جست‌وجوی استاد و پژوهشگر

Bradley D. Olsenمشاهده پروفایل

استاد

• Polymer Science
• Materials Engineering
• Soft Condensed Matter Physics
• +۴ مورد دیگر

Bradley D. Olsen is a full professor in the Department of Chemical Engineering at the Massachusetts Institute of Technology (MIT), where he leads research at the intersection of polymer science, soft matter physics, and bioengineering. His work focuses on designing materials for critical applications in biotechnology, hemostasis, and sustainable polymer development while advancing fundamental understanding of polymer network mechanics and self-assembly. Education: Ph.D. in Chemical Engineering, University of California Berkeley (2007) S.B. in Chemical Engineering, Massachusetts Institute of Technology (2003) Olsen's research spans protein-based materials, block copolymer phase behavior, and mechanochemical hydrogels. He has pioneered methods for quantifying polymer network topology, developing hemostatic nanoparticles, and creating bio-inspired materials for selective biomolecular transport and medical applications. His recent publications emphasize data-driven approaches to polymer characterization and educational outreach in materials science. Scientific Awards: American Physical Society (APS) Fellow (2023) Fulbright Amazonia Scholar (2023) Alexander and I. Michael Kasser Chair in Chemical Engineering (2021) ACS Macro Letters Young Investigator Award (2021) MIT Committed to Caring Honor (2019) AIChE Owens Corning Early Career Award (2019) APS Dillon Medal (2018) Kavli Emerging Leader in Chemistry (2017) ACS Polymer Division Fellow (2016) Camille Dreyfus-Teacher Scholar (2015) Alfred P. Sloan Research Fellow (2014) NSF Career Grant (2013) NIH Postdoctoral Fellowship (2008-2009) Hertz Fellow (2003-2007) Barry M. Goldwater Scholarship (2002) Olsen has received significant grant support including NSF Career (2013) and AFOSR (2012) awards. His teaching activities include innovative international outreach like the 2025 soccer-themed science camp in Brazil. The Olsen Group at MIT explores advanced materials with applications ranging from trauma care to sustainable polymers.

Eleni Chatziمشاهده پروفایل

استاد

• Structural Health Monitoring
• Nonlinear System Identification
• Damage Detection
• +۷ مورد دیگر

Prof. Eleni Chatzi is a Full Professor and Chair of Structural Mechanics at ETH Zurich's Department of Civil, Environmental and Geomatic Engineering. She holds a PhD from Columbia University (2010) and has held roles from Assistant to Full Professor at ETH since 2010. Her research focuses on intelligent structural monitoring and data-driven asset management, emphasizing nonlinear dynamics and sensor integration. Affiliations : Institute of Structural Engineering, European Academy of Wind Energy (EAWE President), Swiss Community for Computational Methods (SWICCOMAS Chair) Research interests include Structural Health Monitoring (SHM), system identification, and advanced simulation tools. She pioneered work on data-driven diagnostics and self-aware infrastructure, supported by grants like the ERC Starting Grant (2015). Awards include the 2020 Walter L. Huber Prize and 2024 SHM Person of the Year Award. Her work spans wind energy infrastructure, metamaterials for vibration control, and AI-driven structural analytics. Over 600 publications and 200k+ citations highlight her impact. She teaches computational science and structural dynamics in ETH's programs and collaborates globally on sustainable infrastructure projects.

• Lithium-Ion Battery Production
• Energy Storage Systems
• Production Engineering
• +۳ مورد دیگر

Prof. Dr.-Ing. Rüdiger Daub serves as Professor and Chair of Production Engineering and Energy Storage Systems at the Technical University of Munich (TUM), operating within the Department of Mechanical Engineering. His leadership encompasses research direction, academic supervision, and strategic development of battery production technologies at TUM's Garching campus (Boltzmannstr. 15), with active industry collaborations driving innovation in sustainable manufacturing. Daub's research program pioneers advanced production methodologies for lithium-ion and solid-state batteries, focusing on electrode manufacturing, electrolyte filling, and cell assembly processes. His work investigates critical parameter interdependencies affecting battery safety and performance, developing inline monitoring systems and digital twin technologies for real-time process optimization. Key contributions include moisture control in electrode production, electrochemo-mechanical characterization of solid-state systems, and robotics solutions for deformable object assembly, all integrated with machine learning for quality assurance in industrial settings. Analysis of his 2023-2025 publications reveals a dominant research trajectory toward solving production bottlenecks in next-generation energy storage. The work demonstrates increasing integration of computational modeling with empirical validation, particularly in solid-state battery manufacturing and high-voltage electrolyte systems. A notable trend is the cross-pollination of robotics, computer vision, and uncertainty quantification techniques to address complex assembly challenges and distribution shifts in quality monitoring, reflecting industry's urgent need for adaptable, data-driven production systems. Leading TUM's specialized laboratories for battery cell production, Daub's team maintains comprehensive facilities for electrode calendering, electrolyte filling, and cell assembly with integrated tracking and tracing capabilities. The research infrastructure supports collaborative projects with automotive OEMs and battery manufacturers to develop scalable production processes, emphasizing environmental sustainability through water-based electrode production and footprint optimization. Current initiatives focus on digital factory modeling and prelithiation technologies for next-generation battery systems.

Dane Morganمشاهده پروفایل

استاد

• Computational materials science
• Ab initio electronic structure
• Multiscale methods
• +۴ مورد دیگر

Dane Morgan is a Professor in the Department of Materials Science & Engineering at the University of Wisconsin-Madison, College of Engineering. His research focuses on computational materials science for materials design, including ab initio electronic structure modeling, multiscale methods, and machine learning applications in materials discovery. His work spans nuclear materials, battery and fuel cell electrodes, and electronic materials. Education : PhD, 1998, University of California, Berkeley MS, 1994, University of California, Berkeley BA, 1992, Swarthmore College Research Interests : Computational materials science, ab initio methods for electronic structure and thermokinetics, machine learning for materials discovery, electrochemical systems modeling, and applications in nuclear materials, batteries, and electronic materials. His work integrates advanced computational techniques with experimental validation. Scientific Awards : 2024 APL Materials, Editors Pick 2023 Microscopy and Microanalysis Best Paper Award (Instrumentation and Software category) 2023 IEEE Transactions on Plasma Science Best Paper Award 2023 Kellet Mid-Career Award 2015 TMS Materials Genome Initiative Ambassador 2006 3M Technical Nontenured Faculty Grant

Quoc Thong Le Giaمشاهده پروفایل

دانشیار

• Numerical Analysis
• Approximation Theory
• Partial Differential Equations
• +۵ مورد دیگر

Quoc Thong Le Gia is an Associate Professor in the School of Mathematics & Statistics at the University of New South Wales (UNSW), Sydney. He holds a PhD in Mathematics from Texas A&M University (2003), an MS in Mathematics from Texas A&M University (2000), and a BSc in Mathematics and Computer Science from UNSW (1998). His research focuses on Numerical Analysis , Approximation Theory , Partial Differential Equations , and Stochastic Processes , with particular expertise in problems on spherical domains. His work bridges theoretical mathematics with practical applications in computational science, data science, and machine learning. Le Gia's recent publications demonstrate a strong focus on numerical methods for PDEs on spheres, stochastic analysis, and machine learning applications. His work shows consistent progression from theoretical foundations to practical implementations, with increasing interdisciplinary applications in recent years. L. F. Guseman Prize in Mathematics, Texas A&M University (2003) As a dedicated academic mentor, Le Gia has supervised numerous PhD, Master's, and Honours students across computational mathematics and data science topics. He has secured significant research funding through ARC Discovery Projects including DP220101811 (2022-2024) and DP180100506 (2018-2020). Professionally, he serves as External Associate Editor for Frontiers in Applied Mathematics and Statistics , Secretary for ANZIAM's Computational Mathematics Group, and Co-chair of Mathematics of Computation and Optimisation (AustMS Special Interest Group).

Jacques Archambaultمشاهده پروفایل

استاد

• Human Papillomavirus (HPV)
• Polyomavirus
• Viral DNA Replication
• +۴ مورد دیگر

Dr. Jacques Archambault is a Professor in the Department of Microbiology and Immunology at McGill University , and an associate member of the Division of Experimental Medicine since 2016. His research focuses on the molecular biology and pathogenesis of human papillomaviruses (HPVs) and polyomaviruses (HPyVs), with an emphasis on their replication mechanisms as episomes in host cells. The Archambault laboratory employs functional genomics, proteomics, and chemical biology approaches to identify cellular pathways exploited by these viruses and develop high-throughput assays for screening small molecule inhibitors of viral replication. Analysis of his recent publications reveals a strong focus on HPV and HPyV replication machinery, including studies on the E1 helicase, UAF1-USP1 interactions, and structural characterization of viral proteins involved in DNA replication. His work bridges virology, oncology, and drug discovery, particularly targeting oncogenic HPV types implicated in anogenital and oropharyngeal cancers, as well as HPyVs like BKPyV and JCPyV that cause pathologies in immunosuppressed patients. Current efforts in the lab aim to elucidate the molecular mechanisms by which HPVs and HPyVs replicate their genomes and to develop antiviral therapies targeting these processes. Techniques such as fluorescence anisotropy, NMR spectroscopy, and crystallography are frequently employed to study protein-DNA and protein-protein interactions critical to viral replication.

Josef Dickمشاهده پروفایل

استاد

• Numerical Integration
• Quasi-Monte Carlo Rules
• Number Theory
• +۵ مورد دیگر

Professor Josef Dick serves as a Professor and Deputy Head in the School of Mathematics & Statistics at the University of New South Wales (UNSW). With a distinguished career in computational mathematics, he has established himself as a leading researcher in numerical integration methods and quasi-Monte Carlo theory. His work bridges theoretical mathematics with practical computational applications across various scientific domains. Dr. Dick earned his PhD in Mathematics from UNSW in 2004 and his MSc in Mathematics from the University of Salzburg in 2001. His academic journey reflects a strong foundation in both theoretical and applied mathematics, which has informed his subsequent research contributions. Professor Dick's research primarily focuses on numerical integration and quasi-Monte Carlo rules , employing techniques from number theory , abstract algebra (particularly finite fields), discrepancy theory , wavelet theory , and statistics . His work provides rigorous analysis of practical algorithms for computational problems, with implementations often provided in Matlab to bridge theory and application. His research has successfully addressed point distributions on the unit cube for numerical integration, completely uniformly distributed sequences for Markov chain quasi-Monte Carlo algorithms, and explicit constructions of uniformly distributed points on the sphere. Analysis of his recent publications (2022-2025) reveals a consistent focus on advancing quasi-Monte Carlo methods, with increasing integration of machine learning techniques and applications to complex computational problems. His work demonstrates strong interdisciplinary connections between pure mathematics, computational science, and practical engineering applications, particularly in uncertainty quantification and high-dimensional numerical integration. Discovery project from Australian Research Council (2012-2014): "Mathematics in the round - the challenge of computational analysis on spheres" Queen Elizabeth II Fellowship from Australian Research Council (2010-2014): "Algebraic methods for Markov Chain Monte Carlo and quasi-Monte Carlo" UNSW Vice Chancellor Fellowship (2006-2009) Professor Dick has supervised numerous PhD and Honours students working on topics including Quasi-Monte Carlo methods, Discrepancy Theory, Markov chain Monte Carlo, and Uncertainty Quantification. His research has been supported by significant grants from the Australian Research Council, including serving as Chief Investigator on multiple projects. Beyond his research, he serves as an Editor for the Journal of Complexity and Journal of Approximation Theory, demonstrating his leadership in the mathematical community. He teaches courses in Algebra and Mathematical Computing for Finance at UNSW.

Pillai Ramesh Shanmughomمشاهده پروفایل

استاد

• Epigenetics
• RNA modifications
• miRNAs
• +۵ مورد دیگر

Ramesh Shanmughom Pillai is a Full Professor at the Department of Molecular Biology, University of Geneva, Switzerland. He holds additional roles as a Visiting Professor at the University of Kumamoto, Japan, and has been a Group Leader at EMBL Grenoble and a postdoctoral fellow at the Friedrich Miescher Institute. His research focuses on RNA modifications, epigenetics, and piRNA pathways in germline biology. Pillai has received prestigious awards including the ERC Consolidator Grant and The RNA Society Scaringe Award. Education: BSc Botany (University of Kerala, India) MSc Biotechnology (IIT Roorkee, India) PhD in Cell Biology (University of Bern, Switzerland) Research Interests: Pillai’s work centers on RNA biology, particularly the role of RNA modifications (e.g., m6A, m6Am) in development and fertility. He investigates piRNA biogenesis, transposon silencing, and the molecular mechanisms of RNA-protein interactions. His studies bridge biochemistry, genetics, and structural biology to elucidate how RNA molecules regulate critical biological processes. Teaching & Service: At the University of Geneva, he teaches Molecular Biology courses (BSc/MSc levels) and advises 5 PhD students and 4 postdocs. He chairs the ERC Consolidator Grant Review Panel and organizes major conferences like the PIWI/piRNAs Meeting and Swiss RNA Workshop. Pillai also serves on editorial boards for Nucleic Acids Research and RNA . Awards: ERC Consolidator Grant (2015) Best PhD Thesis Award (2003) RNA Society Scaringe Award (2005) Grants & Labs: Funded by ERC Starting and Consolidator Grants, his lab explores RNA modification networks in germ cells. Former trainees include Professors Simon Conn (Flinders University) and Hao Wu (CAS, China).

• Differential Privacy
• Machine Learning
• Privacy-Preserving Data Analysis
• +۳ مورد دیگر

Daniel Kifer is a Professor in the Computer Science and Engineering department at Pennsylvania State University, with affiliations to the Huck Institutes of the Life Sciences. His work bridges computer science, privacy-preserving machine learning, and geoscience applications. With over 10,000 citations and a high h-index, he focuses on methods to unify theoretical and applied research. Research Interests: Differential Privacy, Privacy-Preserving Machine Learning, Physics-Informed Neural Networks, Landslide Prediction, and Formal Verification of Privacy Systems. Recent projects include grants from the National Science Foundation: SaTC: CORE: Small (2024): privacy-preserving user data embedding in machine learning pipelines. SaTC: CORE: Medium (2017-2023): formal methods for differential privacy and accuracy optimization. His research outputs span domains like geoscience, database systems, and policy analysis, emphasizing precision and scalability of privacy-preserving algorithms.

Cheuk Wai Taiمشاهده پروفایل

پژوهشگر

• Structure-Property Relationships
• Functional Materials
• Electron Microscopy
• +۶ مورد دیگر

Cheuk Wai Tai is a Senior Staff Researcher at Stockholm University's Department of Environmental and Materials Chemistry since 2009. He manages the transmission electron microscopes and sample preparation equipment at the Electron Microscopy Center and serves as Section Editor for the Journal of Electronic Materials. His work focuses on quantitative structure characterization in functional materials research, particularly within nanoscience and nanotechnology contexts. Education: Ph.D. in Applied Physics, The Hong Kong Polytechnic University, 2004 M.Phil. in Applied Physics, The Hong Kong Polytechnic University, 2001 M.Sc. in Physics, The Chinese University of Hong Kong, 1998 B.Sc. (Hons) in Engineering Physics, The Hong Kong Polytechnic University, 1997 Dip. in Mechanical Engineering (Computer Aided Engineering), Institute of Vocational Education (formerly Haking Wong Technical Institute), Hong Kong, 1992 His research centers on structure-property relationships in functional materials through advanced electron microscopy techniques. Current specializations include Pair Distribution Function (ePDF) & Diffuse Scattering, Energy Materials characterization, and EM sample preparation methodology development. The group maintains strong focus on translating structural data into functional performance metrics for nanomaterials. Recent publications (2013-2019) demonstrate consistent emphasis on electron microscopy applications for energy storage materials (batteries, photocatalysts) and functional ceramics. Key trends include structural disorder analysis in piezoelectrics, development of quantitative TEM methods like SUePDF, and nanoscale characterization of electrocatalyst surface phases. His work bridges materials chemistry with advanced imaging techniques. Scientific recognition includes: Fellow of The Royal Microscopical Society (U.K.) Senior Member of IEEE Marie Curie Fellowship (2007-2009) from European Commission Sir Edward Youde Memorial Fellowship (2003/2004) from Hong Kong S.A.R. Government He teaches Solid State Chemistry (KZ7003) and leads Introduction to Analytical Electron Microscopy (KZ8009), having previously taught Advanced Transmission Electron Microscopy (KZ8010) before 2011. Major grants supporting his work include: "Quantitative structural characterisation using 3D electron-based pair distribution function" (Swedish Research Council) "A Multidimensional Toolkit for Modern Electron Microscopy" (Swedish Foundation for Strategic Research) "Mitigating Ni-rich Li-ion cathode side-reactions" (Swedish Energy Agency, Co-applicant) He leads the Cheuk-Wai Tai group within Stockholm University's chemistry department and oversees operations at the Electron Microscopy Center, where his team develops and applies advanced characterization techniques for functional materials research.

Abhijit Sarkarمشاهده پروفایل

استاد

• Dynamics
• Probabilistic Mechanics
• Uncertainty Quantification
• +۵ مورد دیگر

Abhijit Sarkar is a Professor in the Department of Civil and Environmental Engineering at Carleton University, Ottawa. His work centers on computational dynamics and probabilistic modeling, with office MC 3076 in the Minto Centre for Advanced Studies in Engineering and contact details including phone (613) 520-2600 x6320 and email abhijit_sarkar@carleton.ca . Education: D.Phil. from University of Oxford M.Sc. from Indian Institute of Science (IISc) B.E. from Calcutta University Professional Engineer (P.Eng.) designation His research drives innovation in uncertainty quantification for complex engineering systems. Core interests include dynamics of nonlinear structures, probabilistic mechanics for stochastic finite element methods, and Bayesian inference frameworks for parameter estimation. He pioneers scalable high-performance computing solvers for large-scale systems and sparse learning algorithms to address overfitting in statistical modeling. Recent publications (2022-2024) reveal three dominant trends: (1) Bayesian model calibration for stochastic compartmental systems applied to epidemiology and aerospace, (2) domain decomposition techniques for scalable uncertainty quantification in stochastic PDEs, and (3) sparse learning methods for nonlinear aerodynamic encoding. Key applications span wind turbine vibration analysis, flutter margin prediction, MEMS resonator optimization, and geospatial pandemic modeling. Scientific awards: No awards, fellowships, or medals listed in the source material Graduate supervision includes 6 current students (Ajay Kumar, John Clarabut, Nastaran Dabiran, Sakhi Mittal, Michael Pantano, Brandon Robinson) and 18 graduated students across 17 years (2006-2023). His research leverages high-performance computing for projects in structural dynamics, aeroelasticity, and computational epidemiology, frequently co-supervised with Dominique Poirel and Chris Pettit. Notable grants focus on wind tunnel validation for nonlinear systems and pandemic spread modeling. Based in the Minto Centre for Advanced Studies in Engineering, his computational mechanics group develops algorithms for stochastic dynamics using Carleton University's high-performance computing infrastructure. Collaborations span aerospace engineering (flutter analysis), civil infrastructure (seismic wave propagation), and public health (Covid-19 modeling).

Xiaolei Fangمشاهده پروفایل

دانشیار

• Statistical Learning
• Deep Learning
• Federated Learning
• +۳ مورد دیگر

Xiaolei Fang is Associate Professor in the Edward P. Fitts Department of Industrial and Systems Engineering at North Carolina State University. His research develops advanced statistical learning, deep learning, and optimization methods for industrial applications involving high-dimensional data, with particular focus on condition monitoring, failure prognostics, and system performance optimization. He holds a PhD in Industrial Engineering and MS in Statistics from Georgia Tech. Professor Fang's research integrates machine learning with industrial engineering to solve complex problems in predictive maintenance, quality control, and energy systems. His methodological innovations include federated learning approaches for privacy-preserving prognostics, distributionally robust machine learning models, and tensor-based statistical methods for manufacturing quality diagnostics. He has received multiple prestigious awards including the ISE Outstanding Research Award (2024), Sigma Xi Best PhD Thesis Award (2019), and SAS Data Mining Best Paper Award (2016). His research has been funded by NSF, Cisco Systems, and the US Department of Energy. Professor Fang teaches courses in Quality Design & Control, Statistical Models for Systems Analytics, High-Dimensional Data Analytics, and Optimization Models. He has supervised 9 PhD students to completion and currently advises 7 graduate students working on projects spanning federated learning for prognostics, tensor-based quality control, and machine learning applications in manufacturing and energy systems.

Tyler Johnsonمشاهده پروفایل

دانشیار

• Natural Products Chemistry
• Bioorganic Chemistry
• Medicinal Chemistry
• +۲ مورد دیگر

Tyler Johnson, PhD, is an Associate Professor in the Department of Natural Sciences and Mathematics at Dominican University of California's School of Health and Natural Sciences. His expertise lies in Natural Products Chemistry , Bioorganic Chemistry , and Medicinal Chemistry , with a focus on biomedical applications. Johnson's research emphasizes discovering therapeutic lead compounds and molecular probes from marine and terrestrial natural products. His research team investigates chemotypes like mycothiazole , zampanolide , fijianolide , and latrunculin from Indo-Pacific marine sponges. These compounds exhibit potent cytotoxicity (IC50 1~5 nM) against cancer cell lines through mechanisms including microfilament disruption , mitochondrial complex I inhibition , and microtubule stabilization . Current work explores mycothiazole as a molecular probe for mitochondrial aging. Key publications span 2024-2002, covering topics from sponge-derived anticancer agents to inflammation modulation and environmental toxicology. Johnson's laboratory engages in large-scale natural product isolation, spectroscopic validation, and semi-synthetic medicinal chemistry to optimize therapeutic leads. His work integrates undergraduate and graduate students into interdisciplinary biomedical research.

Maartje Bastingsمشاهده پروفایل

دانشیار

• Biomaterials Engineering
• DNA Nanotechnology
• Supramolecular Materials
• +۵ مورد دیگر

Maartje Bastings is an Associate Professor at the École Polytechnique Fédérale de Lausanne (EPFL) , leading the Programmable Biomaterials Laboratory (PBL) within the School of Engineering (STI) . She holds additional affiliations with the Institute of Materials (IMX) , IBI-STI (Bioengineering), and supervises doctoral programs in Biotechnology and Biological Engineering ( EDBB-GE ) and Materials Science and Engineering ( EDMX-GE ). Her research focuses on DNA-based supramolecular materials engineered to achieve dynamic reciprocity —a two-way interaction between synthetic materials and biological systems. By leveraging DNA as a programmable scaffold, she investigates structural mechanics, valency control, and geometric constraints governing self-assembly and cell communication at bio-interfaces. Key applications include immune system modulation, diagnostics , and vaccine development . Analysis of her 15 most recent publications reveals a focus on multivalent interactions for T-cell activation, spatial patterning in immune signaling, and engineered coatings for DNA origami stability. Subfields span T cell receptor engineering , nanoscale protein dynamics , stimuli-responsive biomaterials , and bio-inspired therapeutic design . PhD Students: Chen Yuduo Hendrickx Pauline Bart M. Kononenko Artem Li Shujie Lou Yameng Meyer Pitt Narita Minako Rousseau Benjamin Bila Hale Caroprese Vincenzo Comberlato Alice Kurisinkal Eva Eugene Paloja Kaltrina Rodríguez Franco Hugo José Tekin Cem Wong Siu Ho Contact: maartje.bastings@epfl.ch

Lev Sarkisovمشاهده پروفایل

استاد

• Carbon Capture
• Material Informatics
• Molecular Simulation
• +۶ مورد دیگر

Lev Sarkisov is a Professor of Chemical Engineering at the University of Manchester, leading the Sarkisov Research Group. His work focuses on advancing porous materials for carbon capture, energy storage, drug delivery, and sensing through multiscale computational workflows integrating molecular simulation, machine learning, and process modeling. He holds a Ph.D. from the University of Massachusetts Amherst (2001) and held roles at the University of Edinburgh, including Head of Chemical Engineering. Notable achievements include securing a £1M Wolfson Foundation grant for sustainable engineering (2022) and receiving the 2013 Royal Academy of Engineering/Leverhulme Trust Senior Research Fellowship. Education: Ph.D., Chemical Engineering, University of Massachusetts Amherst, 2001 M.Sc./B.Sc., Moscow Lomonosov Academy of Fine Chemical Technologies, 1995-1997 Research Interests: The group develops porous materials using AI-driven approaches for carbon capture, energy-efficient separations, and material informatics. Key areas include MOFs, adsorption phenomena, and open-source software for reproducible research. Grants & Awards: £1M Wolfson Foundation Grant (2022) Royal Academy of Engineering/Leverhulme Trust Senior Research Fellowship (2013) Edinburgh University Student Union Teaching Award (2019) Labs & Collaborations: The group collaborates globally, emphasizing open-source tools and reproducibility. Projects include CRAFTED (MOF adsorption database) and PoreBlazer v4.0.