جست‌وجوی استاد و پژوهشگر

• Fluid-Structure Interaction
• Continuum Mechanics
• Numerical Analysis
• +۳ مورد دیگر

Matthias Heil is a Professor of Applied Mathematics at The University of Manchester, specializing in Fluid-Structure Interaction, Continuum Mechanics, and Numerical Analysis. His research focuses on fluid dynamics, solid mechanics, and computational methods, with contributions to the OOMPH-LIB software library. He is affiliated with the Continuum Mechanics and Numerical Analysis research groups, and his work aligns with UN Sustainable Development Goals through initiatives like Digital Futures and the Christabel Pankhurst Institute. Education details are available on his personal webpage. Research interests include fluid-structure interaction in physiological systems, elastic-walled channel flows, and microfluidic applications. His recent work explores sedimentation dynamics, wake instabilities, and multiphysics modeling. He has supervised 20 research works and contributed to projects like the Föppl–von Kármán equations for MEMS membranes. Labs/Teams: Continuum Mechanics Group, OOMPH-LIB developers, Fluid-Structure Interaction research cluster.

Vikram Gaviniمشاهده پروفایل

استاد

• Electronic Structure Calculations
• Multi-Scale Modeling
• High-Performance Computing
• +۴ مورد دیگر

Vikram Gavini is a tenured professor at the University of Michigan's College of Engineering, holding joint appointments in the Departments of Mechanical Engineering and Materials Science and Engineering. He leads the Computational Materials Physics Group, focusing on quantum-mechanical simulations and multi-scale modeling of materials. Ph.D., Mechanical Engineering, Caltech (2007) M.S., Applied Mechanics, Caltech (2004) B.Tech., Mechanical Engineering, IIT Madras (2003) His research spans electronic structure calculations at macroscopic scales, quantum transport in materials, and high-performance computing (HPC) for density functional theory (DFT). Key areas include: Multi-scale modeling of materials defects Time-dependent DFT algorithms Tensor-structured numerical methods Exchange-correlation functional development Recent publications highlight advancements in: Quasicrystal stability analysis Machine learning for DFT functionals Exponential propagators for time integration GPU-accelerated DFT frameworks Scientific accolades include: 2023 ACM Gordon Bell Prize NSF CAREER Award (2011) Humboldt Research Fellowship (2012) AFOSR Young Investigator (2013) Gallagher Young Investigator Award (2015) Institute Silver Medal (2003) Allan Acosta Fellowship (2003) His group has produced influential open-source software like DFT-FE and trained numerous students now at institutions including Intel, LANL, and Stanford University.

• Grid Integration of Renewable Energy
• Power Systems
• Smart Grids
• +۳ مورد دیگر

Professor Mithulan Nadarajah is a Professor at the School of Electrical Engineering and Computer Science, University of Queensland. He holds a Ph.D. from the University of Waterloo (Canada), and prior academic positions at the Asian Institute of Technology (Thailand) and the University of Peradeniya (Sri Lanka). His research focuses on Grid Integration of Renewable Energy (GIRE), with two main themes: Planning and Operational Planning of renewable energy integration. He has supervised 29 PhD students and 50 research masters, and secured over AUD 3.6 million in grants from ARC, Government of India, Indonesia, Malaysia, and industry partners. His work spans smart grids, power systems stability, and energy storage systems. He served as Director of HDR Studies (2019–2025) and coordinated regional energy initiatives at ASEAN and Asian countries. He is a Senior Member of IEEE and Editor for the American Institute of Mathematical Sciences journal. Education: Ph.D. (Waterloo, Canada), B.Sc. (Peradeniya, Sri Lanka), M.Eng. (AIT, Thailand) Research interests include renewable energy grid integration, power system stability, and distributed generation. Over 320 publications include 1 book, 8 book chapters, and impactful journal/conference papers. His international collaborations span ASEAN, Asia, and Europe. Current roles include leadership in HDR programs and energy policy research.

Ruggero Carliمشاهده پروفایل

دانشیار

• Control Systems
• Robotics
• Machine Learning
• +۶ مورد دیگر

Ruggero Carli is an Associate Professor at the Department of Information Engineering, University of Padova. His research focuses on control systems, robotics, and optimization, with emphasis on model-based reinforcement learning, distributed optimization algorithms, and energy systems. His work bridges theoretical advancements with real-world applications, including autonomous robotics, smart grids, and nonlinear control. Key contributions include physics-informed machine learning frameworks, ADMM-based distributed optimization methods, and MPC-driven control solutions for underactuated systems. Research interests include: Model-Based Reinforcement Learning for Robotics Nonlinear Model Predictive Control (NMPC) Distributed Optimization and ADMM Variants Energy Networks and Smart Grids Robot Dynamics and System Identification Recent publications emphasize: Continual learning for driver behavior analysis Physics-informed control for underactuated systems Robust optimization in unreliable networks Autonomous robotic manipulation with large language models His research integrates control theory with modern machine learning techniques, addressing challenges in edge computing, distributed systems, and real-time implementation.

Qiongkai Xuمشاهده پروفایل

مدرس

• Natural Language Processing
• Machine Learning
• Security
• +۳ مورد دیگر

Dr. Qiongkai Xu is a Lecturer in Natural Language Processing (NLP) at Macquarie University's School of Computing, with an honorary fellowship at the University of Melbourne. He holds a PhD in NLP from the Australian National University (ANU). His research focuses on auditing machine learning models, particularly addressing privacy/security issues in NLP/ML models and developing evaluation frameworks. Key areas include text watermarking, adversarial attacks, data leakage mitigation, and model authentication. Recent publications emphasize robustness against backdoor attacks, generative model watermarking, and healthcare applications of LLMs. His work bridges theoretical security advancements with practical NLP challenges. Awards: DAAD Scholarship (2022), Research Pitching Session Winner (MQ, 2022 & 2024) Grants: Leading the Climate Litigation Risk project on AI-driven greenwashing detection. Labs/Teams: Data Horizons Research Centre, Future Communications Research Centre, and Frontier AI Research Centre.

Cristian Rojasمشاهده پروفایل

استاد

• Automatic Control
• System Identification
• Control Theory
• +۷ مورد دیگر

Cristian Rojas is a Professor of Automatic Control at KTH Royal Institute of Technology, specializing in system identification. His research bridges control theory, statistics, and machine learning to develop data-driven methods for analyzing and controlling dynamical systems. He holds an MS in Electronics Engineering from Universidad Técnica Federico Santa María (Chile) and a PhD in Electrical Engineering from the University of Newcastle (Australia). Research focuses on efficient utilization of data for self-learning systems, including topics like continuous-time system identification, robust control, and statistical estimation. Notable contributions include work on subspace identification, input design for sparse systems, and algorithms for H-infinity norm estimation. His methodologies emphasize practical applications in industrial automation, smart infrastructure, and autonomous systems. Recent publications highlight advancements in inverse filtering, decentralized learning systems, and the theoretical underpinnings of data-driven control. He collaborates widely on projects involving Bayesian methods, adversarial systems, and privacy-protected decision-making frameworks. Rojas' work often addresses challenges such as undersampling effects, model consistency, and computational efficiency in real-world control scenarios. His academic contributions include organizing academic ceremonies at KTH and mentoring researchers in the Department of Automatic Control. Current research explores intersections between machine learning interpretability and control theory, with applications to explainable AI in engineering systems.

H. Metin Aktulgaمشاهده پروفایل

دانشیار

• High Performance Computing
• Parallel Computing
• Big Data Analytics
• +۳ مورد دیگر

H. Metin Aktulga is an Associate Professor in the Department of Computer Science and Engineering at Michigan State University's College of Engineering. His research focuses on high-performance computing, parallel algorithms, and numerical methods for large-scale scientific applications. He leads interdisciplinary projects involving collaborations with computational physicists and materials scientists to develop scalable software systems. His work includes the development of PuReMD, a reactive molecular dynamics code, and DOoC+LAF, a task-based middleware for data analytics. He explores parallel computing on emerging architectures, emphasizing energy efficiency and performance optimization. His research spans applications in molecular modeling, nuclear physics, and computational biology. Awards and grants are not explicitly listed, but his contributions are highlighted through collaborations with projects like MFDn (nuclear structure) and SHINES (electronic structure computations). He advises students in computational methods and leads efforts to automate force field optimization using machine learning and big data analytics. Labs and teams include the High-Performance Computing group at MSU, with active participation in interdisciplinary initiatives to bridge simulation and data-driven discovery in materials science and quantum systems.

Pamela Carreno-Medranoمشاهده پروفایل

مدرس

• Human-Robot Interaction
• Robot Learning
• Socially Assistive Robotics
• +۴ مورد دیگر

Dr. Pamela Carreno-Medrano is a Lecturer and Early Career Research Representative in the Department of Electrical and Computer Systems Engineering at Monash University. Her research focuses on Human-Robot Interaction (HRI), robot learning, and socially assistive robotics. She holds a PhD in Information & Communication Sciences (Université de Bretagne-Sud), Master's in Computer Science (École Nationale d’Ingénieurs de Brest), and a Bachelor's in Computer Systems Engineering (Universidad EAFIT). Her work emphasizes human-centered design for intelligent systems, including adaptive navigation algorithms, human-robot collaboration models, and affective computing applications. She leads projects on long-term human-robot interaction and has contributed to interdisciplinary studies on robot ethics and public space integration. Dr. Carreno-Medrano also serves as an Adjunct Lecturer at Universidad EAFIT and collaborates internationally on sustainable aging technologies through the ARC Training Centre for Optimal Ageing. Current research themes include aligning task representations between humans and robots, modeling non-goal-driven human behaviors, and socially aware navigation strategies. She actively supervises postgraduate students in HRI, offering projects on interactive robot learning and embodied AI systems.

Henk Keersمشاهده پروفایل

دانشیار

• Geophysics
• Seismology
• Ocean Acoustics
• +۳ مورد دیگر

Henk Keers is an Associate Professor at the Department of Geosciences of the University of Bergen (UiB). His research focuses on geophysical modeling, seismology, and ocean acoustics, with contributions to seismic wave analysis, teleseismic tomography, and educational innovations in sedimentology. He actively collaborates on projects such as SEDucate, promoting active learning in geoscience education. Key research themes include: Body wave modeling for regional seismology Acoustic inversion techniques for ocean turbulence Tomographic imaging of crustal structures Development of efficient numerical methods for geophysical problems Recent work includes presentations at events like the AdriaArray Workshop (2023) and Nordic Seismology Seminar (2022) , addressing topics such as elastic isotropic modeling and waveform inversion. His research has been funded by institutions including the Research Council of Norway.

• Underwater Acoustics
• Acoustic Remote Sensing
• Geoacoustic Modeling
• +۵ مورد دیگر

Hefeng Dong is a Professor in Acoustic Remote Sensing at the Department of Electronic Systems (IES) at NTNU since 2002. She leads the Acoustics Group and focuses on underwater acoustics, geoacoustic modeling, and marine environmental impact studies. Her research integrates signal processing, seismic inversion, and distributed sensing technologies. Education: BSc/MSc in Physics (Northeast Normal University, 1983/1986), PhD in Geoacoustics (Jilin University, 1994). She held positions at SINTEF Petroleum and conducted research visits at the University of Victoria (2008–2009) and University of Delaware (2014–2015). She is a member of IEEE and the Acoustical Society of America. Research interests span underwater acoustic communication, fiber-optic sensing for geophysics, and environmental monitoring. Recent work includes advancements in distributed acoustic sensing (DAS) for seabed monitoring and underwater localization using vector sensors. Her publications emphasize machine learning applications in geophysical data analysis and signal processing. Notable contributions include studies on shear wave velocity estimation via deep learning and the development of underwater communication protocols using fiber-optic cables. She has also explored the environmental impact of low-frequency sound on marine life, integrating acoustic measurements with ecological studies. Grants and collaborations include projects on quick clay monitoring and geophysical sensing technologies for energy applications. Her academic outreach includes hands-on acoustics training courses at NTNU and international conference participation.

• Optimization
• Computational Mathematics
• Machine Learning
• +۳ مورد دیگر

Dr. Maryam Yashtini is an Assistant Professor at Georgetown University's Department of Mathematics and Statistics (College of Arts and Sciences). She holds a Ph.D. in Applied Mathematics and dual M.S. degrees in Mathematics and Industrial & Systems Engineering from the University of Florida. Her research focuses on optimization, computational mathematics, and machine learning applications in medical imaging and computer vision. Notable contributions include algorithms for MRI image quality improvement and AI-generated image detection. Education: Ph.D., Applied Mathematics, University of Florida M.S., Industrial Engineering, University of Florida M.S., Applied Mathematics, University of Florida Research interests span optimization theory, image processing, and interdisciplinary applications in healthcare. Her work bridges theoretical mathematics with practical challenges in medical imaging, such as pneumonia detection via CNN models and texture-based AI image analysis. Over 20 peer-reviewed publications appear in top journals like the Journal of Global Optimization and SIAM Journal on Imaging Sciences. Scientific Honors: Best Paper Award, Journal of the Operations Research Society of China Best Presentation Award, International Congress of Mathematicians Her research has been featured in Georgetown College News. Current projects emphasize AI-driven medical diagnostics and algorithmic advancements in nonconvex optimization. She actively mentors students and promotes interdisciplinary collaboration to address real-world problems.

Douglas Allaireمشاهده پروفایل

دانشیار

• Multidisciplinary design optimization
• Bayesian optimization
• Multifidelity methods
• +۴ مورد دیگر

Douglas Allaire is an Associate Professor and Sallie and Don Davis '61 Faculty Fellow in the J. Mike Walker ’66 Department of Mechanical Engineering at Texas A&M University, part of the College of Engineering. He leads the Computational Design Laboratory, focusing on computational methods for complex engineered systems. His research spans multidisciplinary design optimization, Bayesian optimization, machine learning, and materials design. Education: Ph.D., Aerospace Engineering, Massachusetts Institute of Technology (2009) M.S., Aerospace Engineering, Massachusetts Institute of Technology (2006) B.S., Aerospace Engineering, Massachusetts Institute of Technology (2004) Research Interests: Bayesian optimization and uncertainty quantification Materials design using machine learning Autonomous experimentation and data fusion Predictive analytics for engineering systems Recent Trends in Publications: His work emphasizes integrating Bayesian methods with materials discovery, autonomous systems, and high-fidelity modeling. Key themes include optimizing multifidelity systems, inverse microstructure design, and real-time decision frameworks. Awards: ASEM Fellow (2024) AIAA Associate Fellow (2023) ASME Young Engineer Award (2018) Advising & Grants: Advised students like Jaylen James (Ph.D. 2022) and Danial Khatamsaz. Active in securing grants for computational design and materials research. Collaborates with institutions like the American Institute of Aeronautics and Astronautics. Labs & Teams: Directs the Computational Design Laboratory, part of the Engineering Systems Design Group. Engages in interdisciplinary projects with Texas A&M’s Multidisciplinary Engineering program.

Lorena Bociuمشاهده پروفایل

استاد

• Analysis and Control of Partial Differential Equations
• Nonlinear Wave Equations
• Fluid-Structure Interactions
• +۴ مورد دیگر

Lorena Bociu is a Professor and Associate Department Head for Academic Affairs in the Department of Mathematics at North Carolina State University's College of Sciences. She joined NC State as an Assistant Professor in 2011, was promoted to Associate Professor in 2017, and became a full Professor in 2022. She was appointed Associate Department Head in August 2024. Bociu was named an NC State University Faculty Scholar in 2016, recognizing her as a top early- and mid-career faculty member pursuing research to solve society's most pressing problems. Bociu's research focuses on Analysis and Control of Partial Differential Equations with applications in nonlinear wave equations, fluid-structure interactions, fluid-solid mixtures, and multiscale interface couplings of Partial and Ordinary Differential Equations. Her work has significant applications in understanding the development of glaucoma and other biomedical problems. She is a member of the Control, Optimization and Modeling research group, the Ordinary Differential Equations, Partial Differential Equations and Analysis faculty research group, and the Nonlinear Analysis Thematic Group. Bociu's recent publications demonstrate a strong focus on multiscale modeling, particularly in tissue perfusion, poroelasticity, and fluid-structure interactions. Her work combines rigorous mathematical analysis with practical applications in biomedical engineering and mechanics. She has developed novel approaches for coupling different types of differential equations to model complex biological systems. Presidential Early Career Award for Scientists and Engineers (PECASE) 2019 NC State University Faculty Scholar 2016-present NC State University Equity for Women Award (Faculty) 2016 Invited Member to Sigma Xi The Scientific Research Honor Society 2020 University of Virginia Excellence in Scholarship in Sciences and Engineering 2008 Bociu has secured significant research funding including an NSF DMS CAREER Award (2016-2021) for 'Control and Sensitivity Analysis for Fluid-Elasticity Interactions and Fluid-Solid Mixtures,' and serves as PI for an NSF DMS Collaborative Research project (2021-2024) on 'Analysis and Control in Multi-Scale Interface Coupling between Deformable Porous Media and Lumped Hydraulic Circuits.' She also serves as co-PI for the NSF Infrastructure MathCREW project focused on Mathematical Connectivity through Research and Equity for Women. Bociu is active in the academic community through the NCSU Differential Equations and Nonlinear Analysis Seminar and SEARCDE conferences.

Mette S. Olufsenمشاهده پروفایل

استاد

• Mathematical Biology
• Cardiovascular Physiology
• Inverse Problems
• +۵ مورد دیگر

Mette S. Olufsen is a Professor in the Department of Mathematics at North Carolina State University (NC State), affiliated with the College of Sciences. She leads the Cardiovascular Dynamics Research Group and directs the DRUMS REU Program. Her research focuses on mathematical biology, cardiovascular physiology, and inverse problems, with expertise in differential equations and fluid mechanics applied to biomedical systems. Olufsen holds a PhD in Applied and Industrial Mathematics from Roskilde University (1998) and an MS in Mathematics and Computer Science from the same institution. Her work integrates computational models with experimental data to study cardiovascular, respiratory, and inflammatory systems. Notable projects include modeling pulmonary hypertension, autonomic cardiovascular control during orthostatic stress, and parameter optimization in single-ventricle patient models. She collaborates extensively on interdisciplinary projects, publishing in journals like Mathematical Biosciences and Biomechanics and Modeling in Mechanobiology . Her research groups span Control, Optimization, and Modeling; Mathematical Biology; and Numerical Analysis. She also contributes to educational initiatives through the REU program, fostering undergraduate research in biomathematics. Olufsen’s labs and teams focus on translating mathematical models into clinical applications, particularly in understanding cardiovascular dynamics under stress and disease. Her work bridges applied mathematics with biomedical engineering, emphasizing practical clinical relevance and parameter identifiability in complex biological systems.

Augusto Gerolinمشاهده پروفایل

استادیار

• Optimal Transport Theory
• Mathematical Physics
• Theoretical and Computational Chemistry
• +۴ مورد دیگر

Augusto Gerolin is an Assistant Professor jointly appointed in the Departments of Mathematics and Statistics and Chemistry and Biomolecular Sciences at the University of Ottawa. He holds a Tier II Canada Research Chair in Artificial Intelligence at the Interface of Chemistry and Mathematics. His research focuses on Optimal Transport Theory, Mathematical Physics, Theoretical and Computational Chemistry, and Machine Learning. Gerolin’s work bridges quantum chemistry, mathematical analysis, and computational methods, with applications in Density Functional Theory and quantum information science. He obtained his PhD from the University of Pisa and was a Marie Skłodowska-Curie fellow at Vrije Universiteit Amsterdam. He is a member of the European Laboratory for Learning and Intelligent Systems (ELLIS) and leads a research group exploring AI-driven solutions in chemistry and mathematics. His current projects include developing optimal transport frameworks for quantum systems, advancing machine learning algorithms in scientific computing, and fostering collaborations across disciplines through initiatives like the OQMG Network. Gerolin’s research has led to advancements in multi-marginal optimal transport, entropy-regularized methods, and the strong-interaction limit of density functional theory. His group actively collaborates with institutions worldwide, including the Fields Institute, IPAM, and the University of Genoa. He has supervised numerous PhD, Master’s, and undergraduate students, many of whom have contributed to cutting-edge studies in computational chemistry, quantum algorithms, and mathematical analysis. Key awards include the Canada Research Chair designation, and his work has been supported by grants from NSERC, MITACS, and the University of Ottawa. Gerolin is also committed to diversity in science, endorsing principles such as the Diversity Axioms, and advocates for academic solidarity with researchers affected by global conflicts.