Sermet DEMİR is an Assistant Professor at the Faculty of Engineering , Department of Mechanical Engineering , Doğuş University. His work focuses on additive manufacturing, orthotic device design, and mechanical property optimization of composite materials. He teaches courses such as Experimental Engineering, Manufacturing Technology, and Computer-Aided Design. Education : BSc and MSc in Mechanical Engineering from Marmara University; PhD in Mechanical Engineering from Marmara University (2018). Research Interests center on biomedical devices, 3D printing, and structural analysis. His publications often employ the Taguchi method, Response Surface Methodology (RSM), and Quality Function Deployment (QFD) for design optimization. Recent works explore triply periodic minimal surface (TPMS) metamaterials, war bow mechanics, and adhesive joint performance. Scientific Awards are not explicitly mentioned in the text. His projects are sponsored by Doğuş University Scientific Research Projects Coordination Unit (grants 2021–22-D1-B02).
Ulf Hedestig is a Senior Lecturer at the Department of Informatics, Umeå University. Based in MIT building, Umeå, Sweden, he focuses on technology-mediated education and digital government research. Contact: ulf.hedestig@umu.se , +46 90 786 61 32. Research Themes : IoT in public spaces, user-centered design, digitalization in education and government, mobile learning environments, knowledge transfer challenges Recent Trends : 2020 work on IoT urban applications, 2018 publications on China's digital strategies and O2O business models Key Collaborations : Mikael Söderström, Daniel Forest, Victor Kaptelinin Scientific Recognition : Excellent Teacher pedagogical qualification Distinguished University Teacher educational qualification
Dr. Quynh Do is an International Lecturer (Assistant Professor) in Logistics and Supply Chain Management at The Management School, Lancaster University . Her research focuses on innovative pathways towards sustainable and circular supply chains , addressing critical issues such as waste reduction, decarbonisation, worker rights, and empowerment of marginalized actors like farmers and workers. Her work explores the digital and social innovations that drive ethical sourcing and systemic change, with applications in the textile, food, and manufacturing sectors . She collaborates with organizations such as Reverse Resources, Renewcell, and the Global Fashion Agenda to promote transparent and responsible sourcing practices. 2025: Big data analytics and supply chain learning for resilience and financial performance. 2024: Circular food waste management in Italian fish manufacturing, stakeholder collaborations in fast fashion, digital platforms for waste exchange, and power dynamics in circular supply chains. 2023: Organizational resilience during the pandemic, legitimacy-seeking behaviors in circular transitions, and data-driven supply chain learning. 2022: Global value chain restructuring post-COVID, institutional adoption of circular practices in seafood, and resource mobilization via bricolage. 2021: Systematic reviews on food waste and supply chain agility during crises.
Prof. Dr. Eda Taşçı is a faculty member at the Faculty of Engineering, Dumlupınar University, specializing in Metallurgical and Materials Engineering. With a career spanning over two decades, she has held positions including Research Assistant (2002-2010), Associate Professor (2011-2022), and Professor (2022-present). She served as Deputy Head of Department (2017-2018) and Vocational School Directorate (2018-2021). Education: PhD in Ceramic Engineering (2004-2010), Master's (2001-2004), and Bachelor's (1997-2001) from Anadolu University. Her research focuses on inorganic materials like ceramics and cement, emphasizing production processes, surface properties, and sustainable applications. Key projects include enhancing glaze chemical resistance, pozzolan-cement interactions, and industrial metallic glaze development. Her publications span topics from ancient mudbrick materials to modern ceramic processing, highlighting interdisciplinary work in material science, environmental engineering, and industrial chemistry. She received the Turkish Cement Manufacturers Association Trailblazers Scholarship in 2008. Email: eda.tasci@dpu.edu.tr
Marina Milovanović is a Professor at the University of Singidunum, Faculty of Informatics and Computing, Department of Mathematics. She holds dual doctoral degrees from the Faculty of Science, University of Kragujevac (Department of Mathematics, 2014) and Faculty of Entrepreneurial Business, Union University (2008), along with Master's and Bachelor's degrees from the Faculty of Mathematics, University of Belgrade (2000-2005 and 1995-2000 respectively). Faculty of Science, University of Kragujevac, Department of Mathematics (PhD, 2014) Faculty of Entrepreneurial Business, Union University (PhD, 2008) Faculty of Mathematics, University of Belgrade (Master's, 2000-2005) Faculty of Mathematics, University of Belgrade (Bachelor's, 1995-2000) Svetozar Marković High School, science and mathematics major (1991-1995) Professor Milovanović specializes in Mathematics Education and Educational Technology, with particular expertise in interactive multimedia applications for teaching mathematics. Her research consistently bridges theoretical mathematics with practical educational technology solutions, evolving from traditional multimedia approaches to incorporating cutting-edge AI and machine learning techniques. She has authored multiple books including 'Interactive multimedia in mathematics teaching' (2015) and collections of solved mathematics problems for entrance exams. Her recent publication record through 2025 demonstrates active engagement in interdisciplinary research, particularly at the intersection of educational technology, artificial intelligence, and practical applications in fields ranging from software engineering to medical diagnostics. Her work shows a clear trajectory from foundational educational technology research toward more sophisticated AI-enhanced learning systems. Professor Milovanović has made significant contributions to semantic web applications in education, particularly through Moodle LMS enhancements, and has explored SCADA applications in industrial contexts. Her collaborative research spans multiple countries and institutions, reflecting an international scholarly network. She has extensive experience developing computer tools for engineering education and has published on diverse topics including petroleum industry processes, environmental management, and financial mathematics. Her work demonstrates consistent application of computational approaches to solve domain-specific problems across multiple disciplines.
Bradley Schmerl serves as a Principal Systems Scientist in the Software and Societal Systems Department (S3D) within Carnegie Mellon University's School of Computer Science. His research advances software engineering practices for modern challenges in distributed heterogeneous systems, self-adaptation, and cyber-physical integration. He leads the ABLE research group and actively mentors students in the Masters in Software Engineering program while teaching core courses like Software Architecture and Software Engineering Practicum. Dr. Schmerl's work addresses critical challenges in composing continuously evolving software systems, including components from untrusted third parties and on-the-fly recomposition for environmental changes. His research develops reusable, analyzable tools for software composition with emphasis on model-based adaptation, uncertainty management, and cross-language integration. Key projects include Rainbow for runtime architecture reflection, Acme for formal architectural foundations, and Cyber-physical Systems research linking software models with physical dynamics. Analysis of his 2023-2025 publications reveals intensifying focus on robotics software architecture (particularly ROS-based systems), explainable AI for architectural tradeoff analysis, and configuration management in adaptive systems. Trends show growing integration of machine learning for auto-tuning, empirical studies of misconfigurations, and dimensionality reduction techniques for visualizing design spaces—consistently bridging theoretical rigor with practical tool development for real-world applications. Scientific Awards: No specific awards were documented in the source materials. Dr. Schmerl serves as Practice Area Lead and mentor in CMU's Masters in Software Engineering program, guiding client projects including Rainbow UI for self-adaptive framework interfaces, CoBot UI for telepresence robots, and Educational Telepresence Tasking Language development. His research receives support through ABLE group projects funded by grants targeting software architecture foundations, adaptation mechanisms, and cyber-physical system validation. As a core member of the ABLE research group, he directs investigations into architecture-based self-adaptation with active projects spanning Rainbow (runtime architecture models for dynamic adaptation), Acme (formal architectural styles and tools), and Cyber-physical Systems (software-physical model integration). The group also maintains legacy work in End-User Architecting, Architecture Evolution, and service-oriented platforms for intelligence analysis through SORASCS.
Prof. Dr. Julia Rieck is a Full Professor of Business Administration at the University of Hildesheim , leading the Department of Business Administration and Operations Research within the Faculty of Mathematics, Natural Sciences, Economics and Computer Science. As Dean of the Faculty , she oversees academic programs, quality management, and research initiatives. Her roles include academic advising for the Business Information Systems (B.Sc./M.Sc.) programs and active participation in examination boards and quality committees. Education: PhD in Political Science (Dr. rer. pol.) with summa cum laude (2008), Habilitation at Clausthal University of Technology (2014), and studies in Business Mathematics (Diploma, University of Hamburg, 2003) and Mathematics (Georg-August-University Göttingen, 2000). Research: Focuses on Operations Research , Supply Chain Management , Project Planning , and Logistics . Her work integrates mathematical modeling , machine learning , and real-world applications , particularly in disaster response , dynamic transportation , and sustainable e-commerce . Projects: Leads third-party funded initiatives like "IT für die sorgende Gesellschaft" (AI in healthcare/social sectors) and contributes to the HULLS real-lab (AI in aging societies). Collaborates with regional companies (e.g., Youco, ADITUS) and institutions (HAWK, University of Hannover). Teaching: Emphasizes practical application through case studies, industry partnerships, and the IT-Speed Dating event for student-company connections. Her courses cover project resource planning , logistics , and digital transformation . Labs & Teams: Active in the Institute of Business Administration & Business Information Systems , contributing to the KET Kompetenzwerkstatt (entrepreneurship support) and interdisciplinary teams in AI and sustainability research.
Markus Lange-Hegermann serves as Professor of Mathematics and Data Science at Ostwestfalen-Lippe University of Applied Sciences (TH OWL) since 2018 and holds a board position at the Institute for Industrial Information Technology (inIT). His career bridges academic research and industrial applications, with expertise in translating machine learning theory into practical engineering solutions for automation and manufacturing sectors. His educational foundation includes a Diplom (Master equivalent) in Computer Mathematics from RWTH Aachen University (2004-2008) followed by a Dr. rer. nat. (PhD equivalent) in algorithmic differential algebra (2008-2014). Prior to academia, he gained industry experience at FEV GmbH as an R&D engineer (2014-2017) and P3 automotive GmbH as a Data Science Consultant (2017-2018). Lange-Hegermann's research centers on probabilistic machine learning with distinctive emphasis on physics-informed approaches. He develops Gaussian process methodologies that incorporate differential equations to model time dependencies, uncertainties, and physical constraints in industrial systems. His work enables robust data-based modeling and optimization for cyber-physical systems, with applications spanning predictive maintenance, process control, and quality assurance in manufacturing. Analysis of his 15 most recent publications (2024-2025) reveals consistent innovation in physics-integrated machine learning, particularly using Gaussian processes to solve partial differential equations and optimal control problems. The research demonstrates strong industrial applicability across domains including medical imaging, material science, automotive engineering, and brewing processes, with recurring themes of anomaly detection in time-series data and uncertainty-aware decision making. His scientific contributions have earned significant recognition: Forschungspreis TH OWL (2024) Top reviewer award at NeurIPS (2023) Outstanding reviewer award at NeurIPS (2021) Best poster award at Bosch AI CON (2019) Borchers Plakette for outstanding dissertation (2014) Springorum Denkmünze for outstanding diploma (2009) As chairman of the Data Science study program and vice chairman of undergraduate examination boards, Lange-Hegermann actively shapes academic curricula while supervising graduate theses. His governance roles include serving on professorship search committees at multiple institutions and contributing to examination regulations. He maintains active research funding through collaborations with industrial partners and reviews proposals for initiatives like It's OWL and 3IA Côte d’Azur. Lange-Hegermann leads the Mathematics and Data Sciences research group within inIT, fostering collaboration between theoretical machine learning and industrial automation. He co-founded AICOmmunityOWL and the Informatics Europe working group on Data Analysis and Reporting, while organizing machine learning reading groups and data science hackathons to bridge academic research with industrial problem-solving.
Prof. Dr. Nadine Buczek serves as Professor of Renewable Energies, Nanotechnology and Photonics at the Department of Applied Natural Sciences, Lübeck University of Applied Sciences (TH Lübeck), a position she has held since 2017. She leads the Energy Materials Laboratory and maintains active affiliations with the Climate and Environmental Protection Group, Materials for Storage and Renewable Energy Systems, and Photovoltaics Group. Her research centers on physical principles of renewable energy systems and photonics, with core expertise in solar technology, thermoelectrics, and nanoscale material engineering. She investigates spin wave phenomena in disordered magnetic materials and develops advanced fabrication techniques for silicon nanowires and superlattices using metal-assisted chemical etching, with applications in sustainable energy conversion and storage. Analysis of her 15 most recent publications (2012-2022) reveals consistent focus on condensed matter physics and nanomaterial engineering. Key trends include theoretical modeling of spin dynamics in alloys, structural characterization of etched semiconductor nanostructures, and optimization of nanofabrication processes for renewable energy applications. Her work bridges experimental nanotechnology with computational physics, primarily targeting semiconductor-based energy solutions. The Energy Materials Laboratory under her direction drives interdisciplinary research in photovoltaics and thermoelectric materials, collaborating closely with the Materials for Storage and Renewable Energy Systems group. Current projects emphasize scalable nanofabrication methods and fundamental studies of charge transport in nanostructured materials to advance next-generation renewable energy technologies.
Professor Gao Min Gao is a distinguished academic at Cardiff University's School of Engineering, holding the position of Professor of Energy Materials and Head of the Thermoelectric Laboratory. With over 25 years of experience in thermoelectric research, he has established himself as a leading expert in energy conversion technologies. His career at Cardiff University spans from Research Assistant/Associate (1993-1999) to his current professorship (2016-Present), with progressive academic promotions reflecting his significant contributions to the field. BSc in Semiconductor Physics from Xidian University, China PhD in Thermoelectrics under Professor D M Rowe at Cardiff University, UK Professor Gao's research focuses on fundamental understanding of thermoelectric processes for energy harvesting applications, with key areas including thermoelectric materials and devices, solution processed solar cells (Perovskite, OPV), concentrated photovoltaic/thermoelectric systems, and magnetocaloric materials. His work has significantly advanced the field, particularly through his early contributions to Peltier module applications for waste heat recovery and the development of improved TE module theory. His current research emphasizes novel characterization techniques for thermoelectric processes and innovative concepts for full-spectrum solar energy harvesting based on hybrid PV-TE systems. His extensive publication record demonstrates consistent high-impact research output across thermoelectrics and solar energy conversion. The articles show a clear progression from fundamental thermoelectric theory to practical applications and hybrid systems, with recent work focusing on spectral splitting, advanced concentrator designs, and novel material systems like Fe11Ti3Al6 alloys. His research bridges fundamental physics with practical engineering applications, particularly in waste heat recovery and solar energy harvesting. Board Member of European Thermoelectric Society (2013-2019) Member of EPSRC Review College (2016-Present) Theme coordinator (Device Physics), UK Thermoelectric Network (2016-Present) Independent expert for EC H2020 Programme (2014-2016) Professor Gao has supervised numerous PhD students, with current projects spanning laser micro-spectroscopy, next-generation photovoltaics, graphene/ceramic composites, and full-spectrum solar energy harvesting. His externally funded research includes significant projects such as the EU-RFCS-funded 'Development of innovative TEG systems optimized for energy harvesting from EAF off-gas cooling water' (2020-2024) and the EPSRC SUPERGEN project on 'Environmental impact of perovskite solar cell' (2019). His Thermoelectric Laboratory at Cardiff University serves as a hub for cutting-edge research in energy materials and conversion technologies.
BAI Jiaming is an Associate Professor at the Department of Mechanical and Energy Engineering, College of Engineering, Southern University of Science and Technology (SUSTech). His research focuses on additive manufacturing (3D printing) of ceramics, nanocomposites, and functional materials, with applications in energy, biomedical engineering, aerospace, and electronics. Education: PhD (2014), MSc (2009), Loughborough University; BSc (2008), Beijing University of Chemical Technology Research interests: Development of high-speed 3D printing systems, additive design optimization, and industrialization of ceramic/nanocomposite manufacturing. His work bridges material science, structural engineering, and applied technologies. Recent publications highlight advancements in ceramic composites for biomedical and energy applications, graphene-based energy storage systems, and process innovations for zirconia and polymer composites. Key themes include material dispersion, thermal properties, and biomimetic design. Scientific Awards: Fellow of the Institute of Materials, Minerals and Mining (FIMMM); Top 2% Scientists worldwide Advising: Actively recruits postdoctoral fellows, PhD/Master's students, and research assistants. His group has secured over 10 national/provincial research grants. Labs: Affiliated with SUSTech’s Shenzhen Key Laboratory of Additive Manufacturing of High-Performance Materials, which focuses on overcoming industrial bottlenecks in metal/polymer/ceramic AM.
Vera Popovich is a researcher in the Department of Mechanical Engineering at Delft University of Technology and a member of Team Vera Popovich. Her work focuses on advanced manufacturing techniques and material behavior analysis. Education: MSc in Engineering (implied PhD) Her research spans additive manufacturing, microstructure engineering, and material degradation mechanisms: Specializes in additive manufacturing processes and their impact on material microstructure. Investigates hydrogen embrittlement in high-strength steels. Pioneers texture control for corrosion resistance in NiTi alloys. Studies fatigue crack propagation in bi-material systems. Recent publications highlight computational modeling of grain structures, interface mechanics in wire-arc additive manufacturing, and advanced characterization techniques for material degradation. She contributes to editorial activities as an editor for Applied Sciences . Scientific Awards: 2012 Poster Prize: X-ray diffraction stress analysis in silicon solar cells She collaborates on projects like the Rhizome initiative (2021-2022) for off-Earth habitat robotics and participates in public engagement, including a 2023 media feature on Delft's 3D-printing lab.
Troy McDaniel is an Assistant Professor at Arizona State University's School of Manufacturing Systems and Networks, specializing in haptic interfaces and assistive technologies for people with disabilities. With over 50 peer-reviewed publications and two authored books, his work bridges engineering, computer science, and healthcare to develop innovative rehabilitation solutions. Ph.D. from Arizona State University His research focuses on haptic perception and human augmentation through wearable technologies, with emphasis on assistive devices for motor and cognitive rehabilitation. Key areas include vibrotactile communication systems, social robotics for elderly care, and machine learning applications for activity recognition. His work prioritizes user-centered design for real-world disability challenges. Recent publications (2023-2025) demonstrate strong trends in haptic neuro-spatial rehabilitation, executive function therapy apps, and social robot companionship systems. His research increasingly integrates privacy-preserving AI for smart city health applications while maintaining clinical validity through partnerships with institutions like Mayo Clinic. Multiple Top 5% teaching awards for faculty at the Ira A. Fulton Schools of Engineering Dr. McDaniel advises graduate students in manufacturing systems and robotics through dissertation committees (MFG 799, CSE 799), with recent projects spanning haptic training simulations to PERACTIV activity monitoring systems. His research funding includes significant NSF grants like the IGERT program on person-centered technologies for disabilities and collaborations with Intel Corp on smart stadium applications. He contributes to ASU's Smart Living Research initiative, developing haptic neuro-spatial rehabilitation devices and social robotics frameworks within interdisciplinary teams focused on translating lab innovations to community health solutions.
Hannah Blum serves as the Alain H. Peyrot Associate Professor in Structural Engineering within the Department of Civil and Environmental Engineering at the University of Wisconsin-Madison. Her research program focuses on infrastructure resilience, next-generation structural design methodologies, and advanced visualization techniques including extended reality applications, supported by funding from federal agencies, industry associations, and private companies. Her academic credentials include a PhD in Civil Engineering from the University of Sydney (2017), complemented by MS (2012) and BS (2010) degrees in Civil Engineering from Johns Hopkins University. Dr. Blum's research program spans critical domains in structural engineering with particular emphasis on steel systems. Key focus areas include: Steel, cold-formed steel, and stainless-steel structural systems Steel deck and joist system behavior Structural stability and reliability analysis Virtual and augmented reality applications in structural steel fabrication Data-driven approaches to structural engineering problems Analysis of her 15 most recent publications (2023-2025) reveals a concentrated research trajectory centered on data-driven design methodologies, advanced material systems (particularly stainless steel and high-strength alloys), and immersive technology integration. Notable trends include machine learning applications for buckling prediction, experimental validation of novel structural systems, and mixed-reality solutions for fabrication processes. Her distinguished recognition includes: University of Wisconsin-Madison Chancellor’s Teaching Innovation Award (2024) College of Engineering Harvey Spangler Award for Innovative Teaching (2023) American Institute of Steel Construction Terry Peshia Early Career Faculty Award (2023) Structural Stability Research Council McGuire Award for Junior Researchers (2022) Structural Stability Research Council Yoon Duk Kim Young Researcher Award (2021) Dr. Blum actively mentors graduate students through CIV ENGR 790 (Master's Research) and 890 (Pre-Dissertator's Research) courses while securing diverse research funding streams. Her professional service includes active participation in steel design standards committees for structural, cold-formed, and stainless-steel systems through organizations like the Structural Stability Research Council. Her experimental and computational research requires specialized facilities for structural testing and digital visualization, though specific laboratory names are not documented in the provided materials. Current projects demonstrate strong industry collaboration, particularly with steel manufacturing and construction technology firms.
Robrecht Abts is a researcher at KU Leuven’s Manufacturing Processes and Systems (MaPS) group, affiliated with the De Nayer Campus. His work bridges electromechanics, materials science, and advanced manufacturing techniques. His research focuses on structure, behavior, and sustainability of materials , with particular emphasis on thermal analysis in additive manufacturing processes like fused filament fabrication (FFF). This includes leveraging deep learning for real-time thermal data interpretation and optimizing electrical discharge machining (EDM) through adaptive pulse classification. Recent publications highlight his expertise in Threshold-free machine learning pipelines Thermal modeling in 3D printing COMSOL simulations for manufacturing processes
