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Yaoxin WuView profile

Assistant Professor

• Deep Learning
• Combinatorial Optimization
• Integer Programming
• +5 more

Yaoxin Wu is an Assistant Professor at the Eindhoven University of Technology, affiliated with the Department of Industrial Engineering and Innovation Sciences. His research bridges deep learning and combinatorial optimization to solve complex problems in transportation, scheduling, and network design. Education : PhD in Computer Science from Nanyang Technological University (2023). Wu specializes in artificial intelligence and operations research , focusing on graph neural networks, stochastic programming, and multi-objective optimization. His work has significant applications in UAV routing and on-demand delivery systems. His 2025 publications highlight trends in neural combinatorial optimization for stochastic job shop scheduling, ride-hailing, and drone logistics. Key subfields include deep reinforcement learning, preference modeling, and topological graph learning. He has supervised 9 students, including PhD candidates Xia Jiang and Igor Smite, and Master’s students like Venkata Roshan Mannepu and Floor Halkes. Wu's research is funded by projects like LEO (Holland High Tech | TKI HSTM) and SURF Cooperative grants. His educational activities include teaching Fundamentals of Algorithmic Programming and AI-Driven Business Operations , emphasizing data-driven methods for manufacturing processes.

Theo SaletView profile

Professor

• 3D Concrete Printing
• Structural Design
• Concrete Technology
• +7 more

Theo Salet serves as Dean and Full Professor of Structural Design/Concrete Structures at the Department of the Built Environment (Eindhoven University of Technology). He pioneered research integrating design and construction in concrete through computational models like 'Parametric Design Tools' and 'Numerical Concrete' for advanced finite element analysis. Current affiliations: 3D Concrete Printing group, EAISI High Tech Systems, Built Environment department Research focus: 3D printing of functional construction materials, self-sensing concrete with embedded nanomaterials, Industry 4.0 applications in construction His recent publications analyze: (1) data-driven approaches to 3D concrete printing process monitoring (2025), (2) electrical conductivity modeling of carbon nanotube-cementitious materials (2024), and (3) real-time residence time measurement in concrete processing (2024). Research is supported by grants from the Dutch Research Council (NWO) and Saint-Gobain Weber Beamix. Key contributions include developing: (1) domain knowledge-enhanced sensor systems for moisture/heat monitoring in additive manufacturing, (2) analytical models for nanocomposite conductivity validation, and (3) experimental frameworks for improving 3D-printed concrete robustness. His lab operates a proprietary 3D concrete printer for structural applications and reinforcement embedding research.

• Bioprocess Engineering
• Downstream Processing
• Biopharmaceuticals
• +4 more

Prof. Dr. Michel H.M. Eppink is a Special Professor in “Industrial Biopharmaceutical Downstream Processing” at the Department of Biotechnology, Faculty of Applied Sciences, Delft University of Technology. With over 35 years of experience in biomolecule processing, he brings extensive industry expertise from Organon NV and Byondis BV to his academic role. His current position is part-time (0.2 FTE), focusing on integrating harvesting, cell disruption, extraction, separation and formulation technologies for biopharmaceuticals. His research interests include developing lean production processes for biopharmaceuticals with emphasis on green technologies, circular economy principles, and cost reduction in medicine production. He specifically investigates fractionation technologies and process integration for biopharmaceutical proteins including monoclonal antibodies and antibody-drug conjugates. His work addresses bottlenecks in process robustness, efficiency, product quality, and waste reduction. Prof. Eppink has received his MSc in Biology/Chemistry from Utrecht University (1993) and PhD in Biochemistry from Wageningen University (1999). Before joining TU Delft in 2024, he served as part-time Professor at Wageningen University from 2014-2024, leading research on biorefinery technologies for proteins, lipids, carbohydrates and pigments from various eukaryotic organisms. His recent publications focus on chromatography optimization and digital twin applications in biopharmaceutical process development, reflecting his expertise in improving efficiency and sustainability in biomanufacturing processes. These works demonstrate trends toward continuous processing, high-throughput screening, and data-driven optimization in downstream biopharmaceutical production. Prof. Eppink actively contributes to education through advanced courses in Bioprocess Design and Downstream Processing at the Biotechnology Academy Delft, as well as lectures at Wageningen University, Leiden University, Utrecht University, Karlsruhe Institute of Technology, Radboud University, and Maastricht University. His office is located in Building 58, room C2.150 at TU Delft, and he can be reached at M.H.M.Eppink@tudelft.nl or +31 683169622.

Annekoos SchaapView profile

Researcher

• Medical Imaging
• Artificial Intelligence
• Machine Learning
• +3 more

Annekoos Schaap is a Doctoral Candidate at Eindhoven University of Technology's Department of Electrical Engineering, working within the Electronic Systems research group and Eindhoven MedTech Innovation Center. Supervised by Professor Sveta Zinger and Part-time Professor Danny Ruijters, her research bridges AI-driven medical imaging and clinical applications. Her primary research interests focus on AI-powered medical image processing for healthcare optimization. Key areas include: Development of machine learning algorithms for cerebral aneurysm treatment Radiogenomic analysis linking MRI imaging with genomic/transcriptomic data Workflow automation to reduce cognitive load in medical interventions Non-invasive prostate cancer prognosis systems Recent publications demonstrate her specialization in correlating quantitative imaging features with underlying genomic landscapes, particularly in prostate cancer research. Her work emphasizes clinical usability through reader studies and real-world implementation of AI-assisted procedural guidance systems. Current projects target reducing X-ray exposure and procedural time in minimally invasive interventions while improving diagnostic accuracy. Annekoos is embedded in two key research ecosystems: Electronic Systems Group : Focusing on hardware/software co-design for medical applications Eindhoven MedTech Innovation Center : Translating engineering solutions to clinical practice

Saskia HaitjemaView profile

Associate Professor

• Cardiovascular Disease
• Data Analytics in Medicine
• Personalized Healthcare
• +3 more

Saskia Haitjema is an Associate Professor at the Central Diagnostic Laboratory of Utrecht University Medical Center (UMC Utrecht) and head of the Utrecht Patient Oriented Database (UPOD). She contributes to the Circulatory Health strategic program and leads transplantation-focused research on infection-immunity interactions. Her educational background includes: Medicine and Linguistics at Utrecht University PhD in Experimental Cardiology (2017) Her research pioneers data-driven diagnostics using routine hematology datasets and the learning healthcare system approach. She focuses on personalizing cardiovascular care through sex-difference analysis in atherosclerosis and AI-enhanced diagnostic workflows, with strong emphasis on multidisciplinary collaboration across UMC Utrecht. Recent 2025 publications demonstrate her dual focus on cardiovascular analytics (pandemic-era risk management, coronary imaging optimization) and translational immunology (pediatric hypogammaglobulinemia, respiratory biomarkers), consistently leveraging machine learning for clinical decision support. No scientific awards were documented in source materials. Dr. Haitjema has supervised graduate students and serves as external advisor for research funding bodies, including Veni grant evaluations at ZonMw and strategic guidance for the ZorgVoorData healthcare initiative. She directs the Utrecht Patient Oriented Database (UPOD) lab, which transforms routine clinical data into structured research assets, and previously co-led UMC Utrecht's Applied Data Analytics in Medicine program (2017-2020).

• Artificial Intelligence
• Robotics
• Materials Science
• +3 more

Dengpeng Huang is an Assistant Professor specializing in Artificial Intelligence and Robotics within the field of Elastomer Technology and Engineering . His work bridges computational modeling with advanced materials, focusing on applications in smart materials and mechanical systems. Research Interests: Development of AI-driven models for predicting elastomer properties Multiscale analysis of rubber composites Electromechanical coupling in dielectric elastomer actuators Meshfree methods for metal cutting and chip formation Ultra-precision polishing of optical surfaces Recent Trends: His 2024–2025 publications emphasize data-driven modeling of rubber's viscoelastic behavior, multiscale analysis of composites, and CNN-based approaches for material characterization. Earlier work (2014–2022) explores tool path optimization, beam modeling, and computational machining. Scientific Recognition: Holds an h-index of 5 according to Scopus citations, with recognition as an AI and robotics expert in the service industry. Advising & Collaboration: Collaborates with researchers like Anna Blume, Evgeny Karaseva, and Tim Bor on elastomer composites. Supervised at least one academic work, though specific students are not named in the provided data. Labs & Teams: Affiliated with simulation and robotics teams in the smart materials sector, likely within an advanced materials or mechanical engineering research group.

Anne ZanderView profile

Assistant Professor

• Operations Research
• Healthcare Systems
• Artificial Intelligence
• +5 more

Anne Zander is an Assistant Professor affiliated with the Digital Society Institute, Mathematics of Operations Research, and TechMed Centre at Karlsruhe Institute of Technology (KIT). Her work bridges mathematical modeling with healthcare systems, focusing on operational efficiency and data-driven decision-making in medical environments. Research Interests: Zander specializes in applying operations research methodologies to healthcare challenges, including ambulatory care logistics, surgical process optimization, and pandemic response planning. Her work incorporates data mining, reinforcement learning, and stochastic programming to improve patient flow, resource allocation, and system resilience. Publications: Recent research spans operating room benchmarking (2024), bed allocation during pandemics (2023), vaccination process optimization (2022), and innovative teaching approaches for operations research (2018). Her work often involves collaborations with healthcare institutions and interdisciplinary teams. Additional Contributions: Zander co-created open datasets for surgical process durations (2022) and participated in media discussions about cross-border healthcare collaboration (2024) and hospital patient retention strategies (2024). Her research aligns with UN Sustainable Development Goals for health and well-being.

• Artificial Intelligence
• Data Mining
• Process Mining
• +5 more

Faiza Allah Bukhsh is an Associate Professor specializing in Artificial Intelligence, Data Mining, Process Mining, Health Informatics, Cybersecurity, and Ethical AI. Her work bridges technical innovation with societal impact, particularly in healthcare systems analysis, telecommunications resilience, and ethical data governance. Digital Society Institute TechMed Centre Datamanagement & Biometrics Her research focuses on Explainable AI , Process Mining , and Privacy Assurance in healthcare systems, with recent work on AI music perception, sepsis treatment analysis, and privacy-utility trade-offs. Key article trends include: AI in music and creative domains Process Mining for healthcare insights Explainable Machine Learning workflows Privacy-preserving analytics Telcom infrastructure resilience

Marcos MachadoView profile

Assistant Professor

• Artificial Intelligence
• Data Science
• Machine Learning
• +6 more

Marcos Machado is an Assistant Professor in Industrial Engineering & Business Information Systems, focusing on Artificial Intelligence, Machine Learning, and Sustainable Industry applications. His research spans Financial Technology (e.g., credit risk assessment, fraud detection), Energy Efficiency in industrial contexts, and Circular Economy strategies for SMEs. Current research trends highlight hybrid AI models integrating traditional time series analysis Key domains: Retail banking, steel industry optimization, commodity price modeling Contributions to UN SDGs: Climate Action, Responsible Consumption, Industry Innovation Recent activities include international collaborations and presentations on circular economy frameworks.

• Artificial Intelligence
• Systems Engineering
• Cyber-Physical Production Systems
• +5 more

Dr. Saber Darmoul is an Associate Professor specializing in Systems Engineering and Multidisciplinary Design. His research bridges artificial intelligence with industrial applications, focusing on cyber-physical production systems, operational resilience, and smart transportation. He actively explores knowledge representation, reinforcement learning, and agent-based modeling to address complex system challenges. Key Research Interests: Operational resilience, AI in manufacturing, multi-agent systems, smart mobility Technical Expertise: Ontology modeling, simulation platforms, immune-inspired control architectures His recent publications (2025-2019) demonstrate a consistent focus on integrating artificial immune systems into control architectures for transportation and manufacturing. Notable trends include: Development of knowledge-based systems for dynamic reconfiguration Application of multi-criteria decision frameworks in production environments Advancing predictive maintenance strategies through distributed systems Exploring 6G system-of-systems engineering While no explicit awards or student advisement information appears in available records, his 15 most recent publications reflect sustained academic productivity and evolving focus from foundational control systems (2017-2019) to advanced applications in Industry 4.0 (2020-2025).

Farzad VazinramView profile

Researcher

• Deep Learning
• Energy Systems
• Optimization
• +5 more

Dr. Farzad Vazinram is a Postdoc Researcher in the Data Management and Biometrics Group at the University of Twente's Faculty of Electrical Engineering, Mathematics, and Computer Science. With a background in Electrical Engineering (Power Systems) and a second M.Sc. in Smart Systems Engineering, he combines industrial experience from CATERPILLAR and Grid Management Company with academic expertise in Scalable Energy-efficient Deep Learning through the MISD project. B.Sc., M.Sc., Ph.D. in Electrical Engineering 2025 Cum Laude M.Sc. award recipient Research Interests span multiple domains: Deep Learning : Energy-efficient training methods, weight transfer optimization Energy Systems : Microgrid modeling, combined gas-electricity networks Optimization : Power flow calculations, anomaly detection algorithms Smart Systems : Data-driven approaches for grid efficiency Recent publications (2025) demonstrate 40% energy consumption reduction in neural network training while maintaining accuracy. His work aligns with UN Sustainable Development Goals for Education and Clean Energy through technical innovations in grid optimization and AI efficiency. Scientific Recognition : Cum Laude award for M.Sc. research (2024) Scopus-indexed publications with 17 citations Active in academic service as a peer-review editor for the 21st International Conference on Artificial Intelligence Applications and Innovations (2025).

Sebastian ThiedeView profile

Professor

• Sustainable Manufacturing
• Smart Industry
• Human-Centered Manufacturing
• +8 more

Sebastian Thiede is a Full Professor specializing in Manufacturing Systems , with extensive research contributions to Smart Industry , Sustainable Manufacturing , and Human-Centered Production . His work bridges advanced technologies like Machine Learning , Simulation , and Artificial Intelligence with industrial applications, addressing critical challenges in Circular Economy , Energy Efficiency , and Factory Decarbonization . Research focuses on Battery Manufacturing , Edge Computing , and Real-Time Locating Systems (RTLS) . Key methodologies include Digital Twinning , Surrogate Modeling , and Data-Driven Process Optimization . Recent publications highlight trends in Autonomous Sensor Data Analysis (2025), Cyber-Physical Architectures for reconfigurable systems, and Circular Transition Methodologies for manufacturing. His work often integrates Human Factors with Smart Automation , emphasizing environmental and economic impacts.

Christoph BruneView profile

Professor

• Machine Learning
• Deep Learning
• Computer Vision
• +7 more

Christoph Brune is a Full Professor at the University of Twente , affiliated with the Digital Society Institute and TechMed Centre . His research bridges Artificial Intelligence , Deep Learning , and Medical Imaging with applications in Robotics and Computational Fluid Dynamics . Keywords: Machine Learning , Network Analysis , Optimal Transport , Medical Image Segmentation Research Trends : Recent publications focus on physics-informed neural networks , equivariant learning for vascular modeling, and operator learning for PDE-constrained problems. His work emphasizes geometric deep learning , implicit neural representations , and robust AI for clinical settings. Scientific Awards : CMI-NEN seed-money award (2022) for medical imaging innovation Collaborative Networks : Active in graph neural networks , optimal transport , and Barron space theory . Key collaborations include Zenodo dataset contributions and ArXiv preprints with multidisciplinary teams in AI, medicine, and engineering.

Estefanía Talavera MartínezView profile

Assistant Professor

• Artificial Intelligence
• Computer Vision
• Deep Learning
• +5 more

Estefanía Talavera Martínez is an Assistant Professor specializing in Datamanagement & Biometrics , with research spanning artificial intelligence, computer vision, and health informatics. Her work addresses surveillance, emotion recognition, and egocentric data analysis.

• Artificial Intelligence
• Edge AI
• Sensors
• +7 more

Amirreza Yousefzadeh is an Assistant Professor specializing in computer architecture design for embedded systems. His research focuses on hardware acceleration for artificial intelligence, particularly in energy-efficient neuromorphic computing and edge AI applications. Research Interests Neuromorphic computing architectures Event-driven AI hardware Sparsity exploitation in neural networks Embedded vision systems Digital circuit design for AI Research Trends Recent work (2024-2025) demonstrates expertise in spiking neural networks (SNNs), activation sparsity, and hardware-software co-design for neuromorphic processors. Key areas include object detection, energy efficiency optimization, and digital implementations of synaptic delays. Technical Contributions Developed SENMap for multi-objective data-flow mapping Created SENSIM simulator for multi-core neuromorphic systems Investigated 3D stacking for memory-dominated architectures Explored temporal sparsity in event-based processing