جست‌وجوی استاد و پژوهشگر

Carmen Guerra-Garciaمشاهده پروفایل

دانشیار

• Aerospace Engineering
• Low-Temperature Plasma Technologies
• Gas Discharge Physics
• +۶ مورد دیگر

Carmen Guerra-Garcia is the Charles Stark Draper Associate Professor of Aeronautics and Astronautics at MIT. She leads the Aerospace Plasma Group, focusing on the intersection of aerospace engineering, low-temperature plasma technologies, and gas discharge physics. Massachusetts Institute of Technology (PhD, SM) 2007-2014 Polytechnic University of Madrid (Ingeniero Aeronautico) 2007 Her research spans aircraft safety (lightning interaction, wind turbines, and charge control systems), plasma-assisted combustion , and space propulsion . She combines multi-physics modeling , computation , and experimentation to address challenges in carbon-free energy and aerospace electrification. Key trends in her recent work include plasma-assisted ignition of sustainable fuels , CO2 conversion for Mars ISRU , and dynamic interactions between plasmas and flames . Her articles highlight advancements in nanosecond pulsed discharges , self-pulsating streamers , and lightning risk mitigation . NSF CAREER Award (2024) Junior Bose Award for Excellence in Teaching (2024) Office of Naval Research Young Investigator Award (2021) International Fulbright Science and Technology Award (2009) First National Award in Aeronautical Engineering, Spanish Ministry of Education (2007) Guerra-Garcia’s grants include NSF CAREER funding for plasma-assisted combustion and ONR support for aerospace safety. She advises PhD students and collaborates with the Space Propulsion Lab and APS/AIAA technical committees.

Paul Kotyczkaمشاهده پروفایل

استاد

• Control Engineering
• Nonlinear Control
• Port-Hamiltonian Systems
• +۸ مورد دیگر

Paul Kotyczka is a Professor at the Technical University of Munich (TUM) in the School of Engineering and Design , Department of Automatic Control Engineering. He leads the Energy-based Modeling and Control Working Group and focuses on modeling, geometric discretization, and control of multi-physical systems, with expertise in nonlinear and passivity-based control. His work spans applications in robotics, mechatronics, and process engineering. Education : Dipl.-Ing. in Electrical Engineering (TUM, 2005), Dr.-Ing. (TUM, 2010), Habilitation (Dr.-Ing. habil., TUM, 2019). Research : Core areas include port-Hamiltonian systems, predictive control of active chassis, structural mechanics modeling, and numerical methods for control. His projects address autonomous driving, distributed parametric systems, and passivity-based control of switching nonlinear systems. Awards : Held a Marie Sklodowska-Curie Fellowship (2015–2017). Grants : Leads DFG projects (e.g., HermInE, INFIDHEM), MSCA-IF, and Franco-German Doctoral Program on port-Hamiltonian systems. Labs : Head of the Energy-based Modeling and Control group at TUM, collaborating internationally (e.g., IIT Bombay, Grenoble INP, LCIS France).

Cody Scarboroughمشاهده پروفایل

استادیار

• Electromagnetics
• Nonlinear Electromagnetics
• Metamaterials
• +۴ مورد دیگر

Cody Scarborough is an Assistant Professor in the Department of Electrical, Computer, and Energy Engineering at the University of Colorado Boulder. He earned his B.S. in Electrical and Computer Engineering from the University of Texas at Austin (2017) and his Ph.D. from the University of Michigan (2022). His research focuses on space-time modulated electromagnetic metamaterials, non-linear electromagnetics, and reconfigurable intelligent surfaces. He leads the Electromagnetic Metamaterials Research Group (EMRG), advancing next-generation communication systems and energy-harvesting technologies. Scarborough has pioneered novel boundary conditions like the interpath relation, reducing computational demands for analyzing space-time periodic structures. His work has earned best student paper awards at EuCAP 2021 and Metamaterials 2021. He teaches courses such as Electromagnetic Fields I and Electromagnetic Metamaterials, mentoring over 100 students. His research group explores parametric amplification, frequency conversion, and bandwidth enhancement through active metamaterial control. Education: B.S. UT Austin (2017), Ph.D. University of Michigan (2022) Labs/Teams: Electromagnetic Metamaterials Research Group (EMRG) Key Contributions: Space-time modulated antennas, N-path network analysis, interpath relation theory He holds provisional patents on parametric time-modulated antennas and efficient computational methods for metamaterial design. Beyond academia, he enjoys music, hiking, and skiing in Boulder's Flatirons.

• Structural Health Monitoring (SHM)
• Machine Learning
• Pattern Recognition
• +۵ مورد دیگر

Professor Nikolaos Dervilis is a faculty member in the Department of Mechanical Engineering at the University of Sheffield, serving as Director of Research and Innovation for the School of Mechanical, Aerospace and Civil Engineering. He holds a BSc from the National and Kapodistrian University of Athens, an MSc in Sustainable and Renewable Energy Systems from the University of Edinburgh, and a PhD from the University of Sheffield in Mechanical Engineering with a focus on machine learning for Structural Health Monitoring (SHM). His research emphasizes SHM, renewable energy systems (particularly wind turbines), data analysis, nonlinear dynamics, and advanced signal processing. His work spans population-based SHM (PBSHM), machine learning applications in structural dynamics, and probabilistic modeling. Recent publications focus on active learning frameworks, Bayesian methods, and generative models for damage prognosis. He collaborates with industry on wind energy and has contributed to datasets for experimental bridges and aerospace components. Notably, he leads efforts in transfer learning and domain adaptation for heterogeneous structural populations. Research highlights include developing frameworks for risk-informed decision support, model selection via approximate Bayesian computation, and digital twin tools for engineering systems. His lab, part of the Dynamics Research Group, addresses challenges in energy systems, composite materials, and condition monitoring of critical infrastructure.

Adi Kurniawanمشاهده پروفایل

پژوهشگر ارشد

• Wave Energy Conversion
• Wave-Structure Interactions
• Multi-Objective Optimization
• +۳ مورد دیگر

Adi Kurniawan is a Research Fellow at the University of Western Australia (UWA) in the School of Earth and Oceans, affiliated with the Marine Energy Research Australia (MERA) and the Great Southern Marine Research Facility (GSMRF). His research focuses on wave energy conversion, wave-structure interactions, and multi-objective optimization. He holds a PhD in Marine Technology from NTNU and has held roles at Aalborg University and the University of Plymouth. Kurniawan co-authored Ocean Waves and Oscillating Systems and contributes to industry standards (Standards Australia Committee EL-066) and journal editing (Journal of Offshore Mechanics and Arctic Engineering). Research Interests: Wave energy converter (WEC) design and optimization Nonlinear wave dynamics and numerical modeling Multi-objective optimization of wave farms Parametric resonance mitigation in WECs Teaching: Previously taught OCEN4007 Renewable Ocean Energy. Active in the Oceans Graduate School, covering oceanography, hydrodynamics, and marine geoscience. Collaborations: Works with industry on sponsored projects, including wave energy device modeling and power prediction. Part of the UN SDGs contributing to sustainable energy solutions (SDG 7, 13, 14). Grants: Lead investigator on projects like 'Advancing ocean renewable energy systems through physics and machine learning' and 'WaveX Albany', totaling over $2M in funding. Projects emphasize scalability, cost reduction, and environmental impact assessments. Labs/Teams: Based at the GSMRF in Albany, collaborating with international partners on WEC testing and deployment strategies.

Silke Glasمشاهده پروفایل

پژوهشگر ارشد

• Reduced basis method
• Variational inequalities
• Wave equations
• +۳ مورد دیگر

Silke Glas is a Postdoctoral Researcher at the Institute of Numerical Mathematics, Ulm University, a position she has held since July 2018. Previously, she served as a Research Assistant at the same institute from April 2016 to July 2018 and at the Chair of Energy Trading and Finance, University of Duisburg-Essen (2012-2016), funded by the German Research Foundation's Priority Programme 1324. Her research visits include the Institut Henri-Poincaré in Paris and SISSA in Trieste. Her educational background features: Diploma in Mathematics and Economics from Ulm University (2006-2012), thesis on "Reduced Basis Method for Variational Inequalities" Master of Mathematics from the University of South Florida (2009-2010) Dr. Glas specializes in model reduction for nonlinear problems, with core expertise in reduced basis methods applied to variational inequalities, wave equations, Hamilton-Jacobi-Bellman equations, and space-time formulations. Her work bridges theoretical numerical analysis with practical applications in energy markets, particularly intraday electricity trading. She has developed novel approaches for noncoercive and parabolic systems, addressing challenges in error estimation and computational efficiency. Her publication trajectory reveals evolving sophistication in handling time-dependent nonlinear systems, with increasing emphasis on financial applications. Early work focused on theoretical foundations of variational inequalities, while recent publications integrate model reduction with optimal control for energy trading problems, demonstrating cross-disciplinary impact. Scientific recognition includes: No formal awards documented in source material Research funding has been secured through the German Research Foundation's Priority Programme 1324. No student advising activities are mentioned, though her collaborative work involves prominent researchers like K. Urban and Anthony T. Patera. Her primary research environment at Ulm University's Institute of Numerical Mathematics supports her focus on computational mathematics and real-world applications.

Carlo Angiuliمشاهده پروفایل

استادیار

• Type Theory
• Programming Language Foundations
• Homotopy Type Theory
• +۳ مورد دیگر

Carlo Angiuli is an Assistant Professor of Computer Science at the Luddy School of Indiana University . He is a leading researcher in type theory , with a focus on its applications to programming language design and logic . His work bridges theoretical foundations and practical implementations, particularly through dependent types , proof assistants , and homotopy type theory . His research interests include: Type Theory (foundational systems for programs and proofs) Programming Language Foundations (formal semantics and reasoning) Homotopy Type Theory (higher-dimensional structures) Dependent Types (expressive type systems) Proof Assistants (formal verification tools) Computational Logic (algorithmic reasoning) Angiuli actively contributes to the programming languages community as a POPL Program Committee member and co-author of a forthcoming textbook on dependent type theory. His students include Johnson He, Huang Xu, Kelton OBrien, and Ian Ray , who joined in Fall 2025. He has received significant recognition, including the Best Paper Award at FSCD 2019 and the School of Computer Science Distinguished Dissertation Award from Carnegie Mellon University. His current work explores advanced type-theoretic frameworks and their implementation in proof assistants like the red* family of tools. Scientific awards: Best Paper Award, FSCD 2019 (Junior Researchers category) School of Computer Science Distinguished Dissertation Award, Carnegie Mellon University He teaches courses such as Modern Dependent Types (CSCI-B619), Programming Language Foundations (CSCI-B522), and Introduction to Computer Science (CSCI-C211).

Niels Aageمشاهده پروفایل

دانشیار

• Topology Optimization
• Acoustics and Vibroacoustics
• Biomechanics
• +۵ مورد دیگر

Niels Aage is an Associate Professor in the Department of Civil and Mechanical Engineering at the Technical University of Denmark (DTU). His research focuses on topology optimization, biomechanics, and multiphysics modeling, with applications in acoustic devices, biomedical implants, and microelectromechanical systems (MEMS). He holds roles such as Vice President of the International Society for Structural and Multidisciplinary Optimization (2023–2027). Education and Professional Background: Conducted a 5-month research visit at the University of Colorado, Boulder (USA) in 2010. Specializes in giga-scale numerical modeling, finite element methods, and topology optimization algorithms. Research Interests: Develops novel methods for topology optimization of fluidic, thermal, and acoustic systems. Explores applications in patient-specific spinal implants, metamaterials with vibroacoustic bandgaps, and nonlinear dynamic substructuring. His work integrates machine learning and reduced-order modeling for efficient simulation. Publications: Over 100 peer-reviewed articles, including recent contributions on connectivity promotion in topology optimization (2025), vibroacoustic metamaterial design (2025), and anatomically conforming spinal fusion cages (2024). Research emphasizes high-resolution modeling and bridging computational design with additive manufacturing. Scientific Awards: ISSMO Haftka Young Investigator Award (2021), Equinor Prize 2020, and Hyperion Innovation Excellence Award (2017). Recognized for contributions to structural optimization and computational mechanics. Advising and Grants: Supervises multiple PhD projects, including work on vibroacoustic shape optimization, quantum-opto-mechanical systems, and smart hearing aid modeling. Engages in collaborative projects funded by industry and academia. Labs/Teams: Collaborates with DTU’s Solid Mechanics group and industry partners on projects involving topology optimization, multiphysics simulation, and biomedical engineering. Active in international conferences and serves on editorial boards.

Simone Brugiapagliaمشاهده پروفایل

دانشیار

• Mathematics of Data Science
• Computational Mathematics
• Numerical Analysis
• +۵ مورد دیگر

Simone Brugiapaglia is an Associate Professor in the Department of Mathematics and Statistics at Concordia University in Montréal, Canada. His academic journey includes a PhD in Mathematical Models and Methods from Polytechnic University of Milan (2016), an MSc in Mathematics from University of Pisa (2012), and a BSc in Mathematics from University of Pisa (2010), all earned cum laude . Prior to his current role, he held postdoctoral positions at École polytechnique fédérale de Lausanne (2016) and Simon Fraser University (2016-2019). Dr. Brugiapaglia's research bridges mathematics, data science, and computational methods. Key interests include: Foundations of deep learning and neural networks Compressed sensing and sparse recovery algorithms High-dimensional approximation theory Numerical methods for PDEs and diffusion equations Physics-informed machine learning Optimization techniques for large-scale problems His work develops rigorous mathematical frameworks for data-driven algorithms. His publications (30+ including two books) consistently focus on high-dimensional computation , featuring recent advances in neural network theory (e.g., generalization bounds, rank collapse), compressed sensing techniques (e.g., greedy algorithms, unrolled networks), and physics-informed learning. A strong trend involves combining traditional numerical methods with deep learning for PDE solutions. Awards & Fellowships: Concordia Research Fellow (2023) Leslie Fox Prize for Numerical Analysis (2nd place, 2019) PIMS Postdoctoral Fellowship (2016-2018) Multiple INdAM scholarships during graduate/undergraduate studies He has supervised over 20 trainees across postdoctoral, graduate, and undergraduate levels. While specific grants aren't detailed, his fellowship history indicates sustained research funding. No explicit research labs or teams are mentioned, but his supervision record and collaborative publications suggest active leadership in research groups focused on computational mathematics.

• Autonomous Ships
• Dynamic Positioning Systems
• Marine Control Engineering
• +۵ مورد دیگر

Roger Skjetne is a Professor in Marine Control Engineering at the Norwegian University of Science and Technology (NTNU), affiliated with the Department of Marine Technology under the Faculty of Engineering. His research focuses on autonomous ships, dynamic positioning systems, energy and power management for hybrid-electric vessels, Arctic stationkeeping, and ice management systems. He leads the SFI Autoship research center, which aims to advance autonomous ship technology for safe and sustainable operations in Arctic and maritime environments. Key projects include DigitalSeaIce (multiscale sea ice observation), ENDURE (safety solutions for autonomous ships), and ZEVS (zero-emission passenger vessel systems). His work integrates control systems, robotics, and environmental monitoring to address challenges in marine autonomy and energy efficiency. Skjetne’s research has produced over 150 publications since 2020, with a focus on hybrid control barrier functions, energy optimization, and autonomous navigation. He collaborates with industry partners to translate academic findings into practical maritime solutions.

• Time Series Analysis
• Statistics
• Regression Modeling
• +۳ مورد دیگر

Rongning Wu is an Associate Professor at the Paul H. Chook Department of Information Systems and Statistics within the Zicklin School of Business at Baruch College, City University of New York . Holding a Ph.D. and M.S. in Statistics from Colorado State University and a B.S. in Applied Mathematics from Southeast University, Dr. Wu specializes in advanced statistical modeling with particular focus on time series analysis and count data models. Education : Ph.D./M.S. in Statistics (Colorado State University), B.S. in Applied Mathematics (Southeast University) Current Roles : Associate Professor, Committee Chair (Statistics Faculty Recruiting), Co-organizer (Department Research Seminar Series) Dr. Wu's research explores robust statistical methodologies for complex time series data, including Least Absolute Deviation estimation , single-index models with time series errors , negative binomial models for count series , and tail-trimmed absolute deviation techniques . His work addresses challenges in parameter estimation, variance modeling, and structural change detection for both finite and infinite variance processes. Recent publications highlight advancements in change-point estimation for count time series , conditional maximum likelihood for INGARCH models , and semiparametric approaches for discrete-valued processes . Dr. Wu's methodological contributions find applications in financial data analysis and public health research, particularly in modeling HIV transmission dynamics related to incarceration. Scientific Honors : Honored Faculty, Baruch College (2009-2011) IMS Travel Award (2009) James L. Madison Memorial Award (2004) Franklin A. Graybill Award (2003) As an active researcher, Dr. Wu has secured multiple PSC-CUNY grants (2008-2022) totaling over $52,000 for projects spanning time series estimation, regression modeling, and empirical likelihood methods. He has served on various academic committees including the ZSB Graduate Curriculum Committee and the Statistics Faculty Recruiting Committee, while maintaining editorial reviewing roles for 15+ international journals.

• Nonlinear Dynamics
• Vibrations of Thin Structures
• Model Order Reduction
• +۷ مورد دیگر

Cyril Touzé is a Professor at ENSTA Paris, affiliated with the Fluid Dynamics and Acoustics team within the Mechanics Unit (UME). His research focuses on nonlinear dynamics of mechanical systems, particularly geometric nonlinear vibrations and invariant manifold theory. Expertise in model reduction methods Specializes in wave turbulence transition Develops passive control techniques using nonlinear dampers Applications in acoustic black hole effect and MEMS structures His recent work explores invariant manifold parametrization for nonlinear oscillators, vibro-impact vibration absorbers, and computational methods for geometrically nonlinear systems. He serves as a Topical Associate Editor for Nonlinear Dynamics and collaborates on experimental validations in structural acoustics. Key contributions include: Advanced reduced-order modeling techniques Wave turbulence analysis in thin structures Innovative vibration control strategies Nonlinear coupling mechanisms in musical acoustics Applications to MEMS and aerospace systems

• Numerical Analysis
• Applied Mathematics
• Scientific Computing
• +۴ مورد دیگر

Virginie Ehrlacher is a Professor at CERMICS, École des Ponts ParisTech (ENPC), France. She specializes in applied mathematics with a focus on high-dimensional problems, numerical analysis, and computational modeling. Her work bridges quantum chemistry, materials science, and machine learning through innovative mathematical frameworks. Education includes: PhD in Mathematics (2012) from ENPC: Mathematical models in quantum chemistry and uncertainty quantification Habilitation (2020) from Université Paris-Dauphine: Mathematical and numerical analysis of high-dimensional and multiscale problems in materials science Research spans multiscale modeling, tensor decompositions for high-dimensional systems, cross-diffusion equations, and scientific machine learning. Her work frequently addresses challenges in quantum mechanics, materials science, and computational physics using advanced numerical techniques. Publications emphasize: Algorithms for high-dimensional PDEs and eigenvalue problems Model reduction techniques (tensor networks, reduced basis methods) Cross-diffusion systems with biological/physical applications Neural networks for scientific computing Awards and distinctions: Irène Joliot-Curie Prize (2023) Chevalier de l’Ordre National du Mérite (2025) Leadership includes: ERC Starting Grant HighLEAP (2023–2028) ERC Synergy project EMC2 (2020–2026) ANR JCJC project COMODO (2019–2023) She co-leads the EMS Topical Activity Group on Scientific Machine Learning. Affiliated with the CERMICS laboratory, she collaborates on interdisciplinary teams tackling multiscale and data-driven modeling challenges.

Anouar Soufianiمشاهده پروفایل

پژوهشگر

• Radiative Heat Transfer
• Computational Fluid Dynamics
• Plasma Physics
• +۳ مورد دیگر

Anouar Soufiani is an active researcher specializing in radiative heat transfer, computational fluid dynamics, and plasma physics. His work focuses on complex thermal systems involving coupled radiation-convection phenomena, molecular gas radiation, and high-temperature spectroscopy. Research Interests: Soufiani's investigations span: Fundamental studies of Rayleigh–Bénard convection under radiation effects High-temperature gas radiative properties (CO 2 , argon plasmas) Development of numerical models for turbulent participating media Experimental spectroscopy at extreme temperatures (up to 6000K) Applications in aerospace thermal protection and geological systems Publication Trends: His recent articles (2024-2025) predominantly explore coupled heat transfer mechanisms in confined geometries, with frequent examination of: Radiation-convection interactions in cubical cavities Validation of reduced-order models against experimental data Molecular radiation effects in plasma environments Advanced measurement techniques for radiative properties This consistent focus demonstrates expertise in multiscale thermal transport modeling.

• Audio Signal Processing
• Acoustic Signal Processing
• Hearing Assistive Technologies
• +۳ مورد دیگر

Ryan Corey (he/him/his) is an Assistant Professor at the University of Illinois Chicago in the Department of Electrical and Computer Engineering, College of Engineering. His research focuses on audio and acoustic signal processing for human listening technologies such as hearing aids and augmented reality systems. Education: MS in Electrical and Computer Engineering from University of Illinois at Urbana-Champaign (2014), BSE in Electrical Engineering from Princeton University (2012) Teaching: Co-instructor for TE 401: Develop Breakthrough Projects, ranked as excellent by students multiple times Corey's research explores signal processing algorithms that combine audio signals from distributed microphone arrays to isolate desired sounds from background noise. His work has potential applications in hearing devices that can enhance specific speaker voices in crowded environments. He also investigates innovative prototyping approaches using unconventional microphone placements on wearable devices. His recent publications focus on advanced beamforming techniques, adaptive filtering methods, and sensor network applications in audio processing. These include topics such as random projections in beamforming, neural adaptive filters, and sound field interpolation using acoustic sensor networks. Scientific Honors: Best Student Paper Award at WASPAA 2019 Intelligence Community Postdoctoral Fellowship (2019) Microsoft Research Dissertation Grant (2018) National Science Foundation Graduate Research Fellowship (2013) Corey leads the Listening Technology Lab and collaborates with Professor Andrew Singer in the Augmented Listening Laboratory. He has mentored students in developing innovative listening technologies, including prototypes for hearing aids with improved directional audio capabilities and devices that modify sound environments for better hearing experiences.