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Tamas KoltaiView profile

Professor

• Operations Management
• Industrial Engineering
• Production Planning
• +12 more

Tamas Koltai is a Professor at the Budapest University of Technology and Economics, affiliated with the Department of Management and Corporate Economics within the Faculty of Economic and Social Sciences. He holds a PhD in Industrial Engineering and is actively contributing to research in operations management, production planning, and industrial engineering. His research interests include: Operations Management Industrial Engineering Production Planning Assembly Line Balancing Human-Robot Collaboration Data Envelopment Analysis (DEA) Supply Chain Management Learning Curves Flexible Manufacturing Systems Performance Evaluation His recent publications focus on applying mathematical programming models (MILP, CP), simulation, and DEA to optimize assembly lines, particularly under learning effects and human-robot collaboration. He explores workload distribution, cycle time optimization, and efficiency evaluation in both manufacturing and service sectors, including healthcare and business simulation games. His work bridges theoretical models with practical industrial applications, supporting managerial decision-making under uncertainty. While no specific scientific awards are listed, his extensive publication record (over 50 papers) and high citation count reflect significant academic impact. He frequently collaborates with researchers such as Imre Dimény, Noémi Kalló, Viola Gallina, and Rita Dénes. There is no public information on PhD students advised or grants received. His work does not mention specific labs or research teams, but his focus on applied operations research suggests strong industry collaboration potential.

• Architecture
• Urban Design
• Public Space Interventions
• +5 more

Isabel Barbas (b.1974) is an Assistant Professor in the Department of Architecture at Universidade Lusófona (ULHT) 's Faculty of Architecture and Urbanism Technology (ECATI) since 2010. She is currently a PhD candidate in Architecture at ULHT with an FCT scholarship (SFRH/BD/148141/2019), expected to complete in 2025. Her work bridges architecture, art, and design through experimental public space interventions. Architecture degree from FAUTL (Technical University of Lisbon) with Erasmus year at La Sapienza, Rome Masters in Drawing from FBAUL PhD candidate at ULHT (completion 2025) Research Focus : Barbas investigates how architecture shapes participatory urban environments through tactical urbanism , artistic installations , and climate-responsive design . Her work explores urban ambiences , critical spatial practices , and the poetics of materiality , particularly in the context of rising sea levels and ephemeral markets. Current projects include the COST Action CA23145 on European urban ambiences (2024-2027) and the SOSClimateWaterfront Horizon 20/20 project (2019) focusing on public space. Publications & Projects : Her recent work appears in Urbanistica Dossier on public space socialization and the Malagueira International Congress discussing poetic urban fragments. Key projects include the award-winning Jardim das Gaivotas urban furniture (Lisbon, 2017), Nine Swings in Porto (2013-2020), and the Slim Moon lighting installation (2013). She maintains an active practice through her studio MAGstudio (founded 2009) and previously in Madrid (2004-2009). Scientific Awards 1st Prize, Enjoy the Tagus competition 1st Prize, HM RIBA`08 Finalist, Lexmarkex European Art Prize Outros Mercadus`08 II Award Teaching & Collaborations : She has collaborated with institutions like CCB and Museu Berardo's educational services (2004-2019) and contributed to architectural media as a member of the Jornal dos Arquitetos editorial team (2012-2014). Her studio projects combine art, architecture, and urban design with a focus on participatory processes.

• Tactile Sensors
• FPGA-Based Systems
• Robotics
• +3 more

Óscar Oballe-Peinado is a professor at the Department of Electronics, School of Engineering, University of Málaga. His work focuses on tactile sensors, FPGA-based embedded systems, and robotics, particularly addressing crosstalk elimination, sensor calibration, and smart preprocessing techniques. He contributes to both academic research and educational innovations, including remote digital electronics laboratories. PhD in Electronics (University of Málaga, 2016) Active researcher in tactile sensor technology and FPGA implementations Supervisor of Raúl Lora Rivera's research on texture detection and compliance recognition His research explores advanced methods for tactile sensor optimization, including hysteresis correction, quantization error reduction, and compliance recognition algorithms. Recent publications (2023-2024) focus on smart tactile preprocessing and embedded FPGA implementations for robotics. The 2023-2024 articles emphasize his contributions to tactile sensor applications in robotic manipulation, texture detection, and compliance recognition using FPGA-based embedded systems. These works align with trends in smart sensor interfaces and real-time processing for robotics. Collaborated on improving remote digital electronics laboratories using Raspberry Pi 4 (2022) Developed FPGA-based tactile sensor suite electronics (2017)

Michel BesserveView profile

Researcher

• Causal Machine Learning
• Complex Systems
• Neuroscience
• +4 more

Michel Besserve is a Senior Research Scientist in the Empirical Inference department at the Max Planck Institute for Intelligent Systems in Tübingen, Germany. His research bridges machine learning theory with applications in neuroscience and complex systems analysis. He leads a research group focused on developing causal machine learning tools to uncover the internal structure and transformations of complex artificial, physical, and socioeconomic systems. Dr. Besserve's primary research interests center on causal machine learning and its applications to understanding complex systems. His work investigates how causality can provide principled ways to study and improve AI algorithms, particularly focusing on the identifiability of causal models and the principle of Independence of Causal Mechanisms (ICM). He develops theoretical frameworks and practical tools for causal inference in complex equilibrium systems, neural circuits, and socioeconomic contexts. His research has significant implications for building trustworthy and interpretable AI systems that can reliably handle real-world complexity. Analysis of Dr. Besserve's recent publications reveals a strong focus on causal representation learning, with significant contributions to independent mechanism analysis and the identifiability of nonlinear generative models. His work spans both theoretical foundations and practical applications, connecting machine learning with neuroscience to understand brain function through causal inference. The interdisciplinary nature of his research is evident in publications spanning top machine learning conferences (NeurIPS, ICML, ICLR) and leading neuroscience journals (Nature, PLOS Biology). Dr. Besserve has established productive collaborations across multiple institutions, particularly with researchers at the Max Planck Institute and ETH Zurich. His work demonstrates how integrating causal principles with machine learning can address fundamental challenges in AI robustness and interpretability, with applications ranging from brain network analysis to economic modeling. His research group focuses on developing the Causal Computational Model (CCM) framework, which aims to create digital representations of real-world systems that integrate data, domain knowledge, and interpretable causal structure. This work has potential applications in climate modeling, industrial digital twins, and economic simulation.

• MRI
• Brain Imaging (EEG, MRI)
• Neuroscience
• +4 more

Franceschiello Benedetta is an Associate Professor at HES-SO Valais-Wallis School of Engineering, specializing in Technical and IT disciplines. She holds a PhD in Mathematical Neuroscience from Université Pierre et Marie Curie (Paris). Her work bridges applied mathematics, computational neuroscience, and neuroimaging, with a focus on visual perception modeling, MRI techniques, and neural dynamics. Teaching: Linear Algebra courses across multiple engineering bachelor programs Expertise: Combines mathematical modeling with neuroscientific applications to study optical illusions, brain connectivity, and ophthalmic diagnostics Key Affiliations: ISMRM, Organization for Human Brain Mapping (OHBM), Association for Research in Vision and Ophthalmology (ARVO) Research Interests: Computational modeling of visual cortex mechanisms underlying geometric optical illusions Development of MRI-based methods for eye structure segmentation and axial length estimation Analysis of brain network reliability through standardized MRI protocols Optimization techniques for medical imaging reconstruction (e.g., weighted LASSO problems) Recent Work Trends: Focus on integrating psychophysical experiments with computational models to elucidate perceptual mechanisms, emphasizing synergistic interactions between physical stimulus parameters. Active in advancing MRI applications for both clinical diagnostics and fundamental neuroscience research.

Hendra I NurdinView profile

Senior Lecturer

• Quantum Control
• Quantum Systems
• Systems Theory
• +10 more

Dr. Hendra I Nurdin is a Senior Lecturer in the School of Electrical Engineering and Telecommunications at the University of New South Wales (UNSW), where he has been a faculty member since 2012. His academic background spans Electrical Engineering and Applied Mathematics with a focus on systems and control theory, with significant research intersections in quantum physics and power engineering. He serves as an Associate Editor for IEEE Control Systems Letters (IEEE L-CSS) starting from January 2024. Dr. Nurdin's research spans quantum systems and control, nonlinear control of microgrids, and neuromorphic computing for nonlinear systems. His work in quantum control intersects with quantum physics while his research on microgrid control intersects with power engineering. His research has resulted in numerous publications across prestigious journals including Nature Communications, Physical Review Research, IEEE Control Systems Letters, and IEEE Transactions on Automatic Control. His recent publications (2020-2025) predominantly focus on quantum reservoir computing, quantum parameter estimation, control of non-Markovian quantum systems, and applications of control theory to energy systems. The research demonstrates a clear trajectory toward practical implementations of quantum computing concepts and advanced control methodologies for both quantum and classical systems. Associate Editor for IEEE Control Systems Letters (IEEE L-CSS) from January 2024 Dr. Nurdin has supervised several PhD students to completion and currently mentors PhD candidate Wen Liu. His research group has produced significant work in quantum control systems, with former students including Wenxing Li, Yihuan Liao, Jiayin Chen, Jiacheng Li, Muhammad Ali, Zhan Shi, and Onvaree Techakesari. He actively seeks new students with strong academic backgrounds for research in his areas of interest, particularly in quantum systems and control. His laboratory work focuses on quantum systems control, quantum reservoir computing, and microgrid control systems. He has developed courses including ELEC9782 Special Topics in Electrical Engineering 2 (Quantum Control) and teaches ELEC4631 Continuous-Time Control System Design.

Christos ThrampoulidisView profile

Assistant Professor

• Machine Learning
• Deep Learning Theory
• Statistical Signal Processing
• +3 more

Christos Thrampoulidis is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of British Columbia (UBC), part of the Faculty of Applied Science. Previously, he held the same position at the University of California, Santa Barbara (2018–2020) and was a Postdoctoral Researcher at MIT (2016–2018). He earned his Ph.D. and M.Sc. in Electrical Engineering from Caltech (2016 and 2012) and a Diploma in ECE from the University of Patras, Greece (2011). His research focuses on machine learning theory , optimization , high-dimensional statistics , and statistical signal processing . Specific interests include the theoretical foundations of deep learning, neural collapse phenomena, and the analysis of overparameterized models. He has contributed to understanding imbalanced data challenges, model compression, and safe optimization techniques. Key academic achievements include the Qualcomm Innovation Fellowship (2014) and the A. Mentzeolopoulos Fellowship . He teaches graduate courses on Optimization and Signals & Systems at UBC. His research group (MILD Group) actively explores topics like language model geometry, federated learning, and algorithmic reasoning in transformers. Notable publications include work on neural-collapse geometry, imbalance-aware learning, and the theoretical underpinnings of transformers. He advises multiple Ph.D., M.Sc., and undergraduate students, fostering a collaborative environment centered on equity, diversity, and inclusion.

Lionel PichonView profile

Researcher

• Electromagnetics
• Electromagnetic Compatibility
• Wireless Power Transfer
• +4 more

Lionel Pichon is a leading researcher at the Laboratory of Electrical and Electronic Engineering of the University of Paris. His primary affiliations include the Department of Electrical and Electronic Engineering of Paris, where he specializes in advanced electromagnetic research. His work focuses on Electromagnetics , Electromagnetic Compatibility (EMC) , and Wireless Power Transfer , with particular emphasis on composite materials, biomedical applications, and automotive systems. Expertise in numerical methods (FDTD, FIT, DGTD) for electromagnetic simulations Pioneer in shielding effectiveness analysis for composite materials Developer of AI-driven approaches for dielectric property characterization Over 70 publications since 2017 highlight his contributions to fields like inductive power transfer systems, medical device EMC, and GPR imaging techniques. Collaborates extensively with institutions globally, including research on antenna design for implantable medical devices and electromagnetic compatibility in healthcare facilities. Current research trends emphasize machine learning integration with traditional electromagnetic analysis to optimize system performance and safety standards.

Oleg KitovView profile

Assistant Professor

• Time-series Econometrics
• Empirical Macroeconomics
• Income Distribution
• +1 more

Oleg Kitov serves as an Assistant Teaching Professor and Robert Martin Fellow in Economics at Selwyn College, University of Cambridge, where he also acts as Convener of Undergraduate Admissions for the Faculty of Economics. His academic profile centers on rigorous econometric methodology applied to macroeconomic phenomena and distributional economics, with significant contributions to time-series analysis and income inequality research. Kitov's primary research domains include Time-series Econometrics, Empirical Macroeconomics, and Income Distribution and Inequality. His methodological innovations focus on structural break detection in linear models using multiplicative indicator saturation, generative modeling of age-income dynamics, and cross-national Phillips curve analysis. His work bridges theoretical econometrics with empirical applications, particularly examining inflation-unemployment relationships across diverse economies and modeling long-term trends in income distribution using historical datasets. His publication trajectory reveals consistent methodological advancement from 2012-2022, with core themes spanning structural break detection in economic time series, generative income distribution modeling, and comparative Phillips curve analysis across 15+ countries. The research demonstrates increasing sophistication in handling non-stationary economic data while maintaining strong policy relevance for central banking and inequality measurement. Scientific Awards: Cambridge Centre for Teaching and Learning Technology-enabled Learning Prize (2021) As Convener of Undergraduate Admissions, Kitov shapes the academic cohort for Cambridge's Economics program while teaching advanced econometrics. His office hours (Tuesdays 11:15am-12:15pm in Room 79) support student engagement with complex quantitative methods, reflecting his dual commitment to research excellence and pedagogical innovation recognized by the 2021 teaching prize.

Peter SollichView profile

Professor

• Non-equilibrium statistical physics
• Soft matter rheology
• Active matter
• +7 more

Prof. Dr. Peter Sollich is a Professor of Theoretical Physics at Georg-August-Universität Göttingen, affiliated with the Institute for Theoretical Physics. His research spans non-equilibrium statistical physics with applications to soft matter, active systems, and complex networks. He maintains a small part-time appointment at King's College London. His primary research interests focus on non-equilibrium statistical physics , particularly soft and active matter rheology, jamming transitions, glassy dynamics, dynamical phase transitions, and inference from dynamical data. His work bridges theoretical physics with applications in materials science and network theory, emphasizing both fundamental mechanisms and quantitative modeling approaches. Analysis of his recent publications reveals strong thematic consistency in studying glassy dynamics and active matter systems , with increasing integration of machine learning techniques for network analysis. Key methodological threads include coarse-grained modeling, spectral analysis of complex systems, and non-equilibrium thermodynamics frameworks. His 2023-2025 work shows growing emphasis on nonreciprocal interactions in active mixtures and physics-inspired machine learning applications. Prof. Sollich actively supervises Bachelor's, Master's, and PhD students, welcoming thesis inquiries in theoretical physics. His group develops analytical and computational approaches to complex dynamical systems, with recent grants likely supporting work on network dynamics and active matter modeling (specific grants not detailed in source text). His research group operates within the Institute for Theoretical Physics at Göttingen, focusing on computational and analytical modeling of disordered systems. Current projects involve elastoplastic modeling of amorphous solids, spectral analysis of heterogeneous networks, and theoretical frameworks for active matter phase separation.

• Applied Probability
• Insurance Mathematics
• Finance
• +3 more

Mogens Bladt is a Professor of Applied Probability and Insurance Mathematics at the Department of Mathematical Sciences, University of Copenhagen. He has held positions since 2018 after 24 years as Principal Researcher at the National University of Mexico (1994-2018), with visiting professorships at Technical University of Denmark and University of Copenhagen since 2001. Research: Focuses on time-inhomogeneous phase-type distributions, matrix-oriented life insurance models, heavy-tailed distributions, and diffusion bridge simulation Teaching: Offers graduate/undergraduate courses in Applied Probability, Stochastic Processes, Risk Theory, and Numerical Analysis Scientific Contributions: Developed R packages for Markov jump processes, phase-type distributions, and diffusion bridges. Holds grants from Mexico and Denmark, including Danish Research Council funding (2007–2008). Supervised 5 PhD, 8 Master’s, and 13 Bachelor’s theses Organized academic workshops and served as Associate Editor for Stochastic Models since 1997

Ye HeView profile

Visiting Assistant Professor

• Mathematical Foundations of Artificial Intelligence
• Machine Learning Theory
• Data Science
• +3 more

Ye He serves as a Hale Visiting Assistant Professor in the School of Mathematics at the Georgia Institute of Technology, hosted by Prof. Molei Tao. His research bridges mathematical theory and artificial intelligence, focusing on developing rigorous frameworks for machine learning and data science applications. Education: Ph.D. in Mathematics, University of California, Davis (advised by Prof. Krishna Balasubramanian) Research Interests: Dr. He specializes in the mathematical foundations of artificial intelligence , with particular emphasis on scalable inference methods for complex data distributions. His work addresses fundamental challenges in sampling theory , diffusion-based generative modeling , and stochastic optimization , aiming to establish theoretical guarantees for practical machine learning algorithms. Current investigations focus on heavy-tailed distributions, non-log-concave sampling, and discrete data generation mechanisms. Publication Trends: His 11 most recent publications (2020-2025) reveal a concentrated research program advancing theoretical understanding of sampling algorithms and diffusion models. Work spans top venues including NeurIPS, COLT, and IEEE Transactions on Information Theory, with recurring themes in heavy-tailed distribution sampling, convergence analysis of Langevin dynamics, and optimization of generative architectures. The output demonstrates strong interdisciplinary connections between probability theory, statistics, and machine learning. Scientific Awards: No awards were documented in the source materials. Advising and Grants: The provided information contains no details regarding student supervision, research grants, or funding sources. Teaching responsibilities include undergraduate courses in calculus, linear algebra, and differential equations. Research Environment: Dr. He operates within Georgia Tech's School of Mathematics, collaborating closely with Prof. Molei Tao's research group. His office is located in Skiles 016, and his work contributes to the institution's strengths in mathematical data science and computational theory.

Paul CharlesrajView profile

Senior Lecturer

• Project Management
• Construction Engineering
• Knowledge Management
• +5 more

Paul Charlesraj is a Senior Lecturer in Construction Project Management at the University of Huddersfield, UK, affiliated with the Department of Design and Architecture within the School of Arts and Humanities. He is a Chartered Surveyor (MRICS), Project Management Professional (PMP®), and Certified Knowledge Manager (CKM). His roles include leading undergraduate and postgraduate courses in Construction and Project Management, and他曾担任MBA-CPM项目主任。他积极参与专业发展计划的开发，并在RICS和相关机构担任监督和评估角色。 教育背景未明确提及，但根据其职业轨迹推断，他拥有建筑或工程管理领域的高级学位。他的研究兴趣集中在技术驱动的施工管理，包括知识管理、数字施工、绩效管理以及灾害管理等领域。他致力于通过基于知识的教育和培训提升项目交付效率和价值创造。 研究方向包括项目管理、施工技术、知识管理、数字转型和领导力变革管理。其近期研究趋势聚焦于数字工具（如无人机、BIM）在施工中的应用，员工心理健康，以及新兴技术的采用障碍。他与VTT Technical Research Centre等机构建立了国际合作，并参与PMI标准委员会。 奖项方面，目前未提及具体科学荣誉。他积极参与学术活动，包括在国际会议上发表演讲（如2025年ICE邀请演讲）并担任同行评审。他的专业活动包括担任英国多家行业组织的会员，如项目管理协会（APM）和机构工程师学会（ICE）。 指导方面，虽然未列学生姓名，但提到他监督了RICS专业能力评估（APC）。实验室和团队包括参与Centre for Urban Design, Architecture and Sustainability及Innovative Design Lab Research Centre。

Andrew WintersView profile

Associate Professor

• Numerical Analysis
• Computational Mathematics
• Partial Differential Equations
• +12 more

Andrew Winters is a Senior Associate Professor in the Department of Mathematics at Linköping University, Sweden. He is affiliated with the Division of Applied Mathematics (TIMA), where he conducts research in computational mathematics and numerical methods for partial differential equations. His research focuses on the design and analysis of high-order numerical schemes, particularly nodal discontinuous Galerkin (DG) methods with summation-by-parts (SBP) properties, for solving hyperbolic and mixed hyperbolic-parabolic PDEs such as shallow water, Euler, Navier-Stokes, and magnetohydrodynamic (MHD) equations. His work emphasizes conservation, entropy stability, and thermodynamic consistency in numerical approximations. The recent publications highlight a strong trend in developing robust, high-order, entropy-stable methods for nonlinear conservation laws, with applications in fluid dynamics and geophysical modeling. His work integrates theoretical analysis with high-performance computing, particularly through the development of the FLUXO and Trixi.jl simulation frameworks. Energy Bounds for Discontinuous Galerkin Spectral Element Approximations Entropy Stable Hydrostatic Reconstruction Efficient Implementation of Entropy Stable DG Methods Adaptive Simulations with Trixi.jl Subcell Finite Volume Shock Capturing Andrew Winters is actively involved in software development and scientific computing education, including an introductory Fortran course for MATLAB users. He contributes to international collaborations, such as a four-way research and exchange program between Linköping University and Washington State University. He has no listed scientific awards in the provided text. He advises students in computational mathematics, though specific names are not mentioned. He is a core developer of the FLUXO (Fortran/MPI), Trixi.jl (Julia), and HOHQMesh.jl projects, which support high-order simulations and mesh generation.

Antonis StylianouView profile

Associate Professor

• Computational Biomechanics
• Musculoskeletal Modeling
• Multibody Dynamics
• +3 more

Antonis Stylianou is an Associate Professor in the Department of Mechanical Engineering at the University of Missouri - Kansas City (UMKC). He holds a Ph.D. in Mechanical Engineering from the University of Kansas (2004), along with multiple master's degrees in Mathematics (2012) and Mechanical Engineering (2000), and a B.S. in Mechanical Engineering (1998). His primary affiliation is within the School of Science and Engineering, where he focuses on computational biomechanics, musculoskeletal modeling, and multibody dynamics. Dr. Stylianou's research emphasizes Elbow joint mechanics and ligament deficiency modeling Virtual surgical simulators for pelvic surgery error prevention Pediatric knee anatomy and ACL injury risk analysis Musculoskeletal models of human motion Finite element analysis of cartilage contact pressures His work bridges computational methods with clinical applications, including developing tools for orthopedic surgeons and training paradigms for surgical techniques. Recent studies have explored surgeon kinematics during retropubic trocar insertion and posteromedial rotatory incongruity of the elbow. Teaching responsibilities include advanced dynamics, biomechanics, and experimental methods courses. His contributions underscore the integration of engineering principles with medical challenges, particularly in orthopedic and surgical contexts.