جست‌وجوی استاد و پژوهشگر

• Nonlinear partial differential equations
• Semilinear elliptic equations
• Nonlocal equations
• +۶ مورد دیگر

Hon To Hardy Chan is a Research Fellow at the Department of Mathematics and Computer Science at the University of Basel, Switzerland, where he works in the Research Group Lenzmann. He currently holds an SNF Ambizione Fellowship, which is a prestigious independent research grant from the Swiss National Science Foundation. Dr. Chan earned his Ph.D. in 2018 from the University of British Columbia with a dissertation titled 'New solutions to local and non-local elliptic equations,' supervised by Juncheng Wei and Nassif Ghoussoub. Prior to this, he completed an M.Phil. in 2013 at the Chinese University of Hong Kong under Kai-Seng Chou, with a thesis on 'Convergence of bounded solutions for nonlinear parabolic equations.' His research focuses on nonlinear partial differential equations, particularly semilinear elliptic equations, nonlocal equations, phase transitions, the Yamabe problem, nonlocal minimal surfaces, construction of solutions, singular solutions, and free boundary problems. Dr. Chan has made significant contributions to the field including the flatness of stable free boundaries, classification of stable nonlocal minimal surfaces, theory of nonlocal ODEs, fractional elliptic gluing schemes, and new boundary singular phenomena. Analysis of his recent publications reveals a strong focus on fractional calculus and nonlocal operators, with particular attention to geometric problems, singular solutions, and boundary behavior. His work bridges pure mathematical analysis with applications in geometry and physics, demonstrating sophisticated techniques that combine classical ODE methods with modern nonlocal analysis. Among his notable achievements is the SNF Ambizione Fellowship, which supports his independent research program. His publications in top mathematics journals reflect the significance and impact of his work in the mathematical community. Dr. Chan teaches courses in advanced mathematics, including 'Elliptic PDEs: Theory and Applications' for the Spring semester of 2025. His research is supported by multiple prestigious fellowships including his current SNF Ambizione Fellowship, previous Severo Ochoa Postdoctoral Fellowship at ICMAT Madrid (2021-2022), and ERC Postdoctoral Fellowship at ETH Zurich (2018-2021). He is actively involved in the mathematical research community, collaborating with prominent mathematicians across Europe and contributing to the advancement of nonlocal partial differential equations and their applications.

• Surface Engineering
• Electrochemistry
• Thin Film Characterization
• +۷ مورد دیگر

Oksana Banakh is a Full Professor at Haute Ecole Arc - Ingénierie, specializing in Surface Engineering. Her work focuses on vacuum surface treatments and thin film characterization for industrial, biomedical, and sustainable applications. She leads the Surface Engineering competence group and has extensive experience in Physical Vapor Deposition (PVD) and Atomic Layer Deposition (ALD) technologies. PhD in Chemistry Postdoc at EPFL (2002) Current affiliation: Haute Ecole Arc - Ingénierie Her research spans three key areas: Biomedical Coatings (TiO2 layers for implants), Additive Manufacturing (3D-printed Nitinol stents), and Sustainable Surface Treatments (eco-friendly extraction methods). She pioneered HIPIMS-based PVD processes for optical components and developed CO2 cleaning techniques for industrial heritage conservation. Current projects include RASOL (solar steel recycling) and EXTRA-Ag (green solvent extraction of precious metals). Past projects involved optimizing surface treatments for titanium alloys (TRIBO-HP), enhancing micro-mold durability (ALD technology), and improving nitinol implant fabrication (NITINOL-AM). She collaborates with institutions like Université de Genève, UTBM, and industry partners including Stryker SA and PANATERE SA. Her work demonstrates cross-disciplinary innovation in materials science, biomedical engineering, and sustainable manufacturing.

Simon Iwan Stingelinمشاهده پروفایل

مدرس

• Numerical Simulation
• Scientific Computing
• Finite Element Method
• +۶ مورد دیگر

Dr. Simon Iwan Stingelin is a Lecturer in Mathematics at the Institute of Applied Mathematics and Physics (IAMP) within the School of Engineering at Zurich University of Applied Sciences (ZHAW). His work focuses on scientific computing and algorithmics, with particular expertise in numerical methods and mathematical modeling. Dr. Stingelin's research interests span multiple domains of applied mathematics and computational science. He specializes in modeling and numerical simulation with emphasis on electrodynamics, fluid dynamics, and mechanics. His work also encompasses optimal control of partial differential equations and model reduction techniques. As an educator, he teaches advanced courses in numerical analysis and higher mathematics, with a focus on practical applications in engineering. His publication record demonstrates expertise across computational mathematics, fluid dynamics, and control theory. Recent work includes Bayesian analysis of NMR spectra, modeling of peristaltic pumps considering viscoelastic materials, and teaching methodologies for finite element methods using NGSolve. His research shows a consistent trajectory from theoretical mathematical problems to practical engineering applications, with publications spanning from foundational mathematical problems to applied engineering solutions. Dr. Stingelin leads and contributes to multiple research projects including Raman for Process Analytics (Deputy Project Leader, ongoing) Automatic Counting of Varroa Mites in Bee Colonies (Project Leader, completed) Position Error Correction Algorithm (Team Member, completed) SCIS Simulation Based Calibration of Infusion Systems (Team Member, completed) Optical Gas Sensor with Ceramic Cell (Team Member, completed) His teaching approach emphasizes hands-on computational methods, particularly through the use of NGSolve for finite element analysis. He has developed course materials that integrate Jupyter notebooks and interactive learning environments to teach Higher Analysis and Numerics to engineering students across multiple disciplines including Computer Science, Mechanical Engineering, Electrical Engineering, Systems Engineering, and Aviation.

Corentin Dumeryمشاهده پروفایل

پژوهشگر

• Computer Vision
• Computer Graphics
• 3D Reconstruction
• +۴ مورد دیگر

Corentin Dumery is a Doctoral Assistant at École Polytechnique Fédérale de Lausanne (EPFL) , affiliated with the Computer Vision Laboratory (CVLAB) and Machine Learning and Optimization Laboratory (MLO) within the School of Computer and Communication Sciences . His research bridges Computer Vision and Computer Graphics , focusing on 3D scene reconstruction, garment modeling, and neural rendering techniques. Previously, he interned at Meta Redmond , worked at CEA Paris-Saclay on polycube mapping, and was a visiting researcher at ETH Zurich under Prof. Olga Sorkine-Hornung . Corentin holds dual MSc degrees in Computer Science from National University of Singapore (NUS) and Télécom Paris . His work emphasizes 3D content creation for AR/VR Diffusion models for garment reconstruction Neural radiance field optimization Polycube mapping for hexahedral meshing His recent publications (2022–2025) span top venues like SIGGRAPH , ICCV , and CVPR , addressing challenges in 3D Gaussian splatting, view-consistent NeRF training, and single-view garment recovery. He also contributes to academic service as an Outstanding Reviewer at CVPR25 and co-organizes workshops like OpenSUN3D . At EPFL, he serves as Head Teaching Assistant for courses CS433 Machine Learning (2023–2024) and CS442 Computer Vision (2023–2024). Additionally, he is the VP/Treasurer of EPIC , EPFL's computer science PhD association.

• Applied Category Theory
• Monotone Co-Design Theory
• Symplectic Geometry
• +۲ مورد دیگر

Dr. Jonathan Lorand is a Lecturer at the Department of Mechanical and Process Engineering at ETH Zürich , where he teaches Applied Category Theory for Engineering I and II . He is affiliated with the Frazzoli Group at the Institute for Dynamic Systems and Control, focusing on systemic thinking through category theory and its applications in engineering and interdisciplinary domains. Lecturer at ETH Zürich Affiliated with Frazzoli Group Co-author of a textbook on applied category theory Member of CODEWIND and Demos Institute boards Research Interests His work bridges applied category theory with practical systems modeling and optimization. Key areas include monotone co-design theory for engineering systems, duality involutions in categorical frameworks, and geometric representation theory. He has contributed to understanding isotropic structures in symplectic and Poisson geometry, as well as categorification techniques for negative information and resource management. Publications & Collaboration His publications span pure and applied mathematics, including work with collaborators like Andrea Censi, Eugene Frazzoli, and Alan Weinstein. He is co-developing a textbook and learning resources for practitioners in applied category theory.

• Robotic Manipulation
• Control Theory
• Differential Geometry
• +۵ مورد دیگر

Fares J. Abu-Dakka is an Assistant Professor in the Mechanical Engineering Program at New York University Abu Dhabi, where he joined in September 2024. Previously, he served as a Lecturer and Researcher in the Faculty of Engineering - Electronic and Informatic Department at Mondragon Unibertsitatea (2023-2024) and as a Senior Scientist and Leader for the Robot Learning Group at the Munich Institute of Robotics and Machine Intelligence, Technische Universität München (2022-2023). His research focuses on advancing robotic manipulation through the integration of control theory, differential geometry, and machine learning, with particular emphasis on enhancing robotic systems' performance, adaptability, and safety. Dr. Abu-Dakka explores how advanced mathematical frameworks and intelligent algorithms can empower robots to interact more effectively with complex environments, with applications spanning industrial automation, prosthetics, and human-robot collaboration. His recent publications demonstrate expertise in geometry-aware motion planning, variable impedance control, safe execution of robotic skills, and human-robot skill benchmarking. He has organized multiple workshops at major robotics conferences including IROS and ECC, focusing on formal methods in robotics systems, certified control synthesis, and variable impedance learning and control. IEEE Senior Member (2024) Member of the ELLIS Society e.V (2024) Dr. Abu-Dakka actively contributes to the robotics community through workshop organization and collaborative research, with recent work examining AI-driven intent detection, neural interfaces, sensory feedback systems for prosthetics, and certified control synthesis with rigorous safety guarantees. His research bridges theoretical control methods with practical robotic applications, emphasizing mathematical rigor while addressing real-world implementation challenges.

Haoyu Chenمشاهده پروفایل

استادیار

• Machine Learning
• Human Behaviour Analysis
• Emotion AI
• +۴ مورد دیگر

Haoyu Chen is a Tenure-track Assistant Professor at the Center for Machine Vision and Signal Analysis (CMVS), University of Oulu. He is also a co-founder of the AI startup Aitomore and an AI-based restaurant Aitofresh. His research focuses on Machine Learning, Human Behaviour Analysis, Emotion AI, and Adversarial Learning, with particular emphasis on Hybrid Intelligence and human-AI interaction. Dr. Chen received his Ph.D. from the University of Oulu, Finland, where he was advised by Academy Professor Guoying Zhao. During his PhD studies, he visited CEL, TU Delft, the Netherlands. Prior to that, he received his B.E. degree from China University of Geosciences, China, and Master degree from University of Oulu, Finland. Before joining as faculty, he conducted Postdoc research in CMVS, University of Oulu, with projects on Emotion AI (Academy Finland project) and trustworthy AI (Infotech project). His research spans multiple areas of artificial intelligence with a focus on understanding human behavior through AI systems. Dr. Chen's work particularly emphasizes emotion recognition, 3D pose transfer, micro-gesture analysis, and the development of hybrid intelligence systems where humans and AI mutually enhance each other's capabilities. His recent work has increasingly incorporated large language models for multimodal understanding of human emotions and behaviors, with significant publications in ICML, CVPR, and NeurIPS. Academy Research Fellow funding (622k euros), ranked 1st in AI panel ELLIS member IEEE Finland Jt. Chapter SP/CAS Best Paper Award Dr. Chen actively supervises multiple students including PhD candidates, Master's students, and research interns. His teaching includes courses on Affective Computing and Computer Graphics at the University of Oulu. He also organizes workshops and seminars, including the MiGA workshop series on Micro-gesture Analysis for Hidden Emotion Understanding and the HAECU tutorial on Human-AI mutual promotion for emotion and cognition understanding.

Nicolas Courtyمشاهده پروفایل

استاد

• Optimal transport
• Statistical learning
• Machine learning
• +۷ مورد دیگر

Nicolas Courty is a Full Professor in Computer Science at University of Brittany South since 2018, where he leads the Obelix research team at IRISA (Institut de Recherche en Informatique et Systèmes Aléatoires). His research sits at the intersection of machine learning and Earth Observation, with a focus on optimal transport theory and its applications. Dr. Courty obtained his PhD in 2002 from INSA Rennes and his Habilitation à diriger des recherches in 2013, with early work focused on computer graphics and animation. Since 2014, he has developed expertise in optimal transport and its applications to machine learning, becoming a principal investigator in an ANR Chair program on AI (OTTOPIA) in 2020, focusing on applied optimal transport for Remote Sensing. Since 2024, he serves as co-director of Cluster SequoIA, a center of excellence in artificial intelligence research and training in Brittany. His research interests span optimal transport, statistical learning, kernel methods, manifold and geometric approaches to machine learning, with applications to computer graphics, vision, and remote sensing. He is particularly known for pioneering work at the intersection of non-Euclidean geometry and optimal transport, developing novel methods for dimensionality reduction, domain adaptation, and representation learning in complex data spaces. Analysis of his recent publications reveals a strong trend toward geometric machine learning, with increasing focus on non-Euclidean spaces (particularly hyperbolic geometry), sliced optimal transport methods, and applications to remote sensing and neuroscience. His work consistently bridges theoretical advances in optimal transport with practical applications in Earth observation, demonstrating a commitment to both theoretical rigor and real-world impact. Best paper prize at Eurographics 2024 Stanford/Elsevier's Top 2% Scientist Rankings Multiple papers accepted at top conferences including NeurIPS (with oral presentations), ECCV, BMVC, and Eurographics As principal investigator of the ANR Chair program OTTOPIA, Dr. Courty has secured significant research funding for applied optimal transport in Remote Sensing. His leadership extends to directing the Obelix research team at IRISA and co-directing Cluster SequoIA, demonstrating his growing influence in the AI research community in Brittany. He is actively involved in mentoring researchers and fostering collaborations across institutions. Dr. Courty leads the Obelix research team at IRISA, which focuses on the intersection of machine learning and Earth Observation. Under his direction, the team has developed innovative approaches to applying optimal transport theory to remote sensing data, contributing significantly to the field of geospatial AI. The team's work has practical applications in environmental monitoring, land use classification, and climate change analysis.

Alexander Heinleinمشاهده پروفایل

استادیار

• Numerical methods for partial differential equations
• Scientific computing
• Domain decomposition methods
• +۶ مورد دیگر

Dr. Alexander Heinlein is an Assistant Professor in the Numerical Analysis group at the Delft Institute of Applied Mathematics (DIAM), Faculty of Electrical Engineering, Mathematics & Computer Science (EEMCS), Delft University of Technology (TU Delft). His work bridges scientific computing and machine learning through scientific machine learning (SciML) , focusing on domain decomposition methods and multiscale approaches for solving complex partial differential equations on modern hardware like GPUs. Research interests include: Developing high-performance computing algorithms for nonlinear PDEs with applications in fluid-structure interaction and photonic crystals Advancing physics-aware machine learning techniques for groundwater heat transport and post-burn contraction prediction Creating parallel preconditioners like FROSch for challenging problems in computational mechanics Building hybrid numerical-ML frameworks with domain decomposition for multi-physics applications His recent publications highlight a 128-235x speedup in biomedical simulations through deep operator networks , and keynote presentations on geometric challenges in machine learning-based surrogate models at international conferences like CASML 2024. Scientific awards include: 2025 NWO Open Technology Programme grant for the RAPID-Wind project on offshore wind turbine foundations Students and collaborations involve: Yuhuang Meng (PhD candidate, 2024) Jing Zhao (co-supervisor) Prof. Jun Zou (Chinese University of Hong Kong collaboration, 2024) He leads software development for COMSOL and Trilinos extensions while maintaining open-source reproducibility standards.

• Machine Learning
• Data Science
• Mathematical Physics
• +۳ مورد دیگر

Rostyslav Hryniv serves as Professor of Applied Mathematics and Statistics at Ukrainian Catholic University (UCU), holding dual leadership roles as Deputy Dean for Research and Head of the Machine Learning Laboratory. His academic profile uniquely bridges rigorous mathematical physics with cutting-edge artificial intelligence applications, reflecting UCU's interdisciplinary research vision. Hryniv's educational foundation includes dual PhDs in Mathematics (1996, 2007) and a higher doctorate (dr hab.) from the Institute of Mathematics of the Polish Academy of Sciences (2009). His postdoctoral trajectory features prestigious appointments as a Humboldt Research Fellow at the University of Bonn (2003-2005) and PIMS Postdoctoral Fellow at the University of Calgary (1998-1999), complemented by teaching roles across institutions in Ukraine, Canada, Poland, and the United States. His research demonstrates a strategic evolution from foundational work in spectral theory and inverse problems to contemporary machine learning. Early career contributions focused on Schrödinger operators with singular potentials, trace formulas, and quantum scattering phenomena. Recent publications (2021-2025) reveal a decisive pivot toward applied AI, including Ukrainian NLP benchmarks, symmetry-based 3D reconstruction, and minimal solvers for computer vision. This transition exemplifies his commitment to leveraging deep mathematical insights for real-world technological challenges. Analysis of his publication trends indicates a clear interdisciplinary trajectory: while maintaining mathematical rigor, his work increasingly addresses Ukrainian language technology and geometric deep learning. The Machine Learning Laboratory under his direction has become a regional hub for AI innovation, particularly in developing resources for low-resource languages and symmetry-aware computer vision systems. Hryniv's scholarly recognition includes: Humboldt Research Fellowship (University of Bonn, 2003-2005) PIMS Postdoctoral Fellowship (University of Calgary, 1998-1999) As Head of UCU's Machine Learning Laboratory, Hryniv leads research initiatives spanning Ukrainian NLP, 3D vision, and quantum machine learning. The lab's recent focus on symmetry-based algorithms and Ukrainian language resources positions it at the forefront of culturally contextual AI development in Eastern Europe, while maintaining connections to his foundational work in mathematical physics through quantum-inspired computing approaches.

• Machine Learning Theory
• Game Theory
• Information Theory
• +۵ مورد دیگر

Prof. Wouter M. Koolen serves as Professor of Mathematical Machine Learning in the Statistics group at the University of Twente and as Senior Researcher in the Machine Learning group at Centrum Wiskunde & Informatica (CWI). He maintains active research affiliations with INRIA-CWI associate teams 6PAC (with Inria Lille) and 4TUNE (with Inria Paris and Grenoble), and holds the distinction of ELLIS Scholar. Dr. Koolen earned both his MSc and PhD cum laude from the Institute of Logic, Language and Computation at the University of Amsterdam, completing his doctoral work titled 'Combining Strategies Efficiently: High-quality Decisions from Conflicting Advice' in January 2011. His academic journey includes being designated a Master of Logic. Prof. Koolen's research spans theoretical machine learning with deep connections to game theory, information theory, statistics, and optimization. His current work focuses on pure exploration in multi-armed bandit models, game tree search algorithms, and provably accelerated learning methods in statistical and individual-sequence settings, which he characterizes as 'learning faster from easy data.' His theoretical contributions consistently demonstrate practical relevance in sequential decision making and statistical inference. Analysis of his recent publications reveals three dominant research threads: martingale-based methods for anytime-valid statistical inference using e-values, adaptive optimization algorithms with provable guarantees, and theoretical foundations of multi-armed bandit problems. His work increasingly bridges theoretical computer science with modern statistical methodology, particularly in sequential analysis and adaptive experimentation. His notable achievements include: NWO VENI grant for innovative research QUT Vice-Chancellor's postdoctoral research fellowship Designation as ELLIS Scholar recognizing European research excellence cum laude distinctions for both master's and doctoral degrees Prof. Koolen actively mentors the next generation of researchers, having supervised multiple PhD students to completion including Hongwei Wen, Clément Lezane, and Tyron Lardy with defenses scheduled for 2025. His research program is supported by competitive grants focusing on theoretical machine learning and statistical methodology. He maintains an active presence in the international research community through conference presentations, workshop organization, and collaborations across European institutions. Within the Machine Learning group at CWI and Statistics group at the University of Twente, Prof. Koolen contributes to a dynamic research environment focused on theoretical foundations with practical applications. His work often intersects with colleagues investigating sequential decision processes, game-theoretic approaches to learning, and robust statistical inference methods.

• Discrete Mathematics
• Convex Geometry
• Probability Theory
• +۱ مورد دیگر

Ambrus Gergely is a Research Fellow affiliated with the Geometry Department at the Hungarian Academy of Sciences. His work bridges discrete mathematics, convex geometry, and probability theory, focusing on geometric configurations, optimization, and combinatorial problems. Research Interests: Discrete Mathematics, Convex Geometry, Probability Theory, Discrete Analysis Grants: Combinatorics in Geometry and Number Theory (2020-2025), Limits of discrete structures (ERC, 2014-2019) His recent publications address vector balancing, Helly-type theorems, and geometric optimization, with a focus on convex bodies, planar sets avoiding unit distances, and extremal problems in discrete geometry. He has made significant contributions to understanding the interplay between convex geometry and probabilistic methods. Scientific Awards: János Bolyai Research Fellowship (Hungarian Academy of Sciences, 2015) Grünwald Géza Memorial Medal (Bolyai János Matematikai Társulat, 2009) Rényi Kató Award (Bolyai János Matematikai Társulat, 2006) Ambrus is part of the GeoScape Research group at the Rényi Institute, collaborating on problems related to convex sets, tight frames, and geometric algorithms.

John H. Maddocksمشاهده پروفایل

استاد

• Nonlinear Mechanics
• Computational Analysis
• Biophysics
• +۶ مورد دیگر

John H. Maddocks is an Honorary Professor at EPFL's School of Basic Sciences (SB), Department of Mathematics. His research focuses on nonlinear mechanics, computational analysis, and the structural dynamics of DNA. He holds a BSc from the University of Glasgow (1978) and a PhD from the University of Oxford (1981). Prior to his honorary role, he served as Full Professor of Applied Analysis at EPFL (since 1997) and previously held positions at the University of Maryland (Full Professor, 1993–1997) and Stanford University. His work spans interdisciplinary fields including robotics, satellite dynamics, and biophysical systems. Maddocks has developed computational tools like the cgDNA+ model for DNA structural analysis and pioneered multiscale simulations of biomolecules. He has advised 12 PhD students at EPFL and contributed to over 100 publications, emphasizing sequence-dependent DNA mechanics and elastic systems. Education: BSc in Mathematics, University of Glasgow (1978) PhD in Mathematics, University of Oxford (1981) Research Interests: Nonlinear mechanics and stability theory Computational modeling of DNA and biomolecules Hamiltonian systems and bifurcation phenomena Scientific computing and visualization tools Award Highlights: Extensive contributions to DNA biophysics and computational mechanics, though no specific named awards are cited. Labs/Teams: Active within EPFL's Laboratory of Computational Vision and Medical Imaging (LCVM). His recent work explores nucleosome energetics, knot mechanics in elastic systems, and multiscale DNA simulations. Collaborations include projects on continuum robotics and single-cell protein dynamics.

Alexandre Terrierمشاهده پروفایل

مدرس

• Biomechanics
• Orthopaedic research
• Mechano-biology
• +۵ مورد دیگر

Alexandre Terrier is a Group Leader and Lecturer at the École Polytechnique Fédérale de Lausanne (EPFL), School of Engineering (STI), within the Department of Biomedical Engineering. He leads the Laboratory of Biomechanical Orthopedics (LBO) and holds teaching roles in multiple departments including SSV, EDBB, and SGM. His expertise spans biomechanics, orthopaedic research, and patient-specific modeling. Terrier's research focuses on joint biomechanics, mechano-biology, and finite element analysis, with applications in orthopaedic surgery and implant design. Education: PhD in Biomechanics (EPFL, 1999), MSc in Physics (EPFL, 1994). Professional experience includes roles as Head of the Joint Biomechanics Group (LBO, EPFL, 2006), Post-Doc at Columbia University (1999), and teaching at Lycée cantonal de Porrentruy (1995). Research interests include patient-specific models of human joints, statistical analysis of bone morphology, and prosthetic implant optimization. Over 120 peer-reviewed articles highlight his contributions to shoulder and hip arthroplasty biomechanics, bone strain analysis, and computational modeling. Terrier advises PhD students in biomechanical engineering and leads interdisciplinary projects combining experimental and computational methods. Labs/Teams: Laboratory of Biomechanical Orthopedics (LBO), collaborating with clinical and engineering partners on surgical innovation and implant development.

• Applied Mathematics
• Algorithms
• Optimization
• +۴ مورد دیگر

Dr. Richard Bödi is a Researcher at the ZHAW School of Engineering's Departement Applied Mathematics, Physics, Systems and Operations. He specializes in applied mathematics, algorithms, optimization, and computer science with a focus on databases and geometry. His work spans NoSQL systems, inventory optimization, and geometric structures like projective planes and collineation groups. Dr. Bödi has contributed to over 30 peer-reviewed publications since 1989, including foundational research on smooth stable planes and ZurichNoSQL query processing. He collaborated on a NoSQL Data Warehouse project for optimizing air transportation logistics and authored articles on decision-making scoring systems. His research interests include algorithmic efficiency in databases, symmetry exploitation in optimization, and geometric modeling. Recent work emphasizes data-driven business analytics and forecast accuracy in supply chains. Notable contributions include collaborative efforts with IBM on inventory budgeting and medical imaging algorithms for brachytherapy. Dr. Bödi’s research demonstrates a blend of theoretical mathematics and applied computational methods. His publications span disciplines from differential geometry to logistics optimization, reflecting a commitment to bridging abstract mathematical concepts with real-world applications.