جست‌وجوی استاد و پژوهشگر

• Chronic Obstructive Pulmonary Disease (COPD)
• Immunology
• Respiratory Medicine
• +۵ مورد دیگر

Andrew Higham is a Research Fellow in the Division of Immunology, Immunity to Infection and Respiratory Medicine. His work focuses on Chronic Obstructive Pulmonary Disease (COPD), inflammation, and respiratory medicine. He completed his PhD in 2014 under Dr. D. Singh, investigating the role of the Liver X Receptor in COPD. His research spans COPD pathophysiology, smoking effects, neutrophil/eosinophil dynamics, and therapeutic strategies. Key research interests include COPD pharmacology, small airway remodeling, electronic cigarette impacts, and type 2 inflammation mechanisms. He has contributed to high-impact studies like the FLAME trial and RECOVERY trial on corticosteroid use in severe COVID-19 cases. His work has been featured in media outlets like COPD News Today and The Express. Higham has published over 40 articles since 2011, with recent focus on COPD subphenotypes, exhaled nitric oxide biomarkers, and long-term smoking effects. He received the British Thoracic Society Early Career Investigator Award in 2014. Education: PhD in Chronic Obstructive Pulmonary Disease (2014) Awards: British Thoracic Society Early Career Investigator Award (2014) Media Coverage: Over 7 media mentions on COPD, e-cigarettes, and lung disease Collaborations: Global networks on COPD and respiratory medicine

Alexander Hoffmannمشاهده پروفایل

استاد

• NFκB signaling
• IRF signaling
• Innate immunity
• +۶ مورد دیگر

Professor Alexander Hoffmann at the Department of Microbiology, Immunology & Molecular Genetics at University of California Los Angeles is a leading systems immunologist focusing on the dynamic regulation of immune signaling networks. His work integrates experimental approaches (biochemistry, molecular biology, live-cell imaging, next-gen sequencing) with computational modeling to decode how immune cells like macrophages and B-cells make stimulus-specific decisions. Education: BA in Physics from Cambridge University (1988), PhD in Biochemistry of TFIID from Rockefeller University (1994), Postdoc in HIV latency control at MIT (1998), and Postdoc in NFκB systems immunology at Caltech (2003). His research uncovers how NFκB and IRF signaling systems govern immune cell responses through temporal dynamics and network architecture. Current projects explore macrophage stimulus specificity , B-cell antibody response regulation , and hematopoietic aging mechanisms , supported by NIH grants as Principal Investigator. Recent publications focus on single-cell signaling fidelity , dynamic gene regulatory logic , and computational approaches to immune modeling . His 15 most recent articles (2024-2025) reveal a strong emphasis on signaling dynamics , immune training , and multi-scale modeling . Scientific Awards: UCLA Undergraduate Research Mentor Award (2023) Thomas M Asher Endowed Chair in Microbiology (2014–present) Hellman Fellow (2007–2008) Ellison Medical Foundation Scholar in Aging Research (2005–2009) Multiple graduate and postdoctoral fellowships He mentors through the Bruins in Genomics program and leads interdisciplinary research at the QCBio Institute , bridging biochemistry , mathematical modeling , and computational biology to understand immune decision-making.

Eleonora Ricciمشاهده پروفایل

مدرس

• Polymer Membranes
• Gas Separation
• Molecular Simulation
• +۲ مورد دیگر

Eleonora Ricci is a Lecturer in Chemical Engineering at the University of Edinburgh's School of Engineering. She obtained her PhD in Chemical Engineering from the University of Bologna (2020), complemented by Master's (2016) and Bachelor's (2014) degrees from the same institution. Previously, she held a Marie Skłodowska-Curie postdoctoral fellowship at Demokritos NCSR in Athens and served as an Adjunct Professor at the University of Bologna. Her research focuses on multiscale modeling of polymer membranes for sustainable gas separations, employing machine learning and molecular simulations to advance materials design. Key investigations include hydrogen storage in polymers, CO2 capture technologies, and thermodynamic modeling of sorption processes. Recent publications demonstrate strong emphasis on machine learning applications for membrane design and computational analysis of gas-polymer interactions. Research consistently bridges molecular-level simulations with industrial-scale performance predictions. She leads projects including 'CharMPol: Characterization and Modeling of high-performance polymers' and coordinates collaborations like the ENFIELD Exchange Scheme focusing on trustworthy AI applications.

Monica Marinescuمشاهده پروفایل

دانشیار

• Electrochemistry
• Energy Storage Systems
• Battery Technology
• +۳ مورد دیگر

Monica Marinescu is a Reader in the Department of Mechanical Engineering at Imperial College London's Faculty of Engineering. Her research focuses on understanding the physical and chemical processes influencing energy storage devices, particularly lithium-ion, lithium-sulfur batteries, and supercapacitors. She develops electrochemical and physical models to identify degradation mechanisms under real-world conditions, with applications in electric vehicles, space missions, and ultra-cold climates. Her affiliations include Electrochemical Science and Engineering, Energy Materials, and Mechanics of Materials. She collaborates with Prof. Alexei Kornyshev and has contributed to advancements like the Revolutionary Electric Vehicle Battery and UAV Zephyr projects. Her work emphasizes modeling solvent consumption in SEI layer growth, validation of Li-S battery models, and optimal cell design strategies. Publications highlight her focus on lithium-ion battery degradation, thermal management, and high-throughput computing methods. She is an editorial board member and has published extensively on topics such as cycle aging analysis, equivalent circuit networks, and degradation mode diagnostics. Her research bridges fundamental electrochemistry with practical engineering solutions for sustainable energy storage systems.

Michael Obersteinerمشاهده پروفایل

پژوهشگر

• Molecular Dynamics
• High-Performance Computing (HPC)
• Sparse Grids Methods
• +۳ مورد دیگر

Dr. Michael Obersteiner is a researcher at the Technical University of Munich (TUM), affiliated with the TUM School of Computer Science and Information Technology and the Department of Computer Science. He is part of the Chair of Scientific Computing in Computer Science (SCCS), led by Prof. Hans-Joachim Bungartz. His research focuses on high-dimensional numerical methods, sparse grids, the combination technique, molecular dynamics, and machine learning applications. He has contributed to the development of scalable algorithms for exascale computing, including the DisCoTec and sparseSpACE frameworks. Education: Michael Obersteiner began his studies in Biochemistry in 2010 before switching to Computer Science in 2011. He completed his M.Sc. in Computer Science in 2016 and is currently pursuing his doctoral dissertation with a focus on high-dimensional numerics and the sparse grid combination technique. Research Interests: His work spans molecular dynamics simulations, high-performance computing (HPC), spatially adaptive sparse grid methods, and applications in uncertainty quantification, machine learning, and plasma physics. He has developed open-source software tools like DisCoTec (for exascale PDE simulations) and sparseSpACE (for adaptive sparse grid operations). Teaching: Obersteiner has been involved in teaching numerical programming, scientific computing, and discrete structures at TUM. His roles include leading exercises for courses like 'Algorithms for Scientific Computing' and 'Numerisches Programmieren.' Projects: He leads projects such as DisCoTec and sparseSpACE, which focus on scalable sparse grid techniques for high-dimensional problems. His research also includes applications in plasma turbulence modeling, hydrology simulations, and machine learning with density estimation. Grants and Collaborations: His work has been supported by projects like EXAHD (funded by DFG/SPPEXA). He collaborates with institutions like BGCE, CSE, and IGSSE. Open student projects are available in areas like adaptive quadrature, machine learning with sparse grids, and uncertainty quantification.

• single-molecule biophysics
• intrinsically-disordered proteins
• GPCRs
• +۴ مورد دیگر

Claudiu Gradinaru is an Interim Chair and Professor in the Department of Chemical and Physical Sciences at the University of Toronto Mississauga, part of the Faculty of Arts & Science. He holds a Ph.D. in Physics from VU University Amsterdam and has conducted postdoctoral research at Leiden University and Sandia National Laboratories. His research focuses on single-molecule biophysics, intrinsically disordered proteins (IDPs), and G protein-coupled receptors (GPCRs), leveraging advanced microscopy and computational techniques. Education: B.Sc. in Physics (University of Bucharest, 1995), Ph.D. in Physics (VU University Amsterdam, 2001). Roles: Interim Chair, Principal Investigator of the Gradinaru Biophysics Lab, and educator in molecular biophysics, optics, and classical mechanics. Research Interests: His lab employs ultrasensitive optical techniques (e.g., single-molecule fluorescence spectroscopy) to study conformational dynamics of IDPs and GPCRs in live cells. Key areas include: Structural ensembles of IDPs using NMR, SAXS, and smFRET. GPCR oligomerization and signaling dynamics. Development of computational methods for ensemble modeling. Advising: Gradinaru mentors numerous graduate and undergraduate students, including PhD candidates Xiaohan Zhou (GPCR dynamics) and Maria Shemeteva (GPCR signaling), and MSc students Aiyan Brown (smFRET analysis) and Xinge Liu (IDP complexes). His lab has produced over 40 publications since 2001, with recent work featured in Biophysical Journal , Journal of the American Chemical Society , and Nucleic Acids Research . Labs & Instruments: The Gradinaru Lab houses custom-built microscopes for multiparameter single-molecule fluorescence, fluorescence correlation spectroscopy, and TIRF microscopy. Collaborations span institutions in Canada, the U.S., the U.K., and Australia.

Anne Skaja Robinsonمشاهده پروفایل

استاد

• Biotechnology
• Pharmaceutical Engineering
• Neurodegeneration
• +۵ مورد دیگر

Dr. Anne Skaja Robinson is a Trustee Professor of Chemical Engineering and Biomedical Engineering at Carnegie Mellon University (CMU), serving as Department Head from 2018–2023. Previously, she held leadership roles at Tulane University and the University of Delaware. Her research focuses on protein folding/aggregation, neurodegeneration (tau pathology), and biopharmaceutical production. She has over 85 publications, 23 Ph.D. graduates, and honors including the NSF PECASE Award and AIChE fellowship. Education: B.S., M.S. (Johns Hopkins University, 1988–1989); Ph.D. (University of Illinois at Urbana-Champaign, 1994). Professional roles include Board Member of AIChE (2015–2017), FDA Advisory Committee member, and editorial board positions for Biotechnology and Bioengineering and Biotechnology Journal . Research interests span: (1) GPCR expression and signaling, (2) protein aggregation control for antibodies and AAVs, (3) tau protein mechanisms in Alzheimer’s, and (4) machine learning for gene therapy vector optimization. Recent work applies computational tools to improve AAV production quality. Articles highlight advancements in GPCR dynamics, tau aggregation kinetics, and biomanufacturing process control. Awards recognize her contributions to biotechnology and engineering education. Her lab actively collaborates on FDA advisory work and NIH/NSF projects.

• Theoretical Chemistry
• Computational Chemistry
• Quantum Chemistry
• +۳ مورد دیگر

Lixin Lu is a Researcher (Postdoctoral Scholar) in the Department of Chemistry at Stanford University, affiliated with the School of Humanities and Sciences. Their work focuses on theoretical and computational chemistry, particularly in developing advanced electronic structure methods and applying them to study molecular dynamics, spectroscopy, and materials science. They are part of affiliated programs including CMAD (Center for Molecular Analysis and Design), ChEM-H (Chemistry, Engineering & Medicine for Human Health), and SSRL (Stanford Synchrotron Radiation Lightsource). Research interests include relativistic quantum chemistry, X-ray spectroscopy, ultrafast phenomena, and computational modeling of molecular systems. Their contributions span multiconfiguration methods, ab initio dynamics, and the analysis of radiation effects in materials. Theoretical frameworks developed by Lu address challenges in high-performance computing and accurate description of molecular excitations. Affiliations with SSRL and ChEM-H highlight collaborative efforts in experimental-theoretical interfaces, such as attosecond X-ray spectroscopy and liquid-phase dynamics. Lu’s work bridges computational innovation with cutting-edge experimental techniques to uncover fundamental insights into chemical and physical processes at the molecular level.

Jianfei Xieمشاهده پروفایل

مدرس ارشد

• Thermal Engineering
• Energy Systems
• Combustion Engineering
• +۳ مورد دیگر

Jianfei Xie is a Senior Lecturer in Mechanical and Manufacturing Engineering at the College of Science and Engineering. His research focuses on thermal energy systems, fluid dynamics, and the application of machine learning in engineering. Key areas include thermal energy storage, combustion optimization, and nanomaterials for enhanced heat transfer. He has published extensively on topics such as packed bed reactors, molecular dynamics simulations, and AI-driven predictive modeling for engine performance. Research Interests: • Thermal systems engineering • Nanofluids and phase change materials • Machine learning applications in energy systems • Combustion processes and fuel efficiency • Micro/nanochannel flow dynamics • High-temperature material characterization Notable contributions include studies on solar thermal power generation, chaos synchronization using reinforcement learning, and the enhancement of molten salt thermal storage using nanoparticles. His work often bridges computational methods with practical engineering challenges, emphasizing interdisciplinary approaches. Publications span Energy Conversion and Management , Fuel , and Chaos, Solitons & Fractals , reflecting a strong focus on energy systems and fluid dynamics. While no awards are explicitly mentioned, his prolific output highlights sustained academic engagement in impactful research areas.

Sabine Kühnمشاهده پروفایل

استاد

• Food Technology
• Sensory Science
• Bioactive Compound Extraction
• +۳ مورد دیگر

Prof. Dr. Sabine Kühn is a Professor of Food Technology and Sensory Science at the Department of Nutritional Science (Fachbereich Oecotrophologie) of Niederrhein University of Applied Sciences. Her roles include leadership in the Food Technology and Sensory Laboratory, where she oversees research and teaching in food processing, sensory analysis, and sustainable resource utilization. She actively participates in university governance as a member of the Department Council, Deputy Chair of the Examination Board, and member of the Senate. Her academic journey includes a Diplom-Ingenieurin in Food Technology (2008) and a PhD (2014) from Rheinische Friedrich-Wilhelms-Universität Bonn, focusing on polyphenol recovery from onion processing residues. Her research emphasizes bioactive compound extraction from agricultural byproducts, microencapsulation techniques, and sustainable food production. Notable projects include CO₂-based extraction of cranberry pomace compounds and flavor masking of polyphenolic extracts. Publications span topics like supercritical fluid extraction, in vitro digestion of polyphenols, and valorization of food industry wastes. Her work bridges food chemistry, environmental engineering, and sensory science, addressing both technical and sustainable challenges in food technology. Laboratory and administrative roles reflect her commitment to advancing practical and innovative solutions in food science education and industry collaboration.

Apurva Narayanمشاهده پروفایل

استاد مدعو

• Artificial Intelligence
• Machine Learning
• Data Mining
• +۶ مورد دیگر

Dr. Apurva Narayan is an Affiliate Professor in Computer Science and Data Science, and an Associate Member in Mechanical Engineering at the University of British Columbia's Irving K. Barber Faculty of Science. His research focuses on artificial intelligence, machine learning (particularly explainable AI and quantum ML), data mining, cybersecurity of cyber-physical systems, and decision-making under uncertainty. He holds a PhD from the University of Waterloo (Systems Design Engineering) and a Bachelor's in Electrical Engineering from Dayalbagh Educational Institute. Education: PhD in Systems Design Engineering, University of Waterloo (2015) Bachelor of Engineering in Electrical Engineering, Dayalbagh Educational Institute (2008) Research Interests: Explainable AI/ML and quantum machine learning Data analytics and mining Safety/security of cyber-physical systems Graph-theoretic analysis of complex systems Decision-making under uncertainty Reverse engineering complex software systems Awards: Systems Society of India Young Scientist Award (under 40) MITACS Accelerate Award (2013) Dr. T.E. Unny Memorial Award (2011) Multiple teaching nominations at University of Waterloo Advising & Grants: Supervises graduate students in AI/ML and cybersecurity. Secured grants including MITACS Accelerate and Waterloo scholarships. Active in patent filings related to timed regular expressions. Labs/Teams: Engaged in collaborations within the UBC Department of Computer Science and cross-disciplinary projects with Mechanical Engineering on system reliability and quantum computing applications.

B. (Bernd) Ensingمشاهده پروفایل

دانشیار

• Computational Chemistry
• Molecular Simulations
• Catalyst Design
• +۷ مورد دیگر

Dr. Bernd Ensing is an Associate Professor at the van 't Hoff Institute for Molecular Sciences (HIMS) within the University of Amsterdam's Faculty of Science. He directs the AI4Science Laboratory, focusing on integrating artificial intelligence with molecular simulations. His research spans computational chemistry, catalyst design, and biophysical systems, emphasizing bio-inspired materials and electron/proton transfer mechanisms. Key research areas include: molecular simulations of catalytic processes (e.g., hydrogenase enzymes), multiscale modeling of soft materials, and machine learning-enhanced data analysis. He develops advanced algorithms like Path-metadynamics and FABULOUS for free energy exploration and reaction coordinate identification. Notable contributions include studies on polyether solubility in water, light-activated proteins' signaling mechanisms, and adaptive resolution simulations (Hybrid-atomistic/coarse-grained methods). His group collaborates on AI-driven material discovery and sustainable chemistry solutions. He has supervised numerous student projects at bachelor/master levels, requiring expertise in quantum chemistry, thermodynamics, or programming. The AI4Science Lab promotes open-source tools via GitHub and community-driven initiatives like PLUMED tutorials.

Jan Wilhelmمشاهده پروفایل

پژوهشگر

• Ultrafast Processes
• Electronic Structure Theory
• Quantum Technologies
• +۳ مورد دیگر

Jan Wilhelm is a Researcher and leader of the Emmy Noether Independent Junior Research Group at the University of Regensburg's Institute for Theoretical Physics. His work focuses on ultrafast electron dynamics and computational methods for electronic structure theory, addressing gaps between experimental capabilities and theoretical predictions in ultrafast processes. He develops low-scaling algorithms for GW calculations to simulate systems with thousands of atoms, enabling studies of materials like 2D heterobilayers and moiré structures. His research intersects quantum technologies, photovoltaics, and nonlinear optics, with collaborations on high-harmonic generation and ultrafast microscopy. Funded by the German Research Foundation (DFG), his work bridges theory and experiment, aiming to understand femtosecond-scale phenomena in materials. Education: Studied physics and mathematics at Karlsruhe Institute of Technology, with a doctorate in theoretical chemistry from the University of Zurich. Prior industry experience in chemical optimization provided insights into applied vs. fundamental research. Research highlights include the development of CUED software for ultrafast dynamics simulations and contributions to the CP2K package. Key projects include ultrafast laser-driven electron dynamics, topological insulator studies, and collaborations with experimental groups at RUN. Future directions involve leveraging the Regensburg Center for Ultrafast Nanoscopy (RUN) to explore uncharted phenomena at atomic scales. Teaching: Lectures on computational methods for nanoscience and condensed matter excitations. Supervised students like Max Graml, who received the Brigitta and Oskar Braumandl Prize.

Can Atacaمشاهده پروفایل

دانشیار

• Computational Materials Science
• Quantum Monte Carlo
• Density Functional Theory
• +۳ مورد دیگر

Can Ataca is an Associate Professor in the Department of Physics at the University of Maryland, Baltimore County (UMBC), affiliated with the College of Natural and Mathematical Sciences. He directs the STEAM Lab (Simulation, Theory and Engineering of Advanced Materials), focusing on computational investigations of materials for energy, device, and filtration applications using methods spanning density functional theory (DFT), quantum Monte Carlo (QMC), and molecular dynamics (MD). He holds a Ph.D. in Physics from Bilkent University's National Nanotechnology Research Center (Turkey), with double undergraduate degrees in Physics and Electrical Engineering. Prior to UMBC, Ataca was a postdoctoral associate at MIT's Department of Materials Science and Engineering and a Senior Research Scientist at Brown University's Department of Chemistry. Ataca's research targets low-dimensional materials, concrete, electrode materials, and molecular systems, emphasizing electronic, magnetic, and thermal properties under extreme conditions. His publications demonstrate extensive use of machine learning for materials property prediction, surface functionalization studies, and quantum mechanical simulations of 2D systems. His group develops computational methodologies bridging multiple length/time scales. No specific awards, students, or grants are detailed in the provided materials. The STEAM Lab collaborates with national laboratories including NASA and welcomes students/postdocs interested in computational condensed matter physics.

John W. Dailyمشاهده پروفایل

استاد پژوهشی

• Combustion
• Space Propulsion
• Wildland Fire Dynamics
• +۳ مورد دیگر

Prof. John W. Daily is an Emeritus Research Professor in the Department of Mechanical Engineering at the University of Colorado Boulder. His research focuses on combustion science, space propulsion systems, wildland fire dynamics, biomass utilization, and medical instrumentation. He holds a B.S. and M.S. from the University of Michigan (1968-1969) and a Ph.D. from Stanford University (1975). Prior to his academic career, he worked at Aerojet Liquid Rocket Company as a Heat Transfer Analyst. He served as Chair of the Mechanical Engineering Department at CU Boulder from 1990 to 1995. His work on optical biopsy technology led to the founding of Precision Biopsy, a medical instrumentation startup. Key areas: Combustion chemistry, propulsion systems, medical device innovation Collaborations: Extensive multi-disciplinary research spanning engineering and biomedical fields Research interests emphasize understanding reacting systems to enhance safety, efficiency, and health applications. His publications span combustion diagnostics, biofuel analysis, and medical device development. His lab work includes pioneering micro-reactor technology for studying large molecule decomposition. Grants: Not specifically listed in available texts Advising: Retired from active advising but contributed to numerous past graduate students He maintains affiliations with industry through Precision Biopsy, focusing on cancer diagnostics via fluorescence spectroscopy. His contributions to both academic research and applied technology highlight his cross-disciplinary impact.