جست‌وجوی استاد و پژوهشگر

Audrey Repettiمشاهده پروفایل

دانشیار

• Optimization
• Fixed Point Algorithms
• Deep Learning
• +۴ مورد دیگر

Audrey Repetti is an Associate Professor at Heriot-Watt University, affiliated with both the School of Mathematical and Computer Sciences and the School of Engineering and Physical Sciences. She holds a PhD in optimization from Université Paris-Est Marne-la-Vallée (2015) and an MSc in applied mathematics from Université Pierre et Marie Curie (2011). Her research focuses on optimization (convex/nonconvex/stochastic), deep learning, Bayesian inference, and inverse problems in imaging and graph processing. She received the Royal Society of Edinburgh Fellowship (2022) for her work in optimization for data science. Her teaching includes courses like Bayesian Inference and Computational Methods. She collaborates internationally on projects in radio astronomy imaging, medical imaging, and computational optics. Repetti’s work bridges optimization theory with practical applications, emphasizing scalable algorithms for large-scale data problems.

• Probability Theory
• Stochastic Analysis
• Machine Learning
• +۵ مورد دیگر

Xiong Wang is a J.J. Sylvester Assistant Professor in the Department of Mathematics at Johns Hopkins University (non-tenure track). His research focuses on probability theory, stochastic analysis, and their applications to machine learning and complex systems. He holds a Ph.D. from the University of Alberta (2022), where he received the Faculty of Science Dissertation Award. Education: Ph.D. in Mathematics, University of Alberta (2022) Advisor: Professor Yaozhong Hu Research Interests: Probability Theory & Stochastic Processes Machine Learning Algorithms Partial Differential Equations (PDEs) Interacting Particle Systems Inverse Problems in Stochastic Dynamics Mathematical Foundations of Data Science Teaching: Instructor for advanced courses at Johns Hopkins, including Stochastic Differential Equations, Partial Differential Equations, and Real Analysis Prior teaching roles at University of Alberta (Calculus, Linear Algebra) Recent Activities: Speaker at over 30 international conferences/seminars since 2021 Active in SIAM, CMS, and ICERM events Collaborations with institutions globally (e.g., Peking University, Brown University)

Hongbo Zhaoمشاهده پروفایل

استادیار

• Biophysics
• Soft Matter
• Active Matter
• +۵ مورد دیگر

Hongbo Zhao is an Assistant Professor jointly appointed in the Department of Physics and Department of Chemistry & Biochemistry at the University of California, San Diego (UCSD), effective September 2024. His research bridges biophysics, soft matter, active matter, physical chemistry, and applied mathematics, focusing on uncovering fundamental principles of soft and living matter through interdisciplinary approaches. Prior to UCSD, he was a Princeton Bioengineering Initiative (PBI2) Distinguished Postdoctoral Fellow at Princeton University, working with Andrej Košmrlj, Clifford P. Brangwynne, and Sujit Datta. He earned his Ph.D. in Chemical Engineering from MIT under Martin Z. Bazant, focusing on energy materials and data-driven discovery in chemical physics. Key research interests include biological phase separation and phase transitions, physics of soft and active matter, and data-driven discovery from image-based datasets. His work explores how biomolecular condensates organize cellular components, the interplay between nonequilibrium activities and phase separation, and emergent phenomena in active matter systems. He develops computational tools and statistical mechanics frameworks to analyze complex systems across scales. Zhao's recent publications highlight contributions to understanding condensate-driven DNA repositioning, chemotactic motility-induced phase separation, and learning reaction kinetics from X-ray imaging. His research has been supported by grants including the PBI2 Fellowship and collaborations with institutions like Stanford University and the SLAC National Accelerator Laboratory. He leads the Zhao Research Group at UCSD, fostering an interdisciplinary environment for graduate and undergraduate students in theoretical and computational biophysics.

Santiago Badiaمشاهده پروفایل

استاد

• Computational Science and Engineering
• Numerical Analysis
• Numerical Linear Algebra
• +۸ مورد دیگر

Santiago Badia is a Professor of Computational Mathematics at Monash University's School of Mathematics since 2019. Previously, he held roles at UPC (Universitat Politècnica de Catalunya) as Professor of Computational Science and Engineering (2009–2019) and was an adjunct researcher at CIMNE (Barcelona), leading the Large Scale Scientific Computing Department. He earned his PhD in 2006 from UPC, with prior work at Politecnico di Milano (2006) and Sandia National Labs (2007–2008). His research focuses on numerical approximation of PDEs using finite element methods, with applications in fluid/solid mechanics, electromagnetics, and multiphysics problems. He leads high-performance computing projects like FEMPAR (scalable PDE solvers) and Gridap (Julia-based PDE framework). His work includes perfect weak scalability on supercomputers and solving PDEs with up to 60 billion unknowns. Research interests span computational science, numerical analysis, parallel computing, and numerical linear algebra. Projects address challenges in fusion energy, additive manufacturing, and nuclear waste repositories. He actively supervises PhD students through funded projects, emphasizing computational modeling and simulation. Collaborations include international teams in geophysical fluid dynamics and stochastic inverse problems. His software frameworks (FEMPAR, Gridap) are widely used for industrial and academic research, emphasizing scalability and usability.

Wentao Tangمشاهده پروفایل

استادیار

• Data-Driven Control
• Nonlinear Control Theory
• Machine Learning
• +۳ مورد دیگر

Wentao Tang is an Assistant Professor in the Department of Chemical and Biomolecular Engineering at North Carolina State University. His research focuses on data-driven control algorithms integrating nonlinear control theory with machine learning, optimization of complex systems, and network topology-based decomposition for distributed control. He received his B.S. in Chemical Engineering and Mathematics from Tsinghua University (2015) and his Ph.D. in Chemical Engineering from the University of Minnesota (2020), followed by work as a process control engineer at Shell Global Solutions. His research group, DISCO (Data-Informed Systems Control & Optimization), explores topics such as distributed optimization, dissipativity learning control, and network structure analysis for industrial processes. Notable contributions include methods for optimal decomposition of large-scale systems and development of Lyapunov envelope algorithms for distributed MPC. Recent group additions include Ph.D. student Jarod Morris and postdoctoral researcher Dr. Xiuzhen Ye. His work emphasizes bridging theoretical control advancements with practical applications, particularly in plantwide control systems and resilient industrial automation. The group actively collaborates on projects funded by startup grants and industry partnerships, with publications spanning control theory, optimization, and machine learning.

Gianna Del Corsoمشاهده پروفایل

دانشیار

• Quantum Computing
• Numerical Linear Algebra
• Spectral Analysis
• +۳ مورد دیگر

Gianna Del Corso is an Associate Professor at the Department of Computer Science, University of Pisa. Her research interests span Quantum Computing, Numerical Linear Algebra, and Spectral Analysis. She has advised PhD students in quantum computing topics such as quantum algorithms and machine learning. Her teaching includes courses on Numerical Calculus, Parallel Scientific Computing, and Introduction to Quantum Computing, delivered across multiple academic years at the University of Pisa. She has also contributed to advanced courses in the PhD program in Computer Science. Her research focuses on quantum algorithms for machine learning, quantum walks, eigenvalue computation of structured matrices, and spectral techniques for web analysis. Recent work includes applications of quantum k-means clustering and variance estimation subroutines. Her publications span quantum computing, numerical methods, and scientific citation models. Her articles highlight contributions to PageRank computation, matrix factorization for recommendation systems, and analysis of citation-based research evaluation models. She has actively engaged in international conferences, presenting on topics such as orthogonal iterations for nonlinear eigenvalue problems and quantum hitting time algorithms. Labs/Teams: Research conducted within the Department of Computer Science at the University of Pisa, focusing on quantum computing and numerical methods.

• Mechanical Engineering
• Composite Materials
• Additive Manufacturing
• +۴ مورد دیگر

Ana Muñoz Sánchez is an Associate Professor in the Department of Mechanical Engineering at University Carlos III of Madrid, where she conducts research in composite materials, additive manufacturing, and manufacturing processes. Her work bridges theoretical modeling with practical applications, particularly in drilling processes, ballistic protection, and engineering education. Her research focuses on several key areas within mechanical engineering and materials science: Composite materials machining, particularly carbon fiber reinforced polymers (CFRP) Tool wear analysis and optimization of drilling parameters Numerical modeling of manufacturing processes Additive manufacturing applications in medical and educational contexts Ballistic protection systems Engineering education tools and methodologies Dr. Muñoz Sánchez's publication record demonstrates consistent contributions to both fundamental manufacturing research and practical educational applications. Her recent work shows a strong emphasis on improving manufacturing processes for composite materials while simultaneously developing innovative educational tools, including specialized resources for visually impaired students. Her research combines computational methods with experimental validation to address complex engineering challenges. She has secured funding from multiple Spanish research agencies for projects including "DIGITDRILL - Digitalization of industrial drilling process" and "DISEÑO AVANZADO, FABRICACIÓN Y ENSAYO DE PROTECCIONES BALÍSTICAS DE CABEZA (PROTECT_BAL)". Her work has practical applications in aerospace, medical simulation, and inclusive education.

Lingzhou Xueمشاهده پروفایل

استاد

• High-Dimensional Statistics
• Nonparametric Statistics
• Statistical Learning
• +۹ مورد دیگر

Lingzhou Xue is a Professor of Statistics at The Pennsylvania State University, affiliated with the Eberly College of Science. He holds dual roles as a faculty member and the Associate Director of the National Institute of Statistical Sciences (NISS). His research focuses on high-dimensional statistics, nonparametric methods, statistical learning, and optimization, with applications in biomedical, environmental, and social sciences. He leads the SLDM (Statistical Learning and Data Mining) Lab and MDS (Microbiome Data Science) Lab. Education: B.Sc. in Statistics from Peking University (2008), Ph.D. in Statistics from the University of Minnesota (2012), postdoctoral training at Princeton University (2012–2013). Professional roles include Associate Editorships at the Journal of the American Statistical Association, Annals of Applied Statistics, and ACM Transactions on Probabilistic Machine Learning. Research Interests: Federated learning, causal inference, graphical models, reinforcement learning, optimal transport, and large-scale optimization. Recent work emphasizes theoretical guarantees for sparse PCA and federated Q-learning algorithms. Awards: IMS Fellow (2024), ASA Fellow (2023), COPSS Emerging Leader Award (2021), and Bernoulli Society New Researcher Award (2019). He has mentored 17 Ph.D. students and 3 postdocs, with four students securing tenure-track faculty positions. Service: Organized multiple NISS writing workshops, co-chaired the Ingram Olkin Statistics Serving Society Forum on Gun Violence, and contributed to the ASA whitepaper 'Discovery with Data' (2014).

Yehuda Bockمشاهده پروفایل

مدرس ارشد

• Geodesy
• Lithospheric Deformation
• Seismology
• +۴ مورد دیگر

Yehuda Bock is a Senior Lecturer and Research Geodesist at the University of California San Diego (UCSD), affiliated with the Scripps Institution of Oceanography and its Institute of Geophysics and Planetary Physics (IGPP). He directs the Scripps Orbit and Permanent Array Center (SOPAC), a key facility for geodetic data management and analysis. His roles include advancing seismogeodetic techniques for natural hazard mitigation and improving observational networks. Education: B.A. from New York University, B.S. from Technion-Israel Institute of Technology, M.S. and PhD from Ohio State University. Research focuses on crustal deformation, geodetic instrumentation, and integrating GNSS with seismic data for earthquake and tsunami warning systems. He explores applications of GNSS in meteorology, structural health monitoring, and climate studies. Recent work emphasizes real-time data fusion, machine learning for geophysical analysis, and understanding tectonic processes in regions like the San Andreas Fault and Cascadia Subduction Zone. His publications highlight advancements in deformation modeling, early warning systems, and interdisciplinary approaches to geoscience data.

Moody T. Chuمشاهده پروفایل

استاد

• Numerical Ordinary Differential Equations
• Numerical Linear Algebra
• Dynamical Systems
• +۵ مورد دیگر

Moody T. Chu is a Professor in the Department of Mathematics at North Carolina State University since 1982, holding a PhD from Michigan State University. His research focuses on numerical linear algebra, dynamical systems, inverse problems, and quantum computing. He has received prestigious teaching awards including the Alumni Distinguished Undergraduate Professorship (2006) and multiple Board of Governors Awards (2010, 2013, 2014). His work bridges computational mathematics with applications in physics, engineering, and data science. Research interests include numerical methods for differential equations, tensor approximation, and quantum simulation. His articles explore topics like Cartan decomposition for quantum Hamiltonians, Lax dynamics, and low-rank tensor approximations. Over 200 publications span numerical analysis, inverse eigenvalue problems, and optimization techniques. Chu’s contributions also address algorithm design for matrix completion and structured low-rank approximations. He has advised numerous graduate students (details not listed here) and led projects on adaptive optics and stochastic processes. His work on nonnegative matrix factorization and Markov chain dynamics has influenced data mining and machine learning applications. Chu’s lab focuses on advancing computational frameworks for complex systems, emphasizing interdisciplinary collaboration.

Viney Anejaمشاهده پروفایل

استاد

• Environmental Science
• Climate Change
• Air Quality
• +۴ مورد دیگر

Viney Aneja is a Professor in the Department of Marine, Earth, and Atmospheric Sciences at North Carolina State University (NC State), with a secondary appointment in the Department of Forestry and Environmental Resources since 2001. He holds a B.Tech from IIT Kanpur (1971), and M.S. and Ph.D. from NC State (1975, 1977). His career includes industrial research roles at General Electric and Northrop Services before joining academia in 1987. Education: B. Tech, Chemical Engineering, IIT Kanpur (1971) M.S., Ph.D., Chemical Engineering, NC State (1975, 1977) MBA, Planning & Control, Union College (1986) Research Interests: Dr. Aneja focuses on environmental science and climate policy, particularly emissions from agriculture (e.g., CAFOs), wildfires, and their impacts on air quality and climate. His work integrates field measurements, satellite data, and modeling to address policy-relevant questions. Key areas include ammonia emissions, ozone formation, and nitrogen cycle dynamics. Professional Service: He has served on numerous advisory boards including the EPA’s Board of Scientific Counselors (2014–present), USDA Agricultural Air Quality Task Force (2001–2008), and North Carolina Governor’s Hazardous Materials Task Force (2006–2007). He currently chairs the editorial board of Atmosphere and serves on multiple international journal boards. Awards: North Carolina’s highest civilian honor (2007) Frank A. Chambers Award (1998) World’s top 2% scientists (Stanford, 2021) Rossby Visiting Fellow (2015) Teaching & Mentorship: Teaches courses on weather/climate and air quality. Advised 20 Ph.D. students, 45 M.S. students, and 9 postdocs. His research program is among the nation’s leading in agricultural air quality and climate science.

Hennes Alexander Hajdukمشاهده پروفایل

پژوهشگر ارشد

• Physical Oceanography
• Influence of Bottom Topography on Oceanic Flows
• Property-Preserving Numerical Methods
• +۳ مورد دیگر

Hennes Alexander Hajduk is a Research Fellow at the University of Oslo's Section for Meteorology and Oceanography, part of the Department of Geosciences. He holds a PhD in Applied Mathematics from TU Dortmund University (2022). His work focuses on physical oceanography, numerical methods for fluid dynamics, and the influence of bottom topography on ocean flows. He develops property-preserving numerical schemes for conservation laws and shallow-water equations, with applications in geophysics and computational fluid dynamics. Education: PhD in Applied Mathematics (TU Dortmund University, 2022). Research Interests: Physical Oceanography: Investigating jet formation in stratified fluids and topographic effects on oceanic flows. Numerical Methods: Specializing in algebraic flux correction schemes, discontinuous Galerkin methods, and entropy-stable algorithms. Geophysical Modeling: Developing tools like FESTUNG for DG-based simulations in MATLAB/Octave. His publications emphasize stability, accuracy, and computational efficiency in simulating complex fluid systems. He collaborates on projects such as The Rough Ocean Research Group, advancing understanding of fluid dynamics in geophysical contexts. Affiliations: Section for Meteorology and Oceanography, University of Oslo Rough Ocean Research Group

• Earthquake Source Studies
• Strong Motion Seismology
• Seismic Wave Propagation
• +۴ مورد دیگر

Ralph Archuleta is a Professor of Earth Science at the University of California, Santa Barbara (UCSB), affiliated with the Earth Research Institute (ERI). His primary research focuses on Earthquake Source Studies and Strong Motion Seismology, emphasizing the dynamics of fault rupture, ground motion prediction, and seismic hazard assessment. He has advised numerous PhD students and collaborated with postdoctoral researchers, contributing to advancements in seismological modeling and field data analysis. Education: B.S., University of Wyoming, 1969 Ph.D., UC San Diego, 1976 Research Interests: Earthquake source mechanics and rupture dynamics Strong ground motion simulation and site effects Seismic wave propagation in heterogeneous media Development of numerical models for fault systems His work integrates field observations, laboratory experiments, and computational simulations to address seismic hazard challenges. Publications (Recent Trends): Archuleta’s recent work explores fault heterogeneity, nonlinear site response, and supershear earthquakes. Key themes include stochastic modeling of slip distributions, radiated seismic energy analysis, and improvements to ground motion prediction methods. Scientific Contributions: While no explicit awards are listed, his extensive publications and leadership in seismology reflect significant scholarly impact. He has mentored over 20 graduate students and supervised postdocs globally, advancing careers in academia and industry. Labs/Teams: Active in the ERI at UCSB, contributing to collaborative projects like the COSMOS Virtual Data Center for strong motion data dissemination.

Francesca Pariseمشاهده پروفایل

استادیار

• Multi-Agent Systems
• Network Theory
• Control Systems
• +۶ مورد دیگر

Francesca Parise is an Assistant Professor in the School of Electrical and Computer Engineering at Cornell University. Her research focuses on multi-agent systems, particularly analyzing large networks of autonomous decision-makers in transportation, finance, and social systems, employing tools from control theory, game theory, and optimization. She holds a PhD from ETH Zurich and was a Postdoctoral Research Fellow at MIT's Laboratory for Information and Decision Systems under Prof. Asuman Ozdaglar. Her work bridges theoretical foundations with practical applications in networked systems and interventions. Education: Bachelor's and Master's from University of Padua, Italy PhD in Control Engineering from ETH Zurich (2016) Her research interests span game-theoretic models for network analysis, learning dynamics in time-varying systems, and optimal intervention strategies. Key themes include synchronization in power grids, epidemic control via testing, and dynamic resource allocation. Recent work emphasizes graphon-based frameworks for large-scale network games and computational methods for equilibrium analysis. She explores both theoretical guarantees and real-world applications in transportation and energy systems. Publications highlight contributions to networked game theory, stochastic systems, and control applications, with a focus on scalable solutions for complex systems. Her work often intersects with interdisciplinary challenges in systems biology and socio-technical networks. She currently holds an office at Frank H. T. Rhodes Hall, Cornell University.

• Natural Language Processing
• Computational Linguistics
• Photonics Engineering
• +۳ مورد دیگر

Ali Mohammed Mohammed Al-Zawqari is a postdoctoral researcher at Vrije Universiteit Brussel (Brussels, Belgium). His research focuses on interdisciplinary areas combining natural language processing (NLP) with engineering applications, particularly in Arabic debate analysis and photonics design. He has collaborated internationally on projects involving machine learning models for argumentation mining and optimization algorithms for antenna systems. Key research interests include computational linguistics for Arabic discourse analysis, generative photonics modeling, and stochastic optimization in microwave engineering. His work bridges computational methods with real-world applications in both humanities and technical domains. Recent publications (2023-2025) highlight advancements in Arabic debate corpus development, neural argumentation classification, and automated antenna design optimization. He actively participates in academic conferences, presenting at events like ICALP 2023/2024 and the International Conference on Microelectronics (ICM 2024). Al-Zawqari holds an ORCID identifier (0000-0002-6649-7705) and has an h-index of 98 with over 500 citations. His research spans collaborations across multiple countries and institutions, reflecting a global approach to interdisciplinary challenges.