جست‌وجوی استاد و پژوهشگر

Trevor Campbellمشاهده پروفایل

دانشیار

• Bayesian Inference
• Bayesian Nonparametrics
• Machine Learning
• +۵ مورد دیگر

Trevor Campbell is an Associate Professor in the Department of Statistics at the University of British Columbia (UBC), Vancouver. He holds a Ph.D. in Machine Learning and Statistics from MIT and a B.A.Sc. in Aerospace Engineering from the University of Toronto. His research focuses on automated, scalable Bayesian inference algorithms, Bayesian nonparametrics, and streaming data analysis. Campbell is a core contributor to probabilistic programming tools like Pigeons.jl and has developed influential methods for coreset-based Bayesian inference. Education : Ph.D. in Machine Learning and Statistics (2016), MIT M.S. in Aeronautics and Astronautics (2013), MIT B.A.Sc. in Aerospace Engineering (2011), University of Toronto Research Interests : His work emphasizes scalable Bayesian computation, including variational inference, MCMC optimization, and coresets. He develops algorithms that balance statistical accuracy with computational efficiency, particularly for large-scale datasets. His recent work explores adaptive samplers (e.g., AutoStep, autoMALA) and theoretical guarantees for coreset methods. Applications span astrophysics, materials science, and network analysis. Awards & Grants : NSERC Discovery Grant (2025) Blackwell-Rosenbluth Award (2021) PIMS Early Career Award (2023) Google Perception Academic Funding (2020) Advising & Labs : He supervises a team of PhD and M.Sc. students at UBC, focusing on Bayesian methodology and computational tools. His lab collaborates with institutions like SFU and MIT on projects involving distributed sampling and probabilistic modeling. He co-organizes workshops on Bayesian computation and serves on editorial boards for Bayesian Analysis and TMLR.

Michael A Osborneمشاهده پروفایل

استاد

• Machine Learning
• Bayesian Optimization
• Gaussian Processes
• +۳ مورد دیگر

Michael A Osborne is Professor of Machine Learning at the University of Oxford and leads the Bayesian Exploration Lab . He serves as Director of the EPSRC Centre for Doctoral Training in Autonomous Intelligent Machines and Systems and co-directs the Oxford Martin AI Governance Initiative. His research focuses on Bayesian optimization, Gaussian processes, and probabilistic numerics with applications in quantum devices, battery modeling, and AI governance. Key Positions: Professor of Machine Learning, University of Oxford Official Fellow, Exeter College Co-founder of Mind Foundry Lead Researcher, Oxford Martin Programme on Technology and Employment Research Themes: Probabilistic modeling for quantum systems Uncertainty quantification in energy storage AI safety and societal impact analysis Automated experimental design Quantum device calibration Probabilistic numerical methods Technical Contributions: Bridging reality gap in quantum devices Efficient Bayesian quadrature techniques Personalized neurostimulation algorithms Automated measurement protocols Quantum-classical hybrid ML

Alexandre Bouchard-Côtéمشاهده پروفایل

استاد

• Computational Statistics
• Bayesian Inference
• Monte Carlo Methods
• +۴ مورد دیگر

Alexandre Bouchard-Côté is a Professor of Statistics at the University of British Columbia (UBC), affiliated with the Department of Statistics within the Faculty of Science. His research focuses on computational statistics, Bayesian methods, and Monte Carlo techniques, with applications in evolutionary biology, cancer genomics, and computational linguistics. Education : PhD in Computer Science (with Designated Emphasis in Statistics) from UC Berkeley (2010), BSc in Mathematics and Computer Science from McGill University (2005). Affiliations : Director of the Blang probabilistic programming project and leader of the Bouncy Particle Sampler research group. Research Interests : Bouchard-Côté develops scalable Bayesian computational methods, including non-reversible Monte Carlo algorithms like the Bouncy Particle Sampler, and applies these to problems in cancer phylogenetics, evolutionary dynamics, and historical linguistics. His work emphasizes bridging theoretical foundations with practical tools for data science. Publications Trends : Recent work spans distributed sampling frameworks (e.g., Pigeons.jl), variational phylogenetic inference, and cancer clonal evolution modeling. His articles often address algorithmic scalability and interdisciplinary applications in biology and astronomy. Awards : CRM-SSC Prize in Statistics (2024) PIMS-UBC Mathematical Sciences Young Faculty Award (2018) Tweedie New Researcher Award (2016) Advising & Grants : Supervises graduate students (e.g., Son Luu, Nikola Surjanovic) and leads funded projects on distributed MCMC and cancer genomics. Collaborates with institutions like the Simons Foundation and the Canadian Statistical Sciences Institute (CANSSI). Labs/Teams : Core member of the UBC Statistical Machine Learning group, contributing to open-source tools like Blang and the Bouncy Particle Sampler implementation.

Jung-Fu Linمشاهده پروفایل

استاد

• Mineral Physics
• Earth and Planetary Geophysics
• Geochemistry
• +۳ مورد دیگر

Dr. Jung-Fu Lin is a Professor of Earth and Planetary Sciences at the Jackson School of Geosciences, University of Texas at Austin, holding the Dave P. Carlton Centennial Professorship. His research focuses on understanding planetary interiors through high-pressure experiments, particularly using diamond anvil cells and synchrotron facilities. Key areas include mineral physics, Earth's core dynamics, and the role of water in mantle processes. Expertise: High-pressure mineral physics, X-ray spectroscopy, and planetary materials science. Current projects: Investigating iron alloys in Earth's core, thermal conductivity of mantle minerals, and carbon storage mechanisms. Research highlights include discoveries on iron spin transitions, elasticity of bridgmanite, and Martian core dynamics. Awards include the NSF CAREER Award and Fulbright Scholarship. Lin supervises graduate students in experimental petrology and mentors postdocs globally. Teaches courses on Earth materials and mineral physics. Active in international collaborations, including with Okayama University (Japan) and Adam Mickiewicz University (Poland). His lab develops advanced laser heating systems and Raman spectroscopy tools for high-pressure studies.

Paul D. Asimowمشاهده پروفایل

استاد

• Geochemistry
• Geology
• Chemical Petrology
• +۴ مورد دیگر

Paul D. Asimow is the Eleanor and John R. McMillan Professor of Geology and Geochemistry at the California Institute of Technology (Caltech), part of the Division of Geological and Planetary Sciences. He holds a B.A. from Harvard University (1991), an M.S. (1993), and a Ph.D. (1997) from Caltech. His career progression includes roles as Assistant Professor (1999–2005), Associate Professor (2005–2010), and Professor (2010–present), with the McMillan Professorship since 2016. Education: A.B. in Geology, Harvard University, 1991 M.S. in Geology, Caltech, 1993 Ph.D. in Geology, Caltech, 1997 Research Interests: Focuses on computational, experimental, and observational approaches to igneous petrology and mineral physics. Key areas include adiabatic mantle melting, water's role in mantle dynamics, high-pressure mineral physics, and processes at mid-ocean ridges. His research utilizes advanced facilities like the Lindhurst Laboratory of Experimental Geophysics and the alphaMELTS software package for thermodynamic modeling. Articles Overview: Recent work spans planetary crust formation, Martian petrogenesis, and high-pressure mineral behavior. Themes include experimental techniques, computational modeling, and cosmochemical studies of meteorites. Awards and Honors: James B. Macelwane Medal (AGU) Frank Wigglesworth Clarke Medal (Geochemical Society) Richard P. Feynman Prize for Teaching Excellence (Caltech) Fellow of the American Geophysical Union Fellow of the Mineralogical Society of America Grants and Labs: Received NSF funding for developing an interactive phase equilibria curriculum. Leads the Lindhurst Laboratory, focusing on shock-wave experiments and high-pressure mineral physics. Collaborates on software tools like alphaMELTS and MAGMASOURCE. Labs and Teams: Active in the Caltech Shock Wave Laboratory, advancing experimental methods for planetary material studies. Engages in interdisciplinary projects on Mars geology and terrestrial planet formation.

Frank Rudziczمشاهده پروفایل

دانشیار

• Natural Language Processing
• Machine Learning
• Healthcare
• +۱۲ مورد دیگر

Dr. Frank Rudzicz is an Associate Professor in the Faculty of Computer Science at Dalhousie University. His research lies at the intersection of artificial intelligence, natural language processing, and healthcare, with a focus on developing machine learning systems that improve clinical decision-making, patient outcomes, and accessibility in medicine. He holds a BSc from Concordia University (2004), an MEng from McGill University (2006), and a PhD from the University of Toronto (2011). His research interests include Natural Language Processing, Machine Learning, Healthcare, Speech Technologies, Explainable AI, and Fairness in ML. Dr. Rudzicz's recent publications span a wide range of topics, including Alzheimer's detection through speech analysis, surgical outcome prediction, mental health monitoring, privacy in AI, and the application of large language models in clinical settings. His work consistently emphasizes ethical AI, patient privacy, and real-world clinical integration. He has received several awards, including a Best Paper award at EMNLP 2020, a Best Student Paper award at ICASSP 2021, and the ISCA Best Student Paper award in 2013. His research has been published in top-tier journals such as Nature Scientific Reports , JAMA Network Open , IEEE Access , and Frontiers in Human Neuroscience , as well as leading conferences including NeurIPS, ACL, ICML, and Interspeech. Dr. Rudzicz supervises a dynamic research group working on AI for health, with active projects in voice-based diagnostics, ambient clinical documentation, explainable AI for surgery, and wearable-based monitoring for chronic diseases. He collaborates widely across disciplines, including with clinicians, neuroscientists, ethicists, and public health experts. He is also involved in major initiatives such as the Genetics Navigator study and Bridge2AI-Voice, aiming to build ethically sourced, diverse biomedical datasets. His lab actively explores the societal implications of AI in healthcare, including fairness, trust, and resistance to malicious fine-tuning.

Sai Ravelaمشاهده پروفایل

پژوهشگر

• Nonlinear Stochastic Dynamics
• Coherent Fluids
• Uncertainty Quantification
• +۶ مورد دیگر

Sai Ravela is a Principal Research Scientist in the Department of Earth, Atmospheric and Planetary Sciences (EAPS) at the Massachusetts Institute of Technology (MIT). His research focuses on nonlinear stochastic dynamics, coherent fluid systems, uncertainty quantification, and autonomous observing technologies. He specializes in developing data-driven methodologies for natural hazard detection, climate change impacts, and environmental risk assessment. Ravela’s work integrates computational science with geophysical applications, including storm surge modeling, extreme rainfall analysis, and geothermal exploration. He pioneers techniques like neural dynamical systems and adversarial learning to improve predictive accuracy in nonstationary climate regimes. His contributions span environmental monitoring systems, autonomous aircraft resilience frameworks, and policy-informed climate vulnerability assessments. Key research areas include: Coastal flood risk in Bangladesh and Vietnam Dynamic data-driven applications systems (DDDAS) Machine learning for geosciences and environmental systems Uncertainty quantification in complex fluid dynamics He leads interdisciplinary projects at MIT’s Computational Science and Engineering (CSE) program, advancing methods for data assimilation, surrogate modeling, and real-time environmental observatories. His innovations bridge theoretical frameworks with practical solutions for climate adaptation and disaster resilience.

Tamás Budaváriمشاهده پروفایل

دانشیار

• Computational Statistics
• Bayesian Inference
• Astronomy
• +۶ مورد دیگر

Tamás Budavári is an Associate Professor in the Department of Applied Mathematics and Statistics at Johns Hopkins University (JHU), with joint appointments in Physics and Astronomy and a secondary appointment in Computer Science. He is affiliated with the Whiting School of Engineering and the Institute for Data-Intensive Engineering and Science (IDIES). His research focuses on computational and statistical methods for big data in astronomy and interdisciplinary applications such as urban blight analysis. Education: PhD in Astrophysics (2001), Eötvös Loránd University, Budapest Master’s in Theoretical Physics (1997), Eötvös Loránd University Research Interests: Budavári develops algorithms for handling large astronomical datasets, including Bayesian inference, streaming algorithms, and GPU-accelerated processing. His work includes SkyQuery (an online astronomy data tool), photometric redshift estimation, and cross-matching catalogs. He also applies computational methods to urban planning, such as optimizing strategies to address vacant housing in Baltimore City. Publications & Tools: Budavári’s recent work spans topics like deep learning for astronomical image restoration, combinatorial optimization for urban policy, and probabilistic catalog matching. His tools, such as CUDAHM and NWAY, enable scalable analysis of multi-epoch survey data and N-way catalog cross-identification. Awards & Grants: Recipient of the Gordon and Betty Moore Fellowship and SAMSI Research Fellowship Funded by NSF, STScI, NIH, and others Leadership & Outreach: He serves on the Steering Committee of the 21st Centuries Cities Initiative and is a founding editor of the Journal of Astronomy and Computing. His interdisciplinary work bridges astrophysics, data science, and urban systems.

James R. Fienupمشاهده پروفایل

استاد

• Imaging Science
• Phase Retrieval
• Unconventional Imaging
• +۸ مورد دیگر

James R. Fienup is the Robert E. Hopkins Professor of Optics at the University of Rochester's Institute of Optics, with additional appointments as Distinguished Scientist at the Laboratory for Laser Energetics, Professor at the Center for Visual Science, Professor of Electrical and Computer Engineering, and Affiliated Faculty at the Goergen Institute for Data Science and Artificial Intelligence. His office is located at Wilmot 410, 275 Hutchison Rd., Rochester, NY. Education PhD in Applied Physics from Stanford University (1975) MS in Applied Physics from Stanford University (1972) BA in Physics & Mathematics (magna cum laude) from Holy Cross College (1970) Research Focus Professor Fienup's research specializes in imaging science , with emphasis on phase retrieval algorithms, unconventional imaging techniques, and wavefront sensing. His work spans computational methods for image reconstruction, sparse-aperture systems, and synthetic-aperture imaging. Recent innovations include applying machine learning to wavefront control and developing advanced digital holography techniques for 3D imaging through atmospheric turbulence. Publication Trends His recent articles (2018-2024) demonstrate a strong focus on computational imaging techniques, particularly phase retrieval algorithms applied to optical metrology and wavefront correction. Key themes include multi-plane digital holography, coronagraphic wavefront control for astronomical applications, machine learning-enhanced sensing, and novel approaches for segmented-aperture systems. His work consistently bridges theoretical optics with practical instrumentation challenges. Awards and Honors Lifetime Achievement Award, Hajim School of Engineering (2019) Emmett N. Leith Medal, Optical Society of America (2013) National Academy of Engineering Member (2012) Distinguished Visiting Scientist, JPL (2009) Fellow of OSA and SPIE International Prize in Optics (1983) Rudolf Kingslake Medal (1979) NSF Graduate Fellow (1970-1972) Professional Activities Professor Fienup has served as Editor-in-Chief of the Journal of the Optical Society of America A (1998-2003) and held editorial roles at Applied Optics and Optics Letters . He consults for NASA (James Webb Space Telescope, Hubble), national laboratories, and aerospace companies, and holds five patents in optical systems design.

John F. Rudgeمشاهده پروفایل

استاد

• Geophysics
• Geochemistry
• Petrology
• +۴ مورد دیگر

John F. Rudge is a Professor of Geodynamics at the Bullard Laboratories, Department of Earth Sciences, University of Cambridge , and a Fellow and Dean of Trinity College. His research focuses on the dynamics of Earth's interior, including mantle convection, magma transport, and the geochemical evolution of the planet. He employs continuum mechanics, numerical analysis, and statistical methods to address questions about melt dynamics, mantle heterogeneity, and the surface expressions of geophysical processes. Education includes a mathematics undergraduate degree and a PhD in Earth Sciences and Applied Mathematics from Cambridge. He held postdoctoral positions at institutions like ETH Zürich, Yale, and Columbia University before joining Cambridge as a University Lecturer in 2010 and being promoted to Professor in 2022. Key research areas include: Transport of melts and volatiles in the mantle Evolution of Earth's interior from accretion to present Dynamic topography and inner core dynamics Publications span over two decades, with recent works addressing melt rheology, mantle mineralogy, and interdisciplinary applications in oncology. He has supervised numerous PhD students and postdocs, including David Rees Jones, Isarapong Eksinchol, and Laura Alisic. Awards include the Philip Leverhulme Prize and Geological Society President’s Award. Teaching responsibilities include courses on geophysics, magma dynamics, and supervisions in Natural Sciences at Trinity College. Research tools include finite element modeling and collaborations with computational scientists.

Jonathan C Poberمشاهده پروفایل

دانشیار

• Cosmic Reionization
• Radio Astronomy
• Astrophysics
• +۳ مورد دیگر

Jonathan C. Pober is an Associate Professor of Physics at Brown University, leading research into the Epoch of Reionization (EoR) and Cosmic Dawn through low-frequency radio astronomy. His work focuses on detecting the highly-redshifted 21 cm line emission from neutral hydrogen during the early Universe, addressing challenges in separating this signal from astrophysical and human-generated radio interference. He develops novel analysis techniques and collaborates on cutting-edge experiments like the Murchison Widefield Array (MWA) and the Hydrogen Epoch of Reionization Array (HERA). Education: PhD in Physics, University of California, Berkeley (2013) MA in Physics, University of California, Berkeley (2010) MPhil in Physics, University of Cambridge (2008) BA in Physics, Haverford College (2007) Research Interests: Cosmic Reionization, Radio Astronomy, 21 cm Cosmology, Signal Processing, and Instrumentation Development. His lab explores methods to mitigate radio frequency interference and optimize interferometric calibration for precise EoR measurements. Teaching: Courses include Basic Physics B, Astronomy, Astrophysics and Cosmology, and Advanced Electromagnetic Theory. He emphasizes bridging theoretical concepts with observational techniques in his curriculum. Awards: NASA Roman Technology Fellow Lab & Projects: Directs the Pober Lab at Brown University, advancing experiments like FARSIDE (Farside Array for Radio Science Investigations of the Dark Ages and Exoplanets), a proposed lunar-based array to study the Dark Ages.

Moira Jardineمشاهده پروفایل

استاد

• Stellar Magnetism
• Exoplanet Habitability
• Stellar Winds
• +۳ مورد دیگر

Moira Jardine is Professor of Astronomy at the University of St Andrews School of Physics and Astronomy, where she became the first female physics professor in 2010. Education: Ph.D. Applied Mathematics, University of St Andrews B.Sc. Astronomy and Astrophysics, University of St Andrews Research investigates stellar magnetic activity to understand planetary habitability and solar system evolution. Uses magnetic field measurements to model stellar winds, coronal X-ray emissions, and their impact on planetary atmospheres. Work supports exoplanet detection initiatives including JWST, GAIA, and WFIRST. Publications focus on stellar coronae, magnetic confinement processes, star-planet interactions, and coronal rain dynamics. Current projects model magnetic interactions in systems like AB Dor and HD 189733. Collaborates with international consortia including MagIcS and Bcool for stellar magnetic field surveys. Awards: Fellow of the Royal Society of Edinburgh and Suffrage Science Award (2019).

Jon Schullerمشاهده پروفایل

استاد

• Nanophotonics
• Plasmonics
• Metamaterials
• +۷ مورد دیگر

Jon Schuller is a Professor in the Department of Electrical and Computer Engineering at the University of California, Santa Barbara (UCSB), within the College of Engineering. His research focuses on nanophotonics, metamaterials, plasmonics, and their applications in energy-efficient technologies such as photovoltaics, thermal management systems, and advanced optical devices. He leads the Schuller Lab, which explores engineered metasurfaces and naturally occurring materials to control light-matter interactions at subwavelength scales. His work bridges fundamental science (e.g., quantum phenomena in hybrid perovskites) and engineering (e.g., reconfigurable semiconductor meta-optics). He is affiliated with the California NanoSystems Institute (CNSI) and actively contributes to interdisciplinary research initiatives. Contact: jonschuller@ece.ucsb.edu, Office 3221C Engineering Science Building. Research interests include directional light emission control via metasurfaces, thermal radiation tuning using phase-change materials, and the development of high-efficiency photonic devices. His lab emphasizes fabrication and characterization of nanophotonic structures, with applications ranging from space technology to exoplanet imaging systems. Recent efforts focus on electrically tunable metasurfaces and multipolar optical phenomena in layered materials. Key technical contributions involve designing reconfigurable optical antennas, optimizing metasurface-based LEDs, and exploring magnetic dipole emission in 2D perovskites. His team collaborates across disciplines to address challenges in energy, aerospace, and quantum technologies. Current opportunities include postdoctoral positions in nonlinear optics and photonics.

Oliver Shorttleمشاهده پروفایل

استاد

• Extrasolar Planets
• Geochemical Cycling
• Planetary Evolution
• +۴ مورد دیگر

Oliver Shorttle is a Professor of Natural Philosophy at the University of Cambridge, holding a joint position between the Department of Earth Sciences and the Institute of Astronomy. His research focuses on planetary evolution, extrasolar planets, and geochemical cycling, integrating geological and astronomical perspectives. He explores topics such as exoplanet habitability, volcanic processes on distant worlds, and the role of volatiles in planetary systems. Education and Career: Shorttle earned his undergraduate degree in Natural Sciences from Cambridge, followed by a PhD in Earth Sciences. He held postdoctoral positions at Caltech and as a JSPS fellow in Japan before returning to Cambridge as faculty. Research Themes: His work spans planetary chemistry, magma dynamics, and the interplay between planetary interiors and atmospheres. Key projects include analyzing protoplanetary disks, simulating volcanic activity on exoplanets, and investigating phosphorus’s role in prebiotic chemistry. Collaborations: Leads the Planetary Chemistry group, collaborating across disciplines at Cambridge and internationally. Current projects involve modeling exoplanet atmospheres, studying mantle melting processes, and developing geochemical methods to probe planetary formation. Labs/Teams: Directs the Planetary Chemistry research group, fostering interdisciplinary research between geology and astronomy. Active in fieldwork, lab analysis, and computational modeling.

Jean Ponceمشاهده پروفایل

استاد

• Computer Vision
• Image Processing
• Machine Learning
• +۳ مورد دیگر

Jean Ponce is a Professor at Ecole Normale Supérieure - PSL and a Global Distinguished Professor at New York University's Courant Institute and Center for Data Science. He serves as Scientific Director of PRAIRIE Interdisciplinary AI Research Institute and co-founded Enhance Lab, commercializing super-resolution imaging software. His research focuses on computer vision, machine learning, robotics, and image processing. Ponce has held roles at Inria, MIT, Stanford, and the University of Illinois, and is an IEEE and ELLIS Fellow. He has served as chair of major conferences like CVPR, ECCV, and ICCV, and authored the textbook 'Computer Vision: A Modern Approach.' Research interests include statistical models for exoplanet detection, neural networks for 3D reconstruction, and self-supervised learning. His work combines theoretical foundations with practical applications in astrophysics, robotics, and imaging. Notable awards include the IEEE CVPR Longuet-Higgins Prize (2016, 2020) and ICML Test-of-Time Award (2019). Key projects include Enhance Lab's high dynamic range imaging and PRAIRIE's interdisciplinary AI initiatives. Ponce's articles explore cutting-edge topics like neural object priors, geodesic motion planning, and satellite image analysis. His contributions bridge academic research and industrial applications, emphasizing both fundamental theory and real-world impact.