جست‌وجوی استاد و پژوهشگر

Karim Sabraمشاهده پروفایل

استاد

• Acoustics
• Structural Health Monitoring
• Biomechanics
• +۷ مورد دیگر

Dr. Karim Sabra is a Professor at the George W. Woodruff School of Mechanical Engineering , Georgia Institute of Technology, specializing in Acoustics and Dynamics . He holds a Ph.D. from the University of Michigan (2003) and joined Georgia Tech in 2007 as an Assistant Professor. His research integrates theoretical and experimental approaches to study wave propagation in diverse fields including structural health monitoring, biomechanics, and ocean acoustics. Key areas of focus include passive imaging techniques using ambient noise and diffuse wave fields, with applications in non-invasive monitoring of mechanical systems and seismoacoustic environments. Education: Ph.D., University of Michigan, 2003 M.S., University of Michigan, 2000 M.Sc., École Nationale Supérieure de Techniques Avancées (France), 2000 Research Interests: Dr. Sabra’s work spans acoustics, structural health monitoring, biomechanical systems evaluation, underwater acoustics, and geophysics . Recent projects include developing passive elastography techniques for soft tissues using physiological vibrations and exploring ambient noise-based tomography for ocean environments. His interdisciplinary approach bridges multi-scale engineering challenges with multi-wave tools (acoustical, electrical, optical). Publications: His work focuses on advanced acoustic technologies, including underwater communication systems, passive acoustic identification tags, and ray-based tomography methods. Themes include seamount effects on sound propagation, machine learning for acoustic modeling, and environmental sensing using shipping noise. Awards: R. Bruce Lindsay Award (2011) Fellow of the Acoustical Society of America (2007) Institute of Acoustics A.B. Wood Medal (2009) Advising & Grants: Dr. Sabra mentors graduate students in acoustics and wave phenomena, emphasizing interdisciplinary collaboration. His research is supported by grants focused on underwater acoustics, environmental sensing, and biomedical applications. Labs/Teams: His research group develops novel sensors and algorithms for oceanographic and biomedical applications, collaborating with industry and academic partners.

Stefano Markidisمشاهده پروفایل

استاد

• High-Performance Computing
• Quantum Computing
• Computational Fluid Dynamics
• +۵ مورد دیگر

Stefano Markidis is a Professor of Computer Science at KTH Royal Institute of Technology, affiliated with the School of Electrical Engineering and Computer Science and the Digital Futures Faculty. He holds a Ph.D. from the University of Illinois at Urbana-Champaign and an MS from Politecnico di Torino. His research focuses on high-performance computing systems, including supercomputers and quantum computers, with expertise in plasma simulations, quantum algorithms, and scalable computational frameworks. Markidis leads the development of the Neko framework for high-fidelity computational fluid dynamics and the iPIC3D particle-in-cell code for plasma physics. He teaches courses such as Quantum Computing for Computer Scientists, High-Performance Computing, and Applied GPU Programming. His work addresses exascale computing challenges, including optimizing algorithms for GPUs, quantum systems, and distributed architectures. Key research interests include: Parallel Programming Models and HPC Frameworks Quantum Computing Applications in Scientific Simulations Physics-Informed Machine Learning Exascale System Optimization Turbulence Modeling and Plasma Dynamics His publications span over 100 articles in journals like Journal of Computational Physics and Scientific Reports , focusing on topics such as scalable CFD, quantum neural networks, and plasma simulation techniques. He has advised numerous students in these areas. Markidis collaborates with institutions like Los Alamos National Laboratory and RISE Research Institutes of Sweden through the Digital Futures initiative, aiming to solve societal challenges via digital technologies.

Simon Dixonمشاهده پروفایل

استاد

• Music Information Retrieval
• Artificial Intelligence
• Music Signal Processing
• +۳ مورد دیگر

Simon Dixon is a Professor of Computer Science and Director of the UKRI Centre for Doctoral Training in Artificial Intelligence and Music (AIM CDT) at Queen Mary University of London. He also serves as Deputy Director of the Centre for Digital Music (C4DM). His work focuses on music informatics, AI, and computational musicology, with emphasis on music signal analysis, performance modeling, and MIR applications. He leads projects funded by UKRI, Innovate UK, and industry partners like Yamaha and Spotify. Dixon has supervised over 20 PhD students and contributed to major initiatives like the Jazz Digital Archives Project and the Dig that Lick study on jazz melodic patterns. His research has been recognized with awards including the Peter Claricoats Award and Turing Fellowship. Education: PhD in Computer Science, BSc(Hons) in related field, with music qualifications (AMusA LMusA) Roles: AIM CDT Director, C4DM Deputy Director, EU H2020 MIP-Frontiers PI Research: Music transcription, expressive performance analysis, MIR, and AI applications in music education Key projects include industry collaborations (e.g., Yamaha for jazz piano modeling), semantic audio analysis, and large-scale music corpus studies. His team has pioneered methods in chord detection, source separation, and alignment algorithms, with top rankings in MIREX evaluations. Publications span journals like TISMIR and ICASSP, with a focus on foundational MIR techniques and AI-driven music systems. His work bridges technical innovation with cultural heritage through projects like JazzDAP and the Dig that Lick analysis of jazz solos.

James Unwinمشاهده پروفایل

دانشیار

• Theoretical Particle Physics
• Astrophysics
• Cosmology
• +۵ مورد دیگر

James Unwin is an Associate Professor in the Department of Physics at the University of Illinois Chicago (UIC), affiliated with the College of Liberal Arts and Sciences. He holds a DPhil in Physics from the University of Oxford (2013) and has held postdoctoral positions at the University of Notre Dame. His research focuses on theoretical particle physics, astrophysics, and cosmology, particularly exploring physics beyond the Standard Model, dark matter models, and interdisciplinary applied mathematics. Current interests include dark matter interactions, primordial black holes, and novel experimental approaches like Coulomb explosion imaging. Research Interests: Dark Matter Models: Including freeze-in mechanisms, annihilation signatures, and cosmological constraints Particle Astrophysics: Supersymmetry, LHC searches, and Grand Unified Theories Interdisciplinary Work: Applications of mathematics to epidemiology (e.g., COVID-19 forecasts via stock market indicators) and social dynamics Recent publications emphasize ultrafast molecular dynamics, XUV spectroscopy, and cosmological impacts of primordial black holes. He has advised PhD students Prolay Chanda and Qingyun Wang, both advanced to candidacy in 2021. Professional activities include visiting roles at UC Berkeley (2022–2023) and a Distinguished Academic Visitor appointment at Queen’s College, Oxford (2023). Affiliations: UIC Department of Physics Adjunct roles at UC Berkeley and University of Oxford Collaborations with institutions like Fermilab and CERN

Diego Donzisمشاهده پروفایل

استاد

• Fluid Dynamics
• Turbulence
• Turbulent Mixing
• +۳ مورد دیگر

Diego Donzis is a Professor in the Department of Aerospace Engineering at Texas A&M University, affiliated with the College of Engineering. He holds the Presidential Impact Fellow title. His work focuses on high-performance computing for fluid dynamics, particularly compressible turbulence, turbulent mixing, and shock-turbulence interactions. Donzis earned his Ph.D. and M.S. in Aerospace Engineering from the Georgia Institute of Technology. Research interests include large-scale simulations of turbulent flows, thermal boundary condition effects on turbulence, and the development of advanced numerical methods like Selected-Eddy Simulations (SES) for extreme-scale computing. His studies explore universality in turbulence scaling, energy spectra dynamics, and the interplay between compressibility and fluid mixing. Publications emphasize turbulence decay laws, shock-turbulence interactions, and the role of thermal non-equilibrium in turbulent flows. Notable contributions include advancing asynchronous algorithms for exascale CFD and analyzing density gradient statistics in compressible turbulence. Awards include the Presidential Impact Fellow distinction. Donzis collaborates on grants such as the Frontera Travel Grant for compressible turbulence research. His work bridges computational methods with fundamental fluid dynamics, addressing challenges in both numerical accuracy and physical modeling.

Daisuke Nagaiمشاهده پروفایل

استاد

• Astrophysics
• Cosmology
• Galaxy Formation
• +۴ مورد دیگر

Daisuke Nagai is a Professor of Physics and Astronomy at Yale University and the Director of Graduate Studies in the Yale Physics Department. His research focuses on theoretical and computational cosmology, including dark matter, dark energy, galaxy clusters, and data science. He holds a Ph.D. from the University of Chicago (2005) and a B.S. from the University of Michigan (1999). Before joining Yale in 2008, he was a Sherman Fairchild Postdoctoral Scholar at Caltech. Positions: Professor (2022–present), Director of Graduate Studies (2022–2025), Co-Director Yale Center for Research Computing (2015–2019). Awards: Stephen Murray Lectureship (2018), Cottrell Scholar (2012), IUPAP Young Scientist Prize (2011). His work uses high-resolution cosmological simulations to study galaxy cluster formation, X-ray observations, and the interplay between dark matter and baryonic processes. He also leads efforts in computational astrophysics and data-driven methods for cosmological analysis.

• Robotics
• Autonomous Systems
• Unmanned Aerial Vehicles (UAVs)
• +۹ مورد دیگر

Nabil Aouf is a Professor of Robotics and Autonomous Systems in the Department of Electrical and Electronic Engineering at City, University of London, a position he has held since January 2019. Previously, from 2006 to 2018, he was Professor of Autonomous Systems at Cranfield University’s Defence and Security campus, where he also served as Head of the System and Autonomy Group and Research Lead of the Centre of Electronic Warfare, Information and Cyber. He earned his PhD in Electrical Engineering from McGill University Faculty of Engineering between 1999 and 2002. His research focuses on Robotics, Autonomous Systems, UAV Navigation, Computer Vision, and Fault-Tolerant Control . Key areas include visual odometry, sensor fusion (vision/IMU, RGBD, thermal-visible), robust control for UAVs, fault diagnosis in inertial systems, 3D perception, and autonomous landing. His work integrates theoretical control methods with real-time implementation in aerospace and defense contexts. His recent publications reflect a strong emphasis on robust optimization, multispectral vision, and real-time autonomous navigation. Trends indicate a focus on enhancing autonomy under uncertainty—through illumination-invariant stereo matching, L∞ optimization, and robust feature matching—particularly for UAVs operating in challenging environments. Nabil Aouf has collaborated extensively with researchers such as M. Richardson, O. Araar, T. Mouats, and M. Boulekchour across numerous projects in UAV control, sensor fusion, and autonomy. While no scientific awards are listed in the provided text, his leadership roles and sustained publication record in high-impact journals and conferences underscore his academic contributions. He has supervised or collaborated with several advisees including S.H. Almutairi, L. Chermak, I. Vitanov, and D. Nam, contributing to both theoretical developments and practical implementations in autonomous systems. His work has applications in aerospace, defense, planetary exploration, and critical infrastructure inspection.

Pietro Bortolottiمشاهده پروفایل

پژوهشگر

• Multidisciplinary design, analysis and optimization (MDAO) of wind energy systems
• Wind turbine rotor design
• Aeroservoelasticity of wind turbines
• +۳ مورد دیگر

Pietro Bortolotti is a senior researcher at the National Wind Technology Center, focusing on wind energy systems. He leads U.S. Department of Energy-funded projects like Big Adaptive Rotor and Holistic Systems Engineering, advancing land-based wind turbine technology through multidisciplinary design and multi-fidelity optimization approaches. Education: Bachelor in Energy Engineering, Politecnico di Milano Master in Sustainable Energy Technology, Technical University of Delft PhD in Mechanical Engineering, Technical University of Munich His research spans MDAO of wind energy systems , rotor design , and aeroservoelasticity , with applications in turbine aerodynamics, noise reduction, and AI-driven environmental monitoring. Recent work includes comparing downwind/upwind turbine configurations, bat tracking systems, and large-scale turbine optimizations. Key trends in his 2024–2025 publications reflect advancements in rotor dynamics , multi-fidelity modeling , and AI integration for wildlife monitoring. Collaborations include institutions like DTU, TU Delft, and NREL, with tools like WISDEM, WEIS, and OpenFAST underpinning his research. His professional journey includes postdoctoral work at NREL (2018–2019), research roles at Technical University of Munich (2013–2018), and a research assistantship at Denmark Technical University (2012–2013). He contributes to projects improving numerical models for wind turbine analysis and optimization.

Peng Gaoمشاهده پروفایل

استاد

• River Morphodynamics
• Geospatial Analysis (GIS, Remote Sensing, UAV)
• Urban Planning
• +۳ مورد دیگر

Peng Gao is a Professor in the Department of Geography and the Environment at Syracuse University, affiliated with the Maxwell School of Citizenship and Public Affairs. His work bridges river geomorphology and urban geospatial analysis, leveraging GIS, remote sensing, and UAV technologies to address environmental and social challenges. Education: Ph.D., Physical Geography, State University of New York at Buffalo (2003) M.S., Physical Geography, Lanzhou University (1993) B.S., Solid Mechanics, Lanzhou University (1990) Professor Gao specializes in river morphodynamics—particularly in the Qinghai-Tibet Plateau—and geospatial applications for urban planning. His research examines braided/meandering river systems, peatland hydrology, and how urban built environments influence social inequities and public health outcomes through spatial analysis. His 2020-2024 publications reveal a dual focus: (1) fluvial processes in high-altitude regions (e.g., neck cutoff dynamics, braided river discharge estimation using Landsat), and (2) urban applications (e.g., green building design, lead poisoning exposure mapping). This reflects a strategic integration of field geomorphology with computational geospatial modeling. Professor Gao actively mentors through SOURCE undergraduate research grants and PhD committees. Current funded projects include peatland mapping in the Andean Altiplano, I-81 Viaduct impact analysis in Syracuse, and studies on urban built environments affecting childhood lead poisoning. His work utilizes UAVs for BVLOS operations and collaborates with Syracuse CoE on urban environmental simulations, emphasizing technical innovation in geospatial data acquisition and analysis.

Ying Sunمشاهده پروفایل

دانشیار

• Geospatial Analysis
• Remote Sensing
• Ecosystem Modeling
• +۵ مورد دیگر

Ying Sun is an Associate Professor at Cornell University's School of Integrative Plant Science, Soil and Crop Sciences Section. Her research integrates geospatial analysis, remote sensing, and ecosystem modeling to study agroecosystem-climate interactions across scales. Key research areas include: Remote sensing of Solar-Induced Chlorophyll Fluorescence (SIF) for photosynthesis quantification Developing high-resolution SIF datasets (OCO-2, ECOSTRESS) using machine learning Modeling carbon-water-energy fluxes in Earth System Models (ESMs) Assessing food-water-climate sustainability in China and Africa She teaches PLSCI 7203: Engineering Plant Sensors and PLSCI 5900: Master of Professional Studies Project . Her lab has produced notable work on Ethiopian land restoration (Nature Sustainability 2022) and Northwest China water depletion (Environmental Research Letters 2022).

Naveen K. Vaidyaمشاهده پروفایل

استاد

• Applied Mathematics
• Mathematical Biology
• Pathogen Dynamics
• +۸ مورد دیگر

Dr. Naveen K. Vaidya is a full Professor at San Diego State University (SDSU) in the Department of Mathematics and Statistics . He received his PhD and M.Sc. in Applied Mathematics from York University, Canada , and M.Sc., B.Sc., and B.Ed. from Tribhuvan University, Nepal . His postdoctoral research was conducted at Los Alamos National Laboratory and Western University, Canada . Research Interests : Dr. Vaidya specializes in applied mathematics and mathematical biology , focusing on modeling infectious diseases such as HIV, SARS-CoV-2, dengue, malaria, and tuberculosis. His work spans within-host and between-host dynamics, integrating differential equations , dynamical systems , optimal control , and biostatistics . Recent projects explore climate impacts on disease spread and machine learning in public health analytics. Scientific Awards : He has received prestigious honors, including the University of Missouri Faculty Scholars (2015/2016) Susan Mann Dissertation Award (2008) NSERC Visiting Fellowships in Canadian Government Laboratories (2008/2009) Travel Support Awards from NSF and MBI (2018) Simons Foundation Collaboration Grant (2020, declined due to NSF grants) Grants and Funding : Dr. Vaidya has secured multiple grants from the National Science Foundation (2016–2021; 2020–2023), Simons Foundation , International Mathematical Union , and SDSU Start-up Funds . He also organized the AMNS-2019 conference in Nepal and led workshops on collaborative research. Labs and Teams : As principal investigator of the SDSU-DiMoLab , he leads a multidisciplinary team studying COVID-19 , HIV , and other infectious diseases. The lab trains graduate and undergraduate students and collaborates internationally, particularly with Tribhuvan University, Nepal .

Brett Hemenway Falkمشاهده پروفایل

پژوهشگر

• Cryptography
• Blockchain
• Computer Security
• +۵ مورد دیگر

Brett Hemenway Falk is a Researcher in the Department of Computer and Information Science at the University of Pennsylvania. He serves as director of the Crypto and Society Lab, focusing on privacy and security in digital environments and facilitating transparency and trust. His work combines rigorous mathematical approaches with practical implementations in cryptography and blockchain technology. Education: Sc.B. in Mathematics from Brown University Ph.D. in Mathematics from UCLA Research Interests include: Cryptography and secure multi-party computation protocols Blockchain technology (cross-chain interoperability, financial network stability, on-chain governance) Privacy-preserving algorithms and data security Coding theory applications in secure systems Scientific Contributions include: Developing practical secure computation protocols Advancing ORAM (Oblivious RAM) architectures Analyzing decentralized governance mechanisms Exploring DeFi network stability and token economics His teaching activities feature the popular MCIT 582 Blockchain course at Penn. Research funding comes from NSF, DARPA, IARPA, ONR, ARL, NIH , and the Laura and John Arnold Foundation.

• Control Technology
• Distributed Optimization
• Machine Learning
• +۴ مورد دیگر

Mikael Johansson is a Professor at Kungliga Tekniska Högskolan (KTH), specializing in Control Technology . He teaches and coordinates courses such as Distributed Optimization (FEL3311) and various advanced-level degree projects in computer science, electrical engineering, and systems engineering. His research spans Control Systems , Machine Learning , and Optimization , with a focus on asynchronous algorithms, federated learning, and applications in energy systems and construction. His work includes 15 recent publications on topics like neural networks, distributed optimization, and battery technology. Notable areas of contribution are in asynchronous learning, federated learning with privacy constraints, and quasi-Newton methods for optimization. His research bridges theoretical advancements with practical applications in urban design, healthcare, and autonomous systems.

Lucie Kruseمشاهده پروفایل

پژوهشگر

• Human-Computer Interaction
• Virtual Reality Exergames
• Serious Games
• +۵ مورد دیگر

Dr. Lucie Kruse is a researcher at the Department of Informatics, University of Hamburg, specializing in Human-Computer Interaction (HCI) and Virtual Reality (VR). Her work focuses on immersive user interfaces for cognitive and physical training, particularly for older adults and those with dementia. She has been an active member of the University of Hamburg's HCI group since 2018 and served on the Ethics Commission since 2023. Her research interests include: Virtual Reality Exergames Serious Games Assistive Technologies Accessibility in VR Mental Health Applications Her publications from 2021-2025 demonstrate expertise in designing VR systems for healthcare, analyzing age-related interaction patterns, and developing inclusive interfaces. She has received multiple awards including the 2024 Honorable Mention for Best Poster at ACM SUI and the 2023 Honorable Mention at ACM CHI. Scientific Awards: Honorable Mention for Best Poster Award at ACM SUI (2024) Runner-Up Prize at Metaverse for the Good (2024) Honorable Mention at ACM CHI'23 Interactive Demo (2023) Honorable Mention at ACM VRST (2021) She has supervised multiple theses on topics like AI agents for mental health, accessibility of chatbots for seniors, and VR exergame design. Her work spans collaborations with institutions like HITLab NZ and Western Sydney University's MARCS Institute.

Sean Welleckمشاهده پروفایل

استادیار

• Large Language Models
• Mathematical Reasoning
• Code Generation
• +۴ مورد دیگر

Sean Welleck is an Assistant Professor at Carnegie Mellon University's School of Computer Science, specifically within the Language Technologies Institute (LTI). He leads the L3 Lab and serves as an advisor for the AI for Math Fund. His academic journey includes a PhD from New York University under Kyunghyun Cho and postdoctoral positions at the Allen Institute for Artificial Intelligence and the University of Washington with Yejin Choi. Dr. Welleck's educational background shows a strong foundation in computer science. He earned his PhD in Computer Science from New York University, where he worked under the mentorship of Kyunghyun Cho and Zheng Zhang. Prior to this, he completed his MSE and BSE in Computer Science from the University of Pennsylvania, demonstrating a long-standing commitment to the field. Dr. Welleck's research focuses on bridging informal and formal reasoning with AI, with particular emphasis on developing learning, inference, and evaluation algorithms for large language models. His work spans multiple cutting-edge areas including mathematical reasoning , code generation , inference algorithms , and AI reasoning agents . A significant portion of his recent work involves combining AI with formal methods for mathematics, where he has developed frameworks like Llemma (an open-source language model for mathematical reasoning) and meta-generation (for inference-time algorithms). His research is characterized by a strong theoretical foundation coupled with practical applications that push the boundaries of what AI systems can achieve in formal reasoning domains. Analysis of Dr. Welleck's recent publications reveals a clear research trajectory focused on enhancing language models' capabilities in formal reasoning and mathematical problem-solving. His work demonstrates an evolution from foundational research in neural text generation to increasingly sophisticated approaches that integrate formal methods with deep learning. Key trends include the development of inference-time algorithms that improve model performance without additional training, frameworks for mathematical reasoning that connect informal and formal proofs, and novel evaluation methodologies for language models. His publications consistently appear in top-tier conferences including NeurIPS, ICLR, ICML, and ACL, reflecting the high impact of his contributions to the field. Dr. Welleck's scientific achievements have been recognized with several prestigious awards: NAACL 2025 Best Paper Award ICLR 2025 Oral Presentation (Top 2%) ICLR 2025 Spotlight Presentation (Top 5%) NeurIPS 2021 Outstanding Paper Award (Top 0.1%) for MAUVE NVIDIA AI Labs Pioneering Research Award (2017 and 2018) As an educator and mentor, Dr. Welleck actively guides the next generation of AI researchers. He currently advises multiple PhD students including Pranjal Aggarwal, Weihua Du, Andre He, and Seungone Kim (some co-advised with other faculty), along with MS students Riyaz Ahuja, Jiewen Hu, Qinyue Tan, and Thomas Zhu, and undergraduate Tate Rowney. At CMU, he teaches advanced courses such as Neural Code Generation and Advanced NLP, and has previously taught at New York University and the University of Washington. His commitment to education extends to creating resources like the Thesis Review Podcast and developing tutorials on neural theorem proving that have been presented at major conferences. Dr. Welleck leads the L3 Lab at CMU, which focuses on the intersection of language, learning, and logic. The lab brings together students and researchers to tackle challenging problems in AI reasoning, with particular emphasis on mathematical reasoning and code generation. Recent initiatives include the development of Llemma, an open-source language model specialized for mathematical reasoning, and work on inference-time algorithms that enable language models to improve their performance through additional computation during inference rather than through additional training.