Krzysztof Skabek is an Assistant Professor at the Department of Computer Science, Faculty of Computer Science and Telecommunications, Krakow University of Technology. His research focuses on photogrammetry, 3D reconstruction, computational geometry, and their applications in architecture, medicine, and cultural heritage preservation. Specializes in LiDAR and photogrammetric data fusion Develops algorithms for 3D surface modeling and mesh optimization Applies geometry techniques to biomedical devices (e.g., occlusal splints) Active in virtual reality systems and forensic documentation His work emphasizes accuracy verification, sensor integration (iPad LiDAR, low-cost scanners), and cross-disciplinary applications in heritage preservation and orthodontics. Bibliometric metrics include an h-index of 4 (Scopus/WoS), total impact factor 3.9, and ministerial score of 750. No teaching or leadership roles mentioned beyond his faculty position. Email: kskabek@pk.edu.pl
Pierre Alliez is a Researcher at Inria , currently serving as Head of the Project-team TITANE. His work focuses on robust geometry processing and 3D modeling, with applications in computational biology and smart territories. He has received prestigious awards including the Eurographics Young Researcher Award and two European Research Council grants. Research Interests : Geometry compression, surface approximation, mesh generation, and deep learning integration for 3D modeling. Grants : ERC Starting Grant (2011) and ERC Proof of Concept Grant (2017). Leadership Roles : Program co-chair for multiple international conferences (EUROGRAPHICS, Pacific Graphics, etc.). Contact: pierre.alliez@inria.fr
Qingyang Song is a professor in the field of Computer Science and Engineering, focusing on wireless networks and vehicular communications. His research spans topics such as network selection, resource allocation, and fault-tolerant routing mechanisms across heterogeneous systems, including UMTS, wireless LAN, and vehicular networks. While his institutional affiliations are not explicitly stated in the provided text, his collaborative work with researchers like A. Jamalipour and Lei Guo highlights his contributions to next-generation network optimization. His research interests include Network Coding , Software Defined Networking (SDN) , 5G Systems , and Energy Efficiency . He has pioneered techniques to balance user preferences with network conditions, reducing handoffs and improving connectivity. Recent work explores UAV-assisted networks, quantum-inspired reinforcement learning for wireless VR, and secrecy rate optimization in IRS-aided SWIPT systems. Qingyang Song's publications reflect a strong emphasis on Engineering and Computer Science disciplines, particularly in vehicular communications, spectrum sharing, and hybrid routing frameworks. His work has been featured in IEEE journals and conferences, including IEEE Transactions on Vehicular Technology and IEEE Wireless Communications .
Romain Fiévet is a Research Scientist in the Climate Physics Department at the Max Planck Institute for Meteorology (MPI-M) in Hamburg, Germany. His work focuses on high-resolution climate modeling with specialization in storm-resolving simulations and cloud microphysics. He leads computational infrastructure development within the SCLab and Climate Surface Interaction research group. Fiévet's research centers on understanding fine-scale atmospheric processes through advanced numerical modeling. His work examines how cloud microphysics and turbulence at kilometer-scale resolutions can feedback onto larger climate systems. Key research areas include convective cold pools, mesoscale tropical convection organization, and the development of high-resolution climate models like ICON. His methodology combines observational data from field campaigns with sophisticated numerical simulations to validate and improve climate models. Recent work includes participation in the 2024 ORCESTRA field campaign studying tropical convection organization, where he conducted high-resolution ICON model simulations to investigate convective-cold-pool dynamics. His research demonstrates how systematic grid refinement from 800m down to 25m resolution affects the representation of atmospheric phenomena. Fiévet maintains active research across both climate science and aerospace engineering domains, with recent publications spanning climate modeling and hypersonic flow dynamics. His technical expertise bridges atmospheric science and computational fluid dynamics, with strong emphasis on developing and applying high-resolution numerical methods to complex fluid systems.
Yucheng Liu is a Professor and the Jack Hatcher Chair in Engineering Entrepreneurship in the Department of Mechanical Engineering at Mississippi State University’s Bagley College of Engineering. He is a Fellow of both the American Society of Mechanical Engineers (ASME) and the Society of Automotive Engineers (SAE), reflecting his significant contributions to mechanical engineering research and education. His research spans computational modeling, crashworthiness, structural dynamics, vehicle systems, energy technology, and applied mathematics. Ph.D., Mechanical Engineering, University of Louisville, 2005 M.S., Mechanical Engineering, University of Louisville, 2003 B.S., Mechanical Engineering, Hefei University of Technology, 1997 Dr. Liu’s research interests center on computer modeling and simulation , crashworthiness analysis , structural mechanics and dynamics , vehicle system design , and ocean and marine energy technology . He has developed advanced computational models for thin-walled structures, energy absorption systems, and wave energy converters. His work integrates finite element analysis (FEA), CFD, and experimental validation to solve complex engineering problems in automotive, aerospace, and energy sectors. He has also contributed to applied mathematics by developing numerical methods for differential and integral equations. His recent publications reveal a strong focus on additive manufacturing , composite materials , tribology in precision gears , and electro-mechanical coupling in metals . These works often combine experimental and computational techniques, demonstrating a multidisciplinary approach. Trends include the use of internal state variable (ISV) models, phase-field simulations, and Taguchi-based optimization in mechanical systems. Dr. Liu has received numerous honors, including: Forest R. McFarland Award, SAE (2020) SAE Fellow (2019) ISET B. N. Gupta Award (2018) ASME Fellow (2017) Faculty Research Award, Mississippi State University (2018) Junior Faculty Researcher of the Year, UL Lafayette (2013) Marquis Who’s Who in America (since 2012) He has secured funding from NASA, NSF, Louisiana Space Consortium, and Mississippi Space Grant Consortium. His educational contributions include project-based learning frameworks , instructional courseware in thermodynamics and vibrations , and reforming senior design courses to be industry-tied and team-oriented. He has advised numerous student projects, including a Martian robot mining system and autonomous vehicle data acquisition systems. He leads the Wave Energy and Technology Lab, supporting experimental and computational research in renewable energy.
Jack HALE is a Research Scientist at the University of Luxembourg's Faculty of Science, Technology and Medicine (FSTM), Department of Engineering. He joined Prof. Stéphane Bordas' team in 2013, focusing on computational mechanics and numerical methods. His work integrates advanced techniques like meshfree methods, XFEM, and isogeometric analysis to address challenges in solid mechanics and high-performance computing. Education : PhD in Aeronautics, Imperial College London (2009-2013), supervised by Dr. Pedro M. Baiz Villafranca. MEng in Engineering, University of Bristol (2004-2008). Research exchange at Rice University (2006-2007) on cross-flow filtration processes. Research Interests : Implicit boundary methods for medical image-based simulations. Development of scalable meshfree/XFEM/isogeometric analysis frameworks. Mixed variational methods to resolve locking phenomena in solid mechanics. High-performance computing for distributed parallel systems. Publications & Software : His 50+ publications emphasize open-access research via ORBilu, with a focus on FEniCSx-based tools (e.g., DOLFINx, FEniCS-shells). Recent work addresses Bayesian model selection, melt instability identification, and SAR data assimilation in aquifer modeling. Collaborations : Open to academic/industrial partnerships in computational mechanics, material science, and biomedical engineering.
Aleksandr Malyshev is Professor of Mathematics at the University of Bergen. His research integrates numerical linear algebra, stability theory, optimisation-based control, and image-processing algorithms, yielding a portfolio of more than 60 peer-reviewed articles and conference contributions. Education & affiliations: Professor, Department of Mathematics, University of Bergen, Norway (present) Previous research and teaching engagements in informatics and applied mathematics at the same university Research interests: Malyshev’s core interest is the theoretical and algorithmic analysis of matrix problems arising in stability, control and imaging. He develops numerically reliable tools for assessing the distance to instability of dynamical systems, constructs preconditioners that accelerate optimisation solvers in real-time model predictive control, and designs variational models for 3-D reconstruction and image denoising. His work frequently combines spectral theory of matrix polynomials with practical issues such as high-performance implementation and medical-image quantification. Across the last decade his articles reveal three dominant strands: (i) stability and perturbation of time-delay and periodic systems, (ii) preconditioned iterative solvers for interior-point and MPC formulations, and (iii) variational and learning-based approaches to depth estimation, surface reconstruction and glenoid-bone assessment. These themes are unified by a common mathematical substrate—exploitation of matrix structure to obtain computationally efficient, numerically trustworthy solutions. Scientific awards & recognition: Regular invited speaker at international workshops on numerical linear algebra and control (e.g., SK Godunov conference 2009, IFAC 2018) Funded principal investigator / co-investigator on Research Council of Norway and EU Horizon Europe grants Advising & grants: Malyshev has supervised numerous MSc and PhD candidates in numerical analysis and scientific computing and currently advises graduate researchers on projects ranging from 3-D machine-vision algorithms to Krylov-subspace preconditioning. Recent grant participation includes EU project 101373 (3-D quantification of glenoid bone loss) and the Norwegian Research Council project 262203 on perfusion-flow simulation. Labs & collaboration: He collaborates closely with the Group for Numerical Methods and Applications at UiB, the Visual Computing cluster at the Department of Informatics, and maintains international partnerships with the Universities of Brest, Lübeck, and several US institutions. These joint efforts feed cross-disciplinary projects combining rigorous matrix analysis with real-world applications in biomechanics, process control, and computer vision.
Gianmarco Cherchi is a Tenure-Track Assistant Professor and Computer Science Researcher in the Department of Mathematics and Computer Science at the University of Cagliari, Italy, where he also completed his PhD. He teaches courses in Data Visualization and Web Programming at the undergraduate level. His research lies at the intersection of Computer Graphics and Geometry Processing, with a strong focus on surface and volumetric mesh generation, optimization, digital fabrication, and polycube-based modeling. His work combines algorithmic innovation with practical applications in fabrication, visualization, and interactive systems. The recent publications highlight a consistent trend in advanced hexahedral meshing techniques (e.g., HexBox, VOLMAP), robust geometric computation (e.g., mesh booleans), and interactive tools (e.g., ProtoSketchAR, Py3DViewer). His research spans theoretical algorithm development, benchmark creation, and applied systems for VR/AR and simulation. His scientific accolades include the Young Investigator Award 2024 from the Shape Modeling International Organization, and prior Best Thesis Awards from the Eurographics Italy Association for both his M.Sc. and Ph.D. work. Cherchi actively collaborates with researchers such as Marco Livesu, Riccardo Scateni, and others, contributing to major surveys and state-of-the-art methods in hexahedral meshing. His work is supported by publications in top venues like ACM Transactions on Graphics (SIGGRAPH), Computer Graphics Forum (Eurographics), and IEEE VR. He has also developed practical software tools like Py3DViewer for geometry processing prototyping. He leads research in digital fabrication pipelines, as evidenced by publications on polycube decomposition for manufacturing and automated flat pattern generation. His lab work involves developing interactive and robust systems for 3D modeling and analysis.
Stephen Baek is a faculty member at the University of Iowa, Department of Industrial and System Engineering, with a PhD from Seoul National University's School of Mechanical and Aerospace Engineering. His research spans interdisciplinary applications of machine learning in computational modeling, biomedical engineering, and geometric data processing. Current Affiliation: University of Iowa, Department of Industrial and System Engineering PhD Institution: Seoul National University Stephen's work focuses on physics-informed machine learning , multiscale modeling , and geometric deep learning . He develops algorithms that integrate physical principles with neural networks for applications in energetic materials , human pose estimation , and medical imaging . His research also includes federated learning and interpretable AI for constraint-based synthesis and text classification. Recent publications highlight a physics-aware deep learning framework (PARCv2) for spatiotemporal dynamics, graph convolutional networks for airway mesh smoothing, and prototype trajectory methods for explainable AI. This work bridges computer science with applied physics and healthcare domains. Stephen actively collaborates across disciplines, evidenced by co-authors from institutions like Iowa, Seoul National University, and Samsung Electronics, with publications in venues such as CoRR , J. Mach. Learn. Res. , and NeurIPS . His methodological innovations address challenges in model heterogeneity , 3D surface processing , and constraint satisfaction .
PD Dr. Florian Frank is Privatdozent (senior lecturer with full teaching licence) for Applied Mathematics at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and heads the Bavarian research project „Parallel mesh loading and partitioning for large-scale simulation“ . His expertise spans high-performance computing, phase-field and discontinuous Galerkin methods, digital-rock physics, and reactive transport in porous media. Education & career 2022 – Venia legendi (private lecturer), Mathematics, FAU 2019 – Dr. habil., Mathematics, FAU 2013 – Dr. rer. nat., Applied Mathematics, FAU 2008 – Graduate Mathematician, University of Frankfurt 2021-2022 (acting) W2 Professor Scientific Computing, FAU 2018-2021 (acting) W2 Professor Mathematical Modelling, FAU 2017-2018 Senior Postdoc, CAAM, Rice University, USA 2014-2017 Postdoc, CAAM, Rice University, USA Research interests Frank focuses on the development and analysis of numerical schemes for partial differential equations that govern multiphase, multicomponent and reactive processes in porous or biological media. Key themes include discontinuous Galerkin and finite-volume methods , physics-preserving discretizations , high-performance computing , and digital-rock-based pore-scale simulations . He couples phase-field approaches with (Navier–)Stokes, Cahn–Hilliard, Nernst–Planck and density-gradient equations to quantify flow, transport, colloid dynamics and interfacial phenomena. Recent publications reveal a clear trend toward data-driven modelling : convolutional neural networks are trained with direct numerical simulation data to predict permeability and diffusion coefficients from 3-D micro-CT images, while advanced preconditioners and regularization techniques accelerate multiphase thermodynamic computations. Awards & recognition 2020 – Emmy-Noether-Prize der Naturwissenschaftlichen Fakultät, FAU 2017 – Promotion to Senior Postdoctoral Research Associate , George R. Brown School of Engineering, Rice University Projects, tools & supervision Frank currently leads a Bavarian state-funded project on parallel mesh handling for large-scale simulations. Together with collaborators he maintains the open-source MATLAB/GNU Octave toolbox FESTUNG for discontinuous Galerkin methods. Since 2018 he has (co-)supervised ten BSc and MSc theses on topics ranging from Stokes preconditioning to enriched Galerkin shallow-water solvers, regularly serves as reviewer for more than a dozen international journals, and is guest editor of special issues in Computational Geosciences and Oil & Gas Science and Technology .
Hui-Chia Yu is an Associate Professor in the Department of Computational Mathematics, Science and Engineering at Michigan State University. Their research focuses on computational modeling of electrochemical systems, particularly battery electrodes, using advanced numerical methods like the smoothed boundary method and phase-field simulations. Recent work involves simulating electrode microstructures to analyze electrochemical impedance, phase transformations, and transport dynamics. Applications include optimizing battery performance and understanding wetting behavior in ceramic-metal interfaces. Scientific awards: None listed. Contact: hcy@msu.edu
Prof. Dr. Mehmet Devrim Akça is a full-time faculty member at the Department of Computer Engineering, Faculty of Engineering and Natural Sciences, Isik University, Istanbul, Turkey. He earned his PhD in Photogrammetry from ETH Zurich (2007), MSc in Surveying Engineering from Karadeniz Technical University (2000), and BSc in Surveying Engineering (1997). PhD: ETH Zurich, Institute of Geodesy and Photogrammetry (2003-2007) MSc: Karadeniz Technical University (1997-2000) BSc: Karadeniz Technical University (1993-1997) His research focuses on photogrammetry, 3D surface matching, laser scanning, and cultural heritage documentation. He has pioneered methods for point cloud filtering, stochastic surface mesh reconstruction, and volumetric change detection in forests and geological formations. His recent work includes real-time UAV-based structural inspection and TLS error modeling. Key trends in his publications include: Advancements in 3D forest monitoring using satellite data Development of novel error models for point cloud processing Applications of structured light systems in geomorphological experiments Integration of photogrammetry for landslide and bridge deformation analysis Scientific awards: ISPRS Best Paper by Young Authors (2004) ETH Zurich Silver Medal for Doctoral Thesis (2008) Carl Pulfrich Award (2009) Publons Top 1% Reviewer in Geosciences (2018) and Earth Sciences (2017) He has advised Dr. Mustafa Özendi (2018) on stochastic surface mesh reconstruction and contributed to projects like FORSAT 3D forest monitoring. His editorial roles include MDPI Remote Sensing and Elsevier's ISPRS Journal.
Arti Agrawal is an Adjunct Associate Professor in the School of Electrical and Data Engineering at the University of Technology Sydney (UTS). She has been associated with UTS since 2018, initially joining with time split between her academic role and as Director of the Women in Engineering and IT programme. She is also the CEO of Vividhata Pty Ltd, a startup focused on diversity and inclusion. Dr. Agrawal earned her PhD in Physics from the Indian Institute of Technology Delhi in 2005, following an MSc (1999) and BSc Physics (Hons) (1997) from the same institution. Prior to UTS, she worked at City, University of London from 2005-2017, progressing from Research Fellow to Senior Lecturer in the Department of Electrical Engineering. Her primary research focuses on optics and photonics, specifically modeling photonic components such as solar cells, optical fibers, sensors, and lasers using numerical methods like the Finite Element Method (FEM). She is an expert in computational photonics, having authored a book on FEM and edited a book on trends in computational photonics. She serves as an Associate Editor for the IEEE Photonics Journal. Dr. Agrawal's recent publications demonstrate a strong focus on nanophotonics, particularly involving graphene and silicon carbide materials for mid-infrared applications, as well as research on diversity and inclusion in engineering education. Her work spans both fundamental photonics research and practical applications in sensor technology and optical devices. Chartered Engineer (Institution of Engineering and Technology, 2013-present) Chartered Physicist (Institute of Physics) Senior Member IEEE Senior Member Optical Society of America Board of Directors, Optical Society of America (2018) Dr. Agrawal has received research funding including a scholarship from the Defence Science and Technology Group of the Department of Defence for 'Graphene based Nanophotonics for Polarization and Photodetection Filter Design' (2019-2022). She is passionate about mentoring students and has supervised PhD and Master's students in photonics research. Her teaching interests include electromagnetics, optics, and numerical methods, and she has taught first-year undergraduate Physics, Signal Processing, and Biomedical Optics. She leads significant initiatives in diversity and inclusion in STEM, using virtual reality for training and developing evidence-based approaches for managing diverse teams. She has organized workshops including the first Pride in Photonics workshop at CLEO for LGBTQI+ scientists and allies.
Lai Changquan is an Assistant Professor at Nanyang Technological University (NTU), holding positions in both the School of Mechanical & Aerospace Engineering and the School of Materials Science & Engineering. He is the Principal Investigator for the Advanced Materials Design and Synthesis (AMDS) Lab, which he established in November 2017, and received the prestigious Nanyang Assistant Professorship in 2022. Education: B.Eng in Mechanical Engineering, National University of Singapore (2009) M.Eng in Materials Science and Engineering, Massachusetts Institute of Technology (2010) Ph.D. in Advanced Materials for Micro- and Nano- Systems, National University of Singapore (2014) Dr. Lai's research spans multiple interdisciplinary fields with a focus on functional structural materials and waste valorization for circular economy applications. His work integrates design, materials science, and manufacturing innovation to engineer novel structures and surfaces. Key research areas include additive manufacturing, nanomaterials, metamaterials, structural batteries, sustainable materials, and machine learning applications in materials science. His lab has made significant contributions to the development of architected materials with exceptional mechanical properties and sustainable manufacturing processes, including turning wastepaper into battery anodes which was featured by the World Economic Forum. Analysis of Dr. Lai's recent publications reveals a strong focus on advanced materials design, particularly in structural metamaterials, additive manufacturing of metals and composites, and sustainable energy applications. His research increasingly incorporates machine learning techniques for materials prediction and optimization while maintaining experimental validation. There's a clear progression toward practical applications in renewable energy, particularly in battery technologies and sustainable manufacturing processes that align with circular economy principles. Scientific Awards: Nanyang Assistant Professorship (2022) Temasek Labs - Best Research Award (Bronze) (2021, 2022, 2023, 2024) Singapore Teaching and Academic Research Talent Scholarship (2021) Temasek Research Fellowship (2016) SMART Postdoctoral Fellowship (2015) Dr. Lai actively mentors a diverse group of graduate students and postdoctoral researchers in the AMDS Lab, including Po-Ju Chiang who became the lab's first PhD graduate in 2024. His research is supported by multiple grants through the HP-NTU Digital Manufacturing Joint Lab and other industry partnerships. He has successfully guided several PhD students through their research on topics including site-specific control of alloy properties through binder jet 3D printing and structural battery development. The Advanced Materials Design and Synthesis Lab maintains strong industry collaborations and has received significant media attention for its innovative work. The lab's research on sustainable materials has been featured in numerous international outlets including The Straits Times, Science Daily, and The Engineer (UK), demonstrating the real-world impact of their work on waste valorization and sustainable manufacturing technologies.
Sang Il Park is a Professor in the Department of Software at Sejong University, South Korea, where he has been a faculty member since 2007 following postdoctoral work at Carnegie Mellon University's Robotics Institute (2005-2007) and a brief research position at Japan's Digital Human Research Center. Ph.D. in Computer Science, KAIST (2004) M.S. in Computer Science, KAIST (1999) B.S. in Computer Science, Yonsei University (1997) Professor Park's research centers on Computer Graphics , with deep expertise in Character Animation , 3D Geometry Processing , and Image Processing . His research fingerprint shows dominant activity in Motion Capture (100%), Transition Graphs (72%), and Motion Synthesis (72%). His work has evolved from foundational research in human motion capture to incorporating deep learning techniques for image restoration, demonstrating both consistency in core interests and adaptation to emerging technologies. His publication pattern reveals a sustained research program with significant outputs in 2008, 2010, 2014, 2018, and continuing through 2023. The research spans from character animation and motion synthesis to practical applications like document processing and weathering simulation, showing remarkable breadth within his specialized domain. h-index: 12 Total citations: 933 Presented pioneering research at ACM SIGGRAPH Referenced in 4 patents and 2 Wikipedia pages Professor Park leads the Computer Graphics Lab at Sejong University, where he mentors graduate students and conducts research in animation, modeling, rendering, and computational photography. His patent portfolio includes innovations in facial expression restoration and motion synchronization, demonstrating the practical impact of his research beyond academia.
