineering, Computing and Applied Sciences. He joined Clemson in August 2019 after serving as a research associate professor at Brown University and a postdoctoral research associate at University of California, Merced. Education: Ph.D. in Fluid Mechanics, Shanghai University, 2012 MS in Fluid Mechanics, Shanghai University, 2008 BS in Engineering Mechanics, Wuhan University, 2005 Dr. Li's research focuses on multiscale modeling of soft matter, complex fluids, biophysics, and collective dynamics using both bottom-up (coarse-grained molecular modeling) and top-down (from continuum descriptions to fluctuating hydrodynamics) approaches, along with high-performance computing. His work spans mathematical theory for coarse-graining and model reduction, statistical methods and machine-learning approaches applied to multiscale modeling, memory effects in complex fluids, and concurrent coupling of heterogeneous solvers for scale-bridging. Analysis of Dr. Li's recent publications reveals a strong trend toward integrating machine learning with traditional computational methods, particularly neural operators for multiscale problems. His work spans diverse applications from bubble dynamics and blood flow to materials science and bioprinting, demonstrating the versatility of his computational approaches across multiple disciplines in engineering and physics. Awards and Recognition: CECAS Dean's Professor Award (2024) Award of Excellence - Junior Faculty (2021-2022) Best Research Poster Award at SC19 (2019) 2nd Place Award of Best Poster Presentation at DOE/EFRC AIM for Composites meeting (2024) Dr. Li actively mentors PhD students including Miles Lu, Ryan Wan, Haizhou Wen, and Ali Mohammadi, who have published significant research in computational mechanics. His research is supported by multiple grants including an NSF Elements grant as PI for 'SciMem: Enabling High Performance Multi-Scale Simulation on Big Memory Platforms', an NSF CDS&E grant as co-PI for 'HAM3R: Heterogeneous Automated Management of Multiscale Methods and Resources', a DOE/EFRC grant as Thrust lead co-PI for 'AIM for Composites', and a NASA EPSCoR grant as Science-PI. Dr. Li leads the MuthComp (Multiscale theory and Computation) research group, which focuses on developing interfaces between Engineering, Applied Mathematics, Physics-based Machine Learning, and High Performance Scientific Computing. The group has active collaborations with institutions including Idaho National Laboratory, University of Tokyo, and Brown University, and has developed open-source software including USERMESO for GPU-accelerated DPD simulations.
Eirik Keilegavlen is a Researcher at the Department of Mathematics, University of Bergen. His primary research focuses on developing mathematical models, numerical methods, and simulation tools for multiphysics processes in porous media, particularly in geothermal energy, CO 2 storage, and subsurface energy systems. He leads the development of the open-source software PorePy, designed for simulating processes in fractured porous media. His work emphasizes coupled problems involving fluid flow, heat transfer, and mechanical deformation. Key research interests include: Mathematical modeling of coupled thermal-hydro-mechanical processes Numerical discretization methods for fractured media Development of open-source simulation tools Applications in geothermal energy extraction and carbon sequestration Recent publications highlight advancements in: Uncertainty quantification for CO 2 leakage Viscous fingering in fractured reservoirs Automated solver selection for multiphysics systems Collaborations involve interdisciplinary teams addressing challenges in geothermal reservoir stimulation, fault mechanics, and high-performance computing. His work bridges theoretical developments with practical applications in energy and environmental systems.
Mine Çağlar is a Professor in the Department of Mathematics at Koç University, specializing in probability theory and stochastic processes with applications in mathematical finance and risk analysis. Her work addresses fundamental problems in Markov additive processes, Lévy processes, and Brownian motion, contributing to both theoretical advances and practical financial modeling. Her academic credentials include: PhD in Statistics and Operations Research from Princeton University (1997) Master’s in Industrial Engineering from Bilkent University (1991) B.A. in Industrial Engineering from Middle East Technical University (1989) Professor Çağlar’s research centers on extreme event analysis in stochastic processes, particularly maximum drawdown, maximum loss, and optimal stopping problems. She investigates path properties of spectrally negative Lévy processes and develops mathematical frameworks for degenerate market models. Her work bridges abstract probability theory with real-world financial applications, including risk management and hedging strategies. Recent publications demonstrate sustained innovation in stochastic analysis, with a focus on long-time behavior of complex processes and boundary-crossing phenomena. Analysis of her 15 most recent publications (2018–2024) reveals a cohesive research trajectory emphasizing Markov additive processes (40% of articles), Lévy process extremes (30%), and financial applications (20%). Key methodological trends include path decomposition techniques, Monge-Ampère equations on Wiener space, and stochastic flow modeling. Her work increasingly integrates fluid dynamics concepts like Çinlar models for turbulence simulation, reflecting interdisciplinary expansion into applied mathematics. Her scholarly recognition includes: Hayri Körezlioğlu Research Award (2013) Parlar Foundation Research Incentive Award (2005)
Sorin Mitran is a Professor in the Department of Mathematics at the University of North Carolina at Chapel Hill. His research focuses on computational simulation of multiscale and multiphysics systems, data-driven constitutive relations for hyperelastic materials, and information geometry for reduced stochastic models. PhD in Aerospace Engineering from Politehnica University Bucharest (1995) Professional background includes fellowships at University of Tokyo (1993), Karlsruhe Institute of Technology (1998-1999), and University of Washington (1999-2002) His research develops numerical tools to predict macro-scale behavior from micro-scale interactions, such as plastic deformation of metals from lattice defect dynamics, microtubule mechanics from molecular dynamics, and protein folding from atomic-level simulations. Mathematical approaches include adaptive computation, machine learning for constitutive law prediction, and information geometry for stochastic process analysis. Recent publications (2023-2018) span computational biology, multiscale fluid dynamics, and medical applications of continuum mechanics. Articles frequently explore data-driven modeling, wave propagation in biological systems, and GPU-accelerated numerical methods like Lattice Boltzmann and Lattice Fokker-Planck formulations.
Dr. Igor Chernyavsky is a Senior Lecturer in Applied Mathematics at the Department of Mathematics, The University of Manchester. His research focuses on complex living systems, particularly transport phenomena and biofluid dynamics in tissue physiology. He leads projects in continuum mechanics, mathematics in life sciences, and uncertainty quantification. His work contributes to UN Sustainable Development Goals through initiatives like Digital Futures, Christabel Pankhurst Institute, and Henry Royce Institute. Research interests include placental hemodynamics, biomimetic models, and multiscale modeling of biological systems. Key projects involve placental circulation modeling for stillbirth prediction, umbilical cord dynamics, and microfluidic studies of blood flow in porous media. He collaborates on placental imaging, bioreactor engineering, and clinical placentology. Recent publications highlight placental oxygenation, umbilical cord solute transfer, and robust fabrication of PDMS microcapsules mimicking red blood cells. His work bridges experimental and theoretical approaches, emphasizing clinical translation. He supervises PhD students and leads grants totaling £ millions, including Maternal & Fetal Health Research Centre (2018-2035) and ROBUST-BIOPRINT (2023-2025). He actively seeks collaborations in biomaterials, fluid dynamics, and biomedical engineering. Labs/teams: Continuum Mechanics Group, Mathematics in Life Sciences Team, and Uncertainty Quantification & Data Science Group.
Dr. Lluis Batet Miracle is a Professor at the Universitat Politècnica de Catalunya (UPC) with the Department of Physics . He leads the Advanced Nuclear Technologies Research Group (ANT) and has contributed extensively to nuclear fusion technology, thermal hydraulics, and liquid metal systems. His work spans reactor safety analysis, tritium processing, and magnetohydrodynamic modeling. Expertise : Nuclear Engineering, Plasma Physics, Computational Fluid Dynamics, Fusion Reactor Design, Tritium Management Notable Projects : CONSOLIDER TECNO-FUS (2009-2013), EURATOM collaborations, HCLL Breeding Blanket Systems for ITER Research Trends : Recent publications focus on helium solubility in liquid metals, bubble dynamics in fusion blankets, and MHD simulations under nuclear conditions. His work combines atomistic modeling, high-fidelity CFD, and experimental validation for tritium and hydrogen systems in fusion reactors. Collaborations : Regularly works with Luis Sedano, Eduardo Ríos, Jordi Martí, Francesc Reventos, and Elisabet Mas de les Valls Grants : Involved in Horizon Europe, EURATOM, and Spanish National Research programs
Antonio Froio is an Associate Professor at the Department of Energy (DENERG) at Politecnico di Torino. His academic career focuses on Industrial and Information Engineering (Area 0009), particularly in Nuclear Power Plants (IIND-07/D). He is an active member of the American Nuclear Society (2021-), Nuclear and Reason Committee (2021-), and Italian Nuclear Association (2020-). Research Interests: Antonio's work centers on breeding blankets , controlled thermonuclear fusion , and thermal-hydraulic analysis . He applies computational engineering (ERC PE8_4) to advance affordable and clean energy (SDG 7). His research includes Design of tritium extraction systems CFD co-simulation for nuclear plants Multiphysics modeling of fusion reactors Uncertainty quantification in nuclear data Recent Publications: His academic output spans thermal-hydraulic assessments, limiter system designs, and computational tools for fusion reactors. Notable contributions include work on EU DEMO systems, PbLi loops, and transient accident simulations. Scientific Awards: Effective member - American Nuclear Society (2021-) Effective member - Nuclear and Reason Committee (2021-) Effective member - Italian Nuclear Association (2020-) Teaching and Supervision: Antonio teaches Computational heat and mass transfer , Monte Carlo methods , and Nuclear fusion reactor engineering courses. He supervises PhD students in the NEMO Research Group including Mauro Spro', Antonio Zurzolo, Marco Caravello, Fabrizio Lisanti, and Alex Aimetta.
Lisa Prahl Wittberg is a Professor of Fluid Mechanics with a specialization in Multiphase Flows at the Department of Engineering Mechanics, KTH Royal Institute of Technology , since 2021. She leads the Biomedical Flows research group , focusing on multiphase flow dynamics in clinical and industrial applications. Her research integrates engineering principles with biomedical challenges, particularly in extracorporeal life support systems, blood pumps, and hemodynamic modeling. She holds an MSc (Mechanical Engineering) and PhD in Fluid Mechanics from Lund University (2007), followed by a Docentship in 2014. Her work addresses pathological processes and device performance in clinical applications, such as ECMO, hemodialysis, and respirators. Key areas include blood flow dynamics in artificial devices, particle transport in biological systems, and improving device safety through computational models. Her publications emphasize cannula design optimization, blood pump performance, and patient-specific vascular modeling. She collaborates on translating engineering insights into clinical practice, enhancing treatment efficacy and reducing complications in critically ill patients. Education: MSc (Lund University), PhD in Fluid Mechanics (2007), Docentship (2014). Labs/Teams: Biomedical Flows research group. Focus Areas: Multiphase flows, biomedical devices, hemodynamics, extracorporeal support systems.
Professor S. Jon Chapman is a faculty member at the Mathematical Institute, University of Oxford, holding the position of Professor of Mathematics and its Applications. He is affiliated with the Oxford Centre for Industrial and Applied Mathematics research group. His educational background includes a DPhil, MA, and BA. Research interests span diverse areas of applied mathematics and scientific modeling: Industrial mathematics and mathematical modeling Partial differential equations and asymptotic methods Fluid dynamics and turbulence theory Biophysical applications including tumor growth and tissue modeling Electromagnetic scattering and superconductivity Materials science and energy systems Publication analysis reveals two primary trends: Recent work (2025) focuses on electrochemical systems (battery modeling, gas-induced bulging) and biological applications (organoid models). Earlier influential publications established expertise in pattern formation, fluid dynamics (ship waves, spiral waves), and transport phenomena in biological systems. Mathematical techniques consistently feature multiscale analysis, asymptotic methods, and nonlinear modeling. Awards and honors recognizing scholarly contributions: Naylor Prize (2015) Julian Cole Prize (2002) Whitehead Prize (1998) Richard C. DiPrima Prize (1994) Johnson Mathematical Prize (1992) No information is available regarding student advising, grants, or laboratory affiliations.
Ricardo Ruiz Baier is a Professor of Computational Mathematics at Monash University in Melbourne, Australia, where he also holds an ARC Future Fellowship. He is affiliated with the Victorian Heart Institute and the Monash Data Futures Institute, highlighting his interdisciplinary research bridging mathematical theory with biomedical applications. His research focuses on the design and analysis of numerical methods for partial differential equations, particularly those that preserve the physical properties of natural phenomena. His expertise includes fundamental topics in numerical analysis and scientific computing such as analysis of finite volume and finite element methods using mixed and augmented formulations, space-time adaptivity and error estimation, perturbed saddle-point problems, multiphase flow and transport in porous media, cardiac electrophysiology and electromechanics, and interface problems. His recent publications reveal a strong emphasis on virtual element methods, poroelasticity models, and cardiac mechanics applications. His work spans theoretical numerical analysis, computational methods development, and practical biomedical applications, particularly in cardiac modeling. The research demonstrates a consistent focus on multiphysics problems and the development of robust numerical schemes for complex coupled systems. Scientific Awards: ARC Future Fellowship FT22 for 'Next-generation methods for transport in poroelastic media with interfaces' Australian Research Council Discovery Project DP21 for 'Towards predictive 4D computational models for the heart' Ruiz Baier actively supervises a large research group with numerous PhD students and postdoctoral researchers working on diverse aspects of computational mathematics. His group has secured funding from multiple sources including Monash Mathematics, the Australian Research Council, IITB-Monash Doctoral Programme, and international government scholarships. He frequently organizes major conferences and workshops, including the Computational Techniques and Applications Conference (CTAC 2024) and MATRIX workshops on numerical analysis. His research group operates at the intersection of mathematics, computational science, and biomedical engineering, with particular focus on developing computational models for cardiac function and mechanics. The group collaborates with international institutions including the University of Oxford and University of Oslo.
Dr. Amirreza Khodadadian is a Lecturer in Mathematics at the School of Computer Science and Mathematics, Keele University, since August 2023. He holds a Ph.D. from the University of Vienna (2017), followed by postdoctoral positions at the Technical University of Vienna and Leibniz University Hannover. His research focuses on uncertainty quantification, numerical methods for stochastic PDEs, finite element methods, computational mechanics, and machine learning applications in nanoelectronics and biological systems. Key research interests include Bayesian inversion, multiscale modeling, reduced-order methods, and the design of nanoscale sensors. He has collaborated with institutions like the University of Oxford and secured an Austrian Science Fund (FWF) grant (476k€) for nanozyme sensor research. Dr. Khodadadian mentors postdoctoral researchers, including Dr. Samaneh Mirsian, and actively publishes in top-tier journals such as Journal of Computational Physics and Computer Methods in Applied Mechanics and Engineering . His work bridges applied mathematics with engineering challenges, emphasizing efficient numerical algorithms for real-world problems like battery degradation, groundwater contamination, and biomedical sensor optimization. Recent projects involve machine learning integration for enhanced predictive modeling. Education: Ph.D. in Mathematics, University of Vienna, Austria (2017) Postdoctoral Fellowships: TU Vienna (2018), Leibniz University Hannover (2018–2022) Grants/Awards: Austrian Science Fund (FWF) Grant: Single Atom Catalysts as Nanozymes in FET Sensors (2023) Advising: Postdoctoral Mentor: Dr. Samaneh Mirsian (Keele University) Dr. Khodadadian’s publications span computational mechanics, stochastic modeling, and interdisciplinary applications, reflecting his expertise in translating mathematical theory into practical engineering solutions.
