جست‌وجوی استاد و پژوهشگر

• Unmanned Aerial Vehicle Design
• Flight Control Technology
• Integrated Navigation Systems
• +۷ مورد دیگر

HAN Song is a Research Professor at the School of Innovation and Entrepreneurship, Southern University of Science and Technology (SUSTech), where he has been working since 2017. He also serves as deputy director of the Aerospace Technology Innovation Center and deputy director of the Unmanned Aerial Vehicle Design and Navigation Control Technology Engineering Research Center (Shenzhen Engineering Research Center). Prior to joining SUSTech, he held various positions at China Aerospace Science and Technology Corporation (CASC), including Professor, Associate Professor, and engineering roles from 2005-2017. Professor Han Song's research focuses on advanced aircraft design and autonomous navigation and control technology. His work spans UAV system development, aircraft aerodynamic layout design, precision guidance systems, and multi-sensor information fusion. He has achieved breakthroughs in high-lift-to-drag aerodynamic layouts, modular composite structures, integrated avionics, and fault-tolerant flight control systems. His research has direct applications in military and civilian unmanned systems with emphasis on practical implementation and technological innovation. 2010 Aerospace Contribution Award of China Aerospace Science and Technology Corporation 2011 Eleventh Five-Year Plan Science and Technology Innovation Award 2016 Twelfth Five-Year Plan Science and Technology Innovation Award First prize for National Defense Science and Technology Progress 5 invention patents in flight control and navigation guidance Professor Han has extensive experience leading major aerospace projects, having served as chief designer for multiple UAV systems including short-range, high-altitude, and medium-range general-purpose platforms. His designs participated in significant national events - a short-range UAV in the 70th anniversary of the Anti-Fascist War parade (2015) and a high-altitude UAV in the 70th National Day parade (2019). He has written nearly 100 technical reports and secured funding for numerous research projects through China Aerospace Science and Technology Corporation. At SUSTech, Professor Han leads research activities at the Aerospace Technology Innovation Center and the Unmanned Aerial Vehicle Engineering Research Center. His team has made notable progress in aerial remote sensing image feature extraction using lightweight convolutional neural networks and underwater bionic flapping wing propulsion technology. He teaches Product Innovation and Design Development Methods and Aviation Vehicle Innovation Design, with his 2020 research group producing four Outstanding Master's Graduates of SUSTech.

• Numerical and analytical modeling of subsurface multi-component, multiphase flows
• Environmental and ecological implications of coastal groundwater flow
• Aquifer parameter estimation
• +۳ مورد دیگر

Dr. Li Hailong is a Chair Professor at the School of Environmental Science and Engineering, Southern University of Science and Technology (SUSTech), Shenzhen. He holds a PhD in Hydrogeology from the University of Hong Kong (2003), an MSc (1991) and BSc (1988) in Applied Mathematics from Fudan University. His academic journey includes professorships at China University of Geosciences-Beijing (2009-2020) and Anshan Normal University (1999-2009). Recipient of 2010 NSFC Outstanding Young Scientist Grant 2022 Dayu Water Conservancy Science and Technology Award 2008 Chutian Professorship (Hubei Province's highest academic honor) Dr. Li's research focuses on multi-component, multi-phase subsurface flows in coastal zones and their ecological/environmental effects. His work spans aquifer parameter estimation, ecohydrology, marine groundwater discharge, and computational fluid dynamics. Recent projects involve submarine groundwater discharge (SGD) quantification in Bohai Sea and Jiaozhou Bay using radium/radon isotopes. His publications in top journals like Nature Geoscience , Geochimica et Cosmochimica Acta , and Water Resources Research have been cited 7,072 times (H-index 49). He has led 19 research projects, including 6 NSFC grants and 973 Program subprojects. Dr. Li serves on editorial boards of Advances in Water Resources and Water Science and Engineering , and was Associate Editor for Hydrogeology Journal (2012-2015).

• Ceramic Composites
• Bioinspired Materials
• High-Temperature Interfacial Phenomena
• +۴ مورد دیگر

Professor Eduardo Saiz Gutierrez is a Chair in Structural Ceramics at the Department of Materials, Faculty of Engineering, Imperial College London. He leads the Centre for Advanced Structural Ceramics (CASC) and coordinates the BioBone Initial Training Network under FP7, involving nine academic and industrial partners. His research spans ceramic composites, graphene-based materials, and bioceramics for bone repair. Research Interests Hierarchical bioinspired ceramic composites High-temperature interfacial phenomena (spreading, capillarity) Graphene synthesis and composite fabrication Biomaterials for bone tissue engineering Advanced processing techniques for ceramics Key Affiliations Centre for Advanced Structural Ceramics (Director) Institute for Molecular Science and Engineering LFC-UK (Laminar Flow Control underpinning technology) The Composites Centre Scientific Contributions He has supervised numerous PhD and MSc students, including Claudio Ferraro, Gil Machado, and Eleonora D'Elia, while securing significant grants for projects like BioBone. His group has developed novel methods for graphene growth, ceramic sintering, and self-healing composites.

Erhan Bayraktarمشاهده پروفایل

استاد

• Mathematical finance
• applied probability
• machine learning
• +۴ مورد دیگر

Erhan Bayraktar is a Professor of Mathematics at the University of Michigan, holding the Susan Smith Chair. He serves as Director of the Quantitative Finance and Risk Management Masters Program, which he established in 2015. His academic career at the University of Michigan spans since 2004, progressing from T. H. Hildebrandt Research Assistant Professor to his current full professorship. Professor Bayraktar earned his Ph.D. from Princeton University in 2004, following dual Bachelor's degrees in Electrical Engineering and Mathematics from Middle East Technical University in Turkey. His academic journey reflects a strong foundation in both theoretical and applied mathematical disciplines. Bayraktar's research focuses on mathematical finance, applied probability, machine learning, mean field games, stochastic analysis, stochastic control, and optimal stopping. His work bridges theoretical mathematics with practical applications in finance and risk management. He has developed sophisticated mathematical frameworks for analyzing complex financial systems, market behaviors, and optimal decision-making under uncertainty. His contributions to mean field games have provided new insights into large-scale interacting systems, while his work on stochastic control has advanced methodologies for optimal decision processes. His publication record demonstrates a consistent trajectory of high-impact research, with recent work focusing on Wasserstein space analysis, graphon particle systems, and applications of machine learning to financial mathematics. His research shows increasing interdisciplinary connections between traditional mathematical finance and modern computational approaches. Susan M. Smith Professorship (2010-present) National Science Foundation CAREER Grant (2010-2016) SIAM Activity Group on Financial Mathematics and Engineering Early Career Prize (2010) Professor Bayraktar has mentored 14 Ph.D. students (13 graduated) and approximately 40 post-doctoral researchers. His students hold prestigious positions in academia and industry, including tenure-track positions at Boston University, University of Colorado, University of Sydney, and University of Toronto. He has secured continuous funding from the National Science Foundation, including the current grant DMS-2507940 (2025-2028) and previous grants totaling over 15 years of continuous NSF support. As Director of the Quantitative Finance and Risk Management Masters Program, Bayraktar has built a robust academic community through the Financial/Actuarial Math seminar series, which hosts about 10 outside speakers annually, and by organizing international workshops in stochastic analysis for finance and insurance in Ann Arbor.

• computer security
• formal methods
• program logics
• +۳ مورد دیگر

Mads Dam is a Professor in Teleinformatics at the School of Computer Science and Communication at Kungliga Tekniska Högskolan (KTH), where he heads the Department of Theoretical Computer Science. His research focuses on computer security, formal methods, and program logics, with particular emphasis on the formal modeling and verification of low-level hardware and software execution platforms for security and application isolation. His educational background includes: PhD in Computer Science from the University of Edinburgh (1990) MSc in Computer Engineering from Aalborg University, Denmark BSc in Information Technology from Aalborg University, Denmark Mads Dam's research interests center on computer security, formal methods, and program logics. His current work focuses on the formal modeling and verification of low-level hardware and software execution platforms such as hypervisors and OS kernels and their underlying hardware. He has made significant contributions to information flow security, verification of microarchitectural systems, and network programming language security. His research bridges theoretical foundations with practical security applications. His recent publications show a strong trend toward verifying low-level systems, with a focus on information flow security for processors, network programming languages (particularly P4), and microarchitectural vulnerabilities. His work combines formal methods with practical security concerns, developing verification techniques that address real-world security challenges in hardware and software systems. The research spans theoretical foundations in temporal and epistemic logics to practical applications in network security and processor verification. His scientific awards and recognition include: Two framework grants from the Swedish Foundation for Strategic Research A junior individual grant from the Swedish Foundation for Strategic Research Project grants and a five-year research fellowship from the Swedish Research Council (VR) Project grants from Ericsson, Microsoft Research, US Air Force, and Vinnova (the Swedish Innovation Agency) Mads Dam has been a principal investigator on numerous research projects and has supervised many graduate students. He has been a partner in several European projects including HATS, S3MS, VerifiCard, LOMAPS, and UaESMC. His research has been supported by substantial grants from major funding bodies, reflecting the significance and impact of his work in computer security and formal methods. He is a founding member of several research centers at KTH, including Access, the CASTOR software research center, and the CDIS center for cyber defense and information security. These centers bring together researchers from multiple disciplines to address complex challenges in cybersecurity and software engineering.

Anthony TUNG Kum Hoeمشاهده پروفایل

استاد

• Database Systems
• Indexing and Searching Complex Objects
• Time Series Analysis
• +۱۳ مورد دیگر

Anthony TUNG Kum Hoe is a Professor in the Department of Computer Science at the National University of Singapore (NUS), where he has established himself as a leading researcher in database systems and data mining. He is also affiliated with the NUS Graduate School for Integrative Sciences and Engineering and serves as a SINGA supervisor. His educational background includes a Ph.D. in Computer Science from Simon Fraser University (2001), an M.Sc. in Information Systems & Computer Science from NUS (1998), and a B.Sc. with 2nd Class Upper Honours in Information Systems & Computer Science from NUS (1997). Professor Tung's research spans several interconnected areas within database systems and data mining. His primary focus is on developing efficient methods for indexing and searching complex data structures including time series, trajectories, trees, graphs, and high-dimensional objects. He has pioneered work in visual query processing, keyword search, and ranking systems. His GENIE (Generic Inverted Index) and LAMP (semi-Lazy Mining Paradigm) projects represent significant contributions to big data analytics, particularly in handling the 'variety' aspect of big data by providing unified frameworks for processing diverse data structures while preserving semantic meaning. His research bridges theoretical database concepts with practical applications in visual data mining, collaborative analytics, and just-in-time model construction. His recent publications reveal a clear evolution from traditional database research toward more complex analytics on diverse data types. While maintaining his core expertise in database indexing and query processing, his work has expanded to incorporate machine learning techniques, particularly in areas like nearest neighbor search, anomaly detection, and predictive analytics. There's a noticeable trend toward interdisciplinary applications, with publications spanning computer vision, natural language processing, transportation systems, and social computing. His research group consistently publishes in top-tier venues including SIGMOD, VLDB, ICDE, and KDD, demonstrating both theoretical rigor and practical relevance. 2005 Best Paper Award for 'Indexing DNA Sequences Using q-grams' 2007 Invited panel speaker on 'Advice for a successful database researcher career in Asia' at SIGMOD 2010 Guest Lecturer for VLDB Database School 2012 VLDB 2012 Research PC Co-chairs 2015 10 Years Best Paper Award, DASFAA 2015 Invited to SIGMOD 2008 and SIGKDD 2008 Program Committees Professor Tung has supervised numerous PhD students and research associates throughout his career, including notable researchers like Zhang Zhenjie (recipient of the 2007 President Graduate Fellowship) and Wang Nan (published in SIGMOD'08). His research group has been consistently productive, with students publishing in top conferences including SIGMOD, ICDE, and VLDB. His professional service is extensive, having served as PC Chair for COMAD'06, Research PC Co-chair for VLDB 2012, and on program committees for virtually all major database and data mining conferences over the past two decades. His research has been supported by various grants that have enabled significant contributions to database technology. His GENIE and LAMP projects represent a cohesive research direction focused on developing systematic approaches to big data analytics. GENIE provides a unified platform for storage and retrieval of big data with various structures, while LAMP introduces a novel paradigm for predictive analytics that combines the strengths of lazy and eager learning approaches. These projects have evolved to incorporate GPU acceleration and parallel processing capabilities, reflecting his commitment to addressing real-world scalability challenges in data-intensive applications.

• Coastal Engineering
• Fluid Dynamics
• Tsunami Modeling
• +۵ مورد دیگر

Lorenzo Melito is a Professor in the Faculty of Engineering at Università Politecnica delle Marche (UNIVPM) in Ancona, Italy. His research focuses on coastal engineering, fluid dynamics, and environmental modeling with particular emphasis on wave dynamics, tsunami inundation, and coastal adaptation to climate change in Mediterranean environments. Dr. Melito's research interests span several critical areas in coastal engineering and environmental fluid mechanics. His work on wave-current interactions, steady streaming, and infragravity dynamics provides fundamental insights into coastal processes. He has developed semi-empirical approaches for tsunami inundation mapping that have been applied to Italian coastlines. His research on munitions mobility in estuaries addresses important environmental contamination issues, while his work on coastal inundation modeling contributes to climate change adaptation strategies for the Marche Region and beyond. His publications demonstrate expertise in both theoretical modeling and experimental approaches to understanding complex coastal phenomena. Analysis of Dr. Melito's recent publications reveals a strong focus on coastal processes in the Adriatic Sea region, particularly in microtidal environments. His work combines theoretical modeling, numerical simulation, and experimental approaches to understand complex wave-bottom interactions, sediment transport, and coastal flooding mechanisms. A recurring theme is the application of fundamental fluid dynamics principles to solve practical coastal engineering problems, with emphasis on Italian coastal regions including the Marche Region and the Tyrrhenian and Adriatic coasts. His research bridges theoretical fluid dynamics with practical coastal management applications, particularly for hazard assessment and climate change adaptation.

Ivan Guoمشاهده پروفایل

دانشیار

• Mathematical Finance
• Optimal Transport
• Stochastic Control
• +۳ مورد دیگر

Associate Professor Ivan Guo is a faculty member at Monash University's School of Mathematics, where he leads research in mathematical finance and stochastic modeling. He obtained his PhD in Mathematics from the University of Sydney in 2014 and currently accepts PhD students. His work bridges theoretical mathematics and practical financial applications, with active projects spanning 2022-2026. Research Focus Dr. Guo's research centers on three interconnected areas: Optimal Transport Applications : Developing transport-based methods for financial model calibration and derivatives pricing Market Microstructure : Analyzing market-making strategies, liquidity, and high-frequency trading dynamics Sustainable Finance : Modeling green investment impacts and energy market transitions using game-theoretic approaches Active Projects Can green investors drive transition to a low-emission economy? (2022-2026) Integrating energy storage into electricity markets (2022-2024) Data61 CRP #46 - Risklab mathematical sciences (2020-2023) Efficient computational techniques for econophysics (2019-2021) The role of liquidity in financial markets (2017-2020) His research consistently addresses model uncertainty, volatility dynamics, and computational methods across 18+ publications since 2012.

Fangbao Tianمشاهده پروفایل

دانشیار

• Computational Fluid Dynamics
• Fluid-Structure Interaction
• Biomechanics
• +۵ مورد دیگر

Associate Professor Fangbao Tian is a distinguished researcher and academic at UNSW Canberra's School of Engineering and Technology, where he also serves as Deputy Head of School for Research since July 2023. Previously, he held positions as Senior Lecturer (2017-2021) and Lecturer (2014-2017) at the same institution after completing postdoctoral research at Vanderbilt University. His academic journey began with a BSc (2006) and PhD (2011) in Theoretical and Applied Mechanics and Engineering Mechanics from the University of Science and Technology of China. Dr. Tian's research focuses on Computational Fluid Dynamics (CFD) tools for complex flows and fluid-structure interaction, with particular emphasis on bio-inspired applications. His work spans modeling laryngeal aerodynamics and vocal-fold vibration, fluid-structure interaction of plates in viscous fluid, fish swimming and insect flight, blood flow dynamics, and non-Newtonian flow phenomena. Recent work has expanded into Martian atmosphere aerodynamics, showing his research's growing interdisciplinary nature. His extensive publication record demonstrates consistent contributions across fluid dynamics, with recent trends showing increasing focus on compressible flows, bio-inspired flight systems, heat transfer applications, and computational methods like Lattice Boltzmann approaches. The research shows strong connections between fundamental fluid mechanics and practical applications in aerospace, biomedical engineering, and environmental systems. UNSW Canberra Goldstar Award 2022 IEEE Outstanding SMCS Chapter Award 2021 Outstanding Volunteer Award 2021 UNSW Canberra Silverstar Award 2018 UNSW Canberra Silverstar Award 2017 Journal of Fluids and Structures Highly Cited Research 2017 ARC DECRA 2016 Dr. Tian actively supervises PhD students across diverse topics including bushfire-enhanced wind loads, bio-inspired flight on Mars, flow control optimization, and fluid-structure interactions in compressible flows. He has secured over $5 million in external funding as Chief Investigator, including significant Australian Research Council projects examining Martian atmosphere aerodynamics, bio-inspired flapping wings, and cardiovascular flow modeling. His editorial roles include Associate Editor for Journal of Fluids and Structures and Scientific Reports, reflecting his standing in the fluid dynamics research community.

Mauro Werderمشاهده پروفایل

مدرس

• Glaciology
• Hydrology
• Numerical Modeling
• +۷ مورد دیگر

Dr. Mauro Werder is a Lecturer at the Department of Civil, Environmental and Geomatic Engineering at ETH Zurich. His work focuses on glaciology, subglacial hydrology, and numerical modeling, combining computational methods with field measurements. He has developed widely used models such as GlaDS (Glacier Drainage System) and BITE (Bayesian Ice Thickness Estimation), and contributed to projects like SHMIP and 4D-Antarctica. Current Projects: Gladder (2025-2028), DIWING (2023-2026), LEAD (2020-2026), 4D-Antarctica (2019-2022), CORDS (2023-2024) Education: PhD in Glaciology (2009, Swiss National Science Foundation funded) His research spans subglacial drainage systems, sediment transport (SUGSET model), Bayesian inversion techniques, and field experiments involving artificial lakes and R-channels. He actively teaches courses on GPU-based PDE solving, applied glaciology, and reproducible scientific computing. Scientific Awards: Swiss National Science Foundation (SNF) Fellowship for Prospective Researchers (2010-2011) European Union (FP7) Marie Curie International Outgoing Fellowship (2011-2014) He collaborates with institutions like the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), and contributes to software development through packages like BITEmodel.jl and Parameters.jl. His fieldwork includes experiments on Greenland's Jakobshavn Isbræ and Switzerland's Plaine Morte glacier.

• Environmental Epidemiology
• Children's Environmental Health
• Molecular Epidemiology
• +۳ مورد دیگر

Margaret Karagas, PhD is the inaugural Department Chair and James W. Squires Professor of Epidemiology at the Geisel School of Medicine at Dartmouth College, with a secondary appointment as Professor of Community and Family Medicine. She also serves as Director of both the Children's Environmental Health and Disease Prevention Research Center and the Center for Molecular Epidemiology at Dartmouth. Her leadership extends to the NIH-funded Environmental Influences on Child Health Outcomes (ECHO) Program, which encompasses over 90,000 participants across the United States. Dr. Karagas earned her PhD from the University of Washington in 1990 and has established herself as a leading researcher in environmental epidemiology. She collaboratively developed an innovative cross-disciplinary postdoctoral and graduate program in Quantitative Biomedical Sciences (QBS) that integrates epidemiology, bioinformatics, and bistatistics. Her research focuses on interdisciplinary studies investigating environmental exposures, host factors, and biological mechanisms affecting health from infancy through adulthood, with particular emphasis on under-studied rural populations. Dr. Karagas employs high-dimensional analytic tools along with biomarkers and sensors to study the exposome, genetic susceptibility, and biological response. Her work has uncovered adverse cardiometabolic, neurodevelopmental, and immune-related health outcomes associated with drinking water contaminants, food-borne toxicants (including in infant first foods), and exposures from woodstoves. Analysis of her recent publications reveals a strong focus on environmental health impacts across the lifespan, with significant contributions to understanding how environmental exposures affect child development, chronic disease risk, and health disparities. Her work frequently employs advanced molecular and analytical techniques to assess environmental exposures and their biological consequences. Dr. Karagas serves on numerous prestigious international consensus panels and committees, including those for the United Nations, World Health Organization, International Agency for Research on Cancer, European Food Safety Authority, US National Institutes of Health, and National Academies of Science, Engineering and Medicine. As an academic leader, she has mentored diverse investigators at all career stages and established innovative training programs. Her research has directly contributed to practice and policy changes regarding environmental health threats. She maintains active collaborations across multiple centers at Dartmouth including the Dartmouth Toxic Metals Superfund Research Program and Dartmouth SYNERGY.

Cynthia D. Rudinمشاهده پروفایل

استاد

• Interpretable Machine Learning
• Sparse Models
• Causal Inference
• +۷ مورد دیگر

Cynthia D. Rudin is the Gilbert, Louis, and Edward Lehrman Distinguished Professor of Computer Science at Duke University, with joint appointments in the Departments of Electrical and Computer Engineering, Statistical Science, Mathematics, and Biostatistics & Bioinformatics. She directs the Interpretable Machine Learning Lab and has held previous positions at MIT, Columbia, and NYU. Her educational background includes: Undergraduate degree from the University at Buffalo PhD from Princeton University (2004) Research Interests: Dr. Rudin's research focuses on interpretable machine learning and its applications across multiple domains. Her work emphasizes creating machine learning models whose reasoning processes people can understand, which includes algorithms for extremely sparse models, interpretable neural networks, interpretable matching methods for causal inference, and dimension reduction for data visualization. She applies these techniques to critical societal problems in healthcare, criminal justice, materials science, and other domains. Her lab has developed practical code for sparse models such as decision lists, decision trees, and additive models that provably optimize accuracy and sparsity. Dr. Rudin's recent publications (2024-2025) demonstrate a strong focus on interpretable AI applications across diverse fields including healthcare (mortality risk scores, breast cancer prediction), materials science (metamaterials design), and environmental justice (location-based health analysis). Her work consistently emphasizes practical implementations with real-world impact, particularly in high-stakes decision-making domains where model transparency is critical. Scientific Awards: Squirrel AI Award for Artificial Intelligence for the Benefit of Humanity (2022) - often described as the "Nobel Prize of AI" INFORMS Society on Data Mining Prize (2024) Guggenheim Fellowship (2022) Three-time winner of the INFORMS Innovative Applications in Analytics Award (2013, 2016, 2019) Winner of the 2023 John M. Chambers Statistical Software Award for PaCMAP Winner of the 2024 Award for Innovation in Statistical Programming and Analytics Dr. Rudin has advised numerous PhD students and postdocs who have co-authored significant publications with her. Her lab has received substantial funding for projects applying interpretable machine learning to healthcare (seizure prediction in ICU patients), criminal justice (crime series analysis), and energy infrastructure (underground electrical distribution networks). Her work on the Series Finder algorithm has been adapted by the NYPD and has been running live in NYC since 2016. She directs the Interpretable Machine Learning Lab at Duke, which includes the Almost-Matching-Exactly Lab focused on interpretable causal inference. Her team develops practical code implementations for all their research, emphasizing usability and real-world application in critical domains.

Guillaume Chiavassaمشاهده پروفایل

استاد

• Wave Propagation
• Numerical Analysis
• Computational Fluid Dynamics
• +۶ مورد دیگر

Guillaume Chiavassa is a Professor in Applied Mathematics at Ecole Centrale de Marseille, affiliated with the Laboratoire M2P2 (Mechanics, Modeling and Physical Processes Laboratory). He leads research in the Thermodynamics, Waves, Digital, Interfaces and Combustion team, focusing on advanced computational methods for complex physical phenomena. His research spans wave propagation in porous media, numerical modeling of plasma flows in Tokamak configurations, multilevel schemes for conservation laws, penalization methods for compressible flows, and wavelets in numerical analysis. Chiavassa's work demonstrates exceptional mathematical rigor applied to challenging physical systems, particularly in nonlinear wave dynamics and computational fluid mechanics. His methodologies bridge theoretical mathematics with practical engineering applications. Analysis of his recent publications reveals a strong focus on wave propagation phenomena across diverse media, with significant contributions to numerical methods for nonlinear systems. His work consistently addresses the mathematical challenges of modeling complex physical behaviors including material softening, fractional attenuation in porous media, and plasma dynamics in fusion devices. The interdisciplinary nature of his research connects applied mathematics with mechanical engineering, geophysics, and nuclear fusion technology. Chiavassa leads the PROSPERO Software project and participates in the ANR Espoir research initiative and the Consortium SEISCOPE. His teaching activities include courses on hyperbolic equations, finite elements, and heat transfer, with practical computational components developed for student instruction. He maintains an active research program through Laboratory M2P2, where his team develops advanced numerical methods for simulating complex physical phenomena with applications ranging from environmental engineering to nuclear fusion research.

Chuanfei Dongمشاهده پروفایل

استادیار

• Star-Terrestrial Planet Interactions
• Magnetic Reconnection
• Turbulence
• +۵ مورد دیگر

Chuanfei Dong is an Assistant Professor of Astronomy at Boston University's College of Arts & Sciences and of Electrical and Computer Engineering at the College of Engineering. His research focuses on understanding plasma physics and its applications to space science, planetary atmospheres, and fusion energy. Dong joined BU in January 2023 after working as a staff scientist at the Princeton Plasma Physics Laboratory. Education: B.S. in Space Science from University of Science and Technology of China M.S. in Earth and Atmospheric Sciences from Georgia Institute of Technology M.S.E. in Nuclear Engineering and Radiological Sciences from University of Michigan M.S. in Planetary and Space Sciences from University of Michigan Ph.D. in Scientific Computing from University of Michigan Research Interests: Dr. Dong's research spans multiple disciplines within space physics and plasma science. His primary interests include Star-Terrestrial Planet Interactions in our Solar System and beyond, magnetic reconnection and turbulence phenomena, wave-particle interactions in space plasmas, and applications of physics-informed machine learning to plasma problems. He also investigates high-intensity laser-plasma interactions with applications to fusion energy research. His work bridges the gap between theoretical plasma physics and observational space science, with particular focus on planetary atmospheres, solar wind interactions, and exoplanet habitability. Dong's interdisciplinary approach combines computational modeling, observational data analysis, and theoretical frameworks to address fundamental questions in space physics. Research Trends: Dong's recent publications demonstrate a strong focus on applying advanced computational techniques to space plasma physics problems. His work spans solar system bodies including Earth, Mars, Mercury, and the Moon, with increasing attention to exoplanet systems. A notable trend is the integration of machine learning approaches with traditional plasma physics modeling, particularly for complex phenomena like Landau damping and magnetic reconnection. His research has significant implications for understanding atmospheric evolution, space weather, and potential habitability of planetary bodies. Scientific Awards: DOE Early Career Research Award (2023) - $875,000 grant for plasma turbulence research Alfred P. Sloan Research Fellow (2024) Metcalf Travel Award Advising and Grants: Dr. Dong mentors undergraduate research assistants and plans to expand his research group with the support of his DOE Early Career Award, which will fund a graduate student and postdoctoral researcher. His research is supported by the Department of Energy and has connections to NASA missions including MAVEN (Mars) and BepiColombo (Mercury). Dong is also involved with the Mauve telescope project as BU institutional PI. His work has been featured in numerous media outlets including Phys.org, Science Daily, and German TV program zdf/3sat. Labs and Teams: Dr. Dong leads a research group focused on computational plasma physics at Boston University. He collaborates with researchers at Princeton Plasma Physics Laboratory and is involved with multiple NASA missions. His team develops advanced computational models to simulate space plasma phenomena, with particular expertise in magnetohydrodynamics (MHD), particle-in-cell methods, and physics-informed machine learning approaches. Dong is also affiliated with BU's Hariri Institute for Computing.

Yvo Pokernمشاهده پروفایل

دانشیار

• Diffusion Processes
• Bayesian Statistics
• Markov Chain Monte Carlo
• +۴ مورد دیگر

Dr Yvo Pokern is an Associate Professor in Statistics at University College London since 2018. His research focuses on computational statistics and machine learning, with expertise in diffusion processes and Bayesian methodology. He earned his PhD in mathematics under Andrew Stuart, a Masters at Paris XI with a dissertation at the Max-Planck-Institute in Leipzig, and was a postdoctoral researcher at Warwick University with Gareth Roberts and Wilfrid Kendall. His primary research interests include statistical inference for diffusion processes (particularly hypoelliptic diffusions and diffusions on manifolds), Bayesian methods such as Markov chain Monte Carlo, and statistical applications in spectroscopy (ENDOR). His work combines theoretical rigor with practical applications in diverse scientific domains. Analysis of his recent publications reveals a consistent theme of developing and applying advanced statistical techniques to complex real-world problems, including traffic flow, fingerprint analysis, and magnetic resonance spectroscopy. Dr Pokern has supervised numerous PhD students, several of whom have gone on to academic careers. Notable former students include Mai Ngoc Bui (now lecturer at the British University Vietnam) and Tjun Yee Hoh (now lecturer at UCL School of Management).