جستجوی استاد راهنمای دانشگاهی

Andy Pavlo is an Associate Professor with Indefinite Tenure in the Computer Science Department at Carnegie Mellon University's School of Computer Science. He is an active member of the CMU Database Group and the Parallel Data Laboratory, where he leads research in database management systems with a focus on self-driving architectures, transaction processing, and large-scale analytics. His work bridges academic research and industry applications through projects like NoisePage, OtterTune (which he co-founded and served as CEO before it ceased operations), and Peloton. Dr. Pavlo's research interests span database management systems with particular emphasis on autonomous database architectures that can self-tune and optimize without human intervention. His work explores transaction processing systems that can handle high-throughput workloads while maintaining consistency, and large-scale data analytics techniques that efficiently process massive datasets. He has made significant contributions to query optimization, database extensibility, and automatic database tuning using machine learning techniques. His recent work on database extensibility revealed critical issues in PostgreSQL's extension ecosystem, showing that approximately 16% of extensions are incompatible with at least one other extension due to API violations and memory errors. His research output demonstrates a consistent focus on practical database systems challenges, with recent publications examining database extensibility, user-defined function optimization, and the cyclical nature of database research. The articles show a strong trend toward making database systems more autonomous, with increasing integration of machine learning techniques for automatic tuning and optimization. His work often combines deep theoretical analysis with practical implementation in open-source systems. Dijkstra Award 2024 for contributions to database systems research Dr. Pavlo actively mentors graduate students, with current advisees including Wan Shen Lim, William Zhang, and Sam Arch (co-advised with Todd Mowry). His former students have gone on to successful careers in both industry and academia. He has secured significant research funding through CMU's affiliate program with major database companies including ClickHouse, DataStax, dbt, Firebolt, MotherDuck, RelationalAI, SingleStore, Spiral, PingCAP/TiDB, Yellowbrick, and Yugabyte. His research is supported by these industry partnerships and likely includes NSF funding given his active participation in the database research community. At CMU, Dr. Pavlo leads the Database Group and organizes several seminar series including "SQL or Death," "Database Building Blocks," and "ML⇄DB Technical Talks." These seminars bring together researchers and practitioners to discuss cutting-edge developments in database systems. He also runs a summer research internship program that has attracted students for multiple consecutive years, indicating a strong research group with ongoing projects and funding.

• Database Management Systems
• Self-Driving Database Architectures
• Transaction Processing Systems
• +۴ مورد دیگر

Craig H. Meyer is a Professor in Biomedical Engineering and Radiology & Medical Imaging at the University of Virginia. He holds a Ph.D. from Stanford University and leads the Rapid MRI Research Group, focusing on developing advanced MRI techniques for cardiovascular disease, neural disorders, and pediatrics. His work integrates physics, signal processing, and machine learning to improve MRI acquisition and processing speed. Education: Ph.D. in Biomedical Engineering, Stanford University. Research Interests: Medical and Molecular Imaging, Signal and Image Processing, Biomedical Data Sciences, Biomechanics, and Cardiovascular Engineering. His innovations include fast spiral imaging, conjugate phase reconstruction, and machine learning-enhanced MRI denoising. Awards: Notably includes the Dean’s Award for Excellence in Team Science (2014), Fellowships from NAI (2021), AIMBE (2015), and ISMRM (2013). He also authored two landmark MRI papers recognized as pivotal in the field. Teaching: Courses include BME 6310 (Computation and Modeling in Biomedical Engineering) and BME 8782 (Magnetic Resonance Imaging). He emphasizes translational research, with applications in clinical MRI advancements and collaborative interdisciplinary projects. Labs/Groups: Rapid MRI Research Group focuses on cutting-edge MRI technologies, including real-time cardiac imaging and artifact reduction through deep learning.

• Medical and Molecular Imaging
• Signal and Image Processing
• Biomedical Data Sciences
• +۲ مورد دیگر

Stratis Ioannidis is a Professor in the Electrical and Computer Engineering Department at Northeastern University, with a courtesy appointment in the Khoury College of Computer Sciences. His research focuses on distributed systems, networking, machine learning, big data, and privacy. He earned his B.Sc. from the National Technical University of Athens, and M.Sc. and Ph.D. from the University of Toronto. Prior to Northeastern, he worked at Technicolor and Yahoo Labs. Education: B.Sc. in Electrical and Computer Engineering (2002, National Technical University of Athens); M.Sc. and Ph.D. in Computer Science (2004, 2009, University of Toronto). Research interests span machine learning, distributed systems, optimization, and privacy. Key projects include the NSF AI Institute for Future Edge Networks and Distributed Intelligence (AI-EDGE), and work on federated learning, continual learning, and privacy-preserving algorithms. His research has been supported by NSF, Google, and Facebook grants. Recent publications emphasize federated learning, continual learning, and edge computing. Awards include the NSF CAREER Award, Søren Buus Outstanding Research Award, and multiple best paper awards. He advises numerous PhD students and collaborates across disciplines, including healthcare and wireless networks. Lab activities include SPIRAL and WIoT labs, focusing on machine learning at the edge and network optimization. Grants include the NSF AI Institute and multiple collaborative projects with industry and academia.

• Distributed systems
• Networking
• Machine learning
• +۳ مورد دیگر

James S. Kim is a Professor of Education at Harvard University's Graduate School of Education, where he conducts policy-relevant research focused on improving literacy outcomes for low-income students and struggling readers. With an Ed.D. from Harvard University (2002), he leads the READS Lab (Research Enhances Adaptations Designed for Scale in Literacy), a research team that partners with school districts to solve literacy challenges through evidence-based interventions. Dr. Kim's research centers on understanding how building students' domain knowledge and reading engagement can foster long-term improvements in reading comprehension. His work emphasizes experimental design and evidence-based interventions, with a particular focus on addressing educational inequality. His research interests include early education, education policy, evidence-based intervention, human development, inequality and education gaps, informal and out-of-school learning, language and literacy development, and teachers and teaching. Kim's most significant contribution is the Model of Reading Engagement (MORE), a spiraled and sustained content literacy intervention co-developed with schoolteachers that has been shown to improve first to third-grade students' reading comprehension in science, English language arts, and math. Notably, research on MORE meets WWC (What Works Clearinghouse) standards without reservation, and long-term follow-up suggests the intervention's impact persists through fourth grade. His publications reveal a consistent focus on content literacy, domain knowledge development, and transfer effects in reading comprehension. Research on MORE meets WWC standards without reservation Long-term effects of MORE persist through fourth grade Commitment to Open Science principles (open data, open materials, preregistration) As a servant leader, Kim builds long-term partnerships with school districts to implement literacy interventions at scale. His READS Lab promotes open science practices while developing practical solutions to literacy challenges. His research on summer reading interventions, parental text messaging, and classroom-based content literacy approaches demonstrates his commitment to translating research into practice. Kim's current work includes scaling the MORE intervention to improve reading comprehension for high-needs students in moderate to high poverty schools through a Department of Education-funded project (2024-2028).

• Early Education
• Education Policy
• Evidence-Based Intervention
• +۷ مورد دیگر

Lara A. Estroff is a Full Professor and the current Chair of the Department of Materials Science and Engineering at Cornell University's College of Engineering. She has been a faculty member since 2005 and served as Director of Graduate Studies from 2015 to 2019. Her academic leadership and research excellence position her at the forefront of bio-inspired materials and biomineralization research. Her educational background includes a B.A. in Chemistry from Swarthmore College (1997) and a Ph.D. in Chemistry from Yale University (2003), followed by an NIH-funded postdoctoral fellowship at Harvard University in the lab of Prof. George M. Whitesides. Dr. Estroff's research centers on the fundamental mechanisms of crystal growth, biomineralization, and pathological mineralization. She investigates how organisms control mineral formation and applies these principles to engineer synthetic materials with complex structures and functionalities. Her work spans biomaterials, tissue engineering, and energy materials—particularly hybrid organic-inorganic perovskites for photovoltaics. She employs advanced characterization techniques and has pioneered in situ methods to monitor crystallization dynamics. Her recent publications reveal a strong trend toward interdisciplinary research, integrating materials science with cancer biology, immunology, and machine learning. The articles emphasize bio-inspired synthesis, mineral-tissue interactions, and the development of functional crystalline materials for medical and energy applications. Faculty Early CAREER Award, National Science Foundation (2009) Fiona Ip Li '78 and Donald Li '75 Excellence in Teaching Award, Cornell College of Engineering (2007) Marilyn Emmons Williams Award, Cornell Undergraduate Research Board (2009) Keynote Speaker, Gordon Research Seminar on Biomineralization (2012) Lawrence Berkeley National Lab Affiliate (2013) Dr. Estroff leads a major DOE-funded project titled “Formulation Engineering of Energy Materials via Multiscale Learning Spirals,” a $3 million, three-year initiative using machine learning to optimize perovskite synthesis for solar cells. She has advised numerous graduate students and postdoctoral researchers, and her lab is known for fostering collaborative, cross-disciplinary research. She has also contributed to educational initiatives at Cornell, particularly in undergraduate research and materials education. Her research group operates at the intersection of chemistry, engineering, and biology, focusing on high-resolution characterization of biominerals, in situ crystal growth studies, and the design of in vitro models for cell-mineral interactions. The lab actively collaborates with institutions including Lawrence Livermore National Laboratory, National Renewable Energy Laboratory, and Johns Hopkins University.

• Biomineralization
• Crystal Growth Mechanisms
• Bio-inspired Materials Synthesis
• +۷ مورد دیگر

Dr Dongbin Wei is an Associate Professor at the School of Mechanical and Mechatronic Engineering , University of Technology Sydney (UTS), with a career spanning academia and industry. He holds a PhD in Materials Processing Engineering from the University of Science and Technology Beijing (2001) and academic appointments from 2005–2012 at the University of Wollongong (Research Fellow to Lecturer) and 2013–2017 at UTS (Senior Lecturer) before his promotion to Associate Professor in 2018. His research lies at the intersection of Mechanical Engineering , Manufacturing Engineering , and Materials Processing , focusing on: Ultrasonic Additive Manufacturing (UAM) Micro Metal Forming and Size Effects Tribology and Lubrication Numerical Simulations of Material Processing Composite Material Fabrication Key contributions include: Development of the Springback Path–Displacement Adjustment (SP-DA) method for stamping accuracy Advancements in femtosecond laser texturing for silicon wettability control Studies on nanolubrication in hot rolling Optimization of micro-deep drawing parameters He has secured competitive grants from the Australian Research Council (ARC) and industry partners like Weir Minerals Australia Ltd , including projects on: Revolutionizing mineral separation via additive manufacturing Super high-speed grinding technologies Mechanics of micro composite drill fabrication As a lead supervisor, he guided the 2022 thesis 'Creation and Validation of 3D Printable Mineral Separation Spiral' . His work bridges theoretical analysis, computational modeling (FEM/FEA), and practical validation in advanced manufacturing systems.

• Additive Manufacturing
• Micro Metal Forming
• Tribology
• +۴ مورد دیگر

Bjorn Sandstede is the Alumni-Alumnae University Professor of Applied Mathematics at Brown University. His research focuses on applied dynamical systems, nonlinear waves, pattern formation, and computational biology. He holds a PhD from the University of Stuttgart and has held faculty positions at The Ohio State University and the University of Surrey before joining Brown in 2008. Sandstede has received numerous awards, including the SIAM J.D. Crawford Prize and the Royal Society Wolfson Research Merit Award. He served as Department Chair at Brown and directed the Data Science Initiative. His work involves interdisciplinary collaborations, such as modeling zebrafish stripe formation and developing computational tools like SCOT for single-cell data integration. Sandstede also mentors extensively, advising over 30 PhD students and postdoctoral researchers. He leads the NSF-funded Institute for Computational and Experimental Research in Mathematics (ICERM) and contributes to initiatives promoting diversity and inclusion in STEM. Education: PhD in Mathematics, University of Stuttgart Undergraduate Degree, University of Heidelberg Research Interests: Applied Dynamical Systems Nonlinear Waves and Pattern Formation Computational Biology Data Science PDE Analysis Awards and Recognition: Alfred P. Sloan Research Fellowship SIAM J.D. Crawford Prize Royal Society Wolfson Research Merit Award Elsevier Jack Hale Award Teaching Excellence Awards from Brown University Fellow of the AMS and SIAM Grants and Leadership: Principal Investigator of NSF grant establishing ICERM Director of Brown's Data Science Initiative Member of Research Advisory Board and Tenure Committees Labs and Teams: Leads the Sandstede Lab at Brown, focusing on computational biology and dynamical systems. Collaborates with the Volkening Lab on zebrafish pattern modeling and the Singh Lab on optimal transport methods.

• Applied Dynamical Systems
• Data Science
• Computational Biology
• +۳ مورد دیگر

Sir Paul Collier is a distinguished Professor of Economics and Public Policy at the Blavatnik School of Government, University of Oxford, where he also directs the Centre for the Study of African Economies. He serves as the Oxford Academic Director of the International Growth Centre, a collaboration between the London School of Economics and Oxford University. Professor Collier has established himself as a leading authority on development economics, particularly focusing on African economies, poverty reduction, and the political economy of development. His academic journey includes significant public service, having served as Director of the World Bank Research Development Department from 1998 to 2003. He currently advises the International Monetary Fund Strategy and Policy Department and writes regularly for major publications including The Independent and The New York Times. His extensive contributions to economic thought have been recognized with numerous prestigious awards. Professor Collier's research interests span a wide range of critical development issues. He has made significant contributions to understanding the "bottom billion" - the poorest populations in the world - and has developed innovative approaches to addressing regional inequality, natural resource management, and the political barriers to development. His work consistently bridges academic rigor with practical policy applications, making complex economic theories accessible to policymakers and the general public alike. His publications reveal a consistent focus on practical solutions to development challenges. Recent works have examined urbanization in developing countries, the relationship between elections and economic performance, infrastructure investment in Africa, and the cultural foundations of economic success or failure. His research demonstrates how institutional design, cultural factors, and appropriate policy frameworks can enable neglected regions to "spiral up" toward prosperity. A.SK Social Science Award (2013) Arthur Ross Prize (for The Bottom Billion) Handesblatt Prize (for The Future of Capitalism) Adam Smith Prize by Glasgow's Philosophical Society (2023) Global Citizenship Award by Belgium's cooperative movement (2018) Professor Collier is actively engaged in advising governments on practical approaches to regional renewal. His recent work emphasizes the importance of local agency, collective learning, and moral leadership in addressing the challenges faced by neglected communities in both developed and developing countries. He challenges conventional economic orthodoxies that prioritize market forces alone and advocates for more nuanced, context-specific policy approaches that recognize the complex interplay of economic, social, and political factors in development processes.

• Development Economics
• African Economies
• Poverty and Inequality
• +۵ مورد دیگر

Dr. Pulin Gong is an Associate Professor in the School of Physics at the University of Sydney. His research focuses on understanding the self-organizing mechanisms of neural circuits' spatiotemporal dynamics and their computational principles. He investigates distributed dynamic computation via propagating neural waves, irregular neural activity variability, and coherent spatiotemporal patterns in large-scale neural data. His work combines experimental and computational approaches to unravel neural coding principles. Research interests include: Distributed dynamic computation (e.g., visual feature integration) Irregular neural dynamics and membrane potential fluctuations Coherent spatiotemporal wave patterns (e.g., spiral waves) Recent projects involve analyzing cortical wave patterns in mice and primates, fractional neural sampling, and Lévy walk dynamics in neural systems. Collaborators include institutions like Fudan University and Kyoto University. Current research student: Andrew LY, working on cortico-cortical loop dynamics and AI applications.

• Neural Dynamics
• Computational Neuroscience
• Spatiotemporal Patterns
• +۳ مورد دیگر

Prof. Dr. Lena Wessel is a Mathematics Education faculty member at the Department of Mathematics Didactics, Faculty of Electrical Engineering, Computer Science and Mathematics, University of Paderborn. She leads teacher training programs and participates in university governance as elected Faculty Council member. Research Focus: Her work centers on comprehension-oriented mathematics instruction at secondary/vocational levels, emphasizing language development, continuity in curriculum design (spiral principle), and digital mathematics tools. As DZLM network partner, she develops professional development programs for secondary educators. LiaNMU project (scalar products, plane reflections) VioLa project (digital discussion formats) LLV.HD project (cross-university learning networks) Key Contributions: She has coordinated major projects like MESUT (language-supported fraction understanding) and LaMaVoC (language in vocational mathematics). Her 2024 publications analyze language-integrated pedagogy and algebraic concept unification in teacher training.

• Mathematics Education
• Teacher Training
• Language Development in Education
• +۴ مورد دیگر

Andreas Müller is a Lecturer at ETH Zürich's Department of Civil, Environmental, and Geomatic Engineering. He holds a Master of Science in Civil Engineering from the Technical University Munich (TUM, 2015) and has worked as a bridge engineer at BUNG Ingenieure AG in Munich (2016–2017). Since 2017, he has been pursuing a PhD under Prof. Taras, initially at Bundeswehr University Munich (UniBw) and later continuing at ETH Zürich's Chair of Steel and Composite Structures. His research focuses on structural stability, particularly the buckling behavior of high-strength steel hollow sections, post-buckling rotation capacity, and the influence of initial imperfections analyzed via 3D surface scans (Reverse Engineering). He also investigates spiral-welded tubes' imperfection assessment and strain hardening effects on aluminum structural sections. Education: Master of Science in Civil Engineering, Technical University Munich (2015) Research interests emphasize bridging experimental and computational methods in structural engineering. He employs machine/deep learning for predictive modeling of buckling phenomena, particularly leveraging Deep Neural Networks (DNN) to enhance accuracy in structural analysis. His work addresses practical challenges in steel and composite structures, including standard compliance (e.g., prEN1993-1-1 updates) and real-world geometry imperfections' impact on load capacities. Advising and grants: As a current PhD candidate, he is supervised by Prof. Taras but has not yet listed advisees. His research is supported by institutional resources at ETH Zürich's Chair of Steel and Composite Structures. Labs/Teams: Active member of the Chair of Steel and Composite Structures at ETH Zürich, contributing to advanced structural mechanics research and engineering innovation initiatives.

• Structural Engineering
• Civil Engineering
• Steel Structures
• +۳ مورد دیگر

Dr. Gary Glover is a Professor of Radiology (Radiological Sciences Lab) at Stanford University , with courtesy appointments in Psychology and Electrical Engineering. His work focuses on the physics and mathematics of MRI, particularly rapid scanning methods using spiral k-space trajectories for functional brain imaging and multimodal neuroimaging (fMRI/EEG/fPET/fNIRS) combined with neuromodulation techniques like TMS and transcranial ultrasound. Academic Appointments: Radiology, Psychology, Electrical Engineering Professional Affiliations: Bio-X, Stanford Cancer Institute, Wu Tsai Neurosciences Institute Research Interests include: Development of blood oxygen level-dependent (BOLD) and viscoelastic contrast in MRI Functional MR Elastography for brain activation mapping Optimization of MR-ARFI for transcranial ultrasound guidance Automated spinal cord segmentation (EPISeg) using machine learning Scientific Awards : National Academy of Engineering (2013) Gold Medal, ISMRM (2000) Steinmetz Award, General Electric (1985) Lauterbur Lecture, ISMRM (2018) Recent Publications analyze: Fast fMRI sampling and spurious signal correction Dissociated patterns in default mode network anti-correlations Neural correlates of collaborative behavior in triadic fMRI Salience network contributions to depression pathophysiology

• Magnetic Resonance Imaging (MRI)
• Functional MRI (fMRI)
• Biomedical Imaging Physics
• +۶ مورد دیگر

Yonghyun Ha is an Associate Research Scientist in the Department of Radiology & Biomedical Imaging at Yale School of Medicine. His research focuses on advancing magnetic resonance imaging (MRI) technologies, particularly in low-field MRI systems, RF pulse design, and imaging hardware innovation. He collaborates with experts like Duy Phan, Haifan Lin, and Nikhil Malvankar, contributing to projects such as RF pulse distortion compensation and novel RF coil development. Research Interests: His work spans low-field MRI systems , RF engineering , imaging algorithm optimization , and hardware design . He explores applications like point-of-care imaging and cost-effective MRI solutions. Articles Trends: Recent publications address gradient-free imaging, field-cycling magnets, and deep learning for data compression. His work bridges engineering and clinical needs, emphasizing practical MRI advancements. Advising & Grants: No formal advisees are listed, but his collaborations suggest involvement in interdisciplinary research teams. No specific grants are mentioned, but his projects imply funding through institutional or NIH channels. Labs/Teams: Active in Yale’s Radiology & Biomedical Imaging department, contributing to MRI technology development and translational research initiatives.

• Medical Imaging
• Biomedical Engineering
• Magnetic Resonance Imaging (MRI)
• +۳ مورد دیگر

Allon Guez is a Professor in the Department of Electrical and Computer Engineering at Drexel University. His research focuses on control systems, robotics, artificial intelligence, medical robotics, and automated decision making. He actively bridges academia and industry through high-tech entrepreneurship. Education PhD in Electrical Engineering, University of Florida MS in Electrical Engineering, University of Florida MBA in Finance, Drexel University BS in Electrical Engineering, Technion - Israel Institute of Technology His research portfolio spans medical robotics, automated decision making systems, and advanced control algorithms. Key areas include wearable safety devices, radiation control in imaging systems, and closed-loop brain stimulation technologies. Notable contributions include founding ControlRad (radiation reduction systems) and GraceFall (fall detection technology). His work demonstrates a strong emphasis on translating academic research into commercial medical devices. Recent publications highlight innovations in: Fetal brainwave monitoring Postural disturbance detection Seizure prediction algorithms Magnetic microrobotics Dynamic CT collimation Cardiac tissue modeling

• Control Systems
• Robotics
• Artificial Intelligence
• +۷ مورد دیگر

Susan E. Clark is an Assistant Professor of Physics at Stanford University, where she investigates cosmic magnetic fields, magnetohydrodynamic processes, and the interstellar medium (ISM) through observation, simulation, and analytic theory. Prior to Stanford, she was a NASA Hubble Fellow and postdoctoral member at the Institute for Advanced Study in Princeton, New Jersey, after earning her Ph.D. in Astrophysics from Columbia University (2017) and B.S. in Physics from the University of North Carolina at Chapel Hill (2012), supported by a Morehead-Cain scholarship. Education: Ph.D., Astrophysics, Columbia University (2017) — NSF Graduate Fellow B.S., Physics, University of North Carolina at Chapel Hill (2012) — Morehead-Cain scholarship Her research focuses on Galactic and extragalactic magnetism, interstellar turbulence, star formation, and polarized cosmological foregrounds. She leads an interdisciplinary group tackling these problems via observational data, numerical simulations, and machine learning techniques. Current projects include characterizing magnetic field alignment, modeling 3D ISM structure, and analyzing data from experiments like the Atacama Cosmology Telescope, Simons Observatory, CMB-S4, CCAT-Prime, LiteBIRD, and the Galactic Australian SKA Pathfinder (GASKAP). Recent publications highlight her work on dust filament misalignment, HI morphology for gas phase separation, equipartition magnetic field estimation, and tomographic MHD simulations of galactic magnetic fields. These studies integrate astrophysics, computational methods, and observational astronomy to decode the universe's magnetic structure and dynamics. Scientific Awards: Alfred P. Sloan Research Fellowship NSF Graduate Fellowship NASA Hubble Fellowship Clark co-founded the Pan-Experiment Galactic Science Group and co-directs Stanford's Center for Decoding the Universe, fostering interdisciplinary collaborations. Her lab includes postdocs, graduate students, and undergraduates, with alumni transitioning to roles in academia and industry. Funding comes from NSF, NASA, and the Sloan Foundation.

• Cosmic Magnetic Fields
• Interstellar Medium
• Astrophysical Magnetic Fields
• +۶ مورد دیگر