Andreas Miroslaus Wichert is an Associate Professor at the University of Lisbon, affiliated with INESC-ID (GAIPS research group). His academic journey includes studies in computer science at the University of Saarland and a PhD in computer science from the University of Ulm (2000). He has taught courses like Artificial Intelligence, Machine Learning, Deep Learning, and Quantum Artificial Intelligence at institutions including University of Lisbon and Technical University Munich. Born in Wrocław, Poland, with a grandfather who was a Polish officer (Katyn massacre memorialized) Current affiliations: University of Lisbon, INESC-ID, GAIPS research group Research Interests Andreas specializes in Artificial Intelligence , Machine Learning , Quantum Artificial Intelligence , and Neural Networks . His work explores intersections between quantum computing and AI, as detailed in his 2024 book Quantum Artificial Intelligence with Qiskit . He also investigates cognitive systems and high-dimensional data indexing through the HEIDI project. Scientific Awards Recipient of multiple teaching excellence awards for courses in: Logic for Programming (24/25) Machine Learning (21/22, 23/24, 24/25) Decision Support Systems (14/15) Intelligent Multimedia Databases (14/15) Advising Currently supervises PhD students Maria Osorio, Jose Miguel Penedo Ramos, and Joaquim Domingos Mussandi. Past advisees include Dr. Luis Tarrataca, Dr. Angelo Cardoso, Dr. Joao Sacramento, Dr. Catarina Pinto Moreira, and Dr. Luis Sa-Couto. Projects & Labs Lead developer of the HEIDI project (High-Dimensional Indexing), and active member of the GAIPS research group since 2009.
Dr Nam Nghiep Tran is a Senior Lecturer in the School of Chemical Engineering at the University of Adelaide, serving as Associate Dean of Internationalisation (Southeast Asia) within the Faculty of Sciences, Engineering and Technology (SET). His work focuses on fostering academic collaborations between Australia and Southeast Asia, particularly Vietnam. Education: BEng (Chemical Engineering) from Can Tho University, MEng (Chemical System Engineering) from the University of Tokyo, and PhD from the University of Adelaide. Dr. Tran's research interests span process control, renewable energy, reaction engineering, thermal/non-thermal plasma technologies, and ESG principles. He plays a key role in promoting sustainable technologies through interdisciplinary research. He co-founded the Adelaide University Vietnamese Students Association (AVA) and leads the South Australia chapter of the Vietnam-Australia Scholars & Experts Association (VASEA), enhancing academic networks between Australia and Vietnam. Additionally, he serves as Associate Editor-in-Chief of Green Processing and Synthesis (GREENPs).
Alessandro Checco is an Assistant Professor in the Computer Science Department at University of Rome La Sapienza. His research focuses on crowdsourcing, distributed systems, and privacy-preserving technologies, bridging theoretical computer science with practical applications that consider human factors in technological systems. He has established himself as a significant contributor to the field of human computation and privacy-aware systems. His educational background includes: 2020: Fellowship of Higher Education from The University of Sheffield, Higher Education Academy 2015: Ph.D. in Mathematics from Hamilton Institute (Design of decentralised algorithms applied to channel/code selection and convex optimisation for throughput fairness of 802.11 networks) 2010: M.Sc. in Mathematical Engineering from University of Roma "Tor Vergata" (110/110 with great distinction) 2009: Erasmus Scholarship at Universiteit Gent, Department of Telecommunications 2007: B.Sc. in Mathematical Engineering from University of Roma "Tor Vergata" (110/110 with great distinction) Checco's research spans multiple areas at the intersection of computer science and social implications of technology. He is particularly interested in Crowdsourcing for Human Computation, Distributed Private Recommender Systems, Information Retrieval, Data Privacy, Distributed Systems, User Data Obfuscation in Web Systems, Societal and Economic Analysis of Online Work, Crowd Workers Unionisation, and Algorithmic Bias. His work often examines how technological systems can be designed to respect user privacy while maintaining functionality, and how crowd work can be structured to be more equitable for workers. His recent publications demonstrate a clear evolution in research focus, beginning with foundational work in wireless networks and distributed algorithms, then shifting toward human computation and privacy-preserving systems. His most recent work increasingly addresses the societal implications of crowd work, including investigations into crowd worker unionization and cooperative models. Several publications examine gender bias in algorithmic systems, reflecting growing attention to fairness and ethical considerations in his field. Among his notable achievements: All That Glitters is Gold-An Attack Scheme on Gold Questions in Crowdsourcing (Best Paper Award) Checco has secured significant research funding and led important projects including the H2020-funded FashionBrain project as Research Director and the EPSRC-funded BetterCrowd project as Research Associate. His work on the FashionBrain project demonstrates his ability to lead large-scale, interdisciplinary research initiatives. He has also received the Technology Innovation Development Award (TIDA) from Science Foundation Ireland. His research has practical applications across multiple domains including recommendation systems (BLC: Private Matrix Factorization Recommenders), peer review assistance using AI, smart farming technologies, and cooperative models for crowd workers (CrowdCO-OP). He has developed frameworks for understanding worker behavior in crowdsourcing platforms and created methods for improving quality control in human computation systems.
Dr. Edgar Galván is an Associate Professor in the Department of Computer Science at Maynooth University's Faculty of Science & Engineering. He is a leading expert in Genetic Programming (GP) and Evolutionary Algorithms, with a focus on semantic-based approaches, neutrality, and multi-objective optimization. His work spans applications in combinatorial optimization, gaming (e.g., Carcassonne), and software engineering, including neuroevolution for deep learning architectures. Current Affiliation: Maynooth University Previous Roles: Senior Researcher at University College Dublin, Trinity College Dublin, and INRIA Paris-Saclay Research interests include: Semantic-based Genetic Programming Multi-objective Evolutionary Algorithms Monte Carlo Tree Search Circular Economy Applications Privacy-Preserving Optimization Neuroevolution in Autonomous Systems His recent publications analyze semantic diversity in GP, neural architecture search, and privacy-aware swarm optimization. Key awards include being ranked among the top 1% of GP researchers by University College London (2020), a Marie Curie Fellowship (2014), and a Best Paper Award at ECTA 2015. Current Projects: REBUILD (Circular Economy Buildings, 2024-2027), VISION (Circular Business Models, 2023-2026) Previous Grants: Stochastic Bio-inspired Algorithms (2014-2017, €267k), circAI (2022-2023, €142k) Dr. Galván serves on program committees for IEEE, ACM, and Springer conferences, and as Scientific Adviser for institutions in Ireland, France, and Mexico. His work bridges theoretical GP analysis with real-world applications in energy optimization and AI.
Sven De Sutter is a Visiting Professor (Paid guest professor) in the Mechanics of Materials and Constructions research group at Vrije Universiteit Brussel (VUB) , Belgium. His affiliation with VUB centres on advancing structural engineering solutions for lightweight, high-performance composite and concrete systems. Research Focus: Fracture mechanics and damage characterisation of cementitious and composite materials Acoustic emission monitoring and digital image correlation for real-time structural health assessment Development of hybrid textile- or fibre-reinforced concrete beams for lightweight construction His investigations integrate experimental validation with analytical modelling, aiming to optimise safety and durability while reducing material usage in civil infrastructure. Publication Trends: Across 26 outputs captured between 2013 and 2019, De Sutter’s work consistently targets composite-concrete hybrid beams , employing acoustic emission as a primary non-destructive evaluation tool. Key themes include fracture monitoring, damage source identification, and the structural performance of carbon- and textile-reinforced lightweight systems, reflecting a trajectory towards sustainable, resilient construction technologies. Scientific Recognition: While the provided text does not list any specific awards or honours, his publications have attracted 230 citations (Scopus) and an h-index of 6, indicating growing impact within the engineering community. Supervision & Grants: De Sutter has co-supervised at least one Master’s thesis (2012) entitled “Analyse en haalbaarheidsstudie van een innovatief lichtgewicht composiet bekistingssyteem voor betonnen balken” . Further doctoral supervision or grant details are not disclosed in the text. Laboratory & Collaboration: He conducts research within the VUB Mechanics of Materials and Constructions laboratories, collaborating closely with colleagues such as Prof. T. Tysmans, Dr. S. Verbruggen, Dr. D. Angelis, and others, forming an active network around advanced composite and concrete experimentation.
Dr. Andrew Ward is an ARC Early Career Industry Fellow at the Australian Centre for Water and Environmental Biotechnology (ACWEB) within the Faculty of Engineering, Architecture and Information Technology at The University of Queensland. He leads research projects focused on wastewater treatment, nutrient recovery, and microalgae biotechnology, with significant industrial experience working with water utilities to scale research to pilot and demonstration levels. Dr. Ward holds a PhD from the School of Chemical Engineering at the University of Adelaide, where his thesis focused on the optimisation of halophilic anaerobic digestion of algal biomass. He also earned a Bachelor (Honours) degree from Flinders University. His educational background provides a strong foundation in chemical and environmental engineering principles applied to water and wastewater treatment. Dr. Ward's research primarily focuses on innovative approaches to wastewater treatment and resource recovery. His work spans nutrient recovery via electrodialysis, Anammox processes for domestic and agricultural wastewater treatment, and the development of algae-bacterial aggregated flocs (ABAF) for wastewater remediation. He investigates microalgae's role in energy and nutrient recovery from a circular economy perspective, aiming to transform wastewater treatment facilities into resource recovery centers. His research integrates biological, chemical, and engineering approaches to create sustainable solutions for water management challenges. Dr. Ward has extensive experience scaling laboratory research to pilot and demonstration levels, working closely with industry partners including Urban Utilities. Dr. Ward's publication record demonstrates a consistent focus on advancing wastewater treatment technologies, with recent articles examining deep learning applications, nutrient recovery systems, and sustainable biogas production. His work shows a clear progression toward more integrated, circular economy approaches to water management, emphasizing resource recovery alongside treatment. ARC Early Career Industry Fellowship (2024-2027): "Circular Economy", via renewable energy and resource recovery ARC Industry Fellowship Advance Queensland Industry Research Fellowship (2020-2023): Aggregated algal/bacterial flocs for wastewater treatment and algae industries Dr. Ward serves as lead investigator for Urban Utilities' wastewater microalgae research program and manages multiple research projects with industry partners. He currently supervises PhD students working on "Recovery of high-value coloured organic compounds from wastewater" and previously supervised research on "Algae Bacteria Aggregated Flocs in the Enhanced Treatment of Wastewater." His research has attracted significant funding from the Australian Research Council, Queensland government, and industry partners, demonstrating the practical relevance and impact of his work. Dr. Ward works within the Australian Centre for Water and Environmental Biotechnology (ACWEB), collaborating with researchers across engineering, microbiology, and environmental science disciplines. His team focuses on developing and scaling technologies that address real-world water treatment challenges while recovering valuable resources from wastewater streams.
Bilal Ahmad serves as a Teaching Fellow in the Department of Mechanical and Aerospace Engineering at the University of Strathclyde, United Kingdom. His academic role combines teaching responsibilities with research in advanced materials joining technologies, focusing on computational modeling of welding processes for structural and polymeric materials. Education PhD in Mechanical Engineering from University of Strathclyde (awarded March 2019) with thesis titled Numerical Optimisation of low alloy steel friction stir welding supervised by Professors Galloway and Toumpis Dr Ahmad's research centers on friction stir welding (FSW) and laser-assisted variants, employing numerical modeling to optimize weld quality in structural steel and polyethylene applications. His work addresses critical challenges in material flow dynamics, thermal management, and mechanical property enhancement during joining processes, contributing to advancements in manufacturing efficiency for automotive and aerospace sectors. Analysis of his 2019-2023 publications reveals consistent focus on computational optimization of welding parameters, with significant citation impact (25+ Scopus citations) for his structural steel research. His work bridges theoretical modeling with practical industrial applications, particularly in joining dissimilar materials where conventional techniques face limitations. Dr Ahmad actively contributes to departmental quality initiatives as Academic Staff on the EGCF project (active since June 2024), developing standardized exam guidelines and quality assurance frameworks for Mechanical and Aerospace Engineering programs. He also engages in educational outreach through activities like the MAE Egg Drop Program, demonstrating commitment to engineering education at multiple levels.
Yingqian Zhang is an Associate Professor in the Information Systems group at the Industrial Engineering and Innovation Sciences department of Eindhoven University of Technology (TU/e). She is affiliated with the Eindhoven Artificial Intelligence Systems Institute (EAISI), specifically with the EAISI High Tech Systems and EAISI Foundational groups. Her research focuses on applying Artificial Intelligence to solve complex decision-making problems across various domains including logistics, transportation, manufacturing, and e-commerce. Dr. Zhang received her PhD in Computer Science from the University of Manchester, UK. Prior to joining TU/e, she served as an Assistant Professor in the Econometrics Institute at Erasmus University Rotterdam and as a postdoc researcher in the Algorithmics group at TU Delft. She was also a visiting professor at the Institute for Advanced Computer Studies at University of Maryland, College Park, USA. Her research expertise lies at the intersection of Artificial Intelligence and optimization, with particular focus on machine learning, deep reinforcement learning, and trustworthy data-driven optimization. Dr. Zhang develops socially aware algorithms that can optimize decisions in data-rich environments. Her work bridges the gap between theoretical AI advancements and practical applications in industrial settings, addressing real-world challenges through innovative algorithmic solutions. She is particularly interested in how AI can support human decision-making while maintaining transparency and trustworthiness. Dr. Zhang's recent publications reveal a strong trend toward applying graph neural networks and reinforcement learning to complex scheduling and optimization problems. Her work demonstrates increasing sophistication in handling stochastic elements in decision-making processes, with applications spanning healthcare diagnostics, logistics, transportation, and manufacturing. She has made significant contributions to the field of neural combinatorial optimization, particularly for job shop scheduling problems and vehicle routing. Dr. Zhang has received several prestigious awards recognizing her contributions to the field: Winner of the MLVRP2023 GECCO competition (2023) Best Paper Award from Omega-International Journal of Management Science (2017) Best Industrial Paper Award (2020) Best Student Paper Award (2019) Best Student Paper Award of ICAART 2022 (2022) As a dedicated mentor, Dr. Zhang supervises numerous PhD students including Mohsen Abbaspour Onari, Abdo Abouelrous, Luca Begnardi, Xia Jiang, Chengpeng Hu, Minshuo Li, Robbert Reijnen, Jesse van Remmerden, Bart von Meijenfeldt, Ya Song, and Igor Smit. Her research is supported by various grants, including the LEO (Learning and Explaining Optimization) project co-funded by Holland High Tech | TKI HSTM via the PPP allowance scheme for public-private partnerships. Dr. Zhang actively contributes to the academic community as the Chair of the Benelux Association for Artificial Intelligence (BNVKI) and as a member of the Technical Board for the European Big Data Value Association (BDVA). She serves as an associate editor for the "Annals of Mathematics and Artificial Intelligence" journal and participates in the technical Program Committee for major AI conferences such as IJCAI, AAAI, AAMAS, and ECAI. She is also on the executive committee of the Data Science meets Optimisation (DSO) working group of EURO to promote collaboration between AI and Operations Research communities.
Nicole Borth is Associate Professor (associate Univ.Prof.) at the University of Natural Resources and Life Sciences, Vienna (BOKU) and Deputy Head of the Institute of Animal Cell Technology and Systems Biology . Her work sits at the intersection of cell engineering, systems biology and biopharmaceutical manufacturing, with CHO and HEK293 cells as primary platforms. Research in a nutshell: Genome-wide CRISPR/Cas deletion and activation screens to map essential loci and boost recombinant protein titres. Epigenetic and synthetic-biology toolboxes (dCas9-DNMT, synthetic promoters, RNA devices) for multiplexed gene-control. Glyco-engineering and biomarker discovery to optimise critical quality attributes of monoclonal antibodies. Low-cost, animal-component-free media design and microfluidic single-cell cloning to shorten development timelines. Between 2022-2025 her group released a rapid succession of papers exploiting nanopore Cas9-targeted sequencing to pinpoint transgene integration sites, unveiled novel stress-biomarkers for difficult-to-express mAbs, and provided public-domain glyco-analytics for the NIST CHO reference line. Parallel projects apply similar tool-chains to AAV production in HEK293 and characterise human diamine oxidase biopharmaceuticals. Awards & funding: Specific prizes not enumerated in supplied text; however, the volume and recency of high-impact publications indicate sustained competitive funding. Contact: nicole.borth@boku.ac.at | Tel +43 1 47654-79064 | Muthgasse 11, 1190 Vienna, Austria.
Nicolai Siim Larsen is an Assistant Professor (Tenure track) in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), specializing in statistical modeling with applications in finance and healthcare analytics. His work bridges theoretical statistics and real-world data challenges. Education : Ph.D. in Stochastic Financial Models based on Matrix-Analytic Methods, Technical University of Denmark (2022) Dr. Larsen's research centers on multivariate phase-type distributions and matrix-analytic methods , with significant contributions to financial risk modeling and healthcare analytics . His expertise spans survival analysis, sensor data processing, and price optimization frameworks, addressing complex stochastic phenomena in insurance and disease progression monitoring. Analysis of his publications reveals a cohesive focus on advancing numerical computational methods for multivariate data structures. His 2022 Ph.D. thesis established foundations for joint density functions and infinitely divisible distributions, with subsequent work directly applied to commercial pricing strategies and edema patient monitoring systems. Statistical analysis and price optimisation of DJ rental services (2022) AI Denmark: Monitoring disease progression in Oedema patients (2022) KomDigital: Optimal pricing strategies for price monitoring (2022) Stochastic Financial Models based on Matrix-Analytic Methods (PhD project 2018-2023) He actively contributes to academic service as an internal examiner for Statistical Genetics (2025) and instructor for R-based data science workshops, demonstrating commitment to both research leadership and pedagogical development within DTU's Statistics and Data Analysis section.
Ernst Gunnar Gran is Associate Professor at the Department of Information Security and Communication Technology at the Norwegian University of Science and Technology (NTNU), where he heads the communication technology discipline. He also holds an adjunct research scientist position at Simula Research Laboratory, where he headed the Cloud department until December 2016. His research spans high performance computing (HPC), HPC interconnection networks, enterprise data centre networks, cloud computing, and data-intensive processing in multi-clouds. He serves as the Scientific Leader of Communication Technologies in the RCN-funded infrastructure project eX3 (Experimental Infrastructure for Exploration of Exascale Computing) and has significant experience with both RCN-funded and EU-funded research projects, including the H2020 project Melodic (Multi-cloud Execution-ware for Large-scale Optimised Data-Intensive Computing). Gran received his M.Sc. and Ph.D. degrees in computer science from the Department of Informatics, University of Oslo, in 2007 and 2014, respectively. Both theses focused on different aspects of resource management in high performance interconnection networks. He previously headed the RCN-funded project ERAC (Efficient and Robust Architecture for Big Data Clouds) and led the design, implementation, and deployment of the multi-homed IP-based research testbed NorNet Core. Gran also has several years of experience as a system administrator and scientific programmer. His research interests center on the intersection of high performance computing and networking, with particular focus on anomaly detection in time series data, HPC interconnection networks, network virtualization, and cloud computing infrastructure. His work demonstrates a consistent evolution from fundamental networking research to applied solutions for modern computing challenges, particularly in IoT security and smart home applications. His recent publications show a strong emphasis on developing lightweight, real-time anomaly detection systems using deep learning techniques. Analysis of his publication trends reveals a clear progression from traditional HPC networking research toward time series anomaly detection applications, particularly for IoT systems. His 15 most recent publications show dual focus areas: approximately 60% concentrate on anomaly detection methods for time series data (particularly for IoT applications), while the remaining 40% maintain his foundational work in HPC networking, virtualization, and cloud infrastructure. This evolution demonstrates his ability to adapt core networking expertise to emerging application domains while maintaining technical depth. While no specific scientific awards are mentioned in the provided text, Gran's leadership roles in significant research projects (eX3, Melodic, ERAC) indicate recognition of his research capabilities within the academic and research funding communities. His position as Scientific Leader of Communication Technologies in the RCN-funded eX3 project further demonstrates his standing in the Norwegian research community. Gran's teaching responsibilities include serving as course coordinator for DCSG1006 Data Communication and Networks, DCSG2001 Interconnected Networks and Network Security, and Networks: Administration, Programming and Security. His research leadership extends to significant grant-funded projects, including the RCN-funded eX3 infrastructure project and the EU H2020 Melodic project. His previous leadership of the ERAC project and the NorNet Core research testbed demonstrates sustained ability to secure and manage substantial research funding. His laboratory and team affiliations include the Department of Information Security and Communication Technology at NTNU, where he heads the communication technology discipline, and Simula Research Laboratory, where he maintains an adjunct position. The NorNet Core research testbed, which he led the development of, represents a significant infrastructure contribution to the networking research community. His current work with the eX3 project suggests ongoing involvement in experimental infrastructure for exascale computing exploration.
Manuela Battipede is Associate Professor of Flight Mechanics & Control at the Politecnico di Torino , Department of Mechanical and Aerospace Engineering (DIMEAS). Since 2002 she has led research and teaching in aerospace guidance, airworthiness, neural-network-based virtual sensors, and trajectory optimisation, coordinating EU H2020 and Clean Sky projects, industrial airworthiness certification contracts, and supervising PhD students in aerospace engineering. Education & Academic Career Joined Politecnico di Torino as a confirmed Associate Professor (Prof.ssa Associata Confermata). Visiting Researcher, West Virginia University, USA (April–September 2002). Research Interests Her work integrates control theory , flight mechanics , and artificial-intelligence-based sensing to enhance safety and efficiency of air and space vehicles. Key themes include: 4-D trajectory optimisation for climate-neutral aviation. Certifiable virtual air-data systems using neural networks. Flutter suppression and intelligent flight control for fixed-wing and rotary-wing aircraft. Low-thrust orbital mechanics, collision avoidance, and end-of-life disposal for satellites. Lighter-than-air platforms and VTOL hybrid drones for earth-observation and fire-monitoring missions. Scientific Awards & Recognition PoCN – Proof of Concept Network (2015), AREA Science Park, Italy. Regular evaluator for SESAR Joint Undertaking, EU H2020, and European Commission programmes. Doctoral Advising & Funding Since 2011 she has served on the PhD board of the Aerospace Engineering doctorate at Politecnico di Torino, currently supervising: Giorgio Antonio Orlando (39th cycle, 2023–) Gabriele Tarascio (39th cycle, 2023–) She has been Scientific Director of >20 competitively funded projects (EU Clean Sky MIDAS, ESA, MIUR-PRIN, EASA certification contracts, etc.) and commercial consultancy contracts exceeding €3 M. Laboratories & Teams Battipede leads the Modelling, Simulation and Control of Aircraft research group at DIMEAS, managing real-time hardware-in-the-loop test rigs, CubeSat development platforms, and an integrated multi-aircraft simulation laboratory for education and industrial validation.
Dr. Murat AYDIN is a full-time Assistant Professor (Dr. Lecturer) at Karabük University, Faculty of Technology, Department of Industrial Design Engineering, where he has also served as Vice-Dean since 2022 and Head of Department since 2021. His entire academic career since 2008 has been spent at Karabük University and briefly at Düzce University. Education PhD in Mechanical Education, Karabük University, 2019 MSc in Mechanical Education, Karabük University, 2009 BSc in Design & Construction Teaching, Bülent Ecevit University, 2006 Associate Degree in Technical Programs, Selçuk University, 2003 Research Interests His work centres on the intersection of digital design and manufacturing, spanning computer-aided design & manufacturing , additive manufacturing , machine learning , image processing , and finite-element analysis . Within these themes he investigates process optimisation, mechanical characterisation of printed parts, formability of sheet metals, and advanced sensing techniques such as digital image correlation. Across 41 publications and 33 conference presentations from 2008 to 2024, a clear evolution is visible: early studies on hydraulic sheet-metal forming have gradually expanded to embrace 3D printing, composite filaments, and machine-learning-driven design optimisation, positioning him at the forefront of Industry 4.0-oriented research. Scientific Recognition & Service Associate Editor, International Journal of 3D Printing Technologies and Digital Industry (2022–present) Editorial board member and frequent organiser of national & international congresses on 3D printing and industrial design Peer reviewer for Journal of Cleaner Production and other leading journals Advising & Funding He currently supervises four master’s students on topics ranging from medical-device prototyping to the use of 3D printing in composite aerated-concrete production. His research has been funded by five competitive grants, including two TÜBİTAK-supported national projects (total ≈ ₺1.8 M) and an EU-UNESCO project on 3D printer design curricula. Laboratory & Team He leads the Additive Manufacturing & Digital Design Laboratory at Karabük University, a facility equipped with multi-material FDM printers, high-resolution DIC systems, and tensile-testing rigs, hosting a multidisciplinary team of graduate researchers and industry collaborators.
Gokhan Serhat is a tenure-track Assistant Professor at the Department of Mechanical Engineering , KU Leuven, stationed at the Bruges Campus. He conducts research within the Mecha(tro)nic Systems Dynamics Group and the M-Group and maintains a guest-scientist affiliation with the Max Planck Institute for Intelligent Systems. Education: Ph.D. in Mechanical Engineering, Koç University, 2018 (Marie Curie Fellow) M.Sc. in Computational Mechanics, Technical University of Munich, 2013 B.Sc. in Mechanical Engineering, Middle East Technical University, 2011 Research interests span computational mechanics, numerical methods, design & topology optimization, structural dynamics, composite materials, fiber-path optimisation, functionally graded structures, and bio-mechanical/haptic modelling. His work integrates high-fidelity simulation, laminate-parameter techniques, and additive-manufacturing constraints to create lightweight, variable-stiffness composite structures and tactile/biomechanical devices. Recent articles (2022-2025) reveal a strong trajectory in composite optimisation (anisotropic topology, lamination parameters, manufacturability) alongside interdisciplinary forays into biomechanics & haptics (fingertip dynamics, tactile displays, skin simulation). The portfolio is evenly split between computational-method development and application-oriented studies in aerospace, automotive, and human-interaction domains. Scientific recognition: Marie Curie Early-Stage Research Fellow (doctoral training grant) Research funding & leadership: Promoter, Flemish project “Fiber path and topology optimization of 3D printed composites” (2023-2027) Promoter, FWO/Flemish project “Concurrent fiber path and topology optimization of 3D printed composites” (2022-2024) He teaches three courses at KU Leuven Bruges: Structural Dynamics , Aerospace Structures & Lightweight Design and Mechatronic Design , and is an active member of the Faculty Council and the Department Council.
Prof. Dr. Michael Felux is full Professor and team leader of the Aviation Infrastructure group at the ZHAW School of Engineering , Zurich University of Applied Sciences. He also co-founded and co-owns the Estonian consultancy Navaid OÜ , providing GNSS/CNS expertise while ensuring non-conflict with his academic role. Education Dr.-Ing. in Mechanical Engineering, TU München (2012 – 2018) Dipl.-Tech. Math. in Mathematics, TU München (2003 – 2009) CAS Hochschuldidaktik (Higher-Education Didactics), PHZH (2021) Research Focus Michael Felux’s research centres on safe, secure and efficient aviation communication, navigation and surveillance (CNS) . He investigates GNSS-based augmentation systems (GBAS, SBAS) for precision approach and landing, develops real-time interference detection & localization techniques to counteract jamming and spoofing, and explores high-integrity navigation solutions for unmanned aerial vehicles (UAVs). Additional interests include environmental optimisation of flight procedures and multi-constellation, multi-frequency signal processing . Across more than 50 peer-reviewed publications since 2015, his work consistently targets the intersection of technical robustness and operational feasibility . Recent papers map GNSS disruption events across European airspace, quantify fuel-burn reductions enabled by GBAS-guided continuous-descent approaches, and introduce cost-efficient machine-learning frameworks for real-time localisation of malicious radio-frequency interference. Scientific Awards & Recognition (no specific awards listed in supplied material) Research Funding & Projects Spoofer Localization – Swiss project leader, ongoing EGNSS DFMC for GBAS based operations – EU project leader, ongoing Making I-CNS A Reality – integrated CNS technology, project leader, ongoing High Integrity Satellite Navigation for UAV using Galileo HAS – project leader, ongoing LINA – Shared large-scale infrastructure for safe testing of autonomous systems, team member, ongoing Collision avoidance system for manned & unmanned aircraft via SDR – completed Emission Reduction using Satellite Navigation for Approach Guidance – completed Laboratory & Team As head of the Aviation Infrastructure team at ZHAW, Prof. Felux directs a multidisciplinary group developing next-generation CNS technologies. The team operates dedicated GNSS/GBAS testbeds, flight-trial aircraft, and spectrum-monitoring networks to validate concepts from simulation through to real-world deployment.
