جست‌وجوی استاد و پژوهشگر

• Forest Economics
• Climate Change Adaptation
• Risk Analysis
• +۴ مورد دیگر

Dr. Rasoul Yousefpour is an Assistant Professor (Akademischer Rat a. Z.) at the Chair of Forestry Economics and Forest Planning, Faculty of Environment and Natural Resources, University of Freiburg, Germany. He has been serving in this position since October 2014. Prior to this, he worked as a Postdoc Scientist at the Max-Planck-Institute for Meteorology in Hamburg and at the Danish Forest and Landscape Centre, University of Copenhagen. His educational background includes: Ph.D. (Dr. rer. nat.) in "Risk Management in Forestry" from the University of Freiburg (2005-2009) MSc in Forestry and Forest Economics from the University of Tehran, Iran (2000-2002) BSc in Natural Resources Engineering-Forestry from the University of Guilan, Iran (1996-2000) Dr. Yousefpour's research focuses on Operations Research in Forestry, including Optimization, Forest Modelling and Decision-making, Climate Change Risk Management, and Adaptive Forest Management. His work examines the Economic Implications of provisioning Ecosystem Goods and Services, particularly related to Biodiversity and Carbon. He also investigates Risk Analysis, Uncertainty Analysis, and Insurance in the context of forest management under climate change. His research integrates theories from Ecophysiology, Carbon cycle, Forest growth, Forest economy, and Decision science, using methods such as Data acquisition, Modelling, Simulation, and Optimization. An analysis of Dr. Yousefpour's recent publications reveals a strong focus on climate change adaptation in forestry, with particular emphasis on risk management and decision-making under uncertainty. His work spans multiple disciplines including Forest Economics, Climate Science, and Decision Theory. The research often employs sophisticated modeling techniques to address complex forestry challenges, particularly related to biodiversity conservation, carbon sequestration, and economic optimization under changing climate conditions. Many studies adopt a multi-criteria approach that balances ecological, economic, and social objectives in forest management. Dr. Yousefpour is actively involved in numerous research projects including EU Cost Action "PESFOR-W", EU Cost Action "CONVERGES", SNF NRP 73 "Sustainable Economy" Project "DIVES", and the EU Marie Skłodowska-Curie RISE action "SuFoRun" which he coordinates. He also serves as Coordinator of the IUFRO Working Group 4.04.07 "Risk Analysis" and as Associate Editor for the Annals of Forest Science.

Haluk Topcuogluمشاهده پروفایل

استاد

• Multicore Architectures
• Parallel Algorithm Design
• Fault Tolerant Computing
• +۵ مورد دیگر

Haluk Topcuoglu is a Full Professor in the Department of Computer Engineering at Marmara University's Faculty of Engineering. He holds a PhD from Syracuse University (1999) and has been at Marmara University since 1999, progressing through roles as Assistant, Associate, and Full Professor. His research focuses on multicore architectures, parallel algorithms, fault tolerance, dynamic optimization, and hybrid evolutionary algorithms. He has led multiple funded projects, including TUBITAK initiatives on reliability optimization and sensor placement. Education: PhD in Computer Science (Syracuse University, 1999), MSc and BSc in Computer Engineering (Boğaziçi University, 1993 and 1991). Research Interests: Task scheduling for multicore systems, reliability-aware computing, dynamic optimization, and applications of evolutionary algorithms in cloud/fog computing. His work emphasizes balancing performance, energy efficiency, and fault tolerance in parallel systems. Awards include the 2008 IBM Faculty Award, 2010 IEEE ISDA Best Paper Award, and Marmara University's 2012 Publication Impact Award. He has supervised 4 PhD and 19 MSc theses, with ongoing research in machine learning-assisted metaheuristics and dynamic monitor selection. Editorial roles include Cluster Computing Journal (SCI-Expanded) and Journal of Aeronautics and Space Technologies. He teaches courses on evolutionary computing, multicore computing, and parallel processing. Current projects include cross-layer reliability frameworks and scheduling algorithms for fog computing, reflecting his focus on industry-relevant computational challenges.

Vahid Seydiمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Data Science
• Deep Learning
• +۶ مورد دیگر

Vahid Seydi is a Research Fellow in Data Science and Machine Learning at the School of Ocean Sciences, Bangor University, UK. He previously served as an Assistant Professor (2014–2020) and award-winning Lecturer (2010–2014) at Azad University South Tehran Branch, Iran. He holds a PhD in AI (2014) from Azad University, with prior M.Sc. (2007) and B.Sc. (2005) degrees in Computer Science and AI. Education : PhD in Artificial Intelligence (2014), Azad University M.Sc. in Artificial Intelligence (2007), Science and Research University B.Sc. in Software Engineering (2005), Azad University Research Interests : Deep Learning, Domain Adaptation, Generative Models, Explainable AI, Reinforcement Learning, Optimization, and applications in marine renewable energy and environmental sustainability. Key Contributions : His work focuses on ML-driven solutions for offshore energy systems, including predictive digital twins, passive acoustic monitoring, and domain adaptation for ecological impact analysis. He has contributed to projects like the SEEC Centre and EcoWind-ACCELERATE initiative. Awards : Global Talent Endorsement (UK Royal Society, 2023) Zillow Kaggle Competition Silver Medal (2017, top 2%) Excellence in Teaching Award (Azad University, 2014–2019) Grants & Projects : Smart Efficient Energy Centre (SEEC), 2019–2023 EcoWind-ACCELERATE, 2022–2026 Morlais Demonstration Zone, 2022–2024 Labs/Teams : Active in Bangor’s Ocean Sciences research groups, focusing on ML integration with marine energy and ecological data systems.

Alexander Schwingمشاهده پروفایل

دانشیار

• Machine Learning
• Computer Vision
• Robotics
• +۷ مورد دیگر

Alexander Schwing is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign (UIUC), with affiliations to the Coordinated Science Laboratory and the Computer Science Department. His research focuses on machine learning, computer vision, and structured prediction, emphasizing algorithms for deep networks, multivariate distributions, and 3D scene understanding. He has held postdoctoral positions at the University of Toronto and completed his PhD at ETH Zurich. Educations: PhD in Computer Science (ETH Zurich, 2014) Diploma in Electrical Engineering & IT (Technical University of Munich, 2010) Research interests include generative modeling, embodied agents, video segmentation, and reinforcement learning. He has developed influential frameworks like XMem for video object segmentation and MaskRNN for instance-level tracking. His work emphasizes reproducibility and open-source releases. Key awards include the NSF CAREER Award, Amazon Research Award, and NVIDIA GPU donations. He has advised over 25 students, many of whom have pursued roles at top tech firms and academia. Current research explores structured prediction, multi-agent systems, and 3D reconstruction. His labs collaborate with industries like Samsung and Adobe, and he teaches courses on machine learning and pattern recognition.

Nancy M. Amatoمشاهده پروفایل

استاد

• Robotics
• Computational Biology
• Parallel and Distributed Computing
• +۳ مورد دیگر

Nancy M. Amato is the Abel Bliss Professor of Engineering and Director of the Siebel School of Computing and Data Science at the University of Illinois Urbana-Champaign (UIUC), within the Grainger College of Engineering. She holds degrees from Stanford (B.S. in Mathematical Sciences and Economics), UC Berkeley (M.S. in Computer Science), and UIUC (Ph.D.). Her leadership roles include CRA Board Chair, IEEE RAS President Elect, and ACM Council Member-at-Large. Amato is a Fellow of AAAI, AAAS, ACM, and IEEE, and a Member of the American Academy of Arts and Sciences. Her research focuses on robotics (task/motion planning, multi-robot systems), computational biology (protein-drug interactions), parallel computing (STAPL project collaboration with Lawrence Rauchwerger), and computational geometry. She pioneered sampling-based motion planning for robotics and computational biology, including protein folding studies. Current projects include the NSF Expeditions 'Mind in Vitro' exploring biological neuron-based computing. Amato has mentored 29 PhD students (many in academia/industry), 30+ masters, 100+ undergrads, and 10+ high school students, prioritizing underrepresented groups. She co-leads the CRA-WP Distributed REU program, fostering diversity in computing. Awards include the IEEE RAS Saridis Leadership Award (2019) and UC Berkeley's Distinguished CS Alumnus Award (2024).

• Healthcare Operations
• Operations Research
• Queueing Theory
• +۳ مورد دیگر

Rene Bekker is an Associate Professor in the Department of Mathematics at the Vrije Universiteit Amsterdam (Faculty of Science). His research focuses on healthcare operations, stochastic modeling, and optimization techniques applied to healthcare logistics and scheduling. He actively collaborates with Tonos Care as a partner since 2019, bridging academic research with practical healthcare challenges. Key research interests include queueing theory, appointment scheduling systems, home healthcare routing, and capacity planning in nursing homes. Notable contributions involve integrating queueing-based approaches with optimization algorithms to address complex healthcare operational problems. Recent work emphasizes adaptive strategies for resource allocation and staffing in multitasking service systems. His publications often explore the intersection of mathematical modeling and real-world healthcare systems, addressing issues like patient preference allocation, reduced waiting times, and efficient staff scheduling. While his profile does not explicitly mention awards, his extensive contributions to healthcare operations research are evident through over 45 peer-reviewed articles and supervised PhD theses. Bekker’s teaching spans courses such as Applied Stochastic Modeling and Stochastic Modelling, reflecting his expertise in connecting theoretical frameworks with practical applications. Ancillary activities and collaborations highlight his commitment to translating academic research into actionable solutions for healthcare institutions.

Christine Ann Shoemakerمشاهده پروفایل

استاد

• Environmental Engineering
• Optimization Algorithms
• Computational Mathematics
• +۴ مورد دیگر

Christine Ann Shoemaker is the Joseph P. Ripley Professor of Engineering Emerita at Cornell University's College of Engineering, holding a position in the Department of Civil and Environmental Engineering. Her academic career spans over five decades, marked by pioneering contributions to optimization algorithms and their environmental applications. She earned her B.S., M.S., and Ph.D. in Mathematics from the University of California, Davis, and the University of Southern California, respectively. Shoemaker's research focuses on developing computationally efficient optimization methods for solving complex environmental challenges, including groundwater remediation, climate modeling, and carbon sequestration. Her work integrates high-performance computing and surrogate models to address large-scale, data-intensive problems. She has held significant leadership roles, including being Cornell Engineering’s first tenured woman faculty member and the first woman department chair. Her awards include membership in the National Academy of Engineering, the INFORMS Harold Hotelling Medal, and the SIAM Geosciences Career Prize. Shoemaker’s international collaborations, such as the UN-sponsored groundwater contamination initiative in developing countries, highlight her commitment to global environmental sustainability. She currently works part-time, continuing her research while mentoring students and contributing to interdisciplinary projects. Education : B.S. Mathematics (UC Davis, 1966), M.S. Mathematics (USC, 1969), Ph.D. Mathematics (USC, 1971) Key Contributions : Development of asynchronous parallel optimization algorithms, climate model parameterization, and green infrastructure optimization Awards : National Academy of Engineering, INFORMS Fellow, AGU Fellow, Distinguished Educator Award (Society of Women Engineers) Her research bridges computational mathematics and environmental science, with applications in hydrology, ecology, and water resource management. She has advised numerous Ph.D. students across disciplines and advocates for women in STEM.

• Multi-objective Stochastic Optimization
• Supply Chain Management
• Operations Research
• +۵ مورد دیگر

BEN ABDELAZIZ Fouad is a Distinguished Professor at NEOMA BS, France, and Head of the M.Sc. in Artificial Intelligence for Business. He holds a PhD in Operations and Decision Systems from Laval University, Canada, and was a Senior Fulbright Scholar at Rutgers University. His research focuses on multi-objective stochastic optimization, supply chain management, and decision support systems, with notable contributions to portfolio selection, metaheuristics, and resilient supply chains. He has served as Guest Editor for leading journals like Annals of Operations Research and has consulted for industries including chemical and logistics sectors. Education: PhD in Operations and Decision Systems, Laval University, Canada HDR (Authorization to Supervise Research), Management, Quantitative Methods Research Interests: Multi-objective stochastic optimization Supply chain resilience and optimization Artificial Intelligence applications in business Portfolio selection models Metaheuristic algorithms Recent Contributions: His recent work addresses cutting-edge topics such as many-objective optimization algorithms (e.g., MGDE), coalition formation in supply chains, and AI-driven decision tools for perishable food management. Articles in top journals like Annals of Operations Research and European Journal of Operational Research highlight his focus on balancing theoretical rigor with practical industry applications. Grants & Awards: Senior Fulbright Scholar (Rutgers Center for Operations Research) Accessed for Dubai Business Award Labs & Teams: He leads key academic programs at NEOMA BS, including the Doctoral School and MSc in Supply Chain Management, fostering interdisciplinary research collaboration.

• Data Privacy
• Evolutionary Computing
• Recommendation Systems
• +۵ مورد دیگر

Dr. Jinli Cao is a full-time Associate Professor in the Department of Computer Science and Information Technology at La Trobe University. She holds a BSc from Hebei University, China, and a PhD from the University of Southern Queensland, Australia (1997). Her research focuses on evolutionary computing, data privacy, deep learning for vulnerability assessment, and decision support systems. She has published over 150 papers in top venues such as VLDB and IEEE Transactions series. Dr. Cao leads an ARC-funded project on software vulnerability risk discovery and has secured three ARC grants. She has supervised 11 PhD, 2 Master’s, and 57 Honours students, many of whom work in academia and industries like Oracle and Commonwealth Bank. Teaching contributions include developing courses in databases, data warehouses, and artificial intelligence. Research Interests: Privacy-preserving data publishing and optimization Evolutionary algorithms for dynamic data partitioning Deep learning applications in cybersecurity and healthcare Graph-based machine learning for access control and anomaly detection Decision support systems and top-k query processing Her recent articles explore cutting-edge topics like privacy-preserving spatial crowdsourcing tasks, graph neural networks for traffic prediction, and cybersecurity frameworks for vulnerability prioritization. Awards include competitive ARC grants totaling $450,000 (2023-2025). She actively serves as an Associate Editor for Health Information Science and Systems and has examined over 100 PhD theses across Australian universities. Teaching highlights: Developed 20+ courses including Database Management Systems, Data Warehousing, and Artificial Intelligence. Coordinates units like Decision Support Systems and Intermediate Programming in Java.

• Quantum Computing
• Quantum Information Theory
• Quantum Error Correction
• +۳ مورد دیگر

Harold OLLIVIER is a Researcher affiliated with the École Normale Supérieure (ENS) in Paris, France, and the INRIA Mathematics department . His research focuses on Quantum Information Theory , Quantum Computing , and Quantum Error Correction , with applications to secure delegated quantum computations, error mitigation, and foundational studies of quantum systems. He contributes to both theoretical advancements and practical implementations of quantum protocols, emphasizing verification and security in quantum networks. His work spans topics such as composably secure delegation of quantum computations, probabilistic error cancellation, and the role of coherence in quantum advantage. Notably, he has explored frameworks for quantum verification, unified protocol design, and the emergence of objectivity in quantum many-body systems. His research bridges theoretical insights with experimental feasibility, particularly in the NISQ (Noisy Intermediate-Scale Quantum) era. OLLIVIER collaborates with institutions like the Kastler Brossel Laboratory and the ENS Physics Department, contributing to advancements in quantum networking, error correction codes, and foundational quantum mechanics. His publications emphasize practical tools for optimizing quantum protocols and addressing challenges in real-world quantum systems.

• Machine Learning
• Materials Science
• Physics
• +۳ مورد دیگر

Qian Yang is an Assistant Professor in the Computer Science and Engineering Department at the University of Connecticut (Storrs). Her research focuses on computational science applied to physical sciences, particularly machine learning for materials, physics, and chemistry. She holds a Ph.D. in Computational and Mathematical Engineering from Stanford University and a B.A. in Applied Mathematics/Computer Science from Harvard College. Before joining UConn, she was a postdoctoral scholar at Stanford's Materials Computation and Theory Group. Her work emphasizes developing scalable models from molecular dynamics data, model reduction for nonlinear systems, and addressing challenges in scientific datasets (e.g., small, clustered, or imbalanced data). Research Interests : Machine Learning for Materials Science, Data-Driven Modeling, Uncertainty Quantification, Chemical Reaction Networks, and Multiscale Simulation. Awards : Best Instructor Award (Stanford, 2015/2016), Best Student/Postdoc Presentation Award (Materials Research Society, 2015). Teaching : Taught courses on Machine Learning for Physical Sciences, Computer Architecture, and computational methods at UConn and Stanford. Known for interdisciplinary approaches and student engagement. Labs/Teams : Leads research on computational modeling and hosts the Materials and Megabytes podcast, featuring interdisciplinary discussions with leading researchers.

Seán McLooneمشاهده پروفایل

استاد

• Intelligent Systems
• Deep Learning
• Manufacturing Competitiveness
• +۴ مورد دیگر

Seán McLoone is a Professor at Queen's University Belfast's School of Electronics, Electrical Engineering and Computer Science. He leads the Energy Power and Intelligent Control (EPIC) Research Cluster and directs the Centre for Intelligent Autonomous Manufacturing Systems (i-AMS). His research focuses on intelligent systems, data-driven modeling, and applications in manufacturing, energy, and robotics. He holds IEEE Senior Membership and IET Fellowship. Education: MEng (Distinction) and PhD in Control Engineering from Queen's University Belfast (1992-1996), with a Postgraduate Certificate in Higher Education Teaching (2000). He previously served as Head of Electronic Engineering at NUI Maynooth (2009-2012). Research interests include deep learning for manufacturing and energy systems, autonomous robotics, and adaptive optimization. He collaborates extensively with industry on projects like sensor characterization, predictive maintenance, and smart grid analytics. Supervised 28 PhD students, many in roles like Senior Consultant at PSC or Lead at Novartis. Awards include Best Paper at W2GIS 2013 and ISSC 2014. Serves as Associate Editor for Transactions of the Institute of Measurement and Control and on Engineering Applications of Artificial Intelligence's editorial board. Active in professional roles like UKRI IEEE Section Chairman and accreditation committees. Labs/Teams: EPIC Research Cluster, i-AMS Centre Projects: Over 20 projects, including smart manufacturing education hubs and energy system modeling.

Henry Hanمشاهده پروفایل

استاد

• Data Science
• Fintech
• Artificial Intelligence
• +۷ مورد دیگر

Dr. Henry Han is a Professor and holds the McCollum Family Chair in Data Science at Baylor University’s Hankamer School of Business, Department of Data Science. His interdisciplinary research spans data science, fintech, artificial intelligence, bioinformatics, health informatics, cybersecurity, and quantum computing. PhD, Applied Mathematical & Computational Sciences, University of Iowa (2004) MS, Computer Science, University of Iowa (2001) MS, Applied Mathematics, University of Iowa (2001) Dr. Han’s research integrates advanced machine learning, optimization, and data analytics techniques to solve complex problems in finance, healthcare, and cybersecurity. His work emphasizes manifold learning , deep neural networks , evolutionary computation , and graph-based learning for real-world applications such as high-frequency trading, automobile damage classification, and single-cell genomics. He applies these methods across domains including financial modeling, biomedical data analysis, and sports forecasting. His recent publications demonstrate a strong trend toward interdisciplinary machine learning , with applications in computational finance, bioinformatics, and intelligent systems. Many of his works utilize optimization-based deep learning and unsupervised representation learning to extract meaningful patterns from high-dimensional data. Dr. Han has received recognition through prestigious appointments: McCollum Family Chair in Data Science He actively collaborates on research projects and has secured funding for advanced data science initiatives, though specific grant details are not listed. His work supports both academic advancement and practical innovation in data-driven decision-making. He advises graduate students and contributes to the development of next-generation data science methodologies. Dr. Han is a key member of the data science research team at the Hankamer School of Business, contributing to interdisciplinary labs and research groups focused on fintech, AI, and health informatics.

Assaf Zeeviمشاهده پروفایل

استاد

• Operations Research
• Statistics
• Machine Learning
• +۹ مورد دیگر

Assaf Zeevi is the Kravis Professor of Business at Columbia University's Graduate School of Business (GSB), specializing in Decision, Risk, and Operations. He holds leadership roles, including former Vice Dean at Columbia Business School and Editor-in-Chief of Stochastic Systems . His research focuses on Operations Research, Statistics, and Machine Learning, with applications in dynamic pricing, healthcare analytics, and recommendation systems. Education: B.Sc. and M.Sc. (Cum Laude), Technion, Israel Ph.D., Stanford University Research Interests: Dr. Zeevi develops theory and algorithms for reinforcement learning, bandit problems, stochastic optimization, and statistical learning. His work bridges theoretical foundations with practical applications in online platforms, healthcare, and revenue management. Awards: 2019 Lanchester Prize NSF CAREER Award IBM Faculty Award Google Research Award Advising & Grants: He has mentored over 14 doctoral students, many now leading academics and industry professionals. His grant-funded research includes projects on secondary spectrum markets, differentiated services design, and stochastic modeling. Funders include NSF, IBM, Google, and the Binational Science Foundation. Labs & Teams: Collaborates across academia and industry, serving on editorial boards (e.g., Operations Research ) and scientific advisory boards for tech startups. Active in INFORMS prize committees and global innovation initiatives.

Nazrul Islamمشاهده پروفایل

پژوهشگر

• Evolutionary Optimization
• Bayesian Optimization
• Multi and Many-Objective Optimization
• +۷ مورد دیگر

Dr. Nazrul Islam is a Researcher in the Department of Computer Science at the University of York , specializing in optimization algorithms and machine learning. His research spans Evolutionary Optimization , Bayesian Optimization , and Uncertainty Quantification , with applications in surrogate models, LLMs, and NLP. Education: Ph.D. in Computer Science (2021), Queen Mary University of London MRes in Computer Science, London South Bank University BEng in Computer Science, London South Bank University Research Focus: Multi and Many-Objective Optimization Robust Optimization and Algorithm Benchmarking Machine Learning techniques (surrogate models, LLMs, NLP)