Prof. Dr. Andreas Walther is a Professor of Macromolecular Materials and Systems at the Department of Chemistry, Johannes Gutenberg University Mainz, Germany. He is also a Research Fellow at the Gutenberg Research College and the Max Planck Institute for Polymer Research. His research focuses on adaptive, bioinspired materials systems, self-assembly processes, and energy-driven functional materials. Key projects include the development of ATP-fueled systems, dissipative systems engineering, and light-actuated materials. Walther leads the Walther Lab, specializing in life-like materials and systems, and contributes to educational initiatives like the livMatS program. His expertise spans hierarchical self-assembly, biomimetic materials, and non-equilibrium systems. Recent work emphasizes communication in chemically fueled networks and programmable DNA coacervates. Publications highlight breakthroughs in ATP-responsive materials, scalable hydrogel synthesis, and light-controlled systems. Awards and grants include DFG funding for livMatS-related research. He advises two PhD students and collaborates widely across institutions.
Georg Stadler is a Professor of Mathematics and Computer Science at New York University's Courant Institute. His research focuses on computational inverse problems, uncertainty quantification, and PDE-constrained optimization, driven by applications in climate modeling, geophysics, and plasma physics. He holds a PhD from the University of Graz (2004) and has been recognized with awards including the Gordon Bell Prize (2015) and the SIAM Computational Science & Engineering Best Paper Prize (2019). Education: Ph.D. (Dr.), Mathematics, University of Graz, Austria, 2004. M.S. (Mag.), Mathematics, University of Graz, Austria, 2001. M.S., Mathematics and Geometry Education, Graz University of Technology and University of Graz, 2001. Research Interests: Large-scale PDE solvers, Bayesian inverse problems, extreme event probability estimation, and optimization under uncertainty. Applications in climate (sea/land ice, tsunamis), plasma physics (fusion), and computational earth science (mantle flow, plate tectonics). Recent Research Trends: His work emphasizes scalable algorithms for high-dimensional Bayesian inverse problems, with applications to tsunamis, stellarator coil design, and ice sheet dynamics. Recent articles highlight advancements in extreme event probability estimation and robust multigrid solvers for incompressible Stokes equations. Awards: Gordon Bell Prize (2015) for extreme scalability of implicit solvers. SIAM Best Paper Prize (2019) for computational science contributions. Young Scientist ASCINA Award and Springer CSE Prize (2011). Advising & Grants: Current PhD student Sonia Reilly and former advisees include Chen Li and Shanyin Tong. His research is supported by NSF, ONR MURI, and the Simons Foundation. He co-leads the Computational Mathematics and Scientific Computing Seminar at Courant. Labs & Collaborations: Active in Courant’s interdisciplinary groups, focusing on high-performance computing and inverse problems. Collaborates with institutions like UT Austin on mantle dynamics and fusion energy projects.
Torgeir Welo is a Professor at the Department of Mechanical and Industrial Engineering , Norwegian University of Science and Technology (NTNU) . He specializes in metal forming , particularly aluminum alloy structures , with a focus on plastic bending behavior , dimensional stability , and 3D forming technologies . His research also encompasses Lean Product Development , emphasizing knowledge reuse and maximizing customer value in automotive and aerospace applications. Key Research Areas : Metal Forming, Aluminum Processing, Springback Control, Lean Development, Additive Manufacturing, Material Substitution Teaching : Courses on Aluminum Technology , Metal Forming Analysis , and Machine Element Design Publications (15 most recent): Focus on springback monitoring , charge weld evolution , flexible forming , machine learning applications , and circular economy frameworks in metal manufacturing.
Jan Emblemsvåg is a Professor at NTNU’s Department of Ocean Space Operations and Civil Engineering in Ålesund, Norway. He has held senior industry roles including Shipyard Director at Vard, SVP Ship Design & Systems at Rolls-Royce Marine, and Managing Director at Midsund Bruk. His academic positions span BI Norwegian Business School, Ålesund University College, and NTNU since 2006. Emblemsvåg’s expertise bridges engineering and management, focusing on sustainable development through rigorous analytical frameworks. Education: PhD from Georgia Institute of Technology (1999), MSc (1995), and Civil Engineering degree from Norwegian Institute of Technology (1994). R&D focuses on six core areas: 1) Product/process development with Lean methodologies, 2) Life-Cycle Costing/Analyses (integrating cost, environmental, and social dimensions), 3) Risk management and uncertainty analysis using Monte Carlo methods, 4) Operations/General management under Lean principles, 5) Project management with Lean integration, and 6) Renewable energy (especially nuclear power’s role in maritime and industrial sectors). Publications emphasize energy policy critiques, nuclear propulsion feasibility, and sustainable shipping. Recent work highlights nuclear energy’s potential as a cost-effective, low-emission solution for maritime transport and industrial needs. Active in policy debates advocating evidence-based energy strategies. Labs/Teams: Leads NTNU’s research initiatives on nuclear propulsion for merchant ships (NuProShip project) and collaborates internationally on maritime safety and evacuation modeling.
Chuan-Fu Lin is an Associate Professor in the Department of Mechanical Engineering at The Catholic University of America (CUA), School of Engineering. His research focuses on energy storage systems , nanotechnology , and materials innovation for renewable energy and advanced manufacturing . He founded and directs the Energy Materials Innovations Laboratory (EMI-Lab) , which develops solid-state electrolytes , Li/Na metal anodes , and low-cost eco-friendly energy storage solutions through atomic layer deposition (ALD) and thin film engineering . 2024 : Secured $637k DOE grant to study interfacial kinetics of conversion materials 2023 : Published on fluorinated SEI layers for sodium batteries 2022 : Advanced LiPON protection strategies 2021 : Developed Al2O3-coated Mg anodes 2020 : NSF collaborative award with Prof. Rubloff and Qi His work spans solid-state batteries , aqueous battery systems , and conversion electrode materials , with a strong emphasis on preventing corrosion , enhancing cycling stability , and reducing overpotential . Key projects include polymer/ceramic hybrid electrolytes , in-operando characterization cells , and scalable current collector designs . Notable scientific awards include the 2024 Charles H. Kaman Award and NSF support for collaborative research.
Maozhen Li is a Professor in the Department of Electronic and Electrical Engineering at Brunel University of London , within the College of Engineering, Design and Physical Sciences . He serves as the Vice-Dean of the NCUT Transnational Education (TNE) programme, overseeing a joint school with North China University of Technology. He has been at Brunel since 2002, progressing from Lecturer to Professor in 2013. Education: PhD, Institute of Software, Chinese Academy of Sciences (1997) Postdoctoral Research, School of Computer Science and Informatics, Cardiff University (1999–2002) His primary research interests lie in high performance computing, big data analytics, and artificial intelligence, with applications in smart grids, smart manufacturing, and cybersecurity. He focuses on developing interpretable, robust, and lightweight AI models, including work in causal AI, parallel machine learning, and edge computing. His research integrates advanced techniques such as deep learning, reinforcement learning, and blockchain for real-world system optimization. An analysis of his recent publications reveals a strong and consistent research trajectory in AI-driven solutions for environmental monitoring (e.g., PM2.5 prediction), industrial defect detection, IoT security, and intelligent transportation. His work frequently combines deep learning with graph-based modeling and federated or reinforcement learning, emphasizing scalability, efficiency, and robustness in distributed and edge environments. Scientific Awards and Recognition: Fellow of the Institution of Engineering and Technology (IET) Fellow of the British Computer Society (BCS) Shortlisted for the Computing UK BIG DATA EXCELLENCE AWARDS 2018 in the category of Most Innovative Big Data Solution Maozhen Li has successfully supervised 25 PhD students and examined over 30 PhD theses externally. He has secured significant research funding from EPSRC, the European Union (Horizon 2020), Innovate UK, and the Royal Society , with projects including Z-BRE4K, IoRL, and TDX-ASSIST. He serves as an Associate Editor for journals such as the Journal of Cloud Computing and the International Journal of Grid and High Performance Computing . Research Groups and Teams: He is affiliated with the Intelligent Engineering Frameworks (IEF) research group at Brunel, contributing to collaborative efforts in AI, IoT, and smart systems. His leadership in transnational education also fosters international research collaboration between Brunel and Chinese institutions.
Jason Hein is an Associate Professor in the Department of Chemistry at the University of British Columbia's Faculty of Science. His research focuses on the development of automated reaction analysis technology and self-driving laboratories that integrate robotics with synthetic organic chemistry. Dr. Hein leads the Hein Lab, which pioneers innovative solutions for mechanistic organic chemistry, catalytic reaction mechanisms, and chemical manufacturing processes. His research interests center on creating modular robotic tools and integrated analytical hardware for automated reaction profiling, with applications in pharmaceutical manufacturing, battery materials processing, and sustainable chemistry. The lab's work combines advanced robotics, artificial intelligence, and process analytical technology to develop self-optimizing chemical systems that accelerate discovery and improve manufacturing efficiency. Analysis of Hein's recent publications reveals a strong focus on AI-driven laboratory automation, with particular emphasis on crystallization optimization for battery materials, computer vision for process monitoring, and interoperable software systems for self-driving laboratories. His work bridges fundamental mechanistic understanding with practical industrial applications, particularly in lithium extraction from waste brines and pharmaceutical process development. NSERC Postdoctoral Fellowship Dr. Hein's research program includes significant grant funding supporting the development of self-driving laboratory technologies and their application to challenging chemical problems. His lab actively collaborates with industry partners in pharmaceuticals and clean energy sectors to translate fundamental insights into deployable technologies. Current projects focus on battery-grade lithium carbonate production, continuous manufacturing processes, and AI-optimized chemical synthesis. The Hein Lab operates as a multidisciplinary research environment combining expertise in organic chemistry, robotics engineering, computer science, and data analytics to create the next generation of autonomous chemical discovery systems.
Stephen W. Hoag is a Professor in the Department of Pharmaceutical Sciences at the University of Maryland School of Pharmacy. His research spans pharmaceutical formulation, process development, and analytical technologies, with a strong emphasis on solid oral dosage forms and controlled release systems. University: University of Maryland School: School of Pharmacy Department: Department of Pharmaceutical Sciences Email: shoag@umaryland.edu Phone: (410) 706-6865 Fax: (410) 706-0346 Address: 20 North Pine Street, Baltimore, MD 21201 Education: B.S. in Biochemistry, University of Wisconsin–Madison, 1982 Ph.D. in Pharmaceutics, University of Minnesota, Twin Cities, 1990 Dr. Hoag's research is centered on two primary areas: (1) the development of systematic methods for formulating immediate and controlled release tablets, utilizing instrumented tablet presses, shear cell analysis, and process analytical technology (PAT) such as Near-Infrared (NIR) and Raman spectroscopy; and (2) the application of mathematical models to understand mass transport in hydrogels, including calcium alginate and silk-elastinlike protein polymers. His work on folic acid supplementation and prenatal vitamins has important public health implications due to the role of folic acid in preventing neural tube defects. Although no recent publications are listed in the provided text, his research output is evident through his co-editorship of the widely used reference work Pharmaceutical Dosage Forms: Tablets (3rd edition, 2008), and his leadership in developing best practices for PAT in pharmaceutical manufacturing. Scientific Awards: No specific awards mentioned in the provided text. Dr. Hoag has actively mentored a large number of graduate students, postdoctoral fellows, and visiting scientists, contributing significantly to pharmaceutical education and workforce development. His laboratory is equipped with state-of-the-art instrumentation for preformulation, formulation, tableting, coating, dissolution testing, and analytical characterization. The lab supports both non-clinical and GMP-level manufacturing research, enabling translational development of dosage forms. He also leads a hands-on short course on tablets and capsules, further extending his educational impact. Research Facilities: Thermal analysis (DSC, MDSC) Solubility and viscosity measurement Moisture analysis (Karl Fisher, LOD) Mechanical testing (Instron) Flow characterization (shear cell, angle of repose) Particle size analysis (laser diffraction, SEM, sieve) Tablet presses (Stoke’s B2, Manesty Beta, fully instrumented) Coating systems (fluid bed, pan coaters) UV/Vis, HPLC, GC, MS instrumentation Environmental stability chambers Granulation, milling, blending equipment Dissolution testing with autosampler
Qipei Mei is an Assistant Professor in the Department of Civil and Environmental Engineering at the University of Alberta's Faculty of Engineering. With an MSc in Computer Science and a PhD in Structural Engineering, he bridges civil engineering with artificial intelligence to enhance infrastructure productivity and sustainability. His research spans AI-driven design automation, robotics for construction safety, and IoT-based condition assessment. PhD, Structural Engineering - University of Alberta (2020) MSc, Computer Science - Georgia Institute of Technology (2018) MSc, Structural Engineering - University of Alberta (2014) B.E., Civil Engineering - Huazhong University of Science and Technology (2011) Mei's work focuses on three key areas: leveraging data-driven methods for design automation, applying sensing/robotics to construction operations, and using digital twins for infrastructure assessment. His team explores generative AI for housing design, robotic construction in remote communities, and smart monitoring systems. Recent publications highlight advancements in: lateral capacity prediction for monopile foundations, transformer-based architectural layout analysis, large language models for building code compliance, vision-language models for safety hazard detection, and sensor networks for bridge monitoring. These works demonstrate interdisciplinary integration of AI, structural engineering, and IoT. Mei actively collaborates with diverse researchers and welcomes graduate students to his Smart Infrastructure Technologies (SITE) Research Group, part of the Infrastructure and Human Tech Lab (IHT-Lab). He teaches advanced topics in structural and civil engineering while pursuing industry-funded projects through NSERC, CFI, and Alberta Innovates.
Edwin Romeijn holds the Jill Stewart Archer Family Chair and Professor position in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology. He served as School Chair from 2015-2024, overseeing the nation's top-ranked industrial engineering program. Previously, he held faculty positions at the University of Michigan, University of Florida, and Erasmus University Rotterdam, and served as Program Director at the National Science Foundation. Education: Ph.D. in Operations Research (1992), Erasmus University Rotterdam M.S. in Econometrics (1988), Erasmus University Rotterdam Romeijn's research centers on optimization theory and applications , with dual focus areas in radiation therapy treatment planning and supply chain management . His radiation therapy work develops algorithms for cancer treatment planning and clinic scheduling, while his supply chain research addresses integrated optimization of production, inventory, and transportation under demand flexibility, resource constraints, perishability, and uncertainty. His methodologies bridge theoretical operations research with real-world healthcare and logistics systems. His publication portfolio demonstrates consistent contributions to optimization methods across diverse application domains, with recent work spanning healthcare systems, renewable energy, sports analytics, and unconventional logistics. The research exhibits strong methodological continuity in stochastic programming, network optimization, and decision-making under uncertainty. Scientific Awards: Fellow of IISE and INFORMS (2017) Richard C. Wilson Faculty Scholar (2012-2013) Multiple best paper awards in industrial engineering conferences Pierskalla Best Paper Award (2003) Young Investigator’s Award at ICCR (2004) Romeijn has advised numerous graduate students and secured significant research funding through NSF and other agencies. His leadership extends to program direction at NSF and chairing Georgia Tech's Industrial and Systems Engineering school. He maintains active collaborations with healthcare institutions and manufacturing enterprises, translating theoretical advances into practical solutions for radiation oncology and supply chain resilience.
Prof. Dr. Nuri Başoğlu is a Professor at Izmir Institute of Technology (IYTE). His educational background includes a BSc in Industrial Engineering from Boğaziçi University, and both MSc and PhD in Production Management from Istanbul University. Research Focus His research spans interdisciplinary domains with emphasis on: Technology Adoption : Healthcare systems, mobile services, and education. Innovation Processes : Sociotechnical systems, product design, and decision support. Information Systems : Strategic implementation and human-computer interaction. Recent publication trends (2010–2013) highlight technology diffusion in healthcare, including telemedicine, health informatics, and e-learning. Cross-cultural studies on mobile services and ERP optimization in manufacturing also feature prominently. No awards, grants, or supervised students are documented in the provided materials.
Marco Badami is a Full Professor at the Department of Energy (DENERG) , Polytechnic of Turin. He serves as Scientific Director for national and EU-funded research projects in energy systems and has been a Course Lecturer for Energy Systems and Industrial Use of Energy since 2010. He supervises PhD students in Energetics and Electrical Engineering . Research Interests: His work spans energy systems optimization, machine learning applications for industrial energy efficiency, smart grids, cogeneration scheduling, blockchain-based energy data immutability, and predictive maintenance algorithms for photovoltaic plants. Current projects focus on AI-driven energy audits, optimized control systems for industrial microgrids, and digital twin architectures. Collaborations: He works with Trigenia Srl, Stogit SpA, and international institutions on commercial research contracts. His scientific contributions include 15+ publications on topics like LSTM forecasting for solar energy, deep reinforcement learning for multi-energy systems, and decentralized peer-to-peer energy trading platforms.
Prof. Dr.-Ing. Udo Fiedler is a faculty member at the Technical University of Central Hesse (THM), Department of Business Administration and Economics, where he serves as Head of the Production Engineering Laboratory and Member of the Senate. His academic work focuses on manufacturing engineering with specialization in high-speed machining, production processes, and machine tools. His research interests include: High-Speed Machining (HSC) and precision manufacturing Green machining of sintered parts in the green state Process optimization using statistical experimental design Machine tool technology and NC programming Industry 4.0 applications in manufacturing education Process monitoring and control for increased manufacturing safety Prof. Fiedler's publication record demonstrates an evolution from fundamental machining processes toward integrating AI with traditional manufacturing. His recent work shows strong emphasis on applying artificial intelligence to quality prediction, optimizing green machining processes, and implementing Industry 4.0 concepts through learning factory approaches, bridging traditional manufacturing engineering with modern digital technologies. His significant scientific contributions include: Development of methods for NC programming of complex workpieces Research on stability lobe diagrams for milling processes Studies comparing different production methods including HSC, EDM, and generative processes Work on mechatronic tool holders for process monitoring Applications in the ophthalmic industry for precision machining of spectacle lenses Prof. Fiedler teaches multiple courses at THM including Factory Planning/Ergonomics, Handling and Assembly Technology, Innovative Manufacturing Processes, and Machine Tools at the bachelor's level, and Learning Factory 1 and 2 at the master's level. He leads current research projects including Klag-Robotics (2023-2025), Loewe Project OST (2018-2021), and GrünSpan (2014-2015), demonstrating sustained research activity across multiple manufacturing domains.
Professor Michael Ramage is a Senior Lecturer in the Department of Architecture at Cambridge University , where he directs the Centre for Natural Material Innovation . He is also a fellow of Sidney Sussex College and co-founder of Light Earth Designs . His academic background includes architecture studies at MIT and professional experience at Conzett Bronzini Gartmann in Switzerland. His research focuses on low-energy structural materials , natural material innovation , and sustainable housing in developing regions, with particular emphasis on engineered timber and bamboo . The 15 most recent publications highlight trends in modular timber construction , 3D printed earthen materials , and climate action in the building sector . Key subfields include circular economy , resource optimization , structural testing , and behavioral impacts on decarbonization . He has secured research funding from the Leverhulme Trust , Engineering and Physical Sciences Research Council (EPSRC) , Royal Society , and British Academy .
Jonathan Boreyko serves as an Associate Professor and John R. Jones III Faculty Fellow in Virginia Tech's College of Engineering, Department of Mechanical Engineering. His research integrates fluid dynamics, heat transfer, and biomimetic engineering to develop sustainable solutions for water and energy harvesting through innovations like Fog Harps and synthetic trees. Dr. Boreyko earned his Ph.D. in Mechanical Engineering from Duke University (2012), following an M.S. and B.S. in Mechanical Engineering and Physics from Trinity College (2007). His academic journey includes postdoctoral research at Oak Ridge National Laboratory and faculty appointments in Biomedical Engineering and Mechanics at Virginia Tech before transitioning to Mechanical Engineering. His research program centers on interfacial phenomena in micro/nano-structured materials, with core expertise in droplet dynamics, phase-change heat transfer (condensation, evaporation, boiling), and biomimetic water harvesting systems. The Nature-Inspired Fluids and Interfaces Lab examines how natural designs—from plant transpiration to insect surfaces—can inform engineered solutions for atmospheric water collection, anti-icing, and thermal management. Analysis of his 15 most recent publications reveals three dominant research thrusts: (1) biomimetic water harvesting systems (Fog Harps for atmospheric water collection), (2) thermal diodes leveraging droplet bridging for directional heat transfer, and (3) anti-clogging/anti-tangling mechanisms in fog harvesting meshes. These works consistently bridge fundamental fluid mechanics with practical sustainability applications. His scientific achievements include: John R. Jones III Faculty Fellow (2020) NSF CAREER Award for Thermal Transport Processes (2017) AFOSR Young Investigator Program Award (2016) 3M Non-Tenured Faculty Award (2016) Multiple best poster awards at APS, Gordon Research Conferences, and MRS meetings Dr. Boreyko has advised graduate students including Weiwei (Ph.D., 2020) and Viverjita (M.S., 2020), with research funded by NSF, AFOSR, and 3M. His lab maintains active collaborations with industry partners and national laboratories for technology translation. The Nature-Inspired Fluids and Interfaces Lab combines experimental fluid dynamics, materials characterization, and computational modeling to develop deployable systems. Current projects include scaling Fog Harps for real-world water harvesting, optimizing synthetic trees for passive desalination, and exploring jumping-droplet phenomena for thermal management in electronics.
