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Saeid NahavandiView profile

Professor

• Artificial Intelligence
• Robotics and Haptics
• Autonomous Systems
• +4 more

Roles and Affiliations: Distinguished Professor Saeid Nahavandi is the inaugural Associate Deputy Vice-Chancellor (Research) and Chief of Defence Innovation at Swinburne University of Technology. He leads defence innovation and research strategy, with a focus on autonomous systems, robotics, and AI. Previously, he served as Pro Vice-Chancellor (Defence Technologies) at Deakin University and founded its Institute for Intelligent Systems Research and Innovation. Research Focus: Specializes in robotics, haptics, autonomous systems, AI, and advanced modelling/simulation. His work bridges academia and industry, with collaborations spanning Airbus, Boeing, NASA, and NATO. He has secured over $150M in funding and established three tech startups. Key research areas include motion simulation, teleoperation systems, and defence technologies. Articles Overview: Over 1,300 publications span AI, robotics, and control engineering. Recent work emphasizes autonomous navigation reviews, uncertainty-aware AI, and motion cueing algorithms. His research addresses real-world applications like driver distraction detection and robotic ultrasound. Awards and Recognition: Recipient of the 2022 Clunies Ross Entrepreneur of the Year Award, 2021 Australian Space Awards Researcher of the Year, and multiple engineering excellence accolades. A Fellow of ATSE, IEEE, and other leading institutions. Grants & Industry Impact: Led ARC Training Centres for automated vehicles and energy storage. Notable grants include a $15M ARC Training Centre for Automated Vehicles in Rural/Remote Regions (2024–2029). Collaborates globally on defence, aerospace, and smart transportation projects. Labs & Teams: Heads Swinburne’s Defence Innovation Group and collaborates with Harvard University (as an Associate) and the University of Windsor (adjunct professor). Advises governments and industries on technology strategy and innovation.

• Acoustic Engineering
• Multiobjective Optimization
• Diesel Particulate Filters
• +3 more

Sinem Öztürk is a Researcher at the Department of Mechanical Engineering, Istanbul Technical University. Her work focuses on acoustic engineering, multiobjective optimization, and material science with applications in diesel particulate filters, 3D-printed materials, and musical instrument acoustics. She has contributed to noise control strategies in both industrial and residential environments, with a particular emphasis on vibration analysis and acoustic performance optimization. Her research interests span acoustic multiobjective optimization, additive manufacturing acoustics, and traditional instrument modal analysis. Key areas include diesel particulate filter design, porous media acoustics, and resonance characteristics of musical instruments like the bendir and baglama. She also explores noise reduction techniques in rubber composites and speaker placement algorithms for indoor environments. Öztürk has published extensively since 2010, with notable contributions in acoustic material studies (2017), 3D-printed material acoustics (2024), and model-based noise control (2023). Her work aligns with UN Sustainable Development Goals related to clean energy and sustainable infrastructure. No scientific awards are explicitly mentioned in the provided information. She has no recorded advisees or grants listed here.

• Variational Inequalities
• Optimal Control
• Numerical Analysis
• +2 more

Dr. Constantin Christof is a researcher at the University of Duisburg-Essen, Germany, leading the AG Optimal Control of Partial Differential Equations research group. His academic activities include teaching Mathematical Imaging (lectures/exercises), Practical Course in Numerical Mathematics (case studies), and Bachelor Seminar Mathematics for the Summer Semester 2025. His research spans Variational Inequalities , Optimal Control , Numerical Analysis , PDE-Constrained Optimization , and Nonsmooth Optimization . Key contributions focus on theoretical foundations of obstacle problems, directional differentiability, and stability analysis for variational inequalities. Recent work extends to machine learning applications like physics-guided neural networks for gas source localization and neural network optimization landscapes. Christof's publication trend (2021-2025) reveals deep specialization in nonsmooth optimization for PDE-constrained problems, with 15+ high-impact journal articles in SIAM, ESAIM, and IEEE venues. His work bridges theoretical analysis (e.g., Lipschitz stability, strong stationarity) and computational methods (semismooth Newton techniques), addressing challenges in rate-independent systems and non-Lipschitzian nonlinearities. Scientific Awards: No awards documented in available records. Advising and Grants: Current information does not specify student supervision or grant funding details. His research group structure suggests active mentorship of junior researchers through collaborative publications. Labs and Teams: Heads the AG Optimal Control of Partial Differential Equations research group, driving interdisciplinary projects connecting mathematical optimization with environmental monitoring and machine learning applications.

Daniel Vázquez VázquezView profile

Assistant Professor

• Chemical Process Simulation
• Sustainable Process Design
• Machine Learning Applications
• +6 more

Daniel Vázquez Vázquez is an Assistant Professor in the Department of Chemical Engineering and Materials Science at IQS School of Engineering, Ramon Llull University. He is an active researcher with a focus on chemical process simulation, sustainable design, and machine learning applications for environmental sustainability. His research interests span multiple areas including inherent safety engineering , multiobjective optimization , process design , and environmental sustainability . Dr. Vázquez applies advanced computational methods to address challenges in chemical engineering, particularly focusing on sustainable process development and optimization. His work integrates chemical engineering principles with data science to create more efficient and environmentally friendly industrial processes. Dr. Vázquez is a key member of the GESPA (Engineering and Simulation of Environmental Processes Group), a consolidated research group recognized by the Catalan Government (2021 SGR 00321). This multidisciplinary team combines chemical engineering, biotechnology, and advanced chemical analysis with a theoretical-experimental approach to improve societal sustainability. His publication record shows a consistent output with 27 research outputs, including high-impact publications in journals like Nature Communications. His research has garnered over 300 citations with an h-index of 9, indicating significant scholarly impact. His work has been referenced in policy sources and picked up by multiple news outlets, demonstrating real-world relevance. Dr. Vázquez is currently leading or participating in significant research projects including M-AD-NESS (Development of Dual Anaerobic Bioelectrochemical Digesters) and GESPA (Grup d'Enginyeria i Simulació de Processos Ambientals), which focus on innovative solutions for wastewater treatment and biomethane production. These projects demonstrate his commitment to addressing pressing environmental challenges through engineering solutions.

• Multiobjective Decision Making
• Combinatorial Optimization
• Scheduling
• +3 more

Özgür Özpeynirci is an Assistant Professor at the Department of Logistics Management, Izmir University of Economics, where he has been employed since February 2008. His research focuses on multiobjective decision making, combinatorial optimization, and scheduling, bridging theoretical and applied aspects of operations research and logistics. B.S., M.S., and Ph.D. in Industrial Engineering from Middle East Technical University (2002, 2004, 2008) Researcher at TÜBİTAK (2001-2007) His scholarly work integrates operations research, decision theory, and logistics. He has pioneered interactive algorithms for multi-criteria sorting and resource allocation, addressing complex problems in transportation, biofuel contracts, and digital transformation. His research often applies mathematical modeling and optimization techniques to real-world scenarios. Scientific awards include the 2011 Best Doctoral Dissertation Award from the International Society on Multiple Criteria Decision Making. His publications span high-impact journals like Management Science and European Journal of Operational Research , emphasizing algorithm design, logistics performance, and multiobjective programming. Co-authors like Vincent Mousseau and Selin Özpeynirci indicate collaborative research across Turkey and global institutions.

• Control Theory and Applications
• Robotics
• Electrical-Electronics Engineering
• +3 more

Dr. Muhammed Orkun Öğücü is an Assistant Professor at Gaziantep University's Faculty of Aviation and Astronautics, Department of Aeronautics and Astronautics Engineering. His academic journey includes a Ph.D. in Electrical-Electronics Engineering from Middle East Technical University (2007-2014), an M.S. in Control and Automation Engineering from Istanbul Technical University (2004-2006), and a B.S. in Electrical Engineering from the same institution (1999-2004). His research focuses on control theory applications , robotic systems , and engineering optimization , particularly in aerospace contexts. Key domains include autonomous systems, aerodynamic optimization, and mobile robotics. He previously worked as an R&D Engineer at Marine Systems Technologies (2015-2016), developing control systems for observation-class submarines. Dr. Öğücü teaches advanced courses including Advanced Robotics , Optimal Control Theory , and Aircraft Stability & Control . He served as Head of Department at Gaziantep University from 2018-2019.

• Computational Intelligence
• Evolutionary Multi-objective Optimization
• Evolutionary Machine Learning
• +5 more

Professor Hisao Ishibuchi is Chair Professor of Computer Science and Engineering at Southern University of Science and Technology (SUSTech) in Shenzhen, China, a role he has held since April 2017. Previously, he spent nearly three decades at Osaka Prefecture University, progressing from Research Associate (1987-1993) to Assistant Professor (1993), Associate Professor (1994-1999), and full Professor (1999-2017). He is an IEEE Fellow , served as Vice-President of the IEEE Computational Intelligence Society (2010-2013) , and is currently President of the Japan Society for Evolutionary Computation (2016-2018) . He is Editor-in-Chief of IEEE Computational Intelligence Magazine (2014-2019) and the Journal of the Japan EC Society (2014-2018). Education: Ph.D. in Engineering, Osaka Prefecture University, 1992 M.S. in Engineering, Kyoto University, 1987 B.S. in Engineering, Kyoto University, 1985 Research Focus: Professor Ishibuchi is internationally recognised as a pioneer of computational intelligence , with seminal contributions to evolutionary multi-objective optimisation , evolutionary machine learning , fuzzy systems , neural networks , and hybrid intelligent systems . He introduced the first multi-objective memetic algorithm and early methods for multi-objective fuzzy rule-based classifier design that balance accuracy and interpretability. Publications & Impact: With over 100 journal papers in top-tier venues such as IEEE Transactions on Evolutionary Computation and nearly 500 conference papers, his work has attracted more than 24 000 Google-Scholar citations and an h-index of 68. His recent articles concentrate on many-objective optimisation, fuzzy machine learning, and transfer learning techniques. Honours & Awards: IEEE Computational Intelligence Society Fuzzy Systems Pioneer Award 2019 IEEE Fellow 2014 JSPS Prize 2007 (Japan’s most prestigious mid-career award) Multiple Best Paper Awards from GECCO, FUZZ-IEEE, SCIS & ISIS, WAC, ACIIDS, HIS-NCEI, and others Teaching & Mentoring: At SUSTech he teaches Advanced Algorithms and Advanced Optimization Algorithms , covering greedy algorithms, hyper-heuristics, memetic algorithms, multi-objective optimisation, and performance assessment. His research group actively recruits post-doctoral fellows and research assistants in evolutionary computation, fuzzy systems, and neural networks. Labs & Teams: He leads the Computational Intelligence Research Group at SUSTech, maintaining active collaboration networks across Asia, Europe, and North America, and supervising several post-doctoral researchers and graduate students working on next-generation intelligent systems.

• decision support systems
• multicriteria analysis
• network optimization
• +9 more

João Manuel Coutinho Rodrigues serves as a Full Professor in the Department of Civil Engineering at the University of Coimbra, with research spanning decision support systems, multicriteria analysis, network optimization, and GIS applications in urban, transportation, and environmental engineering contexts. Education: First degree in Civil Engineering (1980) MSc in Computer Science (1986) PhD in Civil Engineering, University of Coimbra (1993) Research Focus: His work bridges methodological innovations—including network optimization algorithms and multiattribute/multiobjective modeling—with practical applications in urban, regional, environmental, and transportation planning. This dual emphasis on theoretical advancement and real-world problem-solving defines his interdisciplinary approach to civil engineering challenges. Affiliations: He maintains a primary appointment in the Department of Civil Engineering at the University of Coimbra and is actively associated with INESC Coimbra (Instituto de Engenharia de Sistemas e Computadores de Coimbra), a research institute specializing in systems and computer engineering. Advising and Grants: While specific advising roles and grant details are not documented in the provided text, his position implies involvement in academic mentorship and research funding typical of senior faculty. Labs and Teams: His research activities are conducted through INESC Coimbra, where he contributes to projects integrating computational methods with civil engineering applications, as indicated by his project URL (http://kreation.dec.uc.pt/projects/).

Tanja KortemmeView profile

Professor

• Protein Engineering
• Computational Biology
• Systems Pharmacology
• +4 more

Tanja Kortemme is a Professor and Vice Dean of Research at the University of California San Francisco (UCSF) School of Pharmacy , with primary appointments in the Department of Bioengineering and Therapeutic Sciences . She leads research at the intersection of computational biology, protein engineering, and synthetic biology, focusing on designing novel molecular functions and unraveling cellular network principles through predictive modeling and experimental validation. Research Interests : Her work integrates robotics-inspired computational methods with experimental approaches to solve challenges in protein design, including: Developing predictive algorithms for protein structure and function (Rosetta software) Engineering light/phosphorylation-activated protein switches Mapping allosteric regulation in cellular networks Designing SARS-CoV-2 neutralizing receptor traps Publication Trends : Recent articles emphasize machine learning for protein design, multi-input drug-controlled gene expression systems, and structural analysis of SARS-CoV-2 interactions. Collaborations with Nevan Krogan's lab and the QBI Coronavirus Research Group highlight her interdisciplinary impact. Scientific Awards : Chan Zuckerberg Biohub Investigator (2022, 2017) AIMBE Fellow (2019) NSF CAREER Award (2008) Sloan Research Fellow (2005) W.M. Keck Medical Research Award (2013) Grants & Collaborations : Leads NIH-funded research on protein interaction networks, participates in QBI's $67.5M coronavirus therapeutic initiative, and contributes to structural biology consortia like QCRG. Her lab's work on protein design principles has been continuously supported by federal and foundation grants.

• Multiobjective Optimization
• Supply Chain Management
• Artificial Intelligence Assisted Optimal Design
• +13 more

Dr. Wang Zhenkun is an Assistant Professor at the School of System Design and Intelligent Manufacturing, Southern University of Science and Technology (SUSTech), with a joint appointment in the Department of Computer Science and Engineering. He earned his PhD in Circuits and Systems from Xidian University in 2016 and held postdoctoral and research fellow positions at Nanyang Technological University (NTU), City University of Hong Kong, and its Shenzhen Research Institute. Research Interests: Multiobjective optimization, artificial intelligence-assisted design automation, supply chain management, deep learning, and UAV traffic scheduling. Grants & Recognition: Principal Investigator (PI) for a National Natural Science Foundation of China (NSFC) youth project and multiple grants; selected as a high-level C-category overseas talent in Shenzhen. Leadership Roles: Associate Editor of Swarm and Evolutionary Computation (JCR Q1); Chair of IEEE CIS Shenzhen Chapter Student Activities; Organizer of EMO 2021 Competition. Teaching: Courses include Fundamentals of Systems Engineering , Machine Learning System Design , and Intelligent Optimization Algorithms . Group Leadership: Founder of the Computational Intelligence and Advanced Manufacturing (CIAM) group, focusing on developing AI-driven optimization algorithms for logistics, manufacturing, and aerospace applications.

• Blockchain technologies
• Quantum Computing
• Artificial Intelligence
• +7 more

Dr. Ernestas Filatovas is a Senior Researcher and Chief Researcher in the Project at Vilnius University's Institute of Data Science and Digital Technologies (formerly Institute of Mathematics and Informatics), where he has been affiliated since 2013. He leads the Blockchain and Quantum Technologies Group, focusing on cutting-edge research at the intersection of quantum computing, blockchain, and artificial intelligence. Previously, he served as an Associate Professor and Lecturer at Vilnius Gediminas Technical University's Faculty of Fundamental Sciences from 2013 to 2019. Dr. Filatovas earned his Doctor of Technology in Computer Science Engineering from Vilnius University Institute of Mathematics and Informatics in 2012. His dissertation, supervised by Prof. Dr. Olga Kurasova, focused on the interactive solution of multi-criteria optimization problems. His research spans multiple high-impact domains, with particular expertise in blockchain technologies, quantum computing, artificial intelligence, and machine learning. He has pioneered work in quantum blockchain implementations, reproducibility of AI research through blockchain verification, and quantum machine learning applications. His research bridges theoretical computer science with practical applications in financial markets, healthcare, and distributed systems. His extensive publication record—over 50 scientific papers, with more than 25 in Clarivate Analytics-indexed journals—demonstrates consistent productivity and international collaboration. Recent work shows a clear trajectory toward quantum-enhanced AI systems, blockchain-based research verification frameworks, and quantum algorithms for practical problems. Laureate of the 4th LMA Young Scientists' Conference (2014) INFOBALT scholarship 2nd place winner (2014) Lithuanian State Science and Studies Foundation funding recipient (2009, 2010) Recognized as one of Lithuania's most active doctoral students Master's degree with honors (2006) Dr. Filatovas leads multiple significant research projects, including the 2021-2024 project 'Solving the problems of reproducibility of scientific research in the field of artificial intelligence using blockchain technologies' as team leader, and the 2023-2027 project 'Development and validation of quantum machine learning methods using prepared datasets' as Chief Researcher. He has also contributed to international collaborations such as the Spanish-funded 'High Performance Solutions for Modern Scientific Computing Challenges' (2019-2021). His popular science contributions, including the VU news portal article 'Quantum Computing: Who and Why?', demonstrate his commitment to science communication. As a key member of Vilnius University's Blockchain and Quantum Technologies Group, Dr. Filatovas contributes to Lithuania's growing reputation in quantum computing research and blockchain innovation, working closely with international collaborators across Europe.

• Multiobjective Optimization
• Machine Learning
• Healthcare Management
• +2 more

Serpil Sayın is a Professor in the Operations Management and Information Systems department at Koç University's School of Business Administration . She currently serves as the graduate coordinator of the department's PhD program and has contributed to research in optimization, machine learning, and healthcare management. PhD, University of Florida (1992) Bachelor’s, Boğaziçi University (1988) Her research focuses on multiobjective optimization and its intersections with machine learning , operations , and healthcare management , supported by grants from TÜBİTAK. She has published in top-tier journals such as Mathematical Programming , Operations Research , and Management Science . Scientific Awards : TÜBİTAK Encouragement Award (2004) Professional Memberships : MCDM Society INFORMS Section on MCDM Turkish OR Society

Antonios KladasView profile

Professor

• Electric Machines
• Power Systems Engineering
• Renewable Energy Integration
• +5 more

Antonios G. Kladas is a Full Professor at the National Technical University of Athens (NTUA), School of Electrical and Computer Engineering, where he directs the Laboratory of Electrical Machines and Power Electronics. He holds a Diploma from Aristotle University of Thessaloniki (1982) and DEA/PhD degrees from Université Paris VI (1983-1987). His academic career spans roles as Assistant Professor (1996-2001), Associate Professor (2001-2006), and Full Professor (2006-present). Research Focus: Prof. Kladas specializes in electric machine and transformer design, renewable energy systems, industrial drives, and electromagnetic field computation. His work integrates finite-element modeling with optimization techniques for applications in electric vehicles, aerospace actuators, and power grids. Key innovations include hybrid electromagnetic-thermal methodologies and multi-objective evolutionary algorithms for motor design. Awarded the Best Paper Award at ICEM 2010 , he has led projects like the EU Clean Sky Programme and the fuel-efficient 'Pyrforos' vehicle prototype (Shell Eco Marathon 2012). He serves on steering committees for IEEE CEFC and ICEM conferences and has evaluated research programs for Italian/Portuguese agencies. Major Grants: EU Clean Sky (HPEM, EMAS), HERACLITOS II (electric vehicles), CREAM (integrated actuators) Advising: Supervised 14 PhD theses and >100 undergraduate projects on topics ranging from PM motor optimization to wave energy converters. Laboratory Leadership: Oversees 50+ researchers in the NTUA Laboratory of Electrical Machines and Power Electronics, focusing on experimental validation of industrial and renewable energy systems.

Raghu Nandan SenguptaView profile

Professor

• Sequential Estimation
• Statistical Reliability Theory
• Risk Analysis
• +4 more

Dr. Raghu Nandan Sengupta is a Professor at the Department of Management Sciences, Indian Institute of Technology Kanpur (IITK). With a Ph.D. from IIM Calcutta and a BE in Mechanical Engineering from BIT Mesra, he has held visiting positions at institutions including Princeton University, University of Warsaw, and Technische Universität Dresden. His academic leadership includes serving as Head of Department (2017-2020) and Chairman of GATE/JAM-2025. Research Focus: Sequential Estimation, Statistical Reliability Theory, Risk Analysis, Optimization Techniques in Finance, Meta Heuristic Algorithms, and Robust Optimization His publications span topics like reliability-based portfolio optimization , multiobjective decision-making , and brand sensitivity in B2B marketing . Key awards include DAAD Research Stays, EU-NAMASTE Erasmus Mundus Fellowship, and Indo-US S&T Forum Fellowship. He has advised 16 PhD students and contributed to editorial boards of journals including Sequential Analysis and Foundations of Computing and Decision Sciences . Scientific Awards: DAAD Research Stays (2017, 2015) EU-NAMASTE Erasmus Mundus Fellowship (2015) Indo-US S&T Forum Fellowship (2008) Erasmus Mundus Europe Asia Fellowship (2011) He has taught courses such as Probability and Statistics , Quantitative Methods for Decision Making , and Security Analysis & Portfolio Management at IITK, University of Warsaw, and IGIDR Mumbai. His work bridges statistical inference , financial engineering , and operations research , emphasizing practical applications in industry and academia.

• Optimization
• Operations Research
• Renewable Energy Systems
• +3 more

António Ismael Freitas Vaz serves as Associate Professor with Habilitation at the School of Engineering, University of Minho, and holds a Senior Researcher position at the ALGORITMI Research Centre. As a core member of the SEOR (Systems Engineering and Operations Research) R&D Group, he directs research in mathematical optimization methodologies with applications spanning energy systems, additive manufacturing, and biomedical engineering. His institutional profile includes verified metrics: h-index 17, 1,505 citations, and 41 publications including 34 in Q1/Q2 journals. His research program centers on developing advanced optimization frameworks including multi-objective, derivative-free, and semi-infinite programming techniques. Key application domains feature renewable energy integration (demand-response co-optimization, cost-effectiveness analysis), 5-axis 3D printing (curved layer path planning, build orientation optimization), and medical diagnostics (automated tumor detection in wireless capsule endoscopy). Methodological innovations focus on particle swarm optimization, DC programming, and gradient descent complexity for complex constrained problems. Analysis of his 15 most recent publications (2018-2022) reveals three dominant research thrusts: energy systems optimization (33% of output), additive manufacturing (33%), and medical imaging (13%), with foundational optimization theory comprising the remainder. This distribution demonstrates strategic application of core methodologies to high-impact engineering challenges, particularly in sustainable energy transition and advanced manufacturing. The consistent publication in top-tier venues like Renewable and Sustainable Energy Reviews and Applied Energy underscores disciplinary influence. Scientific Awards: No specific awards, fellowships, or medals were documented in the provided profile information. Regarding academic advising, the source material contains no listings of PhD or Master's students supervised. The funding section explicitly indicates zero recorded projects ("Fundings (0)"), suggesting either institutional management of grants outside individual reporting or incomplete profile documentation. His h-index and publication volume imply significant research leadership despite absent grant details. As an integral contributor to the SEOR R&D Group at ALGORITMI, Vaz participates in a cross-disciplinary research ecosystem focused on operational research applications. The group maintains industry partnerships in energy, manufacturing, and healthcare sectors, facilitating translation of optimization algorithms into practical solutions for industrial partners and public administration through the Centre's thematic lines.