جست‌وجوی استاد و پژوهشگر

• Graph Mining
• Network Analysis
• Graph Representation Learning
• +۳ مورد دیگر

Yifei Jin is a WASP Industrial PhD student at KTH Royal Institute of Technology's School of Electrical Engineering and Computer Science, specifically within the Division of Theoretical Computer Science. They are supervised by Professor Aristides Gionis and Associate Professor Sarunas Girdzijauskas at KTH, and also serve as an Experienced Researcher at Ericsson Research and a Visiting Researcher at Yale University under Rex Ying and Leandros Tassiulas. Yifei's research focuses on graph mining , network analysis , and graph representation learning , particularly applied to crowdsourcing data and wireless communication systems. Their work intersects telecommunications network optimization machine learning for graph-structured data AI-driven wireless resource management edge computing and distributed AI as evidenced by their publications spanning 2017–2025. Their academic contributions include 15 recent papers exploring topics such as neural surrogates for voltage drop estimation, wireless ray-tracing models, scalable distributed AI deployment, and vehicle platooning coordination. These publications demonstrate expertise in network traffic reduction KPI conflict analysis graph convolutional networks hyperbolic embeddings for ontologies real-time network diagnostics .

• Machine Learning
• Applied Mathematics
• Computer Vision
• +۶ مورد دیگر

Javier Martinez Torres is a Full-Time Professor in the Department of Applied Mathematics I at the School of Industrial Engineering , University of Vigo. His work bridges machine learning, applied mathematics, and functional data analysis across diverse domains. Education : PhD from University of Vigo (2011) with thesis on slate plate classification using computer vision and machine learning. Research Interests focus on: Machine learning applications in industrial engineering and healthcare Multi-criteria decision-making for sustainable urban planning Functional data analysis for environmental monitoring AI-driven optimization of thermal and mechanical systems Recent publications highlight his interdisciplinary approach, combining machine learning with operations research for pedestrian modeling, grey MCDM in urban development, and LSTM networks for solar energy systems. His work spans public health (SARS-CoV-2 pooling strategies) and digital humanities (AI for document heritage management). He collaborates with the Center for Research in Technologies, Energy, and Industrial Processes at the Vigo campus.

Christophette Blanchet-Scallietمشاهده پروفایل

مدرس

• Applied Probability
• Statistics
• Stochastic Processes
• +۴ مورد دیگر

Christophette Blanchet-Scalliet is a Lecturer in the Department of Mathematics and Computer Science at École Centrale de Lyon, affiliated with the Camille Jordan Institute (UMR CNRS 5208). She obtained her doctorate in 2001 and her Habilitation to Supervise Research in 2016. Currently serving as Director of the Mathematics and Computer Science Department and Head of the Dual Diploma program at Centrale Lyon-ENSAE, she has been active in academia since 2002, with positions at both École Centrale de Lyon (since 2007) and the University of Nice Sophia-Antipolis (2002-2007). Her research spans applied probability , statistics , stochastic processes , stochastic control , Kriging , and robust optimization . She has developed significant expertise in Ornstein-Uhlenbeck processes, backward stochastic differential equations, Hamilton-Jacobi-Bellman equations, and applications in financial mathematics. Her work bridges theoretical probability with practical applications in risk assessment, insurance, and optimization under uncertainty. Analysis of her recent publications reveals a consistent focus on stochastic processes and their applications, with increasing emphasis on computational methods, sensitivity analysis, and optimization techniques. Her research shows strong connections between theoretical probability and practical applications in finance, insurance, and environmental risk assessment, with growing interdisciplinary collaboration across mathematical fields. Dr. Blanchet-Scalliet has supervised five doctoral students since 2015: Mélina Ribaud (2015-2018), Laura Gay (2016-2019), Thierry Gonon (2019-2022), Benoit Nieto (2021-2024), and Noé Fellmann (2021-2024), typically in co-supervision with colleagues from related research groups. She has secured significant research funding through multiple projects including the CIROQUO Consortium (2020-2028), ANR DREAMES (2021-2025), ANR Oquaido Chair (2015-2020), and others. She is actively involved in the CIROQUO research consortium as co-leader, which brings together multiple academic institutions and industry partners including École Centrale de Lyon, Mines Saint-Etienne, University of Toulouse 3, Stellantis France, BRGM, CEA, IFP Energies Nouvelles, and others to advance research in uncertainty quantification and optimization.

Viknesh Andiappanمشاهده پروفایل

دانشیار

• Net-Zero Energy Supply Chains
• Net-Zero Energy Systems
• Process Integration & Optimisation
• +۳ مورد دیگر

Dr. Viknesh Andiappan serves as Associate Professor in Chemical Engineering and Associate Director of the School of Research at Swinburne University of Technology, Sarawak Campus, within the Faculty of Engineering, Computing and Science. His academic foundation includes a PhD and MEng (Hons.) in Chemical Engineering from The University of Nottingham. PhD in Engineering (The University of Nottingham) MEng (Hons.) in Chemical Engineering (The University of Nottingham) His research pioneers net-zero energy systems through mathematical optimization, focusing on supply chain decarbonization, industrial symbiosis, and sustainable agriculture planning. Recent work integrates carbon accounting with palm oil value chains and biomass-to-energy systems, emphasizing practical implementation in Southeast Asian contexts. Analysis of his 15 most recent publications (2020-2022) reveals a dominant focus on decarbonization strategies (47%), biomass supply chains (27%), and process optimization (26%), with growing emphasis on circular economy applications and multi-stakeholder resource allocation frameworks. Key recognitions include: IBAE Young Researcher of the Year Award IChemE Young Researcher Malaysia Award Finalist (2018, 2019) Heriot-Watt University PRIME Award Finalist (2021) He actively supervises research students in net-zero energy systems while leading industry-relevant projects like the Ministry of Plantation Industries white paper on palm oil recovery strategies. His scholarly service includes editorial roles for Process Integration and Optimization for Sustainability and Frontiers in Sustainability. Though no dedicated lab is specified, his research group operates through Swinburne's School of Research, leveraging institutional facilities for computational optimization and sustainability analysis.

Carlos A. Coello Coelloمشاهده پروفایل

استاد مهمان

• Evolutionary Computation
• Multi-objective Optimization
• Genetic Algorithms
• +۵ مورد دیگر

Dr. Carlos A. Coello Coello is a distinguished professor (CINVESTAV 3F Researcher) at the Center for Research and Advanced Studies of the National Polytechnic Institute (CINVESTAV-IPN) in Mexico City. He also serves as a Distinguished Visiting Professor in Computer Science and Computational Intelligence at Tecnológico de Monterrey's School of Engineering and Science, and as a visiting professor at the Basque Center for Applied Mathematics (BCAM) in Spain. Elected to El Colegio Nacional in 2023, he is the first computer scientist to join this prestigious Mexican institution, which groups the country's most important scientists, artists, and intellectuals. Dr. Coello's research focuses on evolutionary computation, particularly multi-objective optimization algorithms. His work spans genetic algorithms, evolutionary strategies, and bio-inspired optimization techniques with applications in engineering. He has made pioneering contributions to evolutionary multi-objective optimization, including developing the first genetic microalgorithm for multi-objective optimization, which has been applied in real-world contexts such as aerospace design. His research bridges computer science, applied mathematics, and operations research. Dr. Coello has published extensively since the 1990s, with recent publications focusing on advanced evolutionary algorithms, surrogate modeling for expensive optimization, and dynamic multi-objective optimization. His work shows a clear progression from foundational evolutionary computation techniques to sophisticated multi-objective and many-objective optimization approaches with practical engineering applications. MCDM Edgeworth-Pareto Prize (2024) Crónica Prize in Science and Technology (2023) SIGEVO Outstanding Contribution Award (2023) IEEE Computational Intelligence Society Evolutionary Computation Pioneer Award (2021) 2013 IEEE Kiyo Tomiyasu Award 2012 National Medal of Science and Arts IEEE Fellow (since 2011) Dr. Coello has successfully secured significant research funding through Mexico's CONACyT, with projects ranging from foundational algorithm development to advanced multi-objective optimization techniques. He has supervised PhD students, including Dr. Luis Vicente Santana-Quintero who received the Arturo Rosenblueth PhD Thesis Award. As Associate Editor for prestigious journals including IEEE Transactions on Evolutionary Computation, Dr. Coello plays an active role in shaping the field. His work has earned him international recognition, including being ranked 289th worldwide and 1st in Mexico among the 1000 most outstanding computer scientists in the 2022 Guide2Research Ranking.

• Artificial Intelligence
• Machine Learning
• Data-Driven Science and Engineering
• +۷ مورد دیگر

Janardhan Rao (Jana) Doppa is a Huie-Rogers Endowed Chair Professor and Berry Distinguished Professor in Engineering at Washington State University (WSU), where he chairs the EECS Graduate Studies committee in the School of Electrical Engineering and Computer Science. His research spans artificial intelligence, machine learning, and data-driven science, with emphasis on: AI-driven adaptive experimental design for scientific discovery Sequential decision-making under uncertainty for agriculture and cyber-physical systems Robust machine learning for high-stakes applications Machine learning for electronic design automation and sustainable computing Current projects include NSF-funded work on agricultural AI and materials discovery. Publication analysis (2019-2026) reveals strong focus on Bayesian optimization methods (37% of recent papers), conformal prediction for uncertainty quantification (21%), and cross-domain applications in materials science (19%), healthcare (15%), and sustainable systems (12%). Major awards include: WSU Faculty Mid-Career Award (2024) Voiland College Outstanding Researcher Award (2024) 4 Best Paper Awards (2021-2023) NSF CAREER Award (2019) Outstanding Paper Award, AAAI (2013) He actively advises 8 PhD students and has graduated 21 PhDs who now hold positions at universities (Minnesota, Houston) and industry labs (Meta, Stanford). Research is supported by NSF, USDA, and industry grants exceeding $5M. He leads a dynamic research group in AI/ML at WSU with collaborations spanning Duke, Stanford, and Oregon State. Group outputs include 100+ publications with 25+ Best Paper nominations/awards since 2013.

John McCallمشاهده پروفایل

استاد

• Data Science
• Artificial Intelligence
• Nature-inspired Computing
• +۵ مورد دیگر

Professor John McCall is a distinguished academic and researcher at Robert Gordon University's School of Computing, Engineering & Technology, where he previously served as Head of School. He currently serves as Director of the National Subsea Centre, leading initiatives to accelerate energy transition through smart technologies applied to industrial and environmental challenges in subsea and related marine sectors. With over 25 years of research experience in nature-inspired computing and artificial intelligence, Professor McCall has established himself as a leading expert in optimization algorithms and explainable AI. Professor McCall's research interests span data science, artificial intelligence, nature-inspired computing, and optimization, with significant applications in energy transition and subsea technologies. His work bridges theoretical foundations with practical implementations, having founded two spinout companies that deliver real-world optimization solutions to industry. He leads both the Complex Optimisation Research Group and the Computational Intelligence Research Group, where his team explores cutting-edge approaches to solving complex computational problems. Analysis of Professor McCall's recent publication record (2023-2025) reveals a strong focus on explainable AI, particularly in the context of evolutionary computation and metaheuristics. His research demonstrates an increasing emphasis on practical applications in energy systems, transportation, and subsea technologies, reflecting his commitment to addressing real-world challenges related to climate change and industrial transformation. The interdisciplinary nature of his work is evident in publications spanning computer science, operations research, renewable energy, and transportation planning. Lead of the Computational Intelligence Research Group ResearcherID: G-1423-2011 Scopus Author ID: 36797474900 ORCID: https://orcid.org/0000-0003-1738-7056 Professor McCall is actively involved in mentoring the next generation of researchers, currently supervising multiple PhD students across diverse topics including explainability of non-deterministic solvers, optimization of electrical machines, and computational intelligence applications in hydrocarbon systems. His research is supported by numerous grants from industry and government sources, with projects totaling millions of pounds focused on solving challenges in energy transition and smart technologies. At the National Subsea Centre, Professor McCall leads a multidisciplinary team working on digital twin technologies, subsea AI applications, and data-driven solutions for the energy sector. His work emphasizes collaboration between academia and industry to develop transformative solutions that address both current challenges and future opportunities in the subsea domain.

• Evolutionary Computation
• Multi-Objective Optimization
• Artificial Intelligence
• +۷ مورد دیگر

Dr. Ciprian Zavoianu is an academic researcher at Robert Gordon University (RGU) in the School of Computing, Engineering & Technology. He leads the Net Zero Operations research programme at the National Subsea Centre and is affiliated with the Complex Optimisation Research Group. His work focuses on applying artificial intelligence, particularly evolutionary computation algorithms, to solve complex real-world optimization problems with practical engineering applications. Dr. Zavoianu earned his academic qualifications from West University of Timisoara, Romania (BSc and MSc in Computer Science) and Johannes Kepler University Linz, Austria (PhD in Computer Science, 2015). His doctoral research focused on enhancing multi-objective evolutionary algorithms for computationally-intensive optimization problems. His primary research interests include: Evolutionary Computation Multi-Objective Optimization Data Mining & Machine Learning (particularly for surrogate modeling) Timetabling and Rostering Parallel/Distributed Computing Dr. Zavoianu's recent publications (2021-2025) demonstrate a strong focus on applying optimization techniques to transportation systems, electrical machine design, and sustainable energy solutions. His work consistently addresses the challenge of computationally expensive optimization through innovative surrogate modeling approaches, enabling practical applications of evolutionary algorithms to real-world engineering problems. He has secured multiple research grants including 'Data For Net Zero' and 'Ferry Passenger and Freight Modelling for Shetland,' demonstrating the practical relevance and industry applicability of his research. Dr. Zavoianu actively supervises five PhD/EngD students across diverse topics including predictive analytics for subsea installations, optimization of electrical machines, and operations optimization for harbor operations. His supervision approach emphasizes bridging theoretical algorithm development with practical implementation in engineering contexts. His laboratory work is centered around the National Subsea Centre, where he leads the Net Zero Operations research programme, focusing on sustainable solutions for the energy transition through advanced computational methods.

Stanisław Szczepańskiمشاهده پروفایل

استاد

• Antenna Design
• Microelectronics
• Machine Learning Applications
• +۳ مورد دیگر

Prof. Stanisław Szczepański is a Professor at the Department of Microelectronic Systems within the Faculty of Electronics, Telecommunications and Informatics at Gdańsk University of Technology. His research focuses on advanced antenna design, microelectronics, and the application of machine learning in electronic systems. His primary research interests include: Antenna Design and Optimization Frequency Reconfigurable Antenna Systems Machine Learning Applications in Electronic Design CMOS Sensor Development Wireless Communication Systems Microfluidic Technologies for Electronics Prof. Szczepański's recent publications demonstrate a strong focus on integrating machine learning techniques with traditional antenna design methodologies. His work spans from highly miniaturized frequency reconfigurable antennas to advanced slot antenna arrays operating at millimeter-wave frequencies. A significant portion of his research involves optimizing antenna parameters using artificial intelligence approaches to overcome computational limitations of electromagnetic simulations. Prof. Szczepański has led multiple research projects funded through the OPUS program, including: CMOS sensor with smart grid of pixels of layered structure for fast acquisition and simultaneous extraction of information from image (UMO-2016/23/B/ST7/03733) Mikroelektroniczny system wizyjny CMOS do endoskopii kapsułkowe z bezprzewodową transmisją danych i mocy zasilającej (UMO-2011/03/B/ST7/03547) He collaborates extensively with Prof. Sławomir Kozieł on projects related to antenna design and optimization, serving as co-investigator on projects focused on unsupervised specification-driven design of antenna structures and rapid quasi-global optimization of high-frequency components.

Dirk Roosمشاهده پروفایل

استاد

• Machine Learning
• Robust Design Optimization
• Stochastic Analysis
• +۱۰ مورد دیگر

Prof. Dr.-Ing. Dirk Roos is a Professor for Computersimulation und Design Optimization at the Department of Mechanical Engineering and Process Engineering, Niederrhein University of Applied Sciences, where he has served since March 2011. He is also the Head of the Institute for Modeling and High Performance Computing (IMH) since December 2016. His academic career includes previous positions as Head of Robust Design Optimization at DYNARDO Dynamic Software and Engineering GmbH (2002-2011) and Technical Solutions Specialist at CADFEM GmbH (2000-2008). Prof. Roos specializes in machine learning, robust design optimization, stochastic analysis, and probabilistic modeling for virtual product development. His research focuses on developing mathematical methods for stochastic structural and fluid simulation, robustness and reliability analysis, multidisciplinary optimization, and software development for complex system development. He has established strong collaborations with academic institutions including RWTH Aachen, Ruhr-Universität Bochum, and industrial partners like Siemens AG and Robert Bosch GmbH. His recent work explores Probabilistic Intelligence, cyber-physical systems, digital twins, and Big Data Analysis, with applications spanning power plant flexibility, renewable energy, turbomachinery, automotive, aerospace, and medical technology. Prof. Roos has successfully secured numerous third-party funded research projects totaling over 1.8 million euros, including collaborations with Siemens AG, Robert Bosch GmbH, and various universities. optiSLang Award 2012 Weimar Optimization and Stochastic Days Gutachter BMBF im Programm FH-Kooperativ since 2019 Mitglied des Scientific Committee International Probabilistic Workshop since 2017 Mitgliedschaft im Graduierteninstitut für angewandte Forschung der Fachhochschule in NRW since 2017 Prof. Roos supervises multiple doctoral students and has successfully completed several cooperative PhD projects. His current research projects include AI-driven optimization for medical care, reinforcement learning for emergency room planning, and machine learning algorithms for power plant component lifetime prediction. The Institute for Modeling and High Performance Computing (IMH) under his leadership develops mathematical methods and software competence in machine learning and CAE-based robust design optimization for virtual product development.