جست‌وجوی استاد و پژوهشگر

• Combinatorial Optimization
• Approximation Algorithms
• Online Algorithms
• +۳ مورد دیگر

Prof. Dr. Sven Oliver Krumke is a Full Professor of Mathematics and currently serves as Dean of the Faculty of Mathematics at the Rheinland-Pfälzische Technische Universität Kaiserslautern-Landau (RPTU). His research focuses on Combinatorial Optimization, Approximation Algorithms, Online Algorithms, Complexity, Algorithmic Game Theory, and Graph Theory with Applications. He leads the Optimization Group (AG Optimierung) within the Department of Mathematics. Education and Academic Path: PhD (Dr. rer. nat.) in Mathematics, University of Würzburg (1997) Habilitation in Mathematics, Technische Universität Berlin (2002), Thesis: 'Online Optimization: Competitive Analysis and Beyond' Professor at RPTU since 2004 Research Projects: Principal investigator in the Research Training Group (GRK 2982) 'MIMO: Mathematics of Interdisciplinary Multiobjective Optimization' Project lead for 'ONE PLAN: Optimization in Emergency Medical Services in Rhineland-Palatinate' Teaching includes courses on Analysis, Mathematical Modeling in Economics, and Reading Courses. He supervises Bachelor's and Master's theses in Optimization-related topics. He has held roles such as Scientific Researcher at the Konrad-Zuse-Zentrum Berlin (1998–2004), and has extensive international collaborations, including a Fulbright-funded stay at SUNY Albany (1992–1993). His work emphasizes practical applications in healthcare logistics, emergency services optimization, and algorithmic solutions for real-world problems.

• Mathematical Optimization
• Nonsmooth Optimization
• Multiobjective Optimization
• +۴ مورد دیگر

Bennet Gebken is a researcher at the Department of Mathematics , Technical University of Munich , affiliated with the Chair of Mathematical Optimization led by Prof. Ulbrich. His work focuses on nonsmooth and multiobjective optimization, particularly in PDE-constrained problems and numerical continuation methods. Research Interests: Bennet specializes in nonsmooth optimization, multiobjective optimization, and regularization techniques. His recent work explores convergence analysis in nonsmooth settings, second-order gradient sampling, and inverse optimization methods for data-driven decision criteria. Publications: His research spans topics like PDE-constrained multiobjective optimization, L1 penalty terms, and the hierarchical structure of Pareto critical sets. Key trends include reduced-order modeling, computational efficiency, and the interplay between optimization and machine learning. Contact: bennet.gebken@tum.de . Based at Boltzmannstr. 3, Garching b. München, Germany.

Mateja Novakمشاهده پروفایل

استادیار

• Model Predictive Control
• Power Electronics
• Multilevel Converters
• +۶ مورد دیگر

Mateja Novak is an Assistant Professor at AAU Energy, Aalborg University, Denmark, within the Department of Applied Power Electronic Systems under the Faculty of Engineering and Science. Her research focuses on model predictive control, multilevel converters, machine learning, and reliability of power electronic systems, contributing to sustainable energy systems and renewable energy integration. She holds a Ph.D. from Aalborg University (2020) and an M.Sc. from Zagreb University (2014). Previously, she was a Postdoc at AAU Energy (2020-2023) and a visiting researcher at Kiel University (2018) and Danfoss (2023). Notable achievements include the EPE Outstanding Young EPE Member Award (2019) and 2nd place in the 2021 IEEE-IES Student and YP Competition. Her work spans projects like ALL2GaN (2023-2026) and AI-Power (2022-2027), addressing GaN IC solutions and AI-driven power electronics advancements. She is actively involved with IEEE societies including the Power Electronics Society and IEEE Women in Engineering. Her research outputs emphasize control strategies for power electronics, reliability analysis, and optimization techniques. Key areas of exploration include thermal stress balancing in converters, statistical model checking, and multiobjective control algorithms. Collaborations with industry partners like Danfoss and academic institutions like Kiel University underscore her interdisciplinary approach to advancing power electronics technology.

• Computational Mechanics
• Structural Crashworthiness
• Additive Manufacturing
• +۵ مورد دیگر

Professor Qing Li is a faculty member at the School of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney. He obtained his PhD from Sydney in 2000, underwent postdoc training at Cornell University (2000-2001), and held academic roles at James Cook University (2004-2006). He joined Sydney in 2006 via a Sesqui senior lectureship, becoming Associate Professor in 2010 and Professor in 2014. He served as Director of Postgraduate Studies (2007-2012) and Director of Biomedical Engineering (2017-2019). Education: PhD (University of Sydney, 2000), ME (UTS), ME (Hunan) His research focuses on computational design, multidisciplinary optimization of nonlinear and time-dependent multifunctional materials, and biomedical applications. Key areas include additive manufacturing , biomechanics , and machine learning in structural reliability. Recent publications emphasize fracture modeling in biomaterials, reliability analysis, and tissue scaffold design. Recent publications highlight Bayesian learning for robotic systems, probabilistic transformation in reliability analysis, and phase field fracture models for additively manufactured composites. Collaborations with industry partners like Cochlear and Stryker span ARC , NHMRC , and MRFF projects. Awards: Clarivate Highly Cited Researcher (2020), Top 50 Australia Research Leader (2020), APACM Computational Mechanics Award (2016) Fellowships: ARC Future Fellow (2013-2017), ARC Australian Postdoctoral Fellow (2001) He supervises PhD students in projects like epidermal electrodes , Silver Diamine Fluoride remineralization , and virtual surgical planning . His leadership extends to the Centre for Advanced Materials Technology and editorial roles in computational methods journals.

• Water Engineering
• Hydrologic Modelling
• Hydraulic Modelling
• +۵ مورد دیگر

Dr. Michael Leonard is an Associate Professor in the School of Architecture and Civil Engineering at the University of Adelaide, affiliated with the Environmental Engineering department. His research focuses on applying numerical and statistical models to environmental engineering challenges, particularly flood risk assessment, hydrological modeling, and water security. He has expertise in stochastic rainfall modeling, multivariate extremes, and climate change impacts. Dr. Leonard’s work emphasizes understanding hazardous environmental systems and improving flood risk management strategies. He is eligible to supervise Masters and PhD students in these areas. His research interests include flood modeling, climate adaptation, and sustainable water resource management. He has contributed to projects such as coastal flood risk analysis, brackish groundwater desalination, and global hydrological change detection. His recent articles highlight trends in flood magnitude, rainfall extremes, and the integration of climate indices to predict hydrological extremes. He has also explored the implications of climate change on water distribution systems and infrastructure resilience. Dr. Leonard collaborates on initiatives like the Global Streamflow Indices and Metadata Archive (GSIM), advancing big data applications in hydrological analysis. His work bridges theoretical models with practical engineering solutions, addressing real-world challenges in water security and environmental sustainability.

• Optimization
• Operational Research
• Computational Mathematics
• +۳ مورد دیگر

Julian Hall is a Professor in the Department of Mathematics at the University of Edinburgh, specializing in optimization and operational research. He is a key developer of HiGHS, an open-source linear optimization software, and previously developed a solver used in global animal feed formulation and oil reservoir management. His research focuses on enhancing the simplex method for linear programming efficiency. Julian holds a PhD from the University of Dundee under Roger Fletcher after studying at Oxford and working at ICI/AstraZeneca. He has received four best paper awards, including COAP recognitions in 2005, 2013, 2015, and 2018. His work bridges academia and industry, with funding from Google and Huawei. Julian emphasizes applied mathematics’ real-world impact, particularly in agriculture and computational sustainability. Education: PhD in Numerical Analysis and Optimization (Dundee), MSc (Dundee), BA (Oxford), King’s School (Macclesfield). Research Interests: Linear programming algorithms, parallel computing, optimization in agriculture and environmental systems. His software HiGHS is deployed in multi-billion-dollar industries, solving problems like optimal animal feed blends and sustainable livestock production. Julian collaborates with industrial partners and mentors PhD students advancing optimization techniques. His recent work includes parallel simplex methods and GPU-accelerated algorithms, reflecting his commitment to scalable computational solutions.

• Mechanical Engineering
• Engineering Design Theory and Methodology
• Multiobjective Optimization
• +۶ مورد دیگر

Christopher A. Mattson is a Professor of Mechanical Engineering at Brigham Young University (BYU) and director of the Design Exploration Research Group. With a PhD from Rensselaer Polytechnic Institute (2003), he has received prestigious awards including the Presidential Early Career Award (PECASE), NSF CAREER Award, Fulbright Scholarship, and ASME Fellowship. Education: PhD, Mechanical Engineering, Rensselaer Polytechnic Institute (2003) MS, Mechanical Engineering, BYU (2001) BS, Mechanical Engineering, BYU (1999) Research Interests: Focus on engineering design theory, multiobjective optimization, and social impact modeling for global development. Applications span poverty alleviation, sustainable design, and modular product platforms. He develops formal methodologies integrating computational optimization with social and environmental factors during conceptual design stages. Teaching Interests: Specializes in team-based engineering design, teaching courses like Advanced Product Development and Global Product Development. His pedagogy emphasizes interdisciplinary collaboration, industry-sponsored projects, and global engagement through travel and cross-cultural partnerships. Scientific Awards: ASME Fellow (2020–Present) NSF CAREER Award (2010–2014) Presidential Early Career Award (PECASE) (2012) Ben C. Sparks Medal (ASME) (2015) Fulbright Scholar (2014–2015) Professional Service: Holds leadership roles in ASME committees, serves as Associate Editor for Journal of Mechanical Design , and contributes to multidisciplinary design optimization societies. His work bridges academia, industry, and international development through sponsored projects and research collaborations.

• Algorithmic Game Theory
• Fair Division
• Computational Economics
• +۳ مورد دیگر

Dr. Georgios Birmpas is a researcher specializing in algorithmic game theory, computational economics, and fair division. He currently serves as Module Co-ordinator for Algorithmic Game Theory (COMP559) and Computational Game Theory and Mechanism Design (COMP326) at an institution not explicitly named. His research focuses on fairness and efficiency in resource allocation problems, often intersecting computer science, economics, and multiagent systems. Key research areas: Algorithmic Fairness Submodular Optimization Strategic Agent Modeling Distortion in Voting Systems Recent contributions include: Fair multiobjective submodular function maximization Existence of approximate equilibria in resource allocation Two-query distortion analysis in matching problems Interdependent value models in fair division His work has appeared in venues such as the International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS) and journals like Mathematics of Operations Research and SIAM Journal on Discrete Mathematics . Collaborators include researchers from institutions across Europe, focusing on theoretical and applied aspects of algorithmic fairness.

Michael Winterمشاهده پروفایل

استاد

• Programming Languages
• Semantics of Programming Languages
• Program Verification
• +۵ مورد دیگر

Michael Winter is a Professor in the Department of Computer Science at Brock University, Canada. He holds a Habilitation from the University of the Federal Armed Forces, Munich (2002), a Ph.D. (Dr. rer. nat.) from the same institution (1998), and a Master of Computer Science (Dipl. Inform.) (1993). His research focuses on programming languages, semantics, program verification, relational methods, category theory, and fuzzy logic. He is the Managing Editor of the Journal on Relational Methods in Computer Science . Winter's academic roles include teaching courses such as COSC 2P05 (Programming Languages), COSC 3P91 (Advanced Object-Oriented Programming), and COSC 5P02 (Logic in Computer Science). He has advised numerous graduate and undergraduate students on topics including fuzzy relational analysis, program verification, and formal methods. His research contributions span over 100 publications in journals and conferences, emphasizing relational algebra, category theory, and their applications in computer science. His work integrates theoretical foundations with practical applications, such as developing tools like RelView for relational computation and exploring applications in ambient intelligence and data visualization. Winter collaborates internationally, contributing to conferences like RAMiCS and publishing in journals like Fuzzy Sets and Systems and Journal of Logical and Algebraic Methods in Programming .

Tamás Tettamantiمشاهده پروفایل

استاد

• Road Traffic Control
• Intelligent Transportation Systems
• Traffic Simulation
• +۳ مورد دیگر

Tamás Tettamanti is a Professor at Budapest University of Technology and Economics, serving as Deputy Head of the Department of Control for Transportation and Vehicle Systems. He holds a PhD (2013), Habilitation (2023), and DSc (2023) in Transportation Engineering, with expertise in road traffic modeling and control. 2007–2010: PhD Student 2010–2013: Assistant Lecturer 2014–2018: Senior Lecturer 2019–2024: Associate Professor 2025–present: Full Professor Education: High School Graduation (2001), DEUG (2004), MSc in Transportation Engineering (2007), Jazz Trumpet Graduate (2008) PhD (2013), Habilitation (2023), DSc (2023) Research focuses on road traffic modeling, intelligent transportation systems (ITS), autonomous vehicle integration, and emission-aware traffic control. His work bridges theoretical developments with real-world applications, including wireless traffic light systems and deep learning for urban mobility peaks. Scientific Awards: BME PhD Research Prize (2012) Literary Awards from Hungarian Scientific Association for Transport (2013, 2017, 2021, 2023) Bolyai János Research Scholarship (2017–2020) Michelberger Master Prize (2022) BME Jubilee Medal (2023) He has led major projects such as Dynamic Adaptive Traffic Control Services (2020–2024) and Deep Learning Anticipated Urban Mobility Peaks (DARUMA) (2021–2024), with industry collaborations including Google-BME Traffic Lab.

Balázs Vargaمشاهده پروفایل

پژوهشگر ارشد

• Road Traffic Modeling and Control
• AI-based Traffic Flow Prediction
• Urban Traffic Simulation
• +۳ مورد دیگر

Dr. Balázs Varga is a Research Fellow at the Department of Control for Transportation and Vehicle Systems, Budapest University of Technology and Economics (BME). He holds a PhD in Transportation and Vehicle Sciences (2021) and an MSc in Vehicle Engineering (2015) from BME. His industry experience includes roles at AVL Hungary as a Software and Function Developer (2016–2018) and academic positions at Chalmers University of Technology (2015) and SZTAKI (2012–2014). Current Role: Research Fellow (2021–present) Teaching: Programming, Control Theory, Traffic Modeling (English language course) Research Interests: Varga specializes in road traffic modeling and control, focusing on AI-based traffic estimation and dynamic traffic management. His work integrates machine learning with mesoscopic and microscopic traffic simulation tools like SUMO to optimize urban mobility and reduce emissions. Projects: He leads the 2020–2024 national development project 'Dynamic, adaptive traffic control services and evaluation tools based on digitally connected data sources' (2019-1.1.1-PIACI KFI). This initiative leverages connected data sources for real-time traffic control and policy evaluation. Key Publications Trends: His recent articles explore topics such as graph neural networks for sensor placement, multiobjective control of emissions, and mixed-reality V2X testing. These works emphasize data-driven approaches, emission reduction, and simulation frameworks for autonomous vehicles.

Amlan Chatterjeeمشاهده پروفایل

دانشیار

• High-Performance Computing
• Big Data
• Graph Compression
• +۵ مورد دیگر

Dr. Amlan Chatterjee is an Associate Professor in the Department of Computer Science at California State University, Dominguez Hills. His work focuses on high-performance computing, big data analytics, and GPU-based graph compression techniques. He has held academic roles since 2015, including Assistant Professor (2015-2021) and currently serves as Associate Professor. His research explores efficient computation on large datasets using multi-core architectures and GPUs, alongside cloud computing optimization and IoT applications in aviation and health monitoring. Education: Ph.D., Computer Science, University of Oklahoma, 2014 M.S., Computer Science, State University of New York, 2009 B.Tech., Computer Science & Engineering, West Bengal University of Technology, 2007 Research Interests: Dr. Chatterjee's research spans graph compression, social network analysis, cloud resource optimization, and IoT-driven solutions for aviation safety and health monitoring. He has mentored students in projects like GPU-based big data processing and cloud computing efficiency. Awards: Graduate Computer Science Scholarship (University of Oklahoma, 2012-13) Computer Science Advisory Board Scholarship (2012) Phillips Petroleum Scholarship (2010-11) Top Undergraduate Rank (1st/68 students) Academic Contributions: He has served on numerous committees, including the Research Chair for Untenured Faculty (2016-17), IEEE conference session chairs, and accreditation boards. His teaching includes courses on data structures, operating systems, and introductory computer science.

Thomas J. Santnerمشاهده پروفایل

استاد

• Design of Experiments
• Computer Simulations
• Statistical Selection and Screening
• +۳ مورد دیگر

Thomas J. Santner is a Professor in the Department of Statistics at Ohio State University. His research focuses on experimental design, particularly in integrating computer simulations with physical experiments. He co-authored influential books including *The Design and Analysis of Computer Experiments* (Springer, 2019) and *The Design and Analysis of Experiments for Statistical Selection, Screening, and Multiple Comparisons* (Wiley, 1995). His work bridges statistics and engineering, addressing challenges in prosthesis design, biomedical systems, and industrial processes. Notably, he collaborates with the Hospital for Special Surgery on bone-implant systems and biomaterials research. Education: Ph.D., Purdue University, 1973. Research interests include: Computer experiments and hybrid simulation-physical experimentation Statistical selection and screening methodologies Applications in biomedical engineering and manufacturing optimization Uncertainty quantification in finite element models Recent work emphasizes optimizing complex systems through calibrated simulators, with applications in injection molding processes and joint replacement design. His articles demonstrate methodological contributions to design efficiency, sensitivity analysis, and multiobjective optimization. Prior roles include former Director of the Department of Statistics' Consulting Service and former Department Chair at Ohio State University.

• Artificial Intelligence
• Machine Learning
• Internet of Things (IoT)
• +۵ مورد دیگر

G. Thippa Reddy is a prolific researcher with a focus on advanced technologies such as artificial intelligence, machine learning, and blockchain, particularly in healthcare, IoT, and cybersecurity domains. His work spans interdisciplinary areas including federated learning, edge computing, and smart city infrastructure. He has collaborated extensively with researchers like Praveen Kumar Reddy Maddikunta, Gautam Srivastava, and Mamoun Alazab, producing over 150 publications in high-impact journals like IEEE Access, IEEE Internet Things Journal, and IEEE Transactions on Industrial Informatics. His research emphasizes practical applications of AI in real-world scenarios, such as privacy-preserving medical systems, secure UAV networks, and inclusive education for individuals with disabilities. He explores cutting-edge topics like the Metaverse's role in Industry 5.0, blockchain-enhanced security frameworks, and the integration of large language models into intelligent transportation systems. Key contributions include frameworks for federated learning in healthcare, optimized routing protocols for underwater communications, and explainable AI (XAI) methods for industrial automation. His work often addresses challenges in scalability, privacy, and ethical deployment of emerging technologies.

• Civil Engineering
• Infrastructure Maintenance
• Climate Change Adaptation
• +۳ مورد دیگر

Irina Stipanovic is an Assistant Professor specializing in Market Dynamics, focusing on infrastructure maintenance, climate change adaptation, and railway systems. Her research integrates advanced technologies like Digital Twins and machine learning to enhance decision-making in civil engineering contexts. Her work addresses critical challenges in infrastructure management, including risk-based maintenance scheduling, structural health monitoring, and flood resilience. She explores innovative methods such as entity-embedding neural networks and vision-based 3D inspections to improve predictive maintenance and asset lifecycle management. Key themes in her research include: Railway earthwork maintenance under climate change Multi-objective decision models for infrastructure prioritization Integration of Structural Health Monitoring (SHM) into Digital Twin platforms Resilience planning for critical infrastructure against floods and other hazards She has contributed to EU initiatives on standardization of Digital Twin applications and has published extensively on railway, tunnel, and bridge management. Her work emphasizes data-driven approaches to sustainable infrastructure solutions.