جست‌وجوی استاد و پژوهشگر

• Time Series Analysis and Forecasting
• Data Analysis/Data Science
• Machine Learning/Deep Learning
• +۳ مورد دیگر

Filipe R. Ramos is an Associate Professor at the Faculty of Social Sciences and Technology of the European University (FCST-UE), while also maintaining collaborative positions at ISCTE-Business School and the IPCB School of Technology (EST-IPCB). He serves as an integrated researcher at the Center for Statistics and Applications of the University of Lisbon (CEAUL), demonstrating his strong commitment to both academic teaching and research. His academic journey includes: PhD in Management, specializing in Quantitative Methods from ISCTE-Business School (2021) Postgraduate studies in online teaching and digital teaching skills from European University of Madrid SLU (2021) Master's degree in Teaching Mathematics from Nova University of Lisbon (2016) Diploma of Advanced Studies in Management from ISCTE-Business School (2014-2015) Trainer Certification from Scientific-Pedagogical Council for Continuing Education (2012) Master's degree in Financial Mathematics from University of Lisbon and ISCTE-Business School (2009-2011) Bachelor's degree in Mathematics (Teaching) from University of Lisbon (1999-2004) Ramos specializes in data analysis and mathematical modeling with three core research areas: Time Series Analysis and Forecasting (particularly applied to Economics, Management, and Finance); Data Analysis/Data Science; and Machine Learning/Deep Learning. His work bridges theoretical mathematical approaches with practical business applications, developing predictive models that address real-world challenges in e-commerce, finance, logistics, and consumer behavior. He has made significant contributions to understanding how digital transformation affects consumer engagement and business operations. His recent publications demonstrate a strong trend toward applying advanced machine learning and deep learning techniques to solve complex business problems. The research spans multiple domains including e-commerce trend analysis, leadership studies, logistics optimization, and financial forecasting. A notable pattern is the integration of traditional statistical methods with modern AI approaches to create more robust predictive models. His work frequently addresses practical business challenges with a strong methodological foundation, often published in Q1 and Q2 journals. Ramos has been actively involved in academic advising across multiple institutions: Doctoral Theses: 3 students currently in progress Master's Dissertations: 27 total (8 in progress, 19 completed) Master's Final Projects: 1 completed His teaching portfolio for the 2024/2025 academic year includes advanced courses such as Time Series Analysis and Forecasting, Business Analytics Research Seminar, and multiple supervised learning modules, reflecting his expertise in data science and machine learning applications.

• Graph algorithms
• Graph theory
• Vertex cover
• +۱۰ مورد دیگر

Christian Laforest is a Professor of computer science at Université Clermont Auvergne, affiliated with Clermont-Auvergne INP. He conducts research at LIMOS (French laboratory of computer science; associated with CNRS, UMR 6158) and teaches at ISIMA (French graduate engineering school focused on computing and applications). His primary research interests include: Graph algorithms and theory (vertex cover, independent dominating set, Steiner tree) Discrete optimization Approximation algorithms (worst case and average case analysis) Online algorithms and adaptive algorithms Random algorithms Multicriteria optimization/approximation Distributed algorithms Professor Laforest's publication record reveals a consistent focus on theoretical computer science with practical applications in network design. His recent work has explored domination problems, vertex cover algorithms, and independent domination set problems across various graph structures. He has made significant contributions to approximation algorithms, particularly in worst-case and average-case analyses, as well as online algorithms that must make decisions without complete future information. His research methodology often involves using Maple for algorithm testing and theoretical calculations on graphs. His notable scientific achievements include: Prix Tangente du meilleur article 2015 for "Sur les algorithmes en ligne" Article primé en 2014 for "Sur les algorithmes d'approximation" Professor Laforest has supervised numerous PhD students throughout his career, including Alexis Irlande, Christian Destré, Fabien Baille, Nicolas Thibault, François Delbot, Romain Campigotto, Raksmey Phan, Benjamin Momège, and Alexis Cornet. His research has been supported by various projects funded by CNRS, RNRT, and the French ANR, including Multipoints, AcTAM, ROM, ROM-EO, ALGOL, TODO, and SHAMAN. He is also active in scientific vulgarization through magazine articles, a published book on graph theory, and a dedicated YouTube channel.

David Shmoysمشاهده پروفایل

استاد

• Algorithms
• Discrete Optimization
• Stochastic Optimization
• +۴ مورد دیگر

David Shmoys is a Professor at Cornell University, affiliated with the School of Operations Research and Information Engineering and the Department of Computer Science . He co-authored the influential book The Design of Approximation Algorithms (2011), which won the INFORMS Lanchester Prize in 2013, and received the Daniel H. Wagner Prize in 2018 for his work on bike-sharing optimization. His research bridges theoretical computer science and operations research, focusing on Approximation algorithms for NP-hard optimization problems Stochastic and deterministic inventory models Computational sustainability applications Shmoys' recent publications highlight applications in COVID-19 college reopening strategies Algorithmic redistricting for fair political representation Optimization of bike-sharing systems Stochastic inventory control Network design for coflow scheduling His methodological work emphasizes linear programming relaxations and sampling-based approaches for uncertain environments. Scientific awards include ACM Fellowship INFORMS Fellowship SIAM Fellowship NSF Presidential Young Investigator Best Paper Prizes at SODA and other conferences He has advised 27 PhD students, many of whom hold faculty positions at institutions like MIT, Waterloo, and Brown, and serves on editorial boards for journals including Mathematics of Operations Research and Operations Research . Cornell Tech's Institute of Computational Sustainability benefits from his leadership as Associate Director.

• Graph Neural Networks
• Combinatorial Optimization
• Federated Learning
• +۳ مورد دیگر

Shiqing Liu is a researcher at the University of Bielefeld, affiliated with the Faculty of Engineering and the Cognitronics & Sensor Technology Group within the Center for Cognitive Interaction Technology (CITEC). Based at office CITEC 3-204, Liu contributes to the university's research in the Socio-Technical World domain, focusing on technologies that enable agents to act and communicate in complex environments. Dr. Liu's research spans several cutting-edge areas in artificial intelligence and optimization. Their work primarily focuses on graph neural networks, combinatorial optimization, and federated learning systems. They have made significant contributions to applying machine learning techniques to solve complex optimization problems including vehicle routing, facility location, and neural architecture search. Their research bridges theoretical computer science with practical applications in distributed systems and privacy-preserving technologies. An analysis of Dr. Liu's recent publications reveals a strong trajectory in developing unified frameworks that combine graph-based learning with combinatorial optimization. Their work increasingly addresses challenges in federated settings where data privacy and distribution heterogeneity present significant obstacles. The research demonstrates a progression from single-objective optimization problems toward more complex multi-objective scenarios, with growing emphasis on practical implementation constraints and real-world applicability. Dr. Liu is actively involved in the Cognitronics & Sensor Technology research group at CITEC, which is part of Bielefeld University's strategic focus on the Socio-Technical World. This center investigates how humans, robots, and AI systems can effectively interact and collaborate in complex environments, aligning with the university's broader mission of "Transcending Boundaries" between disciplines and between science and society.

• Logistics Optimization
• Industry 4.0
• Sustainability in Supply Chains
• +۵ مورد دیگر

Hello Tamas is an Associate Professor at the Department of Constitutional Law within the Faculty of Political Science and Law at the University of Miskolc, Hungary. He has served in this faculty since 2004, progressing from university assistant professor to associate professor in 2014, with continuous contributions to research and education. Education: Ságvári Endre High School (1995) Lawyer degree from University of Miskolc (2001) Ph.D. in Political Science and Law (2004) Research Focus: His work spans logistics optimization, Industry 4.0 integration, and sustainable operations management. Key areas include vehicle routing problems, milkrun supply systems, and cyber-physical logistics frameworks. Technological Specialties: Real-time maintenance optimization IoT applications in logistics Drone-based delivery systems Mathematical modeling for supply chains Sustainability in manufacturing Human-machine resource assignment Methodological Approach: Combines algorithmic optimization (Weiszfeld, Wagner-Whitin, Silver-Meal) with practical implementations across automotive manufacturing, food industry, and healthcare logistics.

• Artificial Intelligence
• Multi-Agent Path Finding
• Robotics
• +۴ مورد دیگر

Jiří Švancara is a Lecturer at the Department of Theoretical Computer Science and Mathematical Logic (KTIML) within the Faculty of Mathematics and Physics at Charles University in Prague. His academic career focuses on Artificial Intelligence, particularly Multi-Agent Path Finding (MAPF), where he has established himself as a prominent researcher with numerous publications in top-tier conferences. His research interests span across Multi-Agent Path Finding , Robotics , Algorithm Design , and Constraint Satisfaction Problems . Švancara has made significant contributions to the field of MAPF, developing novel approaches for large-scale maps, temporal uncertainty handling, and efficient solving methods. His work bridges theoretical foundations with practical applications in robotics and transportation systems. Analysis of his recent publications reveals a strong focus on improving the scalability and robustness of MAPF algorithms. His research explores graph pruning techniques for large maps, handling temporal uncertainty in path execution, and comparing different objective functions for optimization. He has also investigated applications of MAPF in autonomous intersections and train routing systems, demonstrating the practical relevance of his theoretical work. Best Paper Award for 'Multi-agent Path Finding on Real Robots: First Experience with Ozobots' at IBERAMIA 2018 Švancara actively teaches multiple courses including Introduction to Artificial Intelligence, Propositional and Predicate Logic, and Algorithms and Data Structures. His teaching spans both theoretical foundations and practical implementation, with students engaging in programming assignments that reflect current AI challenges. His research collaborations are extensive, with frequent co-authorship with Roman Barták and other researchers in the field, indicating strong integration within the international AI research community. His laboratory work involves practical implementations of MAPF algorithms, including testing on real robots as evidenced by several publications describing experiments with Ozobots and other robotic platforms. This hands-on approach connects theoretical research with tangible robotic applications, providing valuable validation for his algorithmic contributions.

Nadia Lahrichiمشاهده پروفایل

استاد

• Operational Research
• Healthcare Analytics
• Logistics
• +۴ مورد دیگر

Nadia Lahrichi is a Full Professor in the Department of Mathematics and Industrial Engineering at Polytechnique Montréal, where she holds a Tier 1 Canada Research Chair in Healthcare Analytics and Logistics (HANALOG). She serves as Deputy Director at CIRRELT and is an active member of multiple research centers including IVADO (Institute for Data Valorization) and the Interuniversity Research Center on Enterprise Networks, Logistics and Transport. Her research focuses on applying operational research and mathematical modeling techniques to healthcare systems, with particular emphasis on patient flow optimization, resource allocation, scheduling, and logistics. She has developed innovative approaches that integrate machine learning with traditional optimization methods to address complex healthcare challenges. Her work spans multiple healthcare domains including radiology, chemotherapy scheduling, emergency department operations, and pandemic response systems. Professor Lahrichi's publications demonstrate consistent growth in applying advanced analytics to healthcare problems, with recent work focusing on pandemic response systems (particularly related to COVID-19 testing in Nepal), integration of machine learning with optimization techniques, and resilience planning for healthcare systems. Her research bridges theoretical operations research with practical healthcare applications, resulting in tangible improvements to healthcare delivery systems. Tier 1 Canada Research Chair in Healthcare Analytics and Logistics (HANALOG) Deputy Director at CIRRELT Member of CIRRELT (Interuniversity Research Center on Enterprise Networks, Logistics and Transport) Member of IVADO (Institute for Data Valorization) Professor Lahrichi has supervised an impressive number of graduate students throughout her career, including 5 PhD students and 34 Master's students, demonstrating her commitment to training the next generation of researchers in healthcare analytics. Her research has been supported by significant grants that have enabled large-scale projects addressing critical healthcare system challenges. She collaborates extensively with healthcare institutions to ensure her research has direct practical applications and impacts real-world healthcare delivery systems.

Jeffrey W. Ohlmannمشاهده پروفایل

دانشیار

• Operations Research
• Logistics
• Sports Analytics
• +۳ مورد دیگر

Jeffrey W. Ohlmann is an Associate Professor and Director of Graduate Studies in Business Analytics at the Tippie College of Business, University of Iowa. He holds a PhD in Industrial and Operations Engineering from the University of Michigan and a BS in Mathematics from the University of Nebraska-Lincoln. Research focuses on sequential decision making under uncertainty, logistics optimization, and sports analytics 2022 Master of Business Analytics Instructor of the Year recipient 2010 Strategic Innovation Faculty of the Year award winner His recent publications address stochastic vehicle routing, crowdshipping dynamics, and social media applications in sports recruitment. Ohlmann has received significant recognition for teaching excellence and has presented at international conferences including INFORMS and Metaheuristics International Conference. Key research areas: Operations Research, Logistics, Sports Analytics, Stochastic Programming, Dynamic Programming Applications in college football recruiting, delivery systems, and lean production

Patrick Schmidtمشاهده پروفایل

پژوهشگر

• Geometry Processing
• Computer Graphics
• Mesh Processing
• +۳ مورد دیگر

Patrick Schmidt is a researcher in the Department of Computer Science at RWTH Aachen University, Germany, specializing in geometry processing and computer graphics. His work focuses on developing robust algorithms for surface mapping, mesh processing, and automatic differentiation, with significant contributions to both theoretical foundations and practical applications. He actively collaborates with the Computer Graphics group led by Prof. Leif Kobbelt, producing high-impact research published in premier venues including SIGGRAPH and Eurographics. His research spans Geometry Processing, Computer Graphics, and Computational Biology, with core interests in distortion-minimizing surface maps, mesh parametrization, and biomedical applications. Schmidt's innovative approaches include adaptive triangulation methods for bijective surface correspondence and lightweight automatic differentiation frameworks like TinyAD that simplify non-linear optimization in geometry processing. His interdisciplinary work on heart tube formation demonstrates how geometric techniques can advance biological understanding of mammalian organ development. Analysis of Schmidt's publication trajectory reveals a consistent focus on improving the robustness and efficiency of surface mapping techniques. His work progressively addresses challenges in distortion control, topological consistency, and computational performance, culminating in award-winning contributions like adaptive triangulation methods and constant-curvature metric representations. The recurring theme across his research is the unification of geometric theory with practical implementation, emphasizing reproducibility through open-source tools and Graphics Replicability Stamps. Scientific recognition includes: Günter Enderle Best Paper Award at Eurographics 2023 for pioneering adaptive triangulation techniques Consecutive Best Paper Awards at SGP 2022 and 2021 for TinyAD library and homology inference methods Honorable mention for Eurographics 2021 work on layout embedding optimization While specific student advising records aren't documented in public sources, Schmidt's role involves mentoring through collaborative research within RWTH Aachen's Computer Graphics group. His projects frequently secure institutional support for high-impact work, evidenced by consistent publication in top-tier venues and development of widely adopted tools like TinyAD. The group maintains strong industry and academic partnerships focused on advancing geometry processing fundamentals. As a core member of RWTH Aachen's Computer Graphics research team, Schmidt contributes to a vibrant ecosystem centered around Prof. Leif Kobbelt's lab. The team emphasizes both theoretical innovation and practical software development, with recent work bridging computer graphics and biomedical modeling. Current efforts focus on intrinsic optimization frameworks and expanding automatic differentiation applications, positioning Schmidt at the forefront of next-generation geometry processing techniques.

• Combinatorial Optimization
• Logistics Network Design
• Arc Routing Problems
• +۴ مورد دیگر

Maria João Cortinhal is an Associate Professor at ISCTE-IUL, serving as Director of ISCTE Business School since 2023 and holding various academic management positions including coordinator of the third year of the Bachelor's in Management, elected member of the Scientific Council and General Council, and department director. She is affiliated with the Department of Quantitative Methods for Management and Economics at ISCTE Business School. Director of School of Management (2023-2027) President of Scientific Committee (2023-2027) Director of Institutional Course at School of Management (2023-2024) Former Director of School of Management (2019-2023) Former President of Scientific Committee (2019-2023) Her educational background includes a PhD in Statistics and Operational Research from Faculty of Sciences - UL (2005), Master's degree in the same field (1994), and Bachelor's degree (1990). Dr. Cortinhal's research focuses on combinatorial optimization problems, with special emphasis on logistics network design, sectorization and route design, using metaheuristics. Her work spans supply chain management, vehicle routing, waste collection optimization, and energy systems. She has participated as a researcher in FCT-funded projects, including the definition of logistics models for forest biomass supply and expansion of the national network of Justices of the Peace. Her research has evolved from fundamental facility location problems to applied logistics and sustainability projects. Her scientific production shows a strong focus on operational research with applications in logistics and sustainability. The publications demonstrate progression from theoretical optimization problems to practical applications in waste management, supply chain design, and renewable energy systems. Recent work increasingly addresses sustainability challenges in energy systems while maintaining strong methodological foundations in combinatorial optimization. Dr. Cortinhal has supervised 2 doctoral theses to completion, 2 master's dissertations in progress, 6 completed master's dissertations, and 4 master's final projects. Her students' work reflects her research interests, with topics spanning energy sustainability, logistics optimization, community detection in networks, and business applications of operations research. She has contributed to numerous national projects including development of methodologies for vehicle routing for collecting valuables (2016-2019) and planning household waste collection for Seixal municipality (2012-2015). Her external engagement includes media appearances where she discusses management education, economic development through education, and the role of business schools in preparing future executives.

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.

Aysun Bozantaمشاهده پروفایل

استادیار

• Reinforcement Learning
• Natural Language Processing
• Data Science
• +۴ مورد دیگر

Aysun Bozanta is an Assistant Professor at Bogazici University, specializing in data-driven research with applications in reinforcement learning, NLP, and social network analytics. She holds a Ph.D. in Management Information Systems from Bogazici University and completed postdoctoral research at Toronto Metropolitan University's Data Science Lab. Education Undergraduate: Işık University, Mathematical Engineering, MIS (DM) M.A.: Boğaziçi University, MIS Ph.D.: Boğaziçi University, MIS Postdoc: Toronto Metropolitan University - Data Science Lab Research Focus Her work bridges machine learning and real-world problems, with emphasis on: reinforcement learning for logistics (food delivery, courier routing), NLP (sentiment analysis, LLM evaluation), and social media analytics (misinformation, public policy responses). Epidemiologic modeling of COVID-19 interventions and recommender systems are additional key areas. Publication Trends Recent articles (2021-2025) reveal a strong focus on applied AI: reinforcement learning optimizes delivery systems; NLP research evaluates Turkish language models and classifies software requirements; COVID-19 studies model policy impacts; and social media analyses explore misinformation propagation. Transformer architectures and replication studies feature prominently. Awards & Recognition Best Paper Award at CASCON'21 for transformer-based requirements classification TUBITAK Post-Doctoral Research Fellowship (2019-Present) Projects & Collaboration Toronto Metropolitan University Project: "Intraday Trading Data Analytics with Financial News from Twitter" (2019-Present, TUBITAK-funded) Toronto Metropolitan University Project: "Getir-Courier Assignment by Reinforcement Learning" (2019-2020) Erasmus+ Project: "Online Vocational Education Platform" (2017-2019) Boğaziçi University Project: "Industry 4.0 Standardization Roadmap for Turkey" (2017-2019) Boğaziçi University Project: "Context-Aware Location Recommender System" (2016-2019) Labs & Teams Affiliated with Toronto Metropolitan University's Data Science Lab during postdoctoral work, focusing on reinforcement learning and NLP projects.

Jay Howellمشاهده پروفایل

دانشیار

• Problem-based inquiry
• Qualitative methods
• Design-based research
• +۶ مورد دیگر

Dr. Jay Howell is an Associate Professor at the School of Education at the University of Southern Mississippi. He serves as Interim Associate Director and Program Coordinator for Secondary Alternate Route Licensure Programs . Based in Hattiesburg, he contributes to educator training and curriculum development. Bachelor's Degree - Auburn University (2001) Master's Degree - Auburn University (2007) PhD - Auburn University (2014) Dr. Howell's research focuses on problem-based inquiry , design-based research , and visual curriculum materials integration. His work advances teacher education through innovative methods like scaffolded lesson study and inquiry-based instruction in social studies. His recent publications demonstrate trends in geography education (2018-2022), historical inquiry (2017-2020), and teacher professional development (2017-2022). Key themes include authentic pedagogy , civic education , and visual resource design . Honor Society: Phi Kappa Phi As Program Coordinator for Secondary Alternate Route Licensure , he develops pathways for teacher certification while maintaining active research in curriculum & teaching through his personal website . Contact: James.B.Howell@usm.edu | 601.266.5810

• Operations Research
• Quantitative Methods for Business and Economics
• Multiobjective Optimization
• +۳ مورد دیگر

Concepcion Paralera Morales is an Associate Professor at the Universidad Pablo de Olavide, affiliated with the Department of Economics, Quantitative Methods and Economic History. Her research focuses on Quantitative Methods for Business and Economics within the "Quantitative Methods in Business and Economics" research group. PhD in Economics from Universidad Pablo de Olavide (2005) Supervised by Dr. Rafael Caballero Fernández and Dr. Flor María Guerrero Casas Her work primarily addresses multiobjective optimization problems in operational research, particularly location routing and waste management. She has applied metaheuristics like tabu search to real-world case studies in Andalusia, incorporating both economic and social criteria in decision-making processes. Key trends in her publications (2005-2007) include: multicriteria approaches for infrastructure location, integration of equity considerations in risk assessment, and combinatorial optimization techniques. Her research intersects operations research (90-XX) with applications in environmental policy and industrial logistics.

• Distributed Stream Processing
• Data Stream Analytics
• Database Optimization
• +۳ مورد دیگر

Dr. Boris Novikov is a prominent Russian computer scientist affiliated with St Petersburg University and the Higher School of Economics . His research focuses on distributed stream processing, temporal databases, and XML query optimization. Key collaborators include Artem Trofimov , Elena Mikhailova , and Anna Yarygina . Research Areas : Distributed Systems (stream processing, substream management) Database Optimization (vertical partitioning, XML caching) Temporal Data Management Machine Learning (concept drift detection) His recent work on distributed stream processing (2023-2022) explores substream bounding, deterministic models, and resource allocation strategies. Earlier contributions (2012-2008) focused on XML query performance, similarity indexing, and access control frameworks. Technical Trends : Event time alignment in distributed architectures Dynamic residual projection for cybersecurity Bi-objective optimization in database systems Speculative execution with conflict resolution Incremental Lagrangian dual solutions Matrix clustering for partitioning problems