Rohollah Ghasemi is a Senior Lecturer in Mechanical Engineering at the School of Engineering Science , University of Skövde. His research focuses on materials science , manufacturing processes , and tribology , with particular emphasis on compacted/graphite iron microstructure analysis and laser welding optimization . His recent publications (2022–2025) explore advanced manufacturing techniques, including deep learning applications for welding quality prediction and multi-physics simulations for recoil force validation. Earlier works (2014–2019) investigate abrasion resistance , scratch mechanisms , and austempering variables in cast iron systems. Key Research Trends : Process-induced stress modeling, laser welding of dissimilar materials, microstructural engineering of CGI, tribological behavior under load, meta-model optimization Collaborations : Kent Salomonsson, Tobias J. Andersson, Anders E.W. Jarfors, Attila Dioszegi
Konstantinos G. Arvanitis is a Professor in the Department of Natural Resources Management & Agricultural Engineering at the Agricultural University of Athens, affiliated with the Agricultural Engineering Laboratory. He holds a PhD in Electrical and Computer Engineering from the National Technical University of Athens and has held diverse research and teaching roles across Greek institutions. Teaches courses on electrical engineering, sensors, automation, and AI in agriculture Supervised 2 PhD, 9 MSc theses, and guides 6 ongoing PhD projects Member of FEANI, EurAgEng, and IFAC Technical Committee (2000-2017) His research integrates IoT, machine learning, and cyber-physical systems into agricultural practices, focusing on precision agriculture and sustainability. He contributes to editorial boards of journals like Sensors and Energy , and has published over 342 technical works with significant citation metrics. His recent work explores quantum technologies, edge computing, and 3D printing in agricultural systems, emphasizing energy efficiency and educational innovation. He actively reviews for 130+ journals and conferences.
Chrysanthos Maraveas is an Assistant Professor at the Department of Technology and Natural Resources Management & Agricultural Engineering in the School of Environment and Agricultural Engineering at the Agricultural University of Athens since 2022. He earned his PhD in Structural Engineering from the University of Manchester and has held postdoctoral research positions at the University of Liège (EU-funded) and the University of Patras (Greek Ministry of Development-funded). Research Interests : Applications of AI and quantum computing in agriculture Sustainable materials and biodegradable polymers from agricultural waste Internet of Things (IoT) for greenhouse optimization Structural durability and corrosion resistance in agricultural environments Plastic waste management in agrifood systems 4D printing for sustainable agricultural plastics Scientific Recognition : Ranked in the top 2% of scientists worldwide by Stanford University (based on citations and h-index) Publications focus on: Cybersecurity in Agriculture 4.0/5.0 Smart sensors and edge computing for resource management Biopolymer innovations and nanotechnology Structural analysis of agricultural systems Sustainable construction materials from agro-waste Fire resistance in steel and composite structures
Dr. Zoltán Kis serves as a Senior Lecturer (Associate Professor) in the School of Chemical, Materials and Biological Engineering at The University of Sheffield and holds an Honorary Lecturer position at Imperial College London's Department of Chemical Engineering. His research focuses on innovating disease-agnostic RNA vaccine and therapeutics manufacturing platforms through process digitalization and intensification. Dr. Kis earned his Ph.D. in Bioengineering from Imperial College London, complemented by an M.Sc. in Applied Biotechnology and a B.Eng. in Chemical with Biochemical Engineering. His interdisciplinary training bridges chemical engineering, biotechnology, and bioengineering disciplines. His research integrates experimental and computational methodologies to revolutionize mRNA production: Development of continuous enzymatic synthesis, purification, and LNP formulation processes Process intensification through novel unit operations and equipment design Digital twin deployment for real-time monitoring and control Techno-economic modeling to reduce production costs Quality by Digital Design framework implementation for regulatory compliance Analysis of recent publications reveals dominant trends in continuous bioprocessing and digital transformation of mRNA manufacturing. Key subfields include oligo-dT chromatography optimization, tangential flow filtration for mRNA purification, and digital twin applications for process control, with strong emphasis on pandemic-response capabilities and cost reduction strategies. Dr. Kis actively supervises PhD students in mRNA bioprocessing and teaches Biopharmaceutical Manufacturing (CPE336/CPE6043) and Introduction to Bioengineering (BIE103). His industry engagement includes advisory roles on Sanofi's mRNA CMC Board and Pfizer's mRNA Technology Advisory Board. He leads the RNA Manufacturing Innovation Team and has secured substantial research funding, including: £3.7 million CEPI grant for RNAbox platform (2024-2027) £7.6 million UK-SEA Vaccine Manufacturing Hub (2023-2028) £2 million Innovate UK project for automated RNA platform (2023-2025) Multi-million USD Wellcome Leap R3 grant for distributed RNA production His work demonstrates significant impact through industry partnerships, policy advisory roles including WHO mRNA Technology Transfer Hub consultancy, and leadership in advancing global vaccine manufacturing capabilities.
Dr. Kevin Austin is a Research Fellow at the School of Mechanical and Mining Engineering, Faculty of Engineering, Architecture and Information Technology at the University of Queensland. His work is affiliated with the Future Autonomous Systems and Technologies research group, where he focuses on automation and robotics applications in mining engineering. Dr. Austin received his academic qualifications from the University of Queensland, including a Bachelor (Honours) of Engineering and a Doctor of Philosophy. Dr. Austin's research centers on mining automation and autonomous systems for heavy machinery operations. His work spans several key areas including dragline operation planning, excavation sequencing, terrain mapping for autonomous bulldozers, and hyperspectral imaging for ore grade discrimination. He has made significant contributions to the development of algorithms for mining equipment automation, particularly in the areas of Monte-Carlo Tree Search for dragline operation planning and Iterative Closest Point variants for terrain scan matching. His research bridges theoretical advances in robotics and artificial intelligence with practical applications in the mining industry, focusing on improving efficiency, safety, and productivity. Dr. Austin has been involved in numerous research projects funded by industry partners including Caterpillar Inc and the Australian Coal Association Research Program (ACARP). His current research focuses on coal stockpile management for remote bulldozers, semi-autonomous bulldozers for mine site rehabilitation, and articulated truck automated systems. His work demonstrates a strong industry connection with practical applications in mining operations. As an academic supervisor, Dr. Austin has served as an Associate Advisor for multiple PhD and Master's students at the University of Queensland. His supervision portfolio includes research on real-time terrain mapping for autonomous bulldozers, mission planning for autonomous excavation, dragline excavation sequencing, and scan matching for terrain mapping in open-pit mining. His collaborative approach is evident in his work with other faculty members, particularly Professor Ross McAree. Dr. Austin's laboratory and research team work closely with industry partners through the Future Autonomous Systems and Technologies group. They maintain strong connections with mining equipment manufacturers and coal mining operations, ensuring their research addresses real-world challenges in the mining sector. Their facilities likely include simulation environments for mining equipment operation, testbeds for autonomous systems, and data analysis platforms for mining process optimization.
Dr. Xuliang Li is a Postdoctoral Research Fellow at the School of Mechanical and Mining Engineering, The University of Queensland, within the Faculty of Engineering, Architecture and Information Technology. His work focuses on advanced materials processing, particularly in precision machining of brittle solids and nanoscale surface engineering. Research Themes: Brittle material machining, graphene oxide applications, machine learning integration in manufacturing, and multiscale modeling of deformation mechanisms Recent Publications: 14 studies (2019-2024) covering nanoscratch mechanics, laser 3D-printed ceramics, and innovative polishing techniques Funding: Currently developing ductile grinding techniques for dental prostheses applications (2025-2026) Expertise: Available for supervision in advanced manufacturing and materials science domains Key areas include computational modeling of grinding processes, hybrid nanoparticle suspensions, and ductile-to-brittle transition analysis. His collaborations span multiple disciplines in mechanical engineering and materials science.
Davood Pourkargar is an Assistant Professor in the Tim Taylor Department of Chemical Engineering at Kansas State University. He is also a Graduate Faculty Member at the Food Science Institute and a Faculty Researcher at the Johnson Cancer Research Center. His work focuses on integrating data with first-principle models to understand complex systems across multiple scales. Ph.D. in Chemical Engineering from Pennsylvania State University (2015) M.S. in Process Simulation and Control from Sharif University of Technology (2010) B.S. in Chemical Engineering from Sharif University of Technology (2008) His research interests span computational multiscale modeling, digital twin development, applied artificial intelligence, and optimization-based control of complex process networks. Dr. Pourkargar's work integrates process systems engineering with artificial intelligence to address challenging problems in chemical, biological, energy, and food systems. He develops intelligent frameworks for controlling complex process networks, designing cyber-physical architectures for smart manufacturing, and advancing system identification using machine learning and process data analytics. A significant aspect of his research involves physics-informed machine learning applied to cancer dynamics modeling and drug distribution in the human body. Dr. Pourkargar's publication record shows a strong focus on predictive modeling and control of chemical processes, particularly ammonia synthesis systems, polysilicon reactor systems, and food extrusion processes. His recent work increasingly incorporates machine learning techniques, especially transformer architectures and physics-informed approaches, applied to both traditional chemical processes and emerging areas like organ-on-a-chip systems for drug discovery. 2024 Carl R. Ice College of Engineering Outstanding Assistant Professor Award NSF EPSCoR Research Fellowship 2023 Kansas EPSCoR First Award AFOSR Faculty Fellowship Big XII Faculty Fellowship Robert F. Smith School Distinguished Junior Researcher Award from Cornell University (2017) O. Hugo Schuck Best Paper Award (2014) Dr. Pourkargar has successfully mentored numerous graduate students through their master's and doctoral research, with several receiving departmental and college-level awards. His research has been supported by significant grants from the National Science Foundation, Kansas EPSCoR, and K-State's Global Food Systems initiative. His lab has presented extensively at major conferences including AIChE Annual Meetings and American Control Conferences. The Intelligent Systems and Process Systems Laboratory (ISPSL) led by Dr. Pourkargar operates computational and experimental facilities in Durland Hall. The lab is expanding into robotic additive manufacturing and autonomous biomanufacturing, supported by research infrastructure grants. The group maintains active collaborations with the Johnson Cancer Research Center and the Terasaki Institute for Biomedical Innovation.
Dr. Cameron Brown is a Reader (equivalent to Associate Professor) at the Strathclyde Institute of Pharmacy and Biomedical Sciences, University of Strathclyde, Glasgow. He specializes in developing digital design tools and strategies for pharmaceutical manufacturing. Brown joined Strathclyde in 2014 and has progressed through research associate, research fellow, and Chancellor's fellow positions. He currently coordinates the Drug Substance Manufacturing module for the Advanced Pharmaceuticals Manufacturing MSc program. Education: Brown holds a PhD in crystallization process characterization and a Chemical Engineering degree, both from Heriot-Watt University. Research Focus: His work centers on three primary areas: Hybrid modeling approaches : Integrating physics-based and data-driven models to enhance drug substance manufacturing efficiency Self-driving labs : Developing automated systems for drug substance process development with model-based experimental design Digital decision-making : Implementing coupled models through GenAI and LLMs for rapid pharmaceutical process development His research contributes to UN Sustainable Development Goals through improved medicine manufacturing sustainability. Publication Trends: Brown's recent articles focus on pharmaceutical crystallization, digital design methodologies, AI applications in manufacturing, and process optimization. His work demonstrates strong emphasis on translating computational models into industrial practice, particularly in continuous manufacturing and quality-by-design frameworks. Honors: Elected staff officer of British Association of Crystal Growth (2024) Research Leadership: Brown serves as Principal Investigator for PharmaCrystNet and co-investigator on multiple major initiatives including Digital Design and Manufacturing of Amorphous Pharmaceuticals, Future CMAC Manufacturing Hub, Accelerated Discovery and Development of New Medicines Prosperity Partnership, and ARTICULAR. He leads knowledge exchange projects with pharmaceutical companies and manages knowledge transfer partnerships. Professional Engagement: Brown is active in the Acceleration Consortium and serves on the committee of the British Association of Crystal Growth.
Professor James Taylor is a distinguished academic at Lancaster University where he holds a Personal Chair in Control Engineering within the School of Engineering . As Impact Champion for the School of Engineering and lead for Robotics & Control, he has been instrumental in advancing control engineering research. Previously, he served as group lead for Nuclear Science & Engineering (2019-24) and held senior administrative roles including Director of Teaching and Deputy Head of Engineering (2005-2018). Professor Taylor's research spans data-driven modelling and automatic control for challenging, uncertain systems with applications in Energy systems Healthcare Robotics Environmental monitoring His work has attracted over £10m in UK research council funding as co-investigator across 10+ projects. He has made significant contributions through his research on topics such as Electricity theft detection using machine learning Nuclear fuel analysis with hyperspectral imaging Digital twins for nuclear manufacturing Adaptive medical treatment systems Robotic plant phenotyping platforms Scientific recognition includes: Fellow of the Institution of Engineering & Technology (FIET) Member of IET Academic Accreditation Committee Editorial board member for three Elsevier journals Active participation in UK Automatic Control Council Professor Taylor has supervised numerous PhD students to successful completion and co-develops the internationally used CAPTAIN Toolbox (MATLAB) for system identification and control. He has been involved in developing control systems for Hydraulically actuated dual-arm robots Wave energy converters Assisted tele-operation systems Grow-cell agricultural facilities Motion planning algorithms
Jeremy Pruvost is a Professor at Polytech Nantes (University of Nantes) within the Department of Process and Bioprocess Engineering . He directs the GEPEA UMR-CNRS 6144 laboratory and the AlgoSolis UMS-CNRS 3722 platform. His research focuses on microalgae bioprocess engineering , particularly photobioreactor design, CO2 valorization, and sustainable algal biomass production for biofuels and environmental applications. Director, GEPEA UMR-CNRS 6144 (2018–present) Director, UMS AlgoSolis (2015–2019) Co-Chair, ESBES 'Microalgae Bioengineering' (2020–2020) Associate Editor, Algal Research (2019–present) Research Interests : Jeremy Pruvost specializes in photobioreactor engineering , microalgae biotechnology , and sustainable biomass production . His work addresses: Physical-biological interactions in photobioreactors Modeling for photobioreactor design and control Industrial ecology applications to microalgae Thermal regulation in extreme environments CO2 biofixation and biorefinery concepts Article Trends : His publications (over 90 peer-reviewed journals) emphasize algal bioprocess optimization , photobioreactor thermal management , and desert cultivation systems . Key contributions include modeling light-temperature interactions, developing CO2 supply strategies, and integrating photobioreactors into building facades. Education & Administration : He earned his PhD at University of Nantes (2000) and obtained Habilitation à Diriger des Recherches (2005). He has created multiple educational programs including an International Master in Microalgae Bioprocess Engineering (2017–2019) and teaching units on photobioreactors and computational fluid dynamics. Labs & Collaborations : He leads the Bioprocesses Applied to Microalgae team (2011–2018) at GEPEA, a 225-person laboratory. His research involves international collaborations through European Algal Biomass Association and EERA (European Energy Research Alliance).
Dr. Sofia Angeli is a Group Leader at the Institute of Catalysis Research and Technology (IKFT), Karlsruhe Institute of Technology (KIT), Germany, since March 2024. Previously, she held roles as Senior Scientist/Group Leader (2021–2024) and Postdoctoral Researcher (2017–2019) at KIT. Her research focuses on catalysis for energy transition, CO₂ valorization, kinetic modeling, and digitalization of catalytic processes. She leads interdisciplinary projects integrating experimental and computational methods to advance sustainable chemical processes. PhD in Chemical Engineering (2016): Aristotle University of Thessaloniki, Greece. Thesis: Hydrogen production via intensified methane steam reforming processes. M.Sc. in Advanced Materials (2012): Aristotle University of Thessaloniki. Diploma in Chemical Engineering (2009): Aristotle University of Thessaloniki. Research Interests : Sofia’s work spans kinetic modeling, CO₂ conversion, catalytic pollutant removal, and digital tools for catalysis research. She develops novel catalysts for methane reforming and designs data management systems like Adacta and CaRMeN to streamline reaction mechanism analysis. Her projects often address industrial challenges in emissions reduction and renewable energy. Her recent publications emphasize catalytic processes for energy sustainability (e.g., methane oxidative coupling) and automated modeling frameworks. She collaborates widely, contributing to journals like Chemical Engineering Journal and ACS Catalysis . Advising & Teams : As a Group Leader, she oversees researchers in catalysis and data-driven processes. Her team works on cutting-edge projects supported by KIT’s infrastructure and interdisciplinary networks. Labs/Teams : Active in the IKFT and the Deutschmann Group, focusing on catalyst design and process intensification.
Professor Mahdi Mahfouf holds a Chair in the School of Electrical and Electronic Engineering at the University of Sheffield. He has held academic roles since 1997, progressing from Lecturer to Professor in 2005. His research focuses on Fuzzy Logic, Control Systems, and their applications in biomedical and industrial contexts. Mahfouf leads the Intelligent Systems Research Laboratory and has contributed over 370 publications, including influential work on fuzzy modeling and predictive control. Education: Ing.Dipl. (Hons) in Control Systems MPhil in Control Systems (University of Sheffield, 1988) PhD in Control Systems (University of Sheffield, 1991) Research Interests: Fuzzy Logic applications, Artificial Intelligence, Neural Networks, Model-Based Predictive Control, Biomedical Engineering (e.g., ICU Decision Support Systems), and Manufacturing Systems (e.g., granulation processes, surface metrology). Key Achievements: Recipient of the IEE Hartree Premium Award (1992) and MEDIPEX Innovation Award. His work integrates fuzzy logic into real-time systems for aviation, healthcare, and robotics. Grants & Labs: Leads the Intelligent Systems Research Lab. Active in collaborative projects with industry (e.g., pharmaceuticals, aerospace). His research bridges theory and practice, emphasizing data-driven solutions for complex systems.
Bo Wang is an active academic researcher primarily affiliated with multiple Chinese institutions, with strong connections to Tsinghua University, Beijing Jiaotong University, and other leading Chinese universities. His research spans artificial intelligence, machine learning, computer vision, medical image analysis, and intelligent control systems, demonstrating significant interdisciplinary work across computer science, engineering, and biomedical applications. Primary institutional affiliation: School of Computer Science and Technology at multiple Chinese universities Active research areas: AI/ML applications in healthcare, computer vision, federated learning, and intelligent control systems Extensive publication record across top-tier venues in multiple disciplines Wang's research interests focus on the intersection of artificial intelligence and practical applications. His work demonstrates strong expertise in developing novel machine learning architectures for medical image analysis, including applications in CT imaging, MRI, and sperm tracking. He has made significant contributions to federated learning approaches for large language models, sliding mode control systems, and molecular optimization frameworks. His research consistently bridges theoretical advances with practical implementations across healthcare, manufacturing, and environmental monitoring domains. Analysis of Wang's recent publications reveals a strong trend toward interdisciplinary AI applications, particularly in medical imaging and bioinformatics. His work on VAE-GANMDA for microbe-drug association prediction, ACE-QSM for accelerating MRI acquisition, and text-guided molecular optimization demonstrates innovative approaches at the intersection of AI and life sciences. Wang also maintains active research in industrial applications including digital twin technology for energy systems and robust scheduling approaches for multi-factory production. Notable research contributions include: FLFT: A Large-Scale Pre-Training Model Distributed Fine-Tuning Method with Federated Learning VAE-GANMDA: Microbe-drug association prediction model ACE-QSM: Accelerating quantitative susceptibility mapping using diffusion models Digital twin-empowered power consumption prediction systems Wang actively collaborates with researchers across China and internationally, with publications spanning computer science, engineering, medical imaging, and environmental science journals. His work demonstrates strong technical depth across multiple AI methodologies while maintaining focus on practical applications that address real-world challenges in healthcare, manufacturing, and environmental monitoring.
Paul R. Chiarot is a Professor and Chair of the Department of Mechanical Engineering at Binghamton University, State University of New York (SUNY). He holds a BASc, MASc, and PhD in Mechanical Engineering from the University of Toronto. His research focuses on microfluidics, multiphase flows, and electrospray deposition, with applications in advanced manufacturing, biotechnology, and biomedical engineering. Research Interests: Chiarot leads the Microfluidics and Multiphase Flow Laboratory, exploring electrospray printing for electronics packaging, synthetic vesicle fabrication for membrane biology studies, and fluid mechanics of the brain. His work addresses challenges in energy, healthcare, and nanotechnology. Key areas include: Electrospray-based additive manufacturing Microfluidic platform development for asymmetric vesicles Interstitial fluid transport in brain tissues Thermal management solutions for electronics Awards and Grants: He has received the NSF CAREER Award (2016) and the SUNY Chancellor's Award for Excellence in Scholarship (2022). His research is supported by the NSF, NIH, ACS, SRC, and industry collaborators. Lab and Collaborations: The lab's interdisciplinary approach integrates fluid dynamics, materials science, and biotechnology. Recent projects include developing high-throughput vesicle production and modeling cerebral fluid dynamics. Chiarot also contributes to thermal optimization of microchannel heat sinks for data centers and high-performance computing.
Erik Thostenson is a Professor in the Department of Mechanical Engineering at the University of Delaware, with an affiliated appointment in the Department of Materials Science and Engineering. He holds degrees from the University of Delaware (PhD/MS in Materials Science and Mechanical Engineering) and Winona State University (BS in Composite Materials Engineering, Summa Cum Laude). His research focuses on advanced composite materials, nanotechnology, and multifunctional sensor integration into composites. Dr. Thostenson's expertise spans composites processing, nanomaterials characterization (particularly carbon nanotubes), and structural health monitoring. His work emphasizes developing novel fabrication techniques like electrophoretic deposition for hierarchical composites, with applications in aerospace, civil infrastructure, and biomedical wearable sensors. Notable awards include the NSF CAREER Award, Air Force YIP Award, Elsevier Young Composites Researcher Award, and Hayashi International Memorial Award. He has pioneered methods for in situ sensing in composites using nanomaterials, enabling real-time damage detection and smart material systems. His research portfolio includes over 100 publications on topics like carbon nanotube-based sensors, additive manufacturing of composite tooling, and VR-integrated rehabilitation systems. He has also contributed to industry-relevant solutions such as scalable roll-to-roll composite manufacturing and structural repair methodologies.
