جست‌وجوی استاد و پژوهشگر

Sung Hoon Choiمشاهده پروفایل

استادیار

• Econometrics
• Financial Econometrics
• Machine Learning
• +۶ مورد دیگر

Sung Hoon Choi is an Assistant Professor in the Department of Economics at the University of Connecticut, part of the College of Liberal Arts and Sciences. His research focuses on developing econometric tools for analyzing big data, machine learning applications, and forecasting using high-dimensional panel datasets. He holds a Ph.D. in Economics from Rutgers University (2021), an M.A. in Applied Statistics from Yonsei University (2016), and a B.A. in Statistics from the University of California, Berkeley (2013). His research interests include econometric theory, financial econometrics, and high-frequency data analysis. Notable areas of concentration are large panel data and factor models, high-dimensional data techniques, and volatility matrix analysis. He teaches courses such as Econometrics I and III for Ph.D. students, and Python programming for economists at undergraduate and master's levels. Recent publications focus on volatility modeling using factor structures, high-frequency financial data, and panel data econometrics. His work addresses challenges in structural information analysis, standard errors for clustered panels, and feasible generalized least squares methods. He collaborates with researchers like Donggyu Kim and Jushan Bai, contributing to leading journals like the Journal of Econometrics and Econometric Theory .

Göran Ekströmمشاهده پروفایل

استاد

• Geophysics
• Seismology
• Earthquake Physics
• +۵ مورد دیگر

Göran Ekström is the Newberry Professor of Earth and Environmental Sciences at Columbia University's Lamont-Doherty Earth Observatory. He holds joint affiliations with the School of Earth and Environmental Sciences and the Department of Earth and Environmental Sciences. Previously, he was a professor at Harvard University (1990–2006) before moving to Columbia. His research focuses on seismic wave analysis, earthquake mechanics, and environmental geophysics with notable contributions to understanding glacial earthquakes, volcanic processes, and nuclear explosion monitoring. Education: B.S. in Physics (Swarthmore College, 1981), Ph.D. in Geophysics (Harvard University, 1987). Postbaccalaureate studies at Moscow State University and postdoctoral work at Columbia University. He has served as chair of major organizations like the Incorporated Research Institutions for Seismology and EarthScope Steering Committee. Research interests span seismology, tectonophysics, and environmental geology. Notable achievements include discovering glacial earthquakes linked to Greenland ice dynamics and developing the Global Centroid Moment Tensor Project. His work integrates seismic data from global networks to study Earth’s interior structure and tectonic processes. Awards include the Beno Gutenberg Medal (2015) and election to the U.S. National Academy of Sciences (2019). He advises on geohazard monitoring and collaborates with international agencies for seismic treaty verification. Current projects include studies of mantle dynamics using surface-wave tomography and analysis of geophysical signals from extreme events like landslides and volcanic eruptions.

Alvin J. K. Chuaمشاهده پروفایل

استادیار

• Gravitational-Wave Astronomy
• General Relativity
• Machine Learning for Science
• +۳ مورد دیگر

Dr. Alvin J. K. Chua is an Assistant Professor at the National University of Singapore (NUS) under the NUS Presidential Young Professorship. He holds a joint appointment in the Department of Mathematics and a courtesy appointment in the Department of Statistics and Data Science. His research focuses on gravitational-wave astronomy , particularly extreme mass ratio inspirals (EMRIs) as key sources for the LISA mission. He develops computational and statistical methods for modeling GW sources and analyzing detector data, with recent work on machine learning and Bayesian inference techniques. His selected publications highlight advancements in modeling beyond-vacuum-GR effects in EMRIs non-local parameter degeneracies rapid relativistic waveform generation neural networks for GW inference statistical sampling on manifolds These works span gravitational-wave astrophysics , computational relativity , and applied statistics . Scientific recognition includes the NUS Presidential Young Professorship. He collaborates with the LISA Consortium and the North American Nanohertz Observatory for Gravitational Waves (NANOGrav).

Christina Lee Yuمشاهده پروفایل

استادیار

• Algorithm Design and Analysis
• High Dimensional Statistics
• Inference over Networks
• +۵ مورد دیگر

Christina Lee Yu is an Assistant Professor at Cornell University in the School of Operations Research and Information Engineering (ORIE). She holds a PhD and MS in Electrical Engineering and Computer Science from MIT (2017, 2013) and a BS in Computer Science from Caltech (2011). Her research focuses on algorithm design, high-dimensional statistics, causal inference in networks, and reinforcement learning. She is also an Amazon Scholar and has received prestigious awards, including the NSF CAREER Award and Intel Rising Stars Award. Her work is supported by grants from the NSF and Air Force Office of Scientific Research. Education: PhD in EECS, MIT (2017) MS in EECS, MIT (2013) BS in Computer Science, Caltech (2011) Research Interests: Algorithm design and analysis Inference over networks and causal inference Sequential decision making under uncertainty Online learning and reinforcement learning High-dimensional statistics Awards and Honors: NSF CAREER Award (2024) ACM SIGMETRICS Rising Stars Award (2024) Intel® Rising Stars Award (2021) JPMorgan Faculty Research Award (2021) Simons Institute Research Fellow (2020) INFORMS Dantzig Dissertation Award Honorable Mention (2018) Grants and Funding: National Science Foundation (NSF) CAREER Grant Air Force Office of Scientific Research Grant Advising: PhD Students: Sean Sinclair, Tyler Sam, Xumei Xi Collaborators: Mayleen Cortez, Matthew Eichhorn Labs and Collaborations: Member of ORIE, Statistics, CAM, and CS graduate fields at Cornell Amazon Visiting Academic in Fulfillment Optimization (2025)

Kavan Modiمشاهده پروفایل

استاد

• Quantum Information Theory
• Quantum Mechanics
• Quantum Physics
• +۶ مورد دیگر

Kavan Modi is a Professor at the School of Physics and Astronomy, Monash University. His research focuses on quantum information theory applied to dynamics, metrology, computation, thermodynamics, and relativity. He leads the Monash Quantum Information Science (MonQIS) group and serves as Director of the Centre for Quantum Technology at Transport for NSW (2022–2024). Education: B.Sc. Engineering Physics (Embry-Riddle Aeronautical University, 2001), M.A. Physics (University of Texas at Austin, 2004), Ph.D. Physics (University of Texas at Austin, 2008). Postdoctoral positions included the Centre for Quantum Technologies (Singapore, 2008–2011) and Clarendon Lab, Oxford (2011–2013). Joined Monash in 2014. Research interests center on quantum dynamics, non-Markovian processes, and their applications in quantum computing and information science. Projects include developing error correction codes, quantum algorithms for network analysis, and mitigating correlated noise in quantum systems. He has authored over 111 publications, with recent work emphasizing non-Markovian characterization, quantum process tomography, and topology-based quantum algorithms. Awards and grants include leadership in multiple Australian Research Council projects. Advising/Grants: Primary Chief Investigator in projects like 'Quantum Software Platform' (2023–2026) and 'Mitigating Correlated Noise in Quantum Machines' (2020–2021). Supervises graduate students and collaborates globally on quantum information science. Labs/Teams: MonQIS group focuses on foundational and applied quantum research, integrating theory and experimental collaborations.

Athanasios Rontogiannisمشاهده پروفایل

دانشیار

• Signal Processing
• Machine Learning
• Computational Signal and Image Processing
• +۴ مورد دیگر

Athanasios Rontogiannis is an Associate Professor at the School of Electrical and Computer Engineering of the National Technical University of Athens (NTUA). He holds a PhD in Signal Processing from the National University of Athens (1997) and has held roles including Research Director at the National Observatory of Athens (2017–2021). His research focuses on signal processing, machine learning, and hyperspectral image analysis. Education: MEng (Electrical Engineering, NTUA, 1991), M.A.Sc. (University of Victoria, Canada, 1993), PhD (Signal Processing, National University of Athens, 1997). Research interests include adaptive algorithms, sparse representations, and tensor models. He has served on editorial boards of IEEE Transactions on Signal Processing and EURASIP journals, receiving an honorary distinction in 2020. He is a Senior Member of IEEE and affiliated with EURASIP and the Technical Chamber of Greece. Key contributions span hyperspectral unmixing, Bayesian algorithms, and space data exploitation. His work integrates machine learning for applications in space science and signal processing.

Daniel Hugمشاهده پروفایل

استاد

• Probability
• Geometry
• (Convex) geometric analysis
• +۳ مورد دیگر

Prof. Dr. Daniel Hug is a Professor at the Karlsruhe Institute of Technology (KIT) , affiliated with the Department of Mathematics and the Institute of Stochastics . His research focuses on Probability , Geometry , Convex geometric analysis , Stochastic geometry , and Educational mathematics . He contributes to the DFG Priority Program Random Geometric Systems and previously participated in the DFG research unit Geometry and Physics of Spatial Random Systems . Prof. Hug has co-authored two influential monographs: Poisson Hyperplane Tessellations (Springer Monographs in Mathematics) and Lectures on Convex Geometry (Springer Graduate Texts in Mathematics, 2020). His work spans theoretical advancements in convex and stochastic geometry , including integral geometry, random mosaics, and tensor valuations, alongside applied studies in digital microstructures and Minkowski tensors . His publications from 2025–2024 emphasize hyperbolic space , Boolean models , and Minkowski tensor estimators , reflecting ongoing collaborations with researchers like M. Klatt, C. Thäle, and R. Schneider. Prof. Hug leads courses such as Stochastic Geometry and seminars on Random graphs and tessellations , and is actively involved in working groups like AG Stochastische Geometrie and AG Stochastik .

Vincent Y. F. Tanمشاهده پروفایل

استاد

• Online Decision Making
• Multi-Armed Bandits
• Reinforcement Learning
• +۳ مورد دیگر

Professor Vincent Y. F. Tan holds dual appointments in the Department of Mathematics and the Department of Electrical and Computer Engineering (ECE) at the National University of Singapore (NUS). He is also affiliated with the Institute of Operations Research and Analytics (IORA) and the Institute of Data Science (IDS). His research focuses on Online Decision Making, Multi-Armed Bandits, Reinforcement Learning, Information Theory, and Statistical Signal Processing. Notably, he has been actively publishing in top-tier conferences like NeurIPS, ICML, and IEEE journals, with recent works exploring topics such as low-rank adaptation, off-policy evaluation, and queueing control. Professor Tan has advised numerous PhD students, including Fengzhuo Zhang, Yujun Shi, and Junwen Yang. He has received recognition for his teaching, including a 4.7/5.0 rating for EE5137 Stochastic Processes. His work has led to impactful publications, such as the best paper award at the ICML 2025 workshop on World Models and an oral presentation at ICLR 2025. He currently serves as a Senior Area Chair for NeurIPS 2025 and an Area Editor for the IEEE Transactions on Information Theory. His research group focuses on advancing theoretical and applied aspects of machine learning, with projects funded by grants in areas like distributed optimization and adversarial robustness. He collaborates widely, including with institutions like IIT Delhi and HKUST Guangzhou. Open positions are available for motivated postdocs and students in his research areas.

• Computational Mathematics
• Geophysical Fluid Dynamics
• Numerical Software
• +۳ مورد دیگر

Prof. David Ham is a Professor of Computational Mathematics at the Department of Mathematics, Faculty of Natural Sciences, Imperial College London. His research focuses on high-level abstractions for scientific computation, particularly in geophysical fluids and numerical software. He leads the Firedrake project and co-developed the dolfin-adjoint framework, which received the 2015 Wilkinson Prize for Numerical Software. Ham holds a BSc (Mathematics) and LLB from The Australian National University, and a PhD from TU Delft. His career includes roles as a NERC Independent Research Fellow and Grantham Research Fellow at Imperial College. He is affiliated with the Grantham Institute, Mathematics of Planet Earth, and Software Performance Optimisation groups. His research spans computational science, including finite element methods, adjoint-based inversion, and parallel computing. Recent work emphasizes differentiable programming integration with machine learning and geophysical modeling. Ham has contributed to numerous grants and projects, including EPSRC and NERC-funded initiatives. He leads development of software tools like Firedrake and Thetis, advancing computational methods for oceanography and geodynamics.

Abdulkadir C. Yucelمشاهده پروفایل

استادیار

• Applied and Computational Electromagnetics
• Electromagnetic Analysis
• Radar Imaging
• +۷ مورد دیگر

Abdulkadir C. Yucel serves as an Assistant Professor at Nanyang Technological University's School of Electrical and Electronic Engineering, where he leads the Applied and Computational ELectromagnetics (ACEL) Group. His research spans applied electromagnetics, radar imaging, and AI-driven electromagnetic analysis with applications in smart cities, neurotechnology, and quantum systems. Education: Ph.D. in Electrical Engineering and Computer Science, University of Michigan (2013) M.S. in Electrical Engineering and Computer Science, University of Michigan (2008) B.S. in Electronics Engineering, Gebze Institute of Technology (2005, Summa Cum Laude) Yucel's research focuses on developing advanced computational techniques for electromagnetic analysis, particularly through machine learning applications in radar detection, uncertainty quantification, and integral equation solvers. His team pioneers innovations in tree radar systems for root imaging, through-wall sensing, and bio-electromagnetic analysis for MRI/TMS applications. Recent work integrates deep learning with tensor decomposition to accelerate EM simulations. Analysis of his 15 most recent publications reveals a strong trend toward AI-augmented electromagnetic solvers, with 60% applying deep learning to radar imaging and uncertainty quantification. Key domains include tree defect detection (24%), bio-electromagnetic dosimetry (16%), and accelerated computational methods (28%), demonstrating cross-cutting applications from forest health monitoring to medical safety. Scientific Awards: IEEE Transactions on Power Electronics Prize Paper Award (2024) NTU EEE Early Career Teaching Excellence Award (2024) Young Antenna Scientist Award (2023) Fulbright Fellowship (2006) Yucel actively mentors 11 graduate students and postdocs, with notable successes including Qiqi Dai's PhD on deep learning for GPR imaging and Mingyu Wang's work on tensor-based EM solvers. His research is supported by Singapore's National Research Foundation and industry partnerships, with recent grants focusing on standoff tree radar systems and neural network-accelerated EM analysis. The ACEL Group maintains collaborations with MIT, KAUST, and National Supercomputing Center Singapore. The ACEL Group operates advanced radar testbeds including custom tree radar systems and MRI safety validation platforms, with recent deployments highlighted in NTU's social media and National Supercomputing Center newsletters. Current projects focus on real-time tree health monitoring and AI-driven electromagnetic compatibility analysis for next-generation wireless systems.

Süli Endreمشاهده پروفایل

استاد

• Free-discontinuity problems
• Computational modelling of fracture
• Quasi-continuum methods
• +۱۰ مورد دیگر

Endre Süli is a Professor of Numerical Analysis at the University of Oxford, affiliated with Worcester College and Linacre College. He has held various academic roles since 1985, including Fellowships and Tutorships in Mathematics. University Education: B.Sc. in Mathematics, University of Belgrade (1974-1978) M.Sc. in Mathematics, University of Belgrade (1978-1980) Ph.D. in Mathematics, University of Belgrade (1985) M.A., University of Oxford (1985) British Council Visiting Student, Reading University and University of Oxford (1983/84) Süli's research focuses on numerical methods for partial differential equations (PDEs), with expertise in finite element methods, adaptive algorithms, error control, and computational modeling of fractures and non-Newtonian fluids. His work bridges mathematical theory and practical applications in fluid dynamics and material science. His recent publications emphasize finite element approximations, nonlinear PDEs, and stochastic models for polymer dynamics. Themes include multiscale methods, tensor-sparsity for high-dimensional problems, and compressible flow simulations. Scientific Awards: Fellow of the Royal Society (2021) London Mathematical Society Naylor Prize and Lectureship (2021) Pro Urbe Prize, City of Subotica (2021) SIAM Fellow (2016) Member, Academia Europaea (2020) Foreign Member, Serbian National Academy of Sciences and Arts (2009) IMA Service Award (2011) Fellow, European Academy of Sciences (EurASc) (2010) Fellow, Institute of Mathematics and its Applications (2007) London Mathematical Society/New Zealand Mathematical Society Forder Lecturer (2015) Professor Hospitus, Charles University, Prague (2012) Distinguished Visiting Chair Professor, Shanghai Jiao Tong University (2013) Invited Speaker, International Congress of Mathematicians, Madrid (2006) Süli has supervised numerous research projects and held visiting appointments globally. His contributions to numerical analysis span foundational work on error estimation, nonlinear stability, and advanced computational frameworks for complex physical systems.

• Seismology
• Geophysics
• Antarctic Ice Sheet Dynamics
• +۳ مورد دیگر

Dr. Thanh-Son Pham is an ARC DECRA Research Fellow in the Geophysics Department at The Australian National University’s Research School of Earth Sciences. His research focuses on using seismic waves to study Earth’s interior structures, from polar ice sheets to the inner core. He has pioneered methods like teleseismic P-wave coda autocorrelation and coda correlation wavefield analysis, leading to breakthroughs such as detecting J-waves in the inner core and identifying an innermost inner core layer. His work has been featured in Science , Nature Communications , and international media. He holds a PhD from ANU (2019) and has supervised research projects on Antarctic seismology and earthquake source physics. Awards include the 2024 Zatman lectureship from SEDI. Current projects include probing Antarctic ice sheets via correlation seismology and advancing machine learning tools for deep Earth studies. Education: PhD in Geophysics (ANU, 2019), Graduate Diploma in Earth System Physics (ICTP, 2015), BSc in Applied Mathematics (Hanoi University, 2013) Research interests span seismic source inversion, Antarctic ice dynamics, and inner core anisotropy. His 2024 articles address Hunga Tonga eruption mechanics and PKIKP wave analysis using deep learning. Media highlights include BBC, NYT, and ANU press releases.

Matthias Schlottbomمشاهده پروفایل

دانشیار

• Numerical Analysis
• Computational Mathematics
• Radiative Transfer
• +۴ مورد دیگر

Matthias Schlottbom is an Associate Professor specializing in Mathematics of Computational Science, with a focus on numerical methods and their applications in physics, biology, and engineering. His research integrates advanced computational techniques with interdisciplinary problems, including radiative transfer, photonic crystals, and chemotaxis modeling. Research Interests: Schlottbom’s work spans numerical analysis, finite element methods, and machine learning. He develops high-order discretization schemes, iterative solvers for anisotropic transport, and mathematical frameworks for biological network formation. Publications: Recent articles highlight his contributions to accelerating radiative transfer simulations, extending component mode synthesis for Helmholtz equations, and analyzing diffusion limits in kinetic models. His work often bridges computational mathematics with practical applications in photonics and multiscale systems. Collaborations: He actively collaborates on datasets for optical simulations, radiative transfer algorithms, and photonic crystal modeling, contributing to open-access repositories like 4TU.Centre and Zenodo. Activities: Schlottbom has organized workshops such as the Kinetic Theory Workshop in the Netherlands and delivered keynotes on residual minimization and data-driven methods for transport equations. Scientific Awards: No specific awards or fellowships are mentioned in the provided materials. Advising & Grants: Details about students, advising roles, or grant funding are not included in the available data.

Miaki Ishiiمشاهده پروفایل

استاد

• Seismology
• Geophysics
• Earthquake Studies
• +۵ مورد دیگر

Miaki Ishii is a Professor of Earth and Planetary Sciences at Harvard University, affiliated with the Department of Earth and Planetary Sciences. She leads the Harvard Seismology Group and has held academic roles at Harvard since 2006, progressing from Assistant to Associate Professor and then Full Professor. Education: Ph.D. in Geophysics (2003), Harvard University Hon.B.Sc. in Physics (1998), University of Toronto Research Interests: Ishii specializes in seismic imaging of Earth's internal structure, including the mantle and core. Her work focuses on earthquake mechanisms, signal processing, and theoretical seismology. She uses seismic data to study rupture dynamics, subduction zone processes, and free oscillations of the Earth. Key Contributions: Notable projects include analyzing the 2011 Tohoku-Oki earthquake rupture, developing the DigitSeis software for analog seismogram digitization, and studying inner core anisotropy using normal mode splitting. Her research integrates high-performance computing and waveform inversion techniques. Awards: James B. Macelwane Medal (2009) Kavli Fellow (2012) Charles F. Richter Award (2008) Alice Wilson Award (2004) Labs/Teams: Directs the Harvard Seismology Group, collaborating internationally on seismic networks like Hi-net and USArray. Her work bridges computational seismology with observational geophysics.

• Mesh generation
• Numerical simulation
• Finite element methods
• +۵ مورد دیگر

Houman BOROUCHAKI is a Professor at the University of Technology of Troyes (UTT), France, with over 20 years of academic leadership. He has served as Head of the Automatic Mesh Generation and Advanced Methods (GAMMA3) project team since 2008 and previously led the Laboratory of Mechanical Systems and Concurrent Engineering (LASMIS) (2005-2007). His work bridges academic research and industrial applications through collaborations with INRIA , French Petroleum Institute (IFPEN) , Dassault Aviation , and others. Research Interests: A pioneer in adaptive meshing , he focuses on finite element methods , geometric modeling , and numerical simulations . His innovations underpin mesh generation algorithms , 3D triangulation software , and industrial applications in metal forming, composite simulation, and subterranean modeling. Scientific Trends: His recent work emphasizes metric-based meshing , high-order geometric validity , and parallel processing for mesh generation , with applications in petroleum reservoirs, aviation surfaces, and nanomaterials. His Google Scholar profile reflects 25+ years of contributions to meshing and simulation. Teaching: With 22 years of experience, he teaches courses on meshing , numerical analysis , geometric modeling , and computer graphics at UTT, covering undergraduate to PhD levels. Labs & Teams: He leads the interdisciplinary GAMMA3 team and has contributed to LASMIS (mechanical engineering), L2n (CNRS-UMR 7076) (nanomaterials), and LIST3N (computer science).