جست‌وجوی استاد و پژوهشگر

• Renewable Energy Systems
• Microgrids
• Wireless Power Transfer
• +۵ مورد دیگر

Kafeel Ahmed is a Lecturer in Electrical Energy Engineering at Swinburne University of Technology's School of Science, Computing, and Engineering Technologies. He holds a PhD from Swinburne (2023), M.Eng. from the University of Malaya (2016), and B.E. from Mehran University (2010). His research focuses on renewable energy systems, microgrids, wireless power transfer for electric vehicles (EVs), and power conversion technologies. He is affiliated with the New Energy Technologies research group and the Siemens Swinburne Energy Transition Hub, contributing to industrial training programs on PSSE and PSS SINCAL. His academic career includes roles as Senior Lecturer at DHA Suffa University and Lecturer at Indus University in Pakistan, alongside industry experience as an Assistant Manager in distribution network operations at K-Electric. His research spans distributed energy resource integration, DC microgrid control, and misalignment-tolerant wireless EV charging systems. Recent work emphasizes dynamic operating envelopes for grid stability and advanced control strategies for bidirectional power systems. Publications highlight advancements in hosting capacity assessment, voltage stability, and phase-synchronization in EV charging. He is active in supervising PhD candidates and contributes to flagship areas like Affordable and Clean Energy (SDG7), Industry Innovation (SDG9), and Climate Action (SDG13).

• Energy Communities
• Renewable Energy Systems
• Green Hydrogen Production
• +۵ مورد دیگر

Francesco Demetrio Minuto is a Fixed-term tenure-track assistant professor at the Department of Energy (DENERG) at Polytechnic University of Turin, where he is also a member of the Interdepartmental Center Ec-L - Energy Center Lab. His academic activities span across multiple engineering disciplines as an invited member of both the College of Electrical and Energy Engineering and the College of Mechanical, Aerospace, and Automotive Engineering. Dr. Minuto's research focuses on energy communities, techno-economic analysis of renewable energy systems, green hydrogen production, and energy storage technologies including lithium-ion batteries and hydrogen storage. His work addresses critical challenges in sustainable energy transition, with particular emphasis on hydrogen technologies, energy storage optimization, and community-scale renewable energy integration. His research aligns with key Sustainable Development Goals, particularly Goal 7 (Affordable and clean energy), Goal 11 (Sustainable cities and communities), and Goal 13 (Climate action). His recent publication record demonstrates strong expertise in hydrogen technologies, energy storage systems, and renewable energy community modeling, with numerous high-impact publications in leading energy journals. His work often involves sophisticated modeling approaches including stochastic simulation frameworks, MILP optimization, and techno-economic analysis to address real-world energy challenges. Dr. Minuto actively mentors the next generation of energy researchers, supervising five PhD students working on cutting-edge energy projects. His teaching portfolio includes PhD-level instruction on 'Safety of new energy carriers in confined spaces' and undergraduate courses such as 'Energy and Renewable Energy Sources' and 'Applied Thermodynamics and Heat Transfer' across multiple engineering programs. He is currently involved in the EU-funded TIPS4PED research project (2024-2027) under the Horizon Europe program, focusing on Positive Energy Districts and urban energy planning. His research bridges theoretical modeling with practical applications in the energy transition, contributing to both academic knowledge and real-world energy solutions.

Dimitrije D. Čvokićمشاهده پروفایل

استادیار

• Bioinformatics
• Software Development
• Operations Research
• +۵ مورد دیگر

Dimitrije D. Čvokić serves as Assistant Professor and Head of the Department of Computer and Information Sciences within the Faculty of Science and Mathematics at the University of Banja Luka. His academic leadership spans curriculum development across computer science and mathematics disciplines, with teaching responsibilities including Data Structures, Algorithms, Software Engineering, and Mobile Application Development. His research focuses on competitive hub location problems, bioinformatics tool development, and blockchain applications. Čvokić pioneered the integration of Linux-based bioinformatics tools like BioLinux and UGene into biology curricula, while developing novel optimization algorithms for Stackelberg games in logistics networks. His recent work explores large language model terminology standardization and blockchain-based property investment platforms. Analysis of his publication trends reveals a strong trajectory from theoretical optimization (2015-2020) toward applied computational solutions (2021-2024), with increasing interdisciplinary collaboration spanning public health, real estate, and linguistic computing. His work demonstrates consistent methodological rigor in operations research while adapting to emerging technological domains. As an academic advisor, Čvokić has mentored multiple thesis students including Aleksandar Vrhovac (Interval Arithmetic, 2023), Danijela Kovačević (Automatic Control Systems, 2023), and Nemanja Marjanović (CAPTCHA systems, 2022). His teaching innovations include introducing Python programming, RDKit cheminformatics tools, and BASH scripting into diverse science curricula across chemistry, biology, and geodesy programs. Čvokić established computational laboratories featuring specialized software environments including Chemistry Add-in for Word, ChemSketch, UGene, and NET Bio Extension for Excel. His cross-departmental collaborations have modernized computer science instruction across the university's technical and natural science programs through systematic integration of open-source tools and modern development frameworks.

Oktay Gunlukمشاهده پروفایل

استاد

• Discrete Optimization
• Integer Programming
• Mathematical Optimization
• +۵ مورد دیگر

Oktay Gunluk is the Gary C. Butler Family Professor in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology (Georgia Tech). Prior to joining Georgia Tech, he was a professor of practice in the School of Operations Research and Information Engineering at Cornell University and the manager of the Mathematical Optimization and Algorithms group at IBM Research. He also spent three years as a researcher in the Operations Research group at AT&T Labs, where he worked on various large-scale applied optimization projects. His educational background includes: Ph.D. in Operations Research (1995) from Columbia University M.Phil. in Operations Research (1993) from Columbia University M.S. in Industrial Engineering (1989) from Boğaziçi University B.S. in Industrial Engineering (1987) from Boğaziçi University Professor Gunluk's research focuses on theoretical and computational aspects of discrete optimization problems, primarily in integer programming. His seminal work centers on cutting planes for mixed-integer sets, and more recently he has developed integer programming approaches to classification and clustering problems in machine learning with considerations for fairness and interpretability. His work bridges theoretical optimization with practical applications in areas such as quantum computing, fair machine learning, and supply chain optimization. The interdisciplinary nature of his research is reflected in his publications spanning operations research journals, computer science conferences, and machine learning venues. His notable recognition includes: Gary C. Butler Family Professorship at Georgia Tech Professor Gunluk has advised several PhD students including Reka Agnes Kovacs (Oxford, 2023), Connor Lawless (Cornell, 2024), and Ishan Bansal (Cornell, 2024). His research has been supported by collaborations with industry partners including IBM Research where he led the Mathematical Optimization and Algorithms group. His work has addressed optimization challenges in telecommunications, quantum computing, and machine learning with practical implementations. At IBM Research, Gunluk managed the Mathematical Optimization and Algorithms group, leading a team focused on developing advanced optimization techniques for both theoretical advancement and practical application. His work has bridged the gap between academic research and industrial implementation, particularly in the areas of network design, quantum computing algorithms, and fair machine learning systems, demonstrating the real-world impact of his optimization expertise.

Maximilian Schifferمشاهده پروفایل

دانشیار

• Operations Research
• Management Science
• Machine Learning
• +۷ مورد دیگر

Prof. Dr. Maximilian Schiffer is an Associate Professor at the Professorship of Business Analytics & Intelligent Systems , TUM School of Management , Technical University of Munich. He holds a Ph.D. in Operations Research from RWTH Aachen University (2017) and has previously served as a visiting postdoctoral scholar at Stanford University and a postdoctoral scholar at RWTH Aachen. Education: Ph.D. in Operations Research, RWTH Aachen University (2017) Maximilian’s research focuses on developing operations research and prescriptive analytics methods to address societal mobility and transportation challenges, including electric vehicles , autonomous mobility-on-demand systems , and multi-modal transportation networks . His interdisciplinary work bridges robotics , computer science , and transport engineering . Recent publications highlight advancements in autonomous fleet control , electric vehicle charging infrastructure , and collusion detection in dynamic pricing . Scientific Awards: INFORMS TSL Dissertation Prize GOR Doctoral Dissertation Prize Best Paper Award, IEEE Conference on Intelligent Transportation Systems Maximilian is an associate member of the GERAD research group and serves on the editorial board of OR Spectrum and the TSL Dissertation Prize committee . His lab, the Professorship of Business Analytics & Intelligent Systems , trains students in machine learning, optimization, and future mobility systems.

Bernard Gendronمشاهده پروفایل

استاد

• Combinatorial Optimization
• Logistics
• Transportation Networks
• +۵ مورد دیگر

Bernard Gendron is a Full Professor at the Department of Computer Science and Operational Research (DIRO) at the University of Montreal and a researcher at the Interuniversity Research Centre on Enterprise Networks, Logistics, and Transportation (CIRRELT). His research focuses on combinatorial optimization, including polyhedral theory, decomposition methods, metaheuristics, constraint programming, and applications in logistics, transportation, telecommunications, and healthcare. He has held leadership roles, such as Director of CIRRELT (2008–2015) and President of Canadian Operational Research Society (CORS) and INFORMS Transportation Science & Logistics section. He has received awards including CORS Practice (2004), Service (2006), and Merit (2010) Awards. Education: Not explicitly stated in texts, but extensive academic and professional experience implies doctoral-level qualifications. Research Interests: Combinatorial optimization, logistics networks, transportation systems, integer programming, and algorithm design. His work bridges theoretical developments and practical applications, particularly in network design and large-scale optimization problems. Publications: Over 50 peer-reviewed articles, with recent contributions on facility location, network design, and heuristic algorithms. Key areas include subgradient methods, Lagrangian relaxation, and metaheuristic approaches. Editorial & Leadership: Editor-in-Chief of INFOR, member of editorial boards for journals like EURO Journal on Transportation and Logistics. Active in program committees (e.g., Odysseus 2018). Taught courses on optimization, network flows, and mathematical programming. Labs/Teams: Associated with CIRRELT and DIRO research groups focused on transportation and logistics optimization.

Andreas Kloseمشاهده پروفایل

دانشیار

• Operations Research
• Combinatorial Optimization
• Integer Programming
• +۳ مورد دیگر

Andreas Klose is an Associate Professor at the Department of Mathematics, Aarhus University, Faculty of Science. His work focuses on combinatorial optimization and integer programming, particularly for logistics applications. Active in exact solution approaches for bi-objective and uncertain facility location problems Develops metaheuristics for fixed charge transportation challenges Teaches master courses on location planning, vehicle routing, and optimization methods Research Trends: Recent publications emphasize facility location under uncertainty, exact algorithms for capacitated problems, and metaheuristic approaches. Topics span both theoretical and applied operations research. Teaching: Offers courses on Multiple Criteria Optimization , Location Planning , Vehicle Routing , and Metaheuristics , alternating between fall and spring semesters. Collaborations: Frequently collaborates with researchers like Simon Gadegaard and Kim Jörnsten on facility location and transportation problem frameworks.

Thomas W. Kirchstetterمشاهده پروفایل

استاد مدعو

• Pollutant emissions and controls
• Climate and environmental impacts of transportation
• Pollutant measurement technologies
• +۹ مورد دیگر

Thomas W. Kirchstetter serves as an Adjunct Professor of Civil and Environmental Engineering at the University of California, Berkeley, and as the Scientific Division Director for the Energy Analysis and Environmental Impacts Division at Lawrence Berkeley National Laboratory. He also acts as the Chief Scientific Advisor to the Cyclotron Road Program, which focuses on turning scientific concepts into marketable products. With over 25 years of experience at Lawrence Berkeley National Laboratory, Kirchstetter maintains a dual role bridging academic research and national laboratory leadership. Dr. Kirchstetter's research spans several critical environmental areas, with primary focus on pollutant emissions and controls , climate and environmental impacts of transportation , pollutant measurement technologies and community monitoring , and atmospheric aerosol chemical & optical properties . His work has pioneered the development of small, low-cost black carbon sensors and community air quality networks, particularly through partnerships with the West Oakland Environmental Indicators Project and the Environmental Defense Fund. Kirchstetter's research includes quantifying emissions from diesel trucks, developing new air pollutant sensing technologies, and analyzing emissions from dry anaerobic digestion of municipal solid waste-to-energy facilities. His recent publications (2020-2025) reveal a strong focus on urban air quality monitoring, waste-to-energy systems, transportation emissions, and sensor technology development. These works demonstrate consistent application of high-resolution monitoring techniques to address environmental justice concerns, particularly in communities disproportionately affected by air pollution. His research group has made significant contributions to understanding black carbon distribution in urban environments and developing cost-effective monitoring solutions. American Chemical Society Editor's Choice award (2019) Top Environmental Technology Paper in Environmental Science & Technology award (2017) DOE Alexander Hollaender Distinguished Postdoctoral Fellowship (1998) 2016 R&D 100 Award: Cool Roof Time Machine Outstanding Mentor Award by the Department of Energy's Office of Science (2006) As an educator and mentor, Kirchstetter has advised numerous students including postdoctoral fellow Chelsea Preble, graduate student Julien Caubel, and undergraduate researchers Annie Rosen, Kelly Archer, Shannon Chang, and Carter Keeling. His research has been supported by various grants from the Environmental Protection Agency, Department of Energy, and California Energy Commission, focusing on air quality monitoring, emissions characterization, and sustainable waste management solutions. Kirchstetter's work bridges fundamental scientific research with practical applications that inform environmental policy and improve community health outcomes.

Andreas Ernstمشاهده پروفایل

استاد

• Operations Research
• Optimization
• Scheduling
• +۶ مورد دیگر

Andreas Ernst is a Professor in the School of Mathematics at Monash University, Australia. He also serves as a Senior Research Fellow with the Australia Indonesia Centre and a Chief Investigator in the ARC Training Centre OPTiMA. His primary research focuses on optimization and operations research, particularly in developing large-scale integer programming methods and matheuristics for applications in transport, logistics, energy, and mining. Ernst leads the MAXIMA Academy for cross-disciplinary mathematical applications and has contributed to projects such as rail scheduling in Indonesia and coal supply chain optimization. Roles: Professor (Monash University), Senior Research Fellow (Australia Indonesia Centre), Chief Investigator (OPTiMA) Key Research Areas: Scheduling, Combinatorial Optimization, Network Design, Parallel Matheuristics Ernst's work addresses real-world challenges through algorithmic innovation, including hub location problems, vehicle routing, and resource allocation in healthcare. His projects often involve collaboration with industry partners like Equans Services and the Defence Science and Technology Group. He actively supervises PhD/Masters students in areas such as machine learning for discrete optimization and rail scheduling. Notable contributions include the development of benchmark datasets for hub location (Australia Post), convoy movement, and surgical scheduling, hosted on Mathprog-ORlib . His research aligns with UN Sustainable Development Goals, particularly in optimizing resource use and infrastructure planning.

Dimitris Paraskevopoulosمشاهده پروفایل

دانشیار

• Operations Research
• Logistics & Distribution Management
• Transportation
• +۴ مورد دیگر

Dimitris Paraskevopoulos is an Associate Professor (Reader) in Operations and Supply Chain Management at Bayes Business School, City, University of London. He holds a Chemical Engineering degree from the National Technical University of Athens, an MBA, and a PhD in Operational Research from the Athens University of Economics and Business. His research focuses on developing optimization methods for complex problems in manufacturing, service operations, and transportation logistics, including project scheduling, vehicle routing, and network design. He has developed decision support systems for industries and received international awards for his research. Teaching excellence has been a hallmark of Paraskevopoulos’ career, recognized through awards such as a Mary Tasker Award nomination and participation in the International Teachers Programme (ITP) at CEIBS. He teaches Business Analytics, Machine Learning, and Operations Management across MBA and executive education programs at Bayes. He previously served as Course Director for MSc programs in Operations and Supply Chain Management at the University of Bath and Bayes. Paraskevopoulos’ research contributions include over 20 peer-reviewed journal articles in top-tier journals such as Production and Operations Management , European Journal of Operational Research , and Transportation Research Part E . His work explores combinatorial optimization, scheduling algorithms, and metaheuristics. He has led projects funded by EPSRC and collaborated with institutions like the University of Southampton and Athens University of Economics and Business.

• Biomedical Engineering
• Medical Technologies
• Machine Learning
• +۴ مورد دیگر

Jacopo Secco is a Fixed-term Assistant Professor at the Department of Electronics and Telecommunications (DET) of Politecnico di Torino. He is affiliated with the College of Computer, Film, and Mechatronics Engineering and actively contributes to teaching in Bioengineering, Mechatronics, and Physics Engineering programs. PhD in Engineered Personalized Medicine (2025/26) Master's and Bachelor's courses in Electronics Fundamentals and Applications (2023-2025) His research bridges Biomedical Engineering and Electronics , focusing on Memristor-Based Neural Networks for wound classification, Machine Learning in diabetic ulcers, and 3D Analysis of skin pathologies. Recent publications highlight applications in clinical validation, neural network dynamics, and sensor technologies. Patents include tele-rehabilitation tracking , chronic wound infection detection , and skin disease monitoring using CMOS cameras and memristor-based systems. His projects include NeWoTS - Neo-Wound Thermo Scan (2025-2026).

• Public Transportation Optimization
• Electric Vehicle Scheduling
• Operations Research
• +۳ مورد دیگر

Dr. Marcel van Kooten Niekerk serves as an Assistant Professor in the Department of Information and Computing Sciences within the Faculty of Science at Utrecht University. His academic appointment focuses on the Simulation of Complex Systems subgroup, where he applies computational methods to solve challenging transportation problems. He also maintains a professional connection with the transportation industry as a Consultant Logistics and Innovation at Qbuzz BV, demonstrating his commitment to bridging academic research with practical applications in public transport. Van Kooten Niekerk's research spans two primary domains: transportation optimization and theoretical computer science. His more recent work centers on public transportation systems, particularly focusing on the integration of electric vehicles into existing public transit networks. His expertise encompasses complex scheduling challenges including electric bus operations, platform assignments across multiple stations, and the integration of timetabling with vehicle scheduling. Earlier in his career, he made significant contributions to theoretical computer science, particularly in graph theory and combinatorial optimization problems such as partitioning graphs into triangles. His publication record reveals a clear evolution from theoretical computer science toward applied transportation research, with a growing emphasis on sustainability. The most recent publications demonstrate sophisticated approaches to handling uncertainty in electric bus operations, optimizing platform assignments across multiple bus stations in Utrecht, and developing comprehensive frameworks for reliable and sustainable public transport systems. His work consistently applies operations research methodologies, particularly integer linear programming and stochastic optimization techniques, to solve real-world transportation challenges. Van Kooten Niekerk has established himself as a researcher who effectively bridges theoretical computer science with practical transportation applications. His work has gained significant attention, with some publications accumulating over 100 citations and reader captures, indicating substantial impact in the transportation research community. His doctoral thesis on optimizing for reliable and sustainable public transport represents a comprehensive contribution to the field of electric vehicle integration in public transportation systems.

• Integrated Optics
• Photonics
• Semiconductor Physics
• +۴ مورد دیگر

Lionel Bastard serves as a Lecturer at Grenoble INP - Phelma, where he leads the Physical Engineering for Photonics and Microelectronics (IPhy) department and the Master II Photonics and Semiconductors (PhSem) program. His academic affiliation is with the Institute of Microelectronics, Electromagnetism and Photonics - Hyperfrequency and Characterization Laboratory (IMEP-LaHC), specifically within the PHOTO research team. Dr. Bastard's research focuses on integrated photonics with particular expertise in glass-based optical systems. His primary research areas include lasers in integrated optics on glass , generation of millimeter and THz signals via optical channels , and integrated nonlinear optics . His work bridges fundamental optical phenomena with practical applications in telecommunications, sensing, and high-frequency signal generation. Analysis of his recent publications (2022-2024) reveals a strong trend toward advancing characterization techniques for semiconductor interfaces using second harmonic generation, while simultaneously developing integrated photonic solutions for millimeter-wave and THz applications. His work demonstrates consistent innovation in glass-based integrated photonics platforms, with increasing emphasis on reliability engineering through techniques like wafer bonding encapsulation. Throughout his career, Dr. Bastard has maintained a productive research output with publications spanning optics journals including Optics Letters, Applied Physics Letters, and Journal of Applied Physics, as well as conference presentations at major photonics venues like SPIE Photonics West and the International Conference on Transparent Optical Networks. As academic leadership, he directs both a specialized research department (IPhy) and a graduate program (PhSem), indicating his significant role in shaping photonics education and research direction at Grenoble INP. His collaborations span multiple institutions and research teams, particularly with Jean-Emmanuel Broquin and other members of the IMEP-LaHC laboratory. Dr. Bastard's laboratory work centers on the IMEP-LaHC PHOTO team facilities, where his group develops and characterizes integrated photonic devices on glass substrates. Current research directions include improving laser reliability through wafer bonding techniques and advancing optical characterization methods for semiconductor interfaces, suggesting continued innovation in both fundamental and applied photonics research.

• Theoretical Physics
• Computational Materials Science
• Electronic Properties of Materials
• +۳ مورد دیگر

Michele Re Fiorentin serves as a Fixed-term Assistant Professor in the Department of Applied Science and Technology (DISAT) at the Polytechnic University of Turin, where he is affiliated with the Institute of Fundamental Physics and Materials for Nanotechnology. His academic duties encompass teaching advanced courses such as Physics of electrified interfaces at the PhD level and foundational physics courses including Physics II for Engineering Physics students across multiple academic years up to 2025/26. His research program centers on theoretical and computational materials science , with primary interests in the electronic structure of nanomaterials, electrocatalysis for CO 2 conversion, and the optical properties of 2D materials. Employing ab initio quantum mechanical simulations and machine learning techniques, he investigates phenomena ranging from CO 2 reduction mechanisms on silver electrodes to defect engineering in tungsten disulfide monolayers, contributing to sustainable energy technologies and advanced material design. Analysis of his recent publications (2023-2025) reveals a pronounced focus on computational discovery of electrocatalysts for carbon dioxide valorization and the manipulation of quantum properties in low-dimensional systems. Key themes include voltage-dependent reaction kinetics, linear dichroism in monolayers, and strain engineering in van der Waals heterostructures, demonstrating an interdisciplinary approach that integrates physics, chemistry, and data science. Scientific Recognition: Learning to Teach (L2T) certification, Polytechnic University of Turin (June 2024) As a PhD supervisor, Re Fiorentin guides doctoral candidates in the Physics and Materials Science programs, currently mentoring Wei Wang (Materials Science, 39th cycle) and Michele Giovanni Bianchi (Physics, 38th cycle), while having successfully supervised Clara Salvini to completion (Materials Science, 35th cycle). His research is supported by collaborative grants investigating nanomaterials for energy applications and theoretical physics of matter. Within the Institute of Fundamental Physics and Materials for Nanotechnology, he actively participates in research teams focused on computational materials discovery, collaborating closely with senior scientists like Giancarlo Cicero and Francesca Risplendi on projects spanning electrocatalysis, 2D materials, and semiconductor physics.

• Optimization Models
• Systems and Control
• Cyber-Socio-Technical Systems
• +۳ مورد دیگر

Mauro R.U. Salazar Villalon is an Assistant Professor at Eindhoven University of Technology (TU/e) in the Department of Mechanical Engineering and co-affiliated with the Eindhoven AI Systems Institute (EAISI). He obtained his B.Sc. and M.Sc. from ETH Zurich, followed by a Ph.D. from ETH Zurich in collaboration with Ferrari Formula 1. After postdoctoral work at Stanford's Autonomous Systems Lab, he joined TU/e in 2020. Educational Background: B.Sc. & M.Sc. in Mechanical Engineering, ETH Zurich Ph.D. in Mechanical Engineering, ETH Zurich (with Ferrari F1) His research focuses on optimization models for cyber-socio-technical systems, addressing challenges in mobility, pandemics, and material design. He develops methods for sustainable transportation systems, including incentive schemes for user behavior alignment. Recent trends in his publications span hydrogen-based hybrid aero engines, ride-pooling network flows, battery-electric truck infrastructure, and electric vehicle powertrain optimization. His work integrates advanced mathematical modeling with real-world applications. Scientific Awards: ETH Medal (Master & PhD theses) Best Student Paper Awards (2018, 2022) Best Teacher Award (2021) Nominated for TU/e Young Researcher Award (2022) IEEE VPPC 2024 Best Paper Award He contributes to projects like Model Predictive Pandemic Control and National Growth Fund initiatives, while teaching courses on powertrain design, engineering optimization, and optimal control. Collaborations extend to Stanford University and the Ferrari Formula 1 team.