جست‌وجوی استاد و پژوهشگر

Dan Olteanuمشاهده پروفایل

استاد

• Database Systems
• Probabilistic Databases
• Theoretical Computer Science
• +۳ مورد دیگر

Dan Olteanu is a Professor of Computer Science at the University of Zurich (since 2020) and holds a part-time role as a Computer Scientist at RelationalAI. Previously, he was a Professor at the University of Oxford (2016–2020) and had visiting roles at UC Berkeley (2013–2014) and LogicBlox (consulting, 2013–2017). His research focuses on database systems, probabilistic data management, and theoretical foundations of data processing. Education: PhD in Computer Science from Ludwig Maximilian University of Munich (2005), Diplom (M.Sc.) from Polytechnic University of Bucharest (2000). Additional roles include Fellow and Director of IT at St Cross College, Oxford. Research Interests: Factorized databases (FDB), probabilistic databases (SPROUT, ENFrame), Datalog engines (RDFox), query optimization (Distributed Query Optimization), and machine learning over relational data. Publications highlight contributions to incremental query processing, probabilistic inference, and scalable algorithms. Notable work includes the SPROUT query engine, FDB system, and theoretical results on query tractability. Awards: Best Paper Award at ICDT 2019. Grants from ERC, EPSRC, Google, and industry partnerships with Amazon, Microsoft, and others. Students advised include Robert Fink, Maximilian Schleich, and Haozhe Zhang. Active in academic service, editing journals, and organizing conferences like BNCOD and SIGMOD workshops.

Matthias Ihmeمشاهده پروفایل

استاد

• Combustion Science
• Fluid Dynamics
• Computational Fluid Dynamics
• +۵ مورد دیگر

Matthias Ihme is a Professor in the Department of Mechanical Engineering and Photon Science Directorate at Stanford University. His research focuses on large-eddy simulation (LES) of turbulent reacting flows, aeroacoustics, combustion-generated noise, numerical methods, and high-order schemes. He holds a Ph.D. from Stanford University (2008), an M.Sc. in Computational Engineering from the University of Erlangen (Germany, 2002), and a Dipl.-Ing. in Mechanical Engineering from Munich University of Applied Sciences (Germany, 2000). His work bridges computational fluid dynamics, combustion science, and photon science, with notable contributions to supercritical fluid dynamics, machine learning integration in fluid simulations, and high-fidelity atmospheric transport modeling. Recent research emphasizes ultrafast cluster dynamics, shock-induced interface behavior, and stochastic ignition mechanisms in advanced fuel systems. Publications highlight interdisciplinary advancements, including physics-informed ML frameworks for reacting flows and experimental studies using X-ray photon correlation spectroscopy. His projects often involve high-performance computing and collaboration with national labs like SLAC.

Wendy Maoمشاهده پروفایل

استاد

• Materials Science
• Planetary Science
• Geophysics
• +۳ مورد دیگر

Wendy Mao is a Professor of Earth and Planetary Sciences, Photon Science, and (by courtesy) Geophysics at Stanford University, affiliated with SLAC National Accelerator Laboratory. Her research focuses on materials under extreme conditions, particularly high pressure, to understand planetary interiors, energy materials, and novel phases. Key interests include phase transitions in minerals, silicate melts, and light-element alloys, with applications in planetary core modeling and hydrogen storage. Education: Ph.D. in Geophysical Sciences from the University of Chicago (2005). Teaching includes Earth's interior dynamics, mineralogy, and a freshman seminar on diamonds. Research emphasizes high-pressure experimentation using diamond anvil cells and synchrotron X-ray techniques. Recent work explores metallic hydrogen, iron spin states in super-Earths, and amorphization in halide perovskites. Collaborations leverage machine learning and advanced imaging for material characterization. Her lab develops methods to stabilize metastable phases and study ultrafast structural responses under shock compression. The group also investigates defects in quantum sensors and novel synthesis pathways for energy materials.

• Food Science
• Nutrition
• Functional Foods
• +۷ مورد دیگر

Alberta N.A. Aryee, PhD is an Associate Professor in the Department of Human Ecology (Food Science & Biotechnology) at Delaware State University (DSU). She holds a PhD from McGill University and completed postdoctoral research at Agriculture and Agri-Food Canada. Her research focuses on the impact of natural products and bioactive compounds on nutrition, health, and metabolic diseases, with particular emphasis on food processing effects, functional food development, and biofortification strategies. Education: Postdoctoral Research: Agriculture and Agri-Food Canada PhD: McGill University (Dissertation: Enzyme Immobilization and Biodiesel Production) MSc: McGill University (Thesis: Lipase Purification) BSc (Hons): Kwame Nkrumah University of Science and Technology Research Interests: Food science, functional ingredients, nutraceuticals, fermentation science, lipid technology, and sustainable food systems. She explores how processing impacts food matrices, bioavailability of nutrients, and the application of nanotechnology and machine learning in food analysis. Recent Research Trends: Her work spans encapsulation of bioactives, plant-based food innovation, and leveraging AI for nutrient prediction. Studies include optimizing hempseed processing, analyzing fermented cassava chemistry, and designing smart food packaging. Awards: 2020 DSU Faculty Excellence in Research 2018 Hornet Award (Early College High School) Mini Grant for hands-on food science education Advising & Grants: Mentors students in food chemistry and product development. Collaborates on grants focused on value-added food products from agricultural byproducts. Leads experiential learning programs integrating global food challenges. Labs & Teams: Based in DSU’s Baker Annex, her lab focuses on lipid science and functional food formulation. Collaborates with industry partners on sustainable food technologies and nanodelivery systems.

Brian Y. Chenمشاهده پروفایل

دانشیار

• Bioinformatics
• Structural Biology
• Computational Biology
• +۵ مورد دیگر

Dr. Brian Y. Chen is an Associate Professor and Doctoral Program Director in the Department of Computer Science & Engineering at Lehigh University. His research focuses on bioinformatics, structural biology, and machine learning applications in computational biology. He holds a Ph.D. in Computer Science from Rice University and B.A. degrees in Mathematics and Computer Science from Rutgers University. Dr. Chen's work emphasizes developing algorithms to analyze protein structures, protein-protein interactions, and ligand binding mechanisms. He has contributed to tools like DeepVASP-S and MechPPI, which explain molecular interactions and predict binding specificity. His recent projects include Alzheimer’s disease diagnosis using multimodal data and containerization frameworks for bioinformatics software. He previously served as a postdoctoral researcher in Barry Honig's Lab at Columbia University, where he contributed to the Center for Computational Biology and Bioinformatics. His research spans structural bioinformatics, computational methods for protein function prediction, and interdisciplinary applications in medicine and materials science. Key achievements include a nomination for Outstanding Mentorship (2017) and collaborative projects funded by the Army Research Lab and Lehigh University. His lab explores cutting-edge AI techniques for biomedical problems, including interpretable machine learning models and scalable bioinformatics pipelines.

Nan Marie Jokerstمشاهده پروفایل

استاد

• Photonics
• Optoelectronic Devices
• Integrated Optoelectronics
• +۷ مورد دیگر

Nan Marie Jokerst is the J. A. Jones Distinguished Professor of Electrical and Computer Engineering at Duke University's Pratt School of Engineering and Executive Director of the Duke Shared Materials Instrumentation Facility. She previously served as Chair of the Duke Academic Council (2014-2015) and Associate Dean for six years. B.S. in Physics, Creighton University (1982) M.S.E.E., University of Southern California (1984) Ph.D. in Electrical Engineering, University of Southern California (1989) Her research spans chip-scale photonic sensing systems , III-V thin-film lasers on silicon , metamaterials , and heterogeneous integration . She develops optical systems for medical diagnostics, environmental monitoring, and security applications through the Jokerst Laboratory, which combines optical system design, optoelectronic device development, and semiconductor fabrication expertise. Her recent publications show strong focus on terahertz strain mapping using metamaterials, multi-pixel tissue characterization for cancer margin detection, and microfluidic sensing platforms with embedded photodetectors. Key trends include advancing non-destructive structural monitoring and miniaturized biomedical diagnostics through novel photonic integration techniques. IEEE Fellow (2003) Optica Fellow (2001) NSF Presidential Young Investigator Award IEEE Third Millennium Medal IEEE/HP Harriet B. Rigas Medal USC Viterbi School Alumni Award She has advised numerous graduate students in photonic device research and secured major grants including the NSF National Nanotechnology Coordinated Infrastructure ($6M, 2015-2021) and NNCI: North Carolina Research Triangle Nanotechnology Network ($20M, 2020-2026). Her leadership extends to co-founding Triangle Women in STEM and serving on the National Academies Board on Global Science and Technology. The Duke Shared Materials Instrumentation Facility under her direction provides critical cleanroom and characterization resources for interdisciplinary research.

Tianhua Xuمشاهده پروفایل

دانشیار

• Optical Communication Networks
• Optical Sensing Systems
• Optoelectronics
• +۴ مورد دیگر

Dr. Tianhua Xu is a Reader in the School of Engineering at the University of Warwick and an Honorary Lecturer in the Department of Electronic and Electrical Engineering at University College London (UCL). He holds a Ph.D. in Optical Communications and Intelligent Signal Processing from KTH Royal Institute of Technology in Sweden, followed by postdoctoral and research roles at KTH, RISE Acreo, DTU, and UCL. His research focuses on optical communication systems, intelligent signal processing, machine learning, optical sensing, and advanced energy systems. He has secured major grants, including a €1.8M EU Horizon Europe project (SPAR) and a £285K UK National Grid initiative. Dr. Xu serves as an Associate Editor for IEEE Transactions on Communications and the Journal of the European Optical Society-RP, and chairs technical groups in the Optical Society of America. He has authored over 200 publications, including invited book chapters, with a Google Scholar h-index of 32. Current projects involve smart photonic sensing, energy storage systems, and deep learning in optical networks. His lab oversees six PhD students and multiple postdoctoral researchers. He actively recruits students through global scholarships and oversees vibrant research teams in optical communications, sensing, and energy systems.

Scott T. M. Dawsonمشاهده پروفایل

استادیار

• Fluid Mechanics
• Dynamical Systems
• Control Theory
• +۵ مورد دیگر

Scott T. M. Dawson is an Assistant Professor in the Mechanical, Materials, and Aerospace Engineering Department at Illinois Institute of Technology (Illinois Tech). He holds positions in the Armour College of Engineering and leads research at the intersection of fluid mechanics, dynamical systems, control theory, and data science. His work focuses on extracting dynamic models from large datasets to analyze and control turbulent flows and unsteady aerodynamic systems. Education includes a Ph.D. and M.A. from Princeton University (2017, 2013), and B.Eng. and B.S. degrees from Monash University (2010, 2009). Prior to Illinois Tech, he was a postdoctoral scholar at Caltech’s Graduate Aerospace Laboratories under Prof. Beverley McKeon. Research interests emphasize reduced-order modeling, data-driven techniques for fluid flows, and flow control applications. His group’s work is supported by NSF, AFOSR, and DOE grants. Recent projects include sparsity-promoting methods for flow analysis, wavelet-based resolvent analysis, and neural network-driven flow control systems. Publications span over 60 peer-reviewed articles, with a focus on turbulence modeling, transient flow dynamics, and machine learning integration in fluid mechanics. Key contributions include novel algorithms for isolating amplification mechanisms in wall-bounded flows and robust neural network frameworks for closed-loop flow stabilization. Grants and collaborations include multi-year NSF CAREER funding for automated distillation of coherent flow structures. Ongoing efforts explore time-localized spectral methods, nonlinear dimensionality reduction, and hydrogen decarbonization in vehicular systems.

Vikram Deshpandeمشاهده پروفایل

استاد

• Mechanobiology
• Solid mechanics
• Data-driven mechanics
• +۴ مورد دیگر

Vikram Deshpande is a Professor in the Department of Engineering at the University of Cambridge, UK, where he has been employed since 2010. He also maintains significant international connections, having served as a Visiting Professor at the Technical University of Eindhoven (2009-2017) and previously holding positions at the University of California, Santa Barbara and Brown University. His research spans multiple disciplines within solid mechanics and materials science, focusing on fundamental mechanisms that govern material behavior across different scales. His research interests encompass Mechanobiology , where he explores cellular organization mechanisms; Solid mechanics with applications to impact and failure; Data-driven mechanics approaches; Microarchitectured solids including mechanical metamaterials; Fluid-structure interaction in impact scenarios; Chemo-mechanics of battery materials; and Dislocation mechanics for understanding material deformation. His work uniquely bridges fundamental physics with practical engineering applications, particularly in developing materials with tailored mechanical properties. The analysis of his recent publications reveals a strong focus on mechanical metamaterials, cellular mechanics, and electro-chemo-mechanical phenomena in energy storage systems. His research demonstrates a consistent pattern of addressing fundamental scientific questions while maintaining strong connections to practical engineering applications, particularly in materials design, protective systems, and energy technologies. His publications frequently combine experimental approaches with sophisticated modeling techniques across multiple scales. 2024 Zdeněk P. Bažant Medal for Failure and Damage Prevention 2023 Fellow, Royal Academy of Engineering and International Member US National Academy of Engineering 2022 Warner T. Koiter Medal and William Prager Medal 2022 European Research Council (ERC) Advanced Grant 2021 Gili Agostinelli Prize and IIT Bombay Distinguished Alumnus Award 2020 Fellow, Royal Society of London and Rodney Hill Prize Professor Deshpande has served on numerous editorial boards including the Journal of the Mechanics and Physics of Solids (current Associate Editor), Modelling and Simulation in Materials Science and Engineering, and Proceedings of the Royal Society A. He chairs the Royal Society Sectional Committee 4 and serves on the Advisory Board of the European Mechanics Society EUROMECH. His leadership extends to directing the International Conference on Fracture and chairing the EUROMECH Mechanics of Materials Conference committee. His research group at Cambridge, accessible through cambridgesolidmechanics.co.uk, focuses on developing fundamental understanding of material behavior to enable the design of next-generation engineering materials.

Dan M. Ionelمشاهده پروفایل

استاد

• Alternative and Renewable Energy Technologies
• Electric Machines and Power Electronic Drives
• Electromagnetic Devices
• +۳ مورد دیگر

Dan M. Ionel, Professor and inaugural L. Stanley Pigman Chair in Power at the University of Kentucky (UK), directs the Power and Energy Institute Kentucky (PEIK). He is affiliated with the SPARK Laboratory in the Department of Electrical and Computer Engineering, focusing on renewable energy, electric machines, and smart grids. Alternative and Renewable Energy Technologies Electric Machines and Power Electronic Drives Electromagnetic Devices Electric Power Systems Energy Storage Smart Grids and Buildings His research integrates machine learning with electromagnetic design for applications like electric vehicles and aircraft propulsion. Recent work includes axial flux permanent magnet machines with Halbach arrays and cryogenic thermal management systems. Scientific awards include IEEE Fellowship. Research is sponsored by NSF, DOE, NASA, and industry partners like ANSYS, EPRI, and Regal Rexnord. Collaborations span national labs (NREL, ORNL), utilities (LG&E, TVA), and aerospace entities.

Young-Jin Chaمشاهده پروفایل

استاد

• Deep Learning
• Structural Health Monitoring (SHM)
• Autonomous UAVs
• +۵ مورد دیگر

Dr. Young-Jin Cha is a tenured full Professor in the Department of Civil Engineering at the University of Manitoba, affiliated with the Price Faculty of Engineering. He holds a PhD from Texas A&M University and has postdoctoral experience at MIT. His research focuses on deep learning-based structural health monitoring (SHM), autonomous UAVs for infrastructure inspection, and smart transportation systems, with over 100 peer-reviewed publications and $1.2M in grants. He is a Fellow of ASCE and has received notable awards including the 2021 Merit Award and 2022 International Association of Advanced Materials Scientist Award. His work has been cited over 9,200 times globally. Research interests include automated SHM with UAVs, nonlinear system identification, unsupervised deep learning for damage detection, and sustainable infrastructure design. He serves as an editor for journals like Structural Control & Health Monitoring and Engineering Reports . His lab, the Laboratory for Infrastructure Science and Technology (LIST), develops advanced technologies for infrastructure resilience. Key achievements include pioneering deep learning-based SHM with UAVs, top-cited papers in civil engineering journals, and leadership in organizing international conferences. He actively seeks graduate students for research in AI-driven infrastructure solutions.

Jie Xuمشاهده پروفایل

پژوهشگر

• Polymer Electronics
• Flexible Electronics
• Sustainable Materials
• +۴ مورد دیگر

Jie Xu is a Scientist at Argonne National Laboratory and a CASE Affiliated Scientist at the University of Chicago, Pritzker School of Molecular Engineering . Her research focuses on engineering durable, scalable, and sustainable polymer semiconductors for skin-like electronics and autonomous material discovery. Education : PhD in Chemistry (Nanjing University), Postdoctoral Fellow (Stanford University) Her research bridges polymer physics , self-driving laboratories , and AI-guided material synthesis to address challenges in stretchable electronics, recyclable polymers, and energy-efficient manufacturing. She pioneered polymer circuits that remain conductive under extreme deformation and developed the first roll-to-roll mass-production method for stretchable semiconductors. Her 15 most recent articles highlight advancements in AI-driven polymer discovery , biodegradable electronics , and multi-modal energy dissipation . Key themes include autonomous experimentation , hydrogen-bonded polymer systems , and machine learning for conjugated polymers , with applications in wearable medical sensors , soft robotics , and human-computer interfaces . Scientific accolades include the Materials Research Society Postdoctoral Award , MIT Technology Review’s Innovators Under 35 , and recognition as a Scialog Fellow . She serves on editorial boards for APL Machine Learning and Flexible Electronics , and her team at Argonne includes postdocs and students working on self-driving labs and degradable polymers .

Cristina Torres-Machiمشاهده پروفایل

استادیار

• Infrastructure Asset Management
• Sustainability and Resilience in Transportation
• Decision-Making and Optimization Systems
• +۳ مورد دیگر

Cristina Torres-Machi is an Assistant Professor of Civil, Environmental and Architectural Engineering at the University of Colorado Boulder, holding the Beavers Professorship of Construction Engineering. She earned her PhD in Civil Engineering from Universitat Politècnica de Valencia and Universidad Católica de Chile (2015), a MSc in Planning and Management in Civil Engineering (2012) and BSc in Civil Engineering (2008) from Universitat Politècnica de Valencia. Her research focuses on infrastructure asset management, sustainability/resilience in transportation, and decision-making optimization systems. She leads the Innovation for Resilient Infrastructure (IRI) research group, advancing methodologies for long-term infrastructure management. Recognized with awards including the Abertis International Award (2016) and excellence in PhD dissertation (2015/2017), she serves as Chair of ASCE’s Construction Research Council and Vice-Chair of TRB’s Transportation Asset Management committee. Her work integrates machine learning, predictive analytics, and data science to improve pavement performance and lifecycle cost analyses. Professional affiliations include the American Society of Civil Engineers (ASCE) and active roles in TRB committees. Research emphasizes pavement rehabilitation strategies, flood impact modeling, and equitable infrastructure adoption trends. Over 60 publications (60+ citations) span journal articles, conference proceedings, and book chapters, with projects funded by public/private agencies. Current projects explore satellite-based monitoring, EV infrastructure impacts, and disaster-resilient traffic systems. Her recent work addresses procurement fallacies in design-build projects and integrates social vulnerability assessments in natural hazard planning.

Chengzong Pangمشاهده پروفایل

دانشیار

• Power Systems
• Electrical Engineering
• Control Systems
• +۷ مورد دیگر

Chengzong Pang is an Associate Professor and MSECE Graduate Coordinator at the Department of Electrical and Computer Engineering, College of Engineering, Wichita State University. His work focuses on power systems, electrical engineering innovations, and renewable energy integration. He specializes in transient stability analysis, control systems, and smart grid technologies. Research Interests: Dr. Pang's expertise includes advanced control strategies for power electronics (e.g., PMSM, UPQC), machine learning applications for grid stability (LSTM/SVM), and energy storage solutions for renewable integration. His research also addresses challenges in microgrid operation, subsynchronous oscillation mitigation, and battery storage systems. Key Trends in Publications: Over 20 years of publications (2002–2022) emphasize: (1) Machine learning for power system analysis, (2) Control system design for renewable integration, (3) Grid stability enhancement via advanced algorithms, and (4) Smart grid infrastructure optimization. Recent works (2021–2022) highlight transient stability prediction and ANFIS-based power quality solutions. Labs/Teams: Active in energy systems research groups focusing on renewable integration and grid modernization, though specific lab names are not explicitly stated in the provided texts.

Frank Gloriusمشاهده پروفایل

استاد

• Photocatalysis
• C-H activation
• Arene hydrogenation
• +۷ مورد دیگر

Frank Glorius is a Professor of Organic Chemistry at the University of Münster, Germany. With a career spanning multiple institutions, he focuses on developing innovative catalytic methods for organic synthesis and materials science. Born: 1972, Germany Education: PhD in Organic Chemistry (summa cum laude), University of Basel & Max-Planck-Institut für Kohlenforschung Key Research Areas: Photocatalysis, C-H Activation, Organocatalysis, Data Science, and N-Heterocyclic Carbenes His recent work emphasizes photocatalysis , machine learning for reaction modeling , and catalytic hydrogenation . He has pioneered interdisciplinary approaches combining organic chemistry with computational methods. Scientific Awards: Clarivate Highly Cited Researcher (2014-2021) ERC Advanced Grant (2018) Gottfried Wilhelm Leibniz-Award (2013) Alfried Krupp Prize for Young University Teachers (2006) He leads the Glorius Group, which organizes symposia on photochemistry and data science, and engages in industry-academia collaborations through the Forschung der Chemischen Industrie symposium.