جست‌وجوی استاد و پژوهشگر

Alexander Barnesمشاهده پروفایل

استاد

• Magnetic Resonance
• Gyrotron Technology
• NMR Spectroscopy
• +۳ مورد دیگر

Prof. Alexander Barnes is a Full Professor at the Department of Chemistry and Applied Biosciences and Head of the Institute of Molecular Physical Science at ETH Zürich. His research focuses on advanced magnetic resonance techniques, gyrotron technology, and biomolecular structural analysis. Key areas include high-power microwave source development for NMR applications, structural characterization of viral proteins (e.g., SARS-CoV-2), and cryogenic instrumentation for enhanced spectroscopy. Barnes has pioneered innovations in dynamic nuclear polarization (DNP) systems, frequency-agile gyrotrons, and miniaturized superconducting magnets. His work bridges fundamental physics with biomedical and materials science applications. Current projects address protein-membrane interactions, THz gyrotron design, and cryogenic NMR probe optimization. Barnes collaborates extensively with international research groups and industry partners to advance analytical instrumentation and disease-related molecular mechanisms. Publications reflect a sustained focus on experimental and computational methods for magnetic resonance, including breakthroughs in electron beam modeling, frequency-chirped DNP, and ultra-high-field magnet development. His engineering contributions include spherical rotor systems for magic-angle spinning and lightweight gyrotron cavities. Barnes also leads initiatives in solid-state NMR for drug discovery, particularly targeting protein kinase modulation in T-cells and viral assembly mechanisms.

Steven Boxerمشاهده پروفایل

استاد

• Physical Chemistry
• Biophysical Chemistry
• Protein Electrostatics
• +۷ مورد دیگر

Steven Boxer is the Camille Dreyfus Professor of Chemistry at Stanford University. His research focuses on the physical aspects of biological systems, with particular emphasis on protein electrostatics, membrane biophysics, and photosynthesis. He has made significant contributions to understanding the role of electric fields in enzyme catalysis and the photophysics of fluorescent proteins. Dr. Boxer received his PhD in Physical and Physical-Organic Chemistry from the University of Chicago in 1976 and his BS with Honors in Chemistry from Tufts University in 1969. His research program investigates several interconnected themes: Excited State Dynamics in GFP & Split GFP Electrostatics and Dynamics in Proteins Model Membranes Energy and Electron Transfer in Photosynthesis His lab develops innovative experimental methods including Stark spectroscopy to measure electric fields in proteins and has pioneered the use of vibrational probes to map electrostatic environments in biological systems. Recent publications demonstrate his continued leadership in applying physical chemistry approaches to biological problems, with particular focus on quantifying electric fields in enzyme active sites and their role in catalysis. His work bridges chemistry, physics, and biology to provide fundamental insights into biomolecular function. As the Camille Dreyfus Professor of Chemistry, Dr. Boxer has received significant recognition for his contributions to chemical research and education. Dr. Boxer leads an active research group that combines experimental and computational approaches to study biological systems with a strong physical perspective. His lab has developed numerous innovative techniques and continues to train the next generation of scientists at the interface of chemistry and biology. The Boxer Lab maintains a strong research presence with multiple ongoing projects exploring the physical basis of biological phenomena, including advanced instrumentation for measuring electric fields in proteins and developing novel membrane models.

Alan Fershtمشاهده پروفایل

استاد

• Chemistry
• Biophysics
• Molecular Biology
• +۷ مورد دیگر

Alan R. Fersht is a distinguished academic and researcher, currently serving as the Herchel Smith Professor of Organic Chemistry at the University of Cambridge. He has held significant roles such as Director of the MRC Centre for Protein Engineering and Director of the MRC Unit for Protein Function and Design. His research focuses on protein folding, molecular biology, and structural biology, with contributions to understanding protein misfolding in diseases like cancer. Fersht's work integrates biochemical, biophysical, and computational methods to study protein dynamics and chaperone mechanisms. Key honors include Fellowship of the Royal Society (1983), the Copley Medal (2020), and membership in multiple prestigious academies. His awards span decades, recognizing his contributions to biochemistry, structural biology, and protein engineering. Fersht's publications explore protein folding pathways, molecular chaperones (e.g., GroEL), and the thermodynamics of protein stability. His research interests include protein design, folding initiation sites, and the role of electrostatic interactions in folding. Notable studies on barnase-barstar interactions and chaperone-assisted folding highlight his experimental and theoretical approaches to understanding protein behavior. Fersht's work bridges fundamental research and applications in biotechnology and medicine.

Clara Viñas Teixidorمشاهده پروفایل

استاد پژوهشی

• Boron Chemistry
• Organometallic Chemistry
• Nanomaterials
• +۹ مورد دیگر

Prof. Clara Viñas Teixidor (Membership Number: 5772) is a Research Professor at the Institute of Materials Science of Barcelona (ICMAB) , part of the Spanish National Research Council (CSIC) . With a career spanning over 45 years, she has published 359 original scientific papers, 20 book chapters, and co-edited Boron-Based Compounds: Potential and Emerging Applications in Medicine , selected by Wiley for the 2019 International Year of the Periodic Table. Her work bridges chemical and pharmaceutical disciplines, focusing on boron cluster chemistry. Degrees : Chemistry (UAB, 1975), Pharmacy (UB, 1980), PhD in Pharmacy (UB, 1990, supervised by F. Teixidor). Positions : 1991–present at CSIC (ICMAB), including Research Professor since 2006. Research Interests center on Boron chemistry , particularly icosahedral boron clusters , for applications in nanomedicine , catalysis , sensors , and environmental chemistry . Her work pioneers hybrid materials combining boron clusters with polymers, nanoparticles, and metallacarboranes. Scientific Contributions include collaborations with experts in Raman microscopy and radioimaging. Her 2016 Chemical Society Reviews article on boron clusters was highly cited (145 times) and featured on the journal’s cover. Scientific Awards : Medal at ICCC42 (2016), Member of Academia Europaea (2021).

V. Menkovskiمشاهده پروفایل

دانشیار

• Graph Neural Networks
• Machine Learning
• Deep Learning
• +۷ مورد دیگر

Dr. V. Menkovski serves as an Associate Professor in Data Mining at Eindhoven University of Technology's Department of Mathematics and Computer Science. He also holds associate professor positions with EAISI Health and EAISI High Tech Systems, and is an ICMS Affiliated member. His work spans multiple domains of artificial intelligence and computational physics, with significant contributions to fusion energy research. Mathematics and Computer Science, Data Mining (Primary Appointment) EAISI Health (Associate Professor) EAISI High Tech Systems (Associate Professor) ICMS (Affiliated Member) Menkovski's research focuses on Graph Neural Networks, Machine Learning, Deep Learning, and their applications in diverse fields from plasma physics to metamaterials. His work demonstrates strong interdisciplinary connections, particularly between computer science and fusion energy research. He has developed novel approaches for crowd simulation, tokamak plasma monitoring, and metamaterials homogenization using advanced neural architectures. His fingerprint reveals expertise in Quality-of-Experience, Autoencoders, Neural Networks, Annotation, Graph Neural Networks, Video Streaming, Adversarial Machine Learning, and Anomaly Detection. Analysis of his recent publications (2023-2025) shows a clear trend toward applying Graph Neural Networks to complex physical systems, particularly in fusion energy research and materials science. His work increasingly integrates symmetry principles with neural architectures, as seen in his research on equivariant networks for metamaterials and symmetry-informed networks for zeolite analysis. There's also significant focus on practical applications in fake news detection, anomaly detection, and plasma state monitoring. Best Paper Award ICPM 2021 (with Sommers and Fahland) Best Paper Award of LoG 2022 (with multiple co-authors including Huang, Chen, Fang, Zhao, Yin, Pei, Mocanu, Wang, Pechenizkiy, and Liu) Menkovski teaches several advanced courses including Deep Learning, Advanced Topics in Artificial Intelligence, and Sociophysics 2, which runs through August 2025. His supervised work portfolio includes 79 projects, indicating substantial mentorship activity. He has received significant media attention for his research, including coverage by 11 news outlets, blog posts, and mentions on social media platforms. His work on 'Supervised Learning of Process Discovery Techniques Using Graph Neural Networks' was particularly noted in media coverage. His research involves collaboration with multiple institutions and teams, particularly in fusion energy research (Eurofusion Tokamak Exploitation Team, ASDEX-Upgrade team, EUROfusion MST1 Team). He works closely with researchers across disciplines, including physicists working on tokamak plasma and materials scientists studying metamaterials and zeolites.

• Mathematical Biology
• Computational Modeling
• Biophysics
• +۳ مورد دیگر

Sabine Fischer is a Professor for Supramolecular and Cellular Simulations at the University of Würzburg’s Center for Computational and Theoretical Biology (CCTB). She holds a PhD in Mathematics from the University of Nottingham (2009) and completed postdoctoral research at the University of Cambridge (2009-2011) and the University of Frankfurt (2011-2017). Her work focuses on mathematical modeling and data-driven simulations of biological processes across subcellular, multicellular, and multi-tissue scales. Key research areas include agent-based modeling of parasite collective behavior (e.g., Project 7 of SPP 2332 PoP), cell-cell interactions, and morphological modeling with Blender. Her group develops tools for image analysis and applies methods ranging from classical statistics to machine learning. Education: Diploma in Mathematics, University of Würzburg PhD in Mathematical Biology, University of Nottingham (2009) Professional Experience: Postdoc, University of Cambridge (2009-2011) Postdoc, University of Frankfurt (2011-2017) Development Engineer, h.a.l.m. Elektronik GmbH (2017-2018) Professor, University of Würzburg (since 2018) Her research integrates experimental data with computational models to study processes like mouse blastocyst differentiation, tissue-level organization, and parasitic locomotion. Current projects emphasize agent-based modeling of Trypanosoma collective behavior and the development of open-source tools like VESNA for 3D vessel analysis.

• Health Economics
• Applied Microeconomics
• Industrial Organization

Assoc Prof Yang Zhang is an Associate Professor in the School of Economics at the National University of Singapore , Department of Economics. He has been affiliated with the university since 2012, progressing from Lecturer (2012-2015) to Senior Lecturer (2016-2021) and currently Associate Professor since 2022. Education: PhD in Managerial Economics and Strategy (Northwestern University, 2011) BA in Economics (Peking University, 2005) Research Interests: Health Economics Applied Microeconomics Industrial Organization Recent Research Trends focus on leveraging deep learning and structural biology tools for protein structure prediction, RNA modeling, and computational drug design. His work integrates advanced algorithms with large-scale metagenomic data to improve accuracy in complex biological system modeling.

Tony LELIEVREمشاهده پروفایل

استاد

• Applied Mathematics
• Multiscale Modeling
• Molecular Simulation
• +۷ مورد دیگر

Tony Lelièvre is a Professor of Applied Mathematics at the Ecole Nationale des Ponts et Chaussées, part of the Institut Polytechnique de Paris. He holds a PhD (2004) and Habilitation (2009), specializing in multiscale modeling, molecular simulation, and stochastic processes. His research focuses on free energy computations, numerical analysis of complex fluids, and computational statistical physics. He co-authored two books and over 100 papers, receiving significant awards like the ERC Consolidator Grant (2013-2019) and the Grand prix Alcan. Lelièvre has organized numerous international workshops and serves on editorial boards of journals like ESAIM:M2AN and SIAM/ASA Journal of Uncertainty Quantification. His work bridges applied mathematics, numerical analysis, and computational science with applications in materials science and industrial fluid dynamics. Education: PhD in Applied Mathematics, 2004 Habilitation à Diriger des Recherches, 2009 Research Interests: Multiscale modeling of complex fluids Molecular dynamics and free energy calculations Stochastic methods for metastable systems Numerical analysis of PDEs and SDEs Computational statistical physics Recent Contributions: His work on adaptive biasing force methods and hybrid Monte Carlo techniques has advanced the simulation of rare events and free energy landscapes. He contributed to variance reduction techniques in molecular simulations and mathematical analysis of parallel replica algorithms. Awards: ERC Consolidator Grant (2013-2019), Prix CS 2002, Grand prix Alcan (2010), Ordway Visiting Professorship (2012-2013), and several teaching awards. Professional Activities: Organized major conferences like CEMRACS 2013 and IPAM Long Program on Energy Landscapes (2017). Co-edits journals and authored influential textbooks on magnetohydrodynamics and free energy computations.

Emanuel Gullمشاهده پروفایل

استاد

• Quantum Physics
• Condensed Matter Physics
• Theoretical Physics
• +۴ مورد دیگر

Professor Emanuel Gull is a theoretical physicist specializing in numerical methods for quantum many-body systems. He holds a professorship at the University of Warsaw's Faculty of Physics, where he relocated his laboratory from the University of Michigan to conduct research under an ERC Advanced Grant. His work focuses on predictive algorithms for simulating quantum materials, integrating quantum field theory, condensed matter theory, quantum Monte Carlo methods, applied mathematics, and machine learning. This interdisciplinary approach addresses phenomena like magnetism and superconductivity in materials critical to technologies such as MRI machines and electric car batteries. Professor Gull's ERC Advanced Grant, the first awarded to a foreign researcher hosted at the University of Warsaw, underscores the significance of his project. Collaborating with the Quantum Molecular Systems group at the Faculty, his methods support experimental advancements in ultracold matter research. His research aligns with the University's strategic focus on cutting-edge interdisciplinary science. His achievements include the prestigious ERC Advanced Grant, highlighting global recognition of his contributions. The project’s relocation of his laboratory reflects Warsaw's growing role as a hub for quantum research. Grants and institutional support from the University of Warsaw's BMPB office further enable his work, emphasizing collaboration and innovation. Professor Gull's research lab, now based in Warsaw, bridges theoretical and applied physics, fostering advancements in quantum technologies. His work exemplifies the intersection of fundamental science and technological application, with direct implications for energy storage and medical imaging technologies.

• Environmental Microbiology
• Biotechnology
• Wastewater Treatment
• +۵ مورد دیگر

Niall O'Leary is a Senior Lecturer and Principal Investigator in the School of Microbiology at University College Cork (UCC), with affiliations to the Environmental Research Institute and the SUSFERM biotechnology centre. His research focuses on microbial community ecology for wastewater treatment and circular economy solutions in the agri-food sector, alongside innovations in technology-enhanced education. He holds an MA in Teaching and Learning in Higher Education (2014) and has secured significant grants, including SFI funding for wastewater-based epidemiology and EPA support for dairy wastewater valorization. Education: BSc (Hons) Microbiology (1996), PhD Microbiology (2001) from UCC, followed by postdoctoral research. Advanced teaching qualifications include a Postgraduate Diploma (2009) and MA in Teaching and Learning (2014). Research Interests: Microbial bioreactor systems, dairy/meat wastewater treatment, bioconversion to biodegradable plastics, and Lemna cultivation. Recent work includes SARS-CoV-2 wastewater epidemiology and precision fermentation via SUSFERM. Teaching innovations include VR simulations for molecular biology and 360° virtual industrial site visits. Notable Grants: SFI Rapid Response (€44,876), UniCov Study (€188,770), NEWTRIENTS (€650,000), and DAFF-funded projects on duckweed protein and dairy wastewater. Awards: DELTA National Award (2021), UCC President’s Teaching Excellence (2015). Active in outreach, including public workshops on plastic pollution and collaborations with secondary schools via VR biology tools.

• Strongly correlated electron systems
• High-temperature superconductivity
• Disordered systems
• +۳ مورد دیگر

Maciej Maśka is a Professor of Theoretical Physics at the University of Silesia (since 2007), affiliated with the Faculty of Mathematics, Physics and Chemistry. His research focuses on strongly correlated electron systems, high-temperature superconductivity, and ultracold atoms in optical lattices. He leads the Condensed Matter Theory Group (CMTG) and has held leadership roles in numerous international conferences, including chairing the XXXIVth, XXXVIth, and XXXVIIIth International Conferences on Theoretical Physics. Education: M.S. (1988), Ph.D. (1995, with distinction), and Habilitation (2004) from the University of Silesia. His doctoral work addressed antiferromagnetism and superconductivity in the t-J model under Prof. J. Zielinski’s supervision. Research Interests: Superconductivity in magnetic fields and inhomogeneous systems Nanoscopic physics (quantum dots/rings, transport properties) Ultracold atoms in optical lattices (Mott transitions, phase coherence) Electronic structure of molecular and solid-state systems Recent Work Trends: Experimental collaboration with institutions like NIST and Georgetown University focuses on simulating correlated electron phenomena using cold atoms. Key contributions include theoretical models for magnetic phase transitions, superconductivity in novel materials, and molecular formation in quantum gases. Awards include multiple Rector’s Awards (University of Silesia) and distinction in both his M.S. and Ph.D. He serves on the Polish Physical Society’s Election Commission and organizes major international conferences in theoretical physics. Academic Service: Coordinator of Distance Learning initiatives (2004–present), member of the Faculty Council (1995–present), and advisor to the Department of Computer Sciences. Active in promoting computational infrastructure and library resources at the university. Labs/Teams: Leads the Condensed Matter Theory Group (CMTG) and collaborates internationally on projects like the TEAM-funded 'Correlations and Coherence in Quantum Materials' (2011–2014).

Leonhard Möcklمشاهده پروفایل

استاد

• Nano-optical Imaging
• Super-resolution Microscopy
• Glycocalyx Structure and Function
• +۴ مورد دیگر

Leonhard Möckl is a Professor of Nano-optical Imaging at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) and an Associated Group Leader at the Max Planck Institute for the Science of Light. He holds a PhD from LMU Munich (2015) and completed postdoctoral research at Stanford University (2016–2020), focusing on glycocalyx dynamics using super-resolution microscopy and deep learning. His research integrates biophysics, nanotechnology, and computational methods to study cell membrane organization and disease mechanisms. Key research areas include glycocalyx architecture, single-molecule imaging, and nano-optical techniques for biomedical applications. He leads the Physical Glycosciences Research Group, developing tools like XLuminA for automated microscopy design and novel drug delivery systems. Notable contributions include studies on RNA-binding proteins in leukemia treatment and glycocalyx-based drug targeting. Publications span high-impact journals like Nature Biotechnology , Cell , and Nature Communications , reflecting his expertise in imaging, glycobiology, and molecular pharmacology. His work bridges fundamental science and translational medicine, addressing challenges in cancer therapy, infectious diseases, and cell engineering.

Baron G. Petersمشاهده پروفایل

استاد

• Catalysis
• Nucleation and Crystal Growth
• Molecular Simulation
• +۴ مورد دیگر

Baron G. Peters is the William H. and Janet G. Lycan Professor of Chemical & Biomolecular Engineering and Faculty Affiliate in Chemistry at the University of Illinois. His research focuses on reaction rate theory, catalysis, and nucleation/growth mechanisms, with applications in crystal engineering, polymer upcycling, and ice growth inhibition. He holds a PhD from UC Berkeley (2004) and dual BS degrees in Chemical Engineering and Mathematics from the University of Missouri (1999). Affiliations: School of Chemical Sciences, Department of Chemistry Key Research Areas: Rare event simulations, transition path sampling, crystal nucleation theory, and heterogeneous catalysis on amorphous supports Recent work includes developing microkinetic models for polymer upcycling, studying antifreeze protein mechanisms, and advancing methods for predicting catalyst site heterogeneity. His group has pioneered importance learning techniques and population balance models in catalytic systems. Notable awards include the AIChE Separation Division Award (2021) and induction into the Chemical Engineering Academy of Distinguished Alumni (2024). Collaborations span academia and industry, with notable projects on nanocrystal synthesis and pharmaceutical crystallization.

• Semiconductor Device Simulation
• Nanoelectronics
• TCAD (Technology Computer-Aided Design)
• +۳ مورد دیگر

Daniel Nagy is an Assistant Professor at the Department of Electronics and Computing within the Higher Polytechnic School of Engineering at the University of Seville. His research focuses on nanoelectronics, semiconductor device simulation, and TCAD technologies. He leads the ARQCOMP research group, specializing in computer architecture and novel transistor design. Education details are not explicitly provided in the texts, but his academic focus suggests advanced training in electronics engineering or related fields. His research interests include quantum transport phenomena, nanoscale device variability analysis, and computational modeling of FinFETs, nanowire FETs, and nanosheet transistors. He has extensively contributed to TCAD simulation frameworks such as NESS (Nano-Electronic Simulation Software), emphasizing open-source tools. Research Trends: His work emphasizes transistor scaling challenges for sub-2nm nodes, device variability mitigation, and the development of modular simulation tools. Key topics include nanosheet FET optimization, POM-based molecular memory systems, and benchmarking of emerging transistor architectures. His studies often bridge quantum mechanics (via Schrödinger equation modeling) with classical transport phenomena. No scientific awards are mentioned in the provided texts. He has advised no publicly listed PhD/Master’s students. His ARQCOMP group collaborates on next-generation electronic device architectures and simulation methodologies. Labs/Teams: The ARQCOMP group focuses on advancing computer architecture and nanoelectronic device simulation through interdisciplinary approaches, integrating computational physics, materials science, and electrical engineering.

Igor Potemkinمشاهده پروفایل

پژوهشگر

• Polymer Physics
• Soft Matter Physics
• Theoretical Chemistry
• +۵ مورد دیگر

Prof. Dr. Igor Potemkin is an Associated Researcher at Moscow State University, leading a group focused on theoretical studies and computer simulations of polymer self-organization, particularly in complex architectures like arborescent, comb, and gel-like polymers. His work intersects polymer physics, soft matter, and materials science, with collaborations at the DWI and other institutions. Research emphasizes micelle/gel formation, polyelectrolyte interactions, and applications in drug delivery and biomimetic systems. Education details are not explicitly stated in the text, but his academic trajectory is evident through his extensive publication record and project leadership. Key research interests include microgel behavior, interfacial phenomena, and nanoscale structure-property relationships. Notable projects include studies on amphiphilic microgels, ionic liquids, and gradient copolymers. His research group has explored over 100 publications, focusing on topics like microgel self-assembly, polyelectrolyte complexes, and molecular motor design. Collaborations span institutions worldwide, with a strong emphasis on computational modeling and experimental validation. The team includes postgraduates and students involved in projects such as 'Functional Microgels and Microgel Systems' (SFB-985) and Helmholtz-RSCF initiatives. Scientific contributions include advancements in polymer thin films, nanoparticle-stabilized colloids, and the development of novel biomaterials. His work bridges theoretical predictions with practical applications in nanotechnology and biomedical engineering.