جست‌وجوی استاد و پژوهشگر

Wolfgang Tressمشاهده پروفایل

استاد

• Organic Electronics
• Perovskite Solar Cells
• Device Physics
• +۴ مورد دیگر

Prof. Dr. Wolfgang Tress is a faculty member at the ZHAW Zurich University of Applied Sciences , affiliated with the School of Engineering . He leads the Novel Semiconductor Devices Team, focusing on Organic Electronics and Photovoltaics. His research spans perovskite solar cells, device physics, optoelectronic characterization, and mixed ionic-electronic conductors. PhD in Physics from Technical University of Dresden (2012) Master in Electrical Engineering from University of Ulm (2007) His research explores critical aspects of perovskite materials, including device stability, ionic conductivity, and efficiency optimization through advanced modeling and experimental techniques. Key projects involve correlative optoelectronics, interface tailoring, and lead-free perovskite development. Recent publications highlight trends in machine learning applications for perovskite stability analysis, ion migration dynamics, and multifunctional material design. He has authored over 20 peer-reviewed articles in journals like Advanced Energy Materials , Nature Energy , and Science . 2023 : Web of Science Highly Cited Researcher 2019 : ERC Starting Grant 2019 : SPS Award in Applied Physics 2016 : Zeno Karl Schindler Award As project leader, he drives initiatives in perovskite-on-silicon tandem cells, defect engineering, and laboratory digitization. His work combines experimental fabrication with computational modeling to address photovoltaic reliability challenges.

Masoud Akbarzadehمشاهده پروفایل

دانشیار

• Three-Dimensional Graphical Statics
• Structural Form Finding
• Computational Design
• +۶ مورد دیگر

Masoud Akbarzadeh is an Associate Professor of Architecture at the University of Pennsylvania's Stuart Weitzman School of Design, directing the Polyhedral Structures Laboratory (PSL) where he pioneers geometric approaches to structural design and digital fabrication. His work integrates architectural innovation with engineering precision to develop sustainable, high-performance building systems. His academic credentials include: D.Sc. from ETH Zurich's Institute of Technology in Architecture Master of Science in Architecture Studies (Computation) and M.Arch from MIT Degree in Earthquake Engineering from Iran University of Science and Technology B.S. in Civil and Environmental Engineering Dr. Akbarzadeh's research centers on Three-Dimensional Graphical Statics , revolutionizing structural form finding through polyhedral reciprocal diagrams. Key focus areas include computational geometry for spatial structures, digital fabrication of concrete and glass systems, and geometric optimization for environmental performance. His laboratory develops cutting-edge methods for translating theoretical geometry into buildable structures through advanced computational tools. Recent publications reveal accelerating innovation in carbon-capturing concrete using triply periodic minimum surfaces, ultra-thin glass bridges, and machine learning-enhanced structural design. His work consistently bridges theoretical geometric principles with practical applications in sustainable construction and renewable energy infrastructure, demonstrating increasing interdisciplinary impact. Major recognitions include: National Science Foundation CAREER Award (2020) Top 100 Innovators & Entrepreneurs Magazine (2025) SOM award for MIT thesis As principal investigator, he leads significant research initiatives including the NSF CAREER project on 3D graphic statics and collaborative EAGER grants for offshore renewable energy structures. Though specific students aren't listed, he mentors through PSL and teaches graduate courses like Structures II and Technology Lab, fostering next-generation designers in computational structural engineering. CAREER: 3D/Polyhedral Graphic Statics for Education, Design, and Optimization (2020) Collaborative EAGER: Harnessing Ocean Wave Energy with 3D Printed Structures (2024) The Polyhedral Structures Laboratory drives experimental research in geometric structural systems, producing breakthroughs like the Tortuca glass bridge and Terrene 2.0 biomaterial composites. Through partnerships with industry and academic institutions, PSL advances the frontiers of structural design, fabrication technology, and sustainable material applications in architecture.

Holger Kleinkeمشاهده پروفایل

استاد

• Materials Science
• Inorganic Chemistry
• Thermoelectric Materials
• +۳ مورد دیگر

Holger Kleinke is a Professor at the University of Waterloo , specializing in Inorganic Chemistry . His research focuses on thermoelectric materials , crystal structure analysis , and machine learning applications in materials discovery . He can be reached at kleinke@uwaterloo.ca . Research Interests Professor Kleinke's work spans: Thermoelectric materials for energy conversion Zintl phases and intermetallic compounds Machine learning for materials prediction Solid-state synthesis techniques Crystal structure-property relationships Nanostructure engineering for enhanced performance Scientific Contributions Developed explainable AI models for thermoelectric property prediction Pioneered fast cooling methods to improve SnSe thermoelectric performance Investigated complex bismuthide structures like Ba8Zn2-xIn3+xBi10 Explored organic cathode materials through coordination polymer design Optimized electrochemical interfaces for fast-charging batteries

Jianchang Wuمشاهده پروفایل

پژوهشگر

• Perovskite Solar Cells
• Photovoltaics
• Materials Science
• +۳ مورد دیگر

Jianchang Wu is a researcher at Forschungszentrum Jülich GmbH, affiliated with the Helmholtz Institute Erlangen-Nürnberg (HI ERN). His work focuses on advanced materials and automation strategies for photovoltaic technologies, particularly perovskite solar cells. Key Research Areas: Perovskite solar cells, high-throughput experimentation, machine learning in materials optimization, stability engineering, hole transport materials, and thin-film fabrication. Institutional Affiliation: Forschungszentrum Jülich GmbH and HI ERN, a Helmholtz Association institute dedicated to renewable energy research. Recent publications highlight his contributions to automated workflows for material discovery, inverse design of hole transport layers, and stability improvements in perovskite devices. His work integrates robotics, computational modeling, and experimental validation to address multidimensional challenges in energy systems. Labs & Projects: Involved in the Self-driving AMADAP laboratory and the HydroBot project, which explore autonomous optimization and fuel cell-powered robotics.

Kee-Sun Sohnمشاهده پروفایل

استاد

• Materials Informatics
• Artificial Intelligence
• Metaheuristics
• +۴ مورد دیگر

Kee-Sun Sohn is a full-time Professor in the Department of Nano Technology and Advanced Materials Engineering at Sejong University, South Korea, with over 232 research publications and an h-index of 48. His work bridges computational materials science and experimental validation to develop next-generation functional materials. Education: Ph.D., POSTECH (1996) M.S., POSTECH (1992) B.S., Yonsei University (1990) Research Focus: Professor Sohn pioneers Materials Informatics and AI-driven discovery for energy and optical materials. His laboratory integrates Density Functional Theory calculations with experimental synthesis to solve critical challenges in battery technologies (including solid-state electrolytes and hybrid systems) and luminescent materials for advanced lighting. This cross-disciplinary approach accelerates materials development cycles by 3-5× compared to traditional methods. Publication Trends: Recent 2024-2025 outputs reveal concentrated innovation in AI-optimized solid electrolytes (Na/Li systems), novel phosphor discovery through active learning, and multifunctional sensor materials. His team consistently publishes in top-tier energy and materials journals, demonstrating leadership in merging machine learning with materials characterization. Research Leadership: Professor Sohn directs a multidisciplinary team collaborating with industry partners including Hyundai Steel Co. His group maintains active projects in crystal structure determination, materials database mining, and industrial-scale materials prototyping, with current funding supporting 12+ researchers across computational and experimental domains.

Marcus Ciceroneمشاهده پروفایل

استاد

• Physical and Biological Chemistry
• Label-free vibrational imaging
• THz Dynamics of Liquids and Glasses
• +۳ مورد دیگر

Professor Marcus T. Cicerone is a faculty member in the Department of Chemistry and Biochemistry at Georgia Institute of Technology. His research spans physical chemistry, materials science, and biomedical engineering, with a focus on ultrafast spectroscopy and molecular dynamics. Education: Ph.D. in Chemistry (University of Wisconsin, 1994), B.S. in Chemistry (Brigham Young University, 1989) Research interests include: Development of broadband coherent anti-Stokes Raman scattering (BCARS) microscopy for label-free biochemical imaging Molecular dynamics of liquids and glasses Stabilization of dried protein pharmaceuticals Applications in cancer biology, viral infection, and vaccine development His recent publications highlight innovations in optical instrumentation, molecular transport modeling, and planetary analogue biosignature detection. Scientific Awards: Arthur S. Flemming Award (2017) Fellow of the American Physical Society (2016) Washington Academy of Sciences Award (2015) Department of Commerce Bronze Awards (2012, 2008) Professor Cicerone's work combines fundamental physics with practical applications in biopharmaceuticals and advanced materials.

Synge Todoمشاهده پروفایل

استاد

• Quantum Many-Body Physics
• Computational Physics
• Quantum Computing
• +۵ مورد دیگر

Synge Todo is a Professor in the Department of Physics, Graduate School of Science at the University of Tokyo , with joint appointments at the Mathematics and Informatics Center , Institute for Solid State Physics , Institute for Physics of Intelligence , Quantum Software Project , and Next-Generation AI Research Center . Born in 1968, he earned his B.Sc. and Ph.D. from the University of Tokyo and subsequently held post-doctoral positions at ETH Zürich. Education Ph.D. (Science), University of Tokyo, 1996 B.Sc. (Physics), University of Tokyo, 1991 Research Interests Todo’s research integrates quantum many-body physics , computational physics , and quantum computing . He develops advanced Monte-Carlo algorithms , tensor-network techniques , and renormalization group methods to study strongly correlated electron systems , lattice QCD , quantum phase transitions , and machine-learning applications in physics . His recent work explores fault-tolerant quantum computing architectures , non-variational quantum ground-state preparation , and universal scaling laws in deep neural networks . Scientific Awards Prizes for Science and Technology, The Commendation for Science and Technology by MEXT Japan (April 2019) Grants & Collaborations He currently leads several JSPS KAKENHI projects, including “ Quantum-circuit design for computational materials science ” (2023-26) and “ Enhancement of detailed-balance-violating MCMC methods ” (2020-24). He also co-leads interdisciplinary teams focusing on data assimilation for materials discovery and scalable high-performance computing . Laboratories & Software Todo heads research activities in the HΦ quantum lattice model solver and the MateriApps portal, providing open-source tools for large-scale simulations in condensed-matter and materials science.

• polymer materials
• biophysics
• chemical physics
• +۱۲ مورد دیگر

Sadaki Samitsu is a Professor (non-tenure-track) at Waseda University's Graduate School of Advanced Science and Engineering and Faculty of Science and Engineering (Department of Nanoscience and Nanoengineering). Additionally, he serves as a Principal Researcher at Japan's National Institute for Materials Science in the Data-driven Polymer Design Group , with prior roles at Kyoto University and NIMS. Education : PhD in Applied Physics (2006.03), Graduate School of Engineering, The University of Tokyo MS in Advanced Materials Science (2003.04), Graduate School of Frontier Sciences, The University of Tokyo BS in Applied Physics (2001.04), School of Engineering, The University of Tokyo His research bridges polymer materials and soft matter physics , focusing on materials informatics , porous materials , and phase separation mechanisms. Key applications include thermal insulation , sound absorption , and photonic devices leveraging X-ray CT and refractive index engineering . Recent work highlights machine learning-driven inverse design of auxetic metamaterials, self-healing polymer gels via physical entanglements, and photocatalytic nanofiber membranes for wastewater treatment. His publications demonstrate expertise in combining structural color , nano-crystallization , and adhesive fracture mechanics . Scientific Awards : SPSJ Publicity Committee Award (2018) Young Scientist Lecture Award (2017) Poster Presentation Award (2016) NIMS President Award for Young Scientists (2015) He mentors researchers in polymer hybrid materials , with contributions to epoxy adhesive toughening via polyrotaxane dispersion, durable superhydrophobic composites , and active learning frameworks for optimizing adhesive formulations from small datasets.

• Multi-scale Modeling
• Bayesian Uncertainty Quantification
• Geotechnical Engineering
• +۹ مورد دیگر

Dr. Hongyang Cheng is an Assistant Professor at the University of Twente's Civil Engineering & Management Department, specializing in multi-scale modeling of granular materials and Bayesian uncertainty quantification for geotechnical applications. His work bridges physics-based and data-driven approaches, focusing on soil mechanics from quasi-static to dynamic behaviors, with applications in geohazard mitigation, laser sintering, and pharmaceutical powder processing. Education: PhD in Multiscale characterization of geosynthetic-reinforced soil, Hiroshima University (2013–2016) Master's in Civil Engineering, Hiroshima University (2011–2013) Dr. Cheng's research spans multi-scale modeling of granular materials, including Discrete Element Method (DEM) and Finite Element Method (FEM) integrations, and Bayesian uncertainty quantification frameworks like GrainLearning. His work addresses geotechnical challenges such as dike safety, offshore infrastructure resilience, and soil-structure interactions under extreme loading, utilizing machine learning surrogates to enhance computational efficiency. Scientific awards include the Japanese Government Scholarship (2011), Best Student Paper at DEM2016, and IACMAG Excellence in 2022. He leads EU-funded projects like POSEIDON (offshore geohazards) and TUSAIL (upscaling particle systems), supervises postdocs/PhD students, and co-leads Working Group 1 for COST Action ON-DEM to promote open-source DEM tools. Recent publications emphasize DEM's role in bio-cemented soils, vegetation effects on soil mechanics, and sintering kinetics. His teaching includes undergraduate courses on Soil Mechanics and graduate-level GeoRisk Management, integrating probability theory, stochastic modeling, and Python-based risk assessment tools.

Adam Fontecchioمشاهده پروفایل

استاد

• Nanophotonics
• Electro-optics
• Liquid Crystals
• +۷ مورد دیگر

Adam Fontecchio is a Professor in the Electrical and Computer Engineering Department at Drexel University and the inaugural Director of the Center for the Advancement of STEM Teaching and Learning Excellence (CASTLE) . He has held leadership roles including Vice-Dean of the Graduate College, Vice-Chair of the IEEE Philadelphia Section, and Associate Dean for Academic Affairs in the College of Engineering. PhD, Brown University, 2002 MS, Physics, Brown University BA, Physics, Brown University Fontecchio's research spans nanophotonics , liquid crystal technology , and smart textile engineering , with applications in biomedical monitoring, hyperspectral imaging, and educational outreach. His work includes tunable electro-optic devices, holographic polymer dispersed liquid crystals, and wearable RFID sensors. He integrates engineering education into K-12 curricula through NSF-funded programs like STEM GK12. His educational research focuses on project-based learning , team-teaching , and STEM engagement , particularly among underrepresented populations. Recent publications highlight advancements in wearable sensors, tunable optical systems, and interdisciplinary educational frameworks. 2015 Delaware Valley Engineer of the Year NASA New Investigator Award Drexel Graduate Student Association Outstanding Mentor Award Drexel University ECE Outstanding Research Achievement Award International Liquid Crystal Society Multimedia Prize Drexel President’s Award for Diversity (2018) Fontecchio has secured over $33M in grants, including NSF STEM GK12, DoE GAANN, and NSF Nanoscale Undergraduate Education awards. He mentors high school students through Drexel’s Summer Mentorship Program and leads initiatives to enhance STEM education via academic makerspaces and international partnerships.

• River Ice Processes
• Water Resources Engineering
• Frazil Ice Formation
• +۵ مورد دیگر

Vincent McFarlane is an Assistant Professor in Water Resources Engineering at the Department of Civil and Environmental Engineering, Faculty of Engineering, University of Alberta. He is part of the River Ice Research Group at the University of Alberta, with his research primarily focused on ice formation and the river freeze-up process. Dr. McFarlane teaches courses including CIV E 330 - Introduction to Fluid Mechanics and CIV E 739 - Advanced Topics in Fluid Mechanics and Hydraulics. Dr. McFarlane received his educational background entirely from the University of Alberta: Ph.D. in Water Resources Engineering (2014-2018) - Thesis: "Laboratory and Field Measurements of Frazil Ice Characteristics" M.Sc. in Water Resources Engineering (2011-2013) - Thesis: "Laboratory Studies of Suspended Frazil Ice Particles" B.Sc. in Civil Engineering (2007-2011) Dr. McFarlane's research interests center around river ice processes, with a particular focus on frazil and anchor ice formation, river supercooling and energy budget, and ice jam flooding. His work combines experimental field and laboratory studies to investigate ice formation mechanisms in rivers, with emphasis on how these processes affect river hydraulics and morphology. His research spans from fundamental ice physics to practical applications for flood management and infrastructure design in cold regions. Dr. McFarlane has received numerous scientific awards and recognitions for his work, including: NSERC Postdoctoral Fellowship NSERC Postgraduate Scholarship - Doctoral Graduate Student Teaching Award President's Doctoral Prize of Distinction R. Larry Gerard Medal Queen Elizabeth II Graduate Scholarship Best Student Paper Award from IAHR Best Student Paper Prize from CGU-HS Committee on River Ice Processes As a passionate teacher, Dr. McFarlane enjoys sharing the wonders of water resources engineering with students of all ages. He has professional experience in both academia and industry, having worked as a Hydrotechnical Specialist at Stantec Consulting Ltd. before joining the University of Alberta as an Assistant Professor. He is also a Professional Engineer (P.Eng.) registered with the Association of Professional Engineers and Geoscientists of Alberta (APEGA).

• Educational Psychology
• Cognitive Psychology
• Game-Based Learning
• +۴ مورد دیگر

Mylène Sanchiz serves as a Lecturer in Psychology within the Department of Human and Social Sciences of Education at the School of Education, University of Poitiers. She is affiliated with the CeRCA research laboratory (UMR 7295) and represents the SHSE department of INSPÉ since 2022. Her teaching portfolio includes Educational Psychology for MEEF 1st/2nd degree programs, Cognitive Psychology, Ergonomics of Human-Machine Interfaces, and Neuroscience and Learning for MEEF 4. Her research centers on cognitive processes in learning through games and digital tools, investigating interactions between individual characteristics (executive functions, knowledge, motivation), task requirements, and tool design. Key focuses include board/video game effects on socio-emotional skills and executive functions across educational cycles, and digital tool impacts on reading comprehension, motivation, and task interpretation. Methodologically, she examines learner strategies, behaviors, and perceived task requirements from both fundamental and interventional perspectives. Recent publications reveal strong thematic consistency in game-based learning and digital document comprehension. Her 2023-2024 work emphasizes contextual factors in game mechanics, prior knowledge effects, and flow states in learning environments. Earlier research (2019-2020) established foundations in hypertext processing, concept mapping, and individual differences in information search, demonstrating an evolution toward applied educational interventions while maintaining cognitive psychology rigor. Dr. Sanchiz actively contributes to academic governance as SHSE department representative and through research seminars. Her work bridges cognitive theory with classroom applications, particularly in leveraging games for socio-emotional development and optimizing digital tools for comprehension training from early childhood through primary education.

• Machine Learning
• Data Mining
• Computational Material Science
• +۴ مورد دیگر

Prof. Dr. Stefan Sandfeld is the Director of the Institute for Advanced Simulation (IAS) at Forschungszentrum Jülich , leading the Materials Data Science and Informatics (IAS-9) group. His research focuses on integrating machine learning, data mining, and computational methods to address challenges in materials science, particularly in analyzing dislocation dynamics, microstructure evolution, and high-throughput microscopy data. Key areas include developing AI-driven tools for electron microscopy analysis, creating FAIR-compliant data ecosystems (e.g., Helmholtz Knowledge Graph), and advancing materials informatics for semiconductor and alloy systems. He spearheads projects like the NFDI-MatWerk consortium, aiming to standardize materials data management. His work bridges experimental and computational approaches, with applications in SiC crystal growth, dislocation modeling, and defect characterization. Recent contributions include self-supervised learning frameworks for microscopy images and ontology-based systems for dislocation data (DISO). His research also explores generative models for accelerated materials design and data-driven approaches for microstructure-property relationships. Scientific highlights include: High-throughput analysis of in-situ TEM experiments for dislocation dynamics. FAIR data ecosystems via semantic web technologies (e.g., Helmholtz Knowledge Graph). Machine learning for nanoindentation analysis and 4H-SiC wafer defect detection. He collaborates with Helmholtz institutes and industry partners, driving digitalization in materials science through interdisciplinary initiatives. His lab focuses on advancing AI tools for material discovery, process optimization, and understanding multiscale material behavior.

• Electron Microscopy
• Materials Science
• Physics of Matter
• +۴ مورد دیگر

Stefano Frabboni is a Full Professor at the University of Modena and Reggio Emilia (UNIMORE) within the Department of Physical, Computer and Mathematical Sciences. His academic career focuses on experimental physics, particularly in electron microscopy and material science. He teaches courses such as General Physics III , The Profession of Physicist , and Physics Laboratory I , emphasizing mechanical and electromagnetic wave phenomena, data analysis, and laboratory techniques. His research interests span Electron Microscopy , Materials Science , and Quantum Physics , with a strong emphasis on Orbital Angular Momentum (OAM) applications in electron beam shaping and magnetic field analysis. He has contributed extensively to High Entropy Alloys , Computational Ghost Imaging , and Nanoscale Magnetic Spectroscopy . The articles reflect a focus on electron beam manipulation , phase shifts in materials , and high-resolution imaging techniques . Recent article trends include optimizing substrate bias voltage in HEA films , Mo content effects on coatings , enhancing TEM resolution via computational methods , and fabricating 3D nanoarchitectures with direct-write approaches. Sub-fields covered in his work are electron vortex generation , quantum state discrimination , OAM sorting , plasmonic excitation analysis , and defect characterization in semiconductors .

Amin Arbabianمشاهده پروفایل

دانشیار

• Biomedical Engineering
• Sensing Systems
• Internet of Things (IoT)
• +۵ مورد دیگر

Amin Arbabian is an Associate Professor in the Department of Electrical Engineering at Stanford University. His research spans biomedical devices, sensing systems, and Internet of Things (IoT) technologies, with a focus on wireless power transfer and miniaturized sensor design. Education: BSc in Electrical Engineering, Sharif University of Technology (2005) MSc in Electrical Engineering and Computer Sciences, UC Berkeley (2007) PhD in Electrical Engineering and Computer Sciences, UC Berkeley (2011) Arbabian's lab specializes in end-to-end design of RF/microwave systems for medical implants, sensing interfaces, and terascale IoT networks. Key projects include ultrasonically powered implants for neural stimulation and drug delivery, mm-wave radar systems for gesture recognition, and ultrasound wake-up radios for ultra-low-power IoT devices. His recent publications highlight advancements in wireless neural implants, adaptive radar sensing, and photoelastic modulation for time-of-flight imaging. These works span disciplines including Electrical Engineering, Biomedical Engineering, and Applied Physics. Scientific Awards: Best Student Paper Award, ISSCC 2018 Best Student Paper Award, PIERS 2015 1st Place Best Paper Award, 2016 IEEE Biomedical Circuits and Systems Conference Best Student Paper Award, SPIE Security and Defense 2016 Arbabian collaborates with Stanford faculty in Chemistry, Comparative Medicine, and Radiology. His lab's funding sources include NSF, DARPA, NIH, ARPA-E, and ONR. The group also explores industrial applications like semiconductor manufacturing optimization with AI-driven digital twins.