جست‌وجوی استاد و پژوهشگر

Yuri Bazilevsمشاهده پروفایل

استاد

• Computational Mechanics
• Isogeometric Analysis
• Fluid-Structure Interaction
• +۵ مورد دیگر

Yuri Bazilevs is the E. Paul Sorensen Professor of Engineering at Brown University's School of Engineering and Co-Director of the Mechanics of Undersea Science and Engineering Center. His research focuses on computational mechanics, isogeometric analysis, fluid-structure interaction, and high-performance computing. Prior to Brown, he held positions at UC San Diego, where he advanced to Full Professor in 2014 after a rapid tenure. He earned his PhD in 2006 and postdoc training in computational engineering at UT Austin's ICES. Research interests span computational fluid dynamics, solid mechanics, and advanced discretization methods like isogeometric analysis (IGA) and meshfree approaches. He has developed novel formulations for complex phenomena such as underwater explosions, composite material failure, and hypersonic flow dynamics. His work integrates cutting-edge numerical methods with practical engineering applications in aerospace, energy, and biomedical systems. Recent publications highlight advancements in IGA for architected materials, RKPM-based crack modeling, and stabilized formulations for compressible flows. His contributions bridge theoretical mechanics with computational innovation, addressing challenges in multiphysics coupling and large-scale simulations. Collaborations span academia and industry, emphasizing practical validation and real-world impact. Bazilevs' expertise includes variational multiscale methods, peridynamics for fracture mechanics, and immersive particle methods for fluid-structure interaction. His work has been applied to wind turbine aerodynamics, gas turbine optimization, and cardiovascular flow analysis. He actively contributes to computational infrastructure development, such as the tIGAr software framework for IGA automation.

Chris De Saمشاهده پروفایل

دانشیار

• Machine Learning
• Distributed Systems
• Optimization Algorithms
• +۵ مورد دیگر

Chris De Sa is an Associate Professor in the Department of Computer Science at Cornell University, affiliated with the Cornell Machine Learning Group and leading the Relax ML Lab. His research focuses on algorithmic, software, and hardware techniques for high-performance machine learning, particularly relaxed-consistency stochastic algorithms like asynchronous and low-precision stochastic gradient descent (SGD). He earned his Ph.D. from Stanford University under advisors Kunle Olukotun and Chris Ré. His work emphasizes constructing efficient, parallel, and distributed machine learning frameworks for deep learning and data analytics. Education: Ph.D. in Computer Science, Stanford University (2017) Research Interests: Algorithmic techniques for scalable ML, quantization, distributed optimization, hyperbolic geometry in ML, and reliable measurement of ML systems. His group develops frameworks for efficient inference/training and explores the intersection of ML with domains like agriculture and plant science through courses like PLSCI 7202. Recent Highlights: DARPA YFA Grant (2024), NSF CAREER Award, Google Research Scholar Award, and multiple best paper recognitions. Key contributions include QuIP quantization methods, Coneheads attention mechanisms, and theoretical advances in decentralized training. Awards: NSF CAREER Award DARPA YFA Grant (2024) Google Research Scholar Award Mr. & Mrs. Richard F. Tucker Teaching Award Grants & Advising: Advises 8 Ph.D. students (including Ruqi Zhang, Yucheng Lu, A. Feder Cooper) and holds leadership roles in MLSys conferences. Active in grant-funded research (e.g., NSF Robust Intelligence). Labs/Teams: Leads the Relax ML Lab and participates in Cornell’s Institute for Digital Agriculture (CIDA).

Annalisa Maneraمشاهده پروفایل

استاد

• Nuclear Engineering
• Thermal-Hydraulics
• Computational Fluid Dynamics (CFD)
• +۵ مورد دیگر

Prof. Annalisa Manera is a Full Professor at ETH Zurich's Department of Mechanical and Process Engineering since July 2021, specializing in nuclear systems and multiphase flows. Previously, she held a professorship at the University of Michigan's Nuclear Engineering Department from 2011 to 2021. Her research focuses on advanced experimental techniques for single-phase and multiphase flows, high-resolution CFD validation, and computational tools for nuclear systems. She co-directs the Experimental and Computational Multiphase Flow (ECMF) Lab and the High Resolution Imaging Lab. Education: M.Sc. in Nuclear Engineering (University of Pisa, summa cum laude) and Ph.D. in Nuclear Engineering (Delft University of Technology). Awards include the ANS Bal-Raj Sehgal Memorial Award (2022) and the US DOE CASL Director’s Award (2016), alongside being an American Nuclear Society Fellow. Her work bridges nuclear safety, thermal-hydraulics, and computational modeling, with contributions to polaron physics, electron-phonon interactions, and material simulations. Courses taught include Nuclear Energy Conversion and Beyond-Design-Basis Safety.

Hank Childsمشاهده پروفایل

استاد

• Scientific Visualization
• High Performance Computing
• Computer Graphics
• +۱ مورد دیگر

Hank Childs is a Professor in the School of Computer and Data Sciences at the University of Oregon, specializing in scientific visualization and high-performance computing. He leads the Research Group on Computing and Data Understanding at eXtreme Scale (CDUX) and has held leadership roles including Interim Executive Director of the School of Computer and Data Sciences. His educational background includes a Ph.D. (2006) and B.S. (1999) in Computer Science from the University of California at Davis. Prior to academia, he worked for 14 years at Lawrence Livermore and Lawrence Berkeley National Laboratories, where he served as architect of the VisIt open-source visualization tool. Research interests center on visualizing extreme-scale scientific datasets from supercomputers, with a focus on in situ visualization for cosmology, seismology, and fluid dynamics. He has pioneered projects like VTK-m and Ascent, and his work explores power-performance tradeoffs and data-parallel algorithms for GPUs. Recent publications emphasize scalable visualization techniques for exascale computing, with 15 notable works from 2021-2020 covering particle advection, in situ triggering, and power-aware frameworks. His research has been honored with multiple best paper awards at IEEE LDAV, EGPGV, and SC conferences. DOE Early Career Award (2012) University of Oregon Faculty Excellence Award (2018) 4+ million dollars in research funding since 2013 5 Best Paper awards in 2021 alone As an educator, he received four consecutive CIS Best Teacher Awards (2014-2019). He has served as Associate Editor for IEEE Transactions journals and organized numerous visualization workshops including Dagstuhl seminars and Shonan workshops.

• Computational Fluid Dynamics (CFD)
• Smoothed Particle Hydrodynamics (SPH)
• Multiphase Flow Modeling
• +۴ مورد دیگر

Fabian Fritz holds an M.Sc. degree and works at the Technical University of Munich (TUM) within the Chair of Aerodynamics and Fluid Mechanics . His research focuses on computational fluid dynamics (CFD) and numerical simulation of multiphase flows, particularly using Smoothed Particle Hydrodynamics (SPH) . He collaborates on projects like PBF-LB/M (additive manufacturing) and contributes to Lagrangian fluid mechanics benchmarking frameworks. Research Trends: His publications emphasize numerical methods (SPH, level-set, finite-volume), multiphase flow modeling , heat transfer , and thermoacoustic stability . Recent work includes hardware-agnostic code optimization and adaptive mesh refinement techniques. Education: Completed a master’s thesis on Diffusive-Interface Modeling of Multiphase Flows with Surface-Tension Effects , supervised by P.D. Dr.-Ing. habil. Stefan Adami.

• Control Theory
• Machine Learning
• Nonlinear Estimation
• +۶ مورد دیگر

Prashant Mehta is a Professor of Mechanical Science and Engineering at the University of Illinois at Urbana-Champaign , affiliated with the Coordinated Science Laboratory . His research focuses on controlled interacting particle systems and machine learning applications , particularly in human activity recognition using motion sensors. Education: Ph.D. in Mathematics, Cornell University (2004) M.S. in Electrical & Computer Engineering, University of Massachusetts Amherst (1996) B.E. in Electrical & Electronics Engineering, Birla Institute of Technology & Sciences (1993) Mehta's work has pioneered the feedback particle filter (FPF) algorithm for nonlinear estimation, applied in robotic systems and gesture recognition. His research spans control of combustion instabilities in jet engines, mean-field games , and dynamical systems in aerospace engineering. His publications emphasize nonlinear control theory and stochastic filtering , with recent trends in sensor data pattern recognition and cyber-physical systems . He has received multiple scientific awards , including the MURI award for the Cyberoctopus project and Excellence in Undergraduate Advising Awards . Scientific Honors: MURI Award (2019) for Cyberoctopus Excellence in Undergraduate Advising (2010, 2008) Outstanding Teaching Assistant Award (1994) Senior Member, IEEE Control Systems Society Member, ASME Energy Systems Subcommittee Member, SIAM Dynamical Systems Group Mehta has supervised students like Jin Kim (IEEE CDC Best Student Paper, 2019) and co-founded the startup Rithmio , acquired by Bosch Sensortec . His laboratory develops gesture-detection filters for applications in soft robotics and human-machine interfaces .

Behzad Fatahiمشاهده پروفایل

استاد

• Unsaturated Soil Mechanics
• Dynamic Soil-Structure Interaction
• Ground Improvement Techniques
• +۶ مورد دیگر

Professor Behzad Fatahi is a distinguished academic in Civil and Environmental Engineering at the University of Technology Sydney (UTS), specializing in geotechnical engineering, railway infrastructure, and sustainable construction technologies. With a career spanning over 16 years at UTS, he has served as Deputy Head of School - Teaching and Learning (2024-present), Head of Discipline (2020-2024), and School Research Coordinator (2012-2017). His research focuses on unsaturated soil mechanics , dynamic soil-structure interaction , and green infrastructure solutions . Academic Appointments : Professor (2024-present), Associate Professor (2017-2024), Senior Lecturer (2011-2017), Lecturer (2008-2011) Research Leadership : Supervised 21 PhD students to completion, developed groundbreaking techniques for landfill waste reuse and tyre-derived aggregates in railway construction His work on seismic resilience of LNG tanks and bioengineered soil stabilization has received international recognition, including the 2023 Best Research Paper Award at the Australasian Association for Engineering Education conference. Professor Fatahi's industry experience includes geotechnical engineering roles at Coffey International and SES Engineering prior to academia. Key Research Contributions : Developed green corridor models for railway lines using coupled flow-deformation equations Pioneered AI-integrated teaching frameworks for civil engineering education Advanced machine learning techniques for intelligent compaction and structural buckling analysis As a Category 1 supervisor , he mentors graduate researchers in Civil Engineering , Geomechanics , and Earthquake Engineering . His peer-reviewed work (>240 publications) demonstrates technical excellence and innovation across multiple geotechnical domains.

• Power System Control
• Smart Grids
• Communication Systems
• +۱۱ مورد دیگر

Lars Nordström is a Professor at the Division of Electric Power and Energy Systems within KTH Royal Institute of Technology, Stockholm, Sweden. His work bridges control systems , communication networks , and power systems , with a focus on future architectures, functionality, and quality aspects of ICT for power grid operations. He has led initiatives such as the Swedish Centre of Electric Power Engineering and served as Thematic Leader for Smartgrids in KIC InnoEnergy. In 2014, he was a Visiting Professor at Washington State University. Education : Ph.D., MSc.EE Nordström's research explores the intersection of smart grids , machine learning , and cybersecurity for power systems. Key areas include: Wide-Area Monitoring and Control (WAMC) systems Decentralized control strategies for DC microgrids Impedance modeling using neural networks Data-driven methods for islanding detection ICT reliability and protocol design for grid operations His recent publications emphasize machine learning applications in power systems, including LSTM networks for EV charging management, graph attention networks for stability monitoring, and digital twin approaches for cyber-attack mitigation. These works span disciplines such as Smart Grids, Power Electronics, and Data Science. Scientific Recognitions : Senior Member, IEEE Senior Member, CIRED Senior Member, Cigre Past Chairman, Swedish IEC TC57 Mirror Committee Nordström actively teaches and examines graduate courses like Communication and Control in Electric Power Systems and Computer Applications and Machine Learning in Electric Power Systems . His work influences industry practices through collaborations on digital substations, energy market analysis, and resilience strategies.

• VLSI Design
• Computer-Aided Design
• Spectral Graph Theory
• +۴ مورد دیگر

Zhuo Feng is Professor of Electrical and Computer Engineering at Stevens Institute of Technology, directing the HUDSON Lab and holding a Ph.D. from Texas A&M University. His research develops spectral graph methods for VLSI design, including circuit simulation, power grid verification, and machine learning applications. Funded by NSF CAREER and multiple grants, his work has produced award-winning algorithms like GRASS for graph sparsification. Recent publications focus on spectral methods for circuit stability analysis, physics-informed neural networks, and explainable AI frameworks. He teaches graduate courses in VLSI design and GPU programming while co-founding LeapLinear Solutions. NSF CAREER Award (2014) ACM/IEEE DAC Best Paper Award (2013) Multiple Best Paper Nominations (ICCAD 2008, 2006)

• Machine Learning
• Energy Harvesting
• Low Latency Systems
• +۳ مورد دیگر

Kiwan Maeng is an Assistant Professor in Computer Science and Engineering, focusing on the intersection of machine learning systems, privacy-preserving techniques, and low-latency computing architectures. His research emphasizes algorithm-system co-design for scalable and secure AI implementations. Research Trends : His recent work explores retrieval-augmented generation systems, low-latency diffusion models, privacy-preserving federated learning, and energy-harvesting intermittent computing frameworks. Publications highlight collaborations across machine learning, cryptography, and hardware-software co-design. Key Projects : He leads a 3-year NSF SaTC grant (2024-2027) addressing privacy-preserving data embedding for untrusted ML services, and contributes to serverless video analytics frameworks (SVDE) and VR streaming optimization (PIRATE). Technical Contributions : His scholarship spans 17 conference contributions and 3 journal articles since 2007, with notable work on memory encryption for edge devices, secure MPC-based inference, and sustainable AI systems. Current research focuses on balancing privacy guarantees with model utility while optimizing for environmental efficiency.

Karen Livescuمشاهده پروفایل

استاد

• Speech Processing
• Language Processing
• Machine Learning
• +۵ مورد دیگر

Karen Livescu is a Professor at the Toyota Technological Institute at Chicago (TTIC), a philanthropically endowed graduate institute for computer science located on the University of Chicago campus. She also serves as a courtesy faculty member in the Department of Computer Science at the University of Chicago and is an Affiliated Scholar at the Data Science Institute there. Her research focuses on advancing speech and language processing through innovative machine learning approaches. Education: PhD in Electrical Engineering and Computer Science from MIT (2005) S.M. from MIT Department of Electrical Engineering and Computer Science (1999) A.B. in Physics from Princeton University (1996) Karen's research spans multiple dimensions of speech and language processing with particular emphasis on speech recognition, spoken language understanding, and multimodal processing. She has made significant contributions to articulatory feature-based speech recognition, self-supervised learning for speech representation, and sign language processing. Her work consistently bridges machine learning techniques with linguistic and speech science knowledge, focusing on creating more robust, interpretable, and inclusive speech processing systems that can handle diverse languages and modalities. Her recent publication trajectory reveals a strong focus on self-supervised learning for speech representation, multilingual speech processing, and sign language understanding. She has been instrumental in developing benchmark frameworks like SUPERB and ML-SUPERB that have become standard evaluation tools in the speech community. Her work increasingly addresses critical challenges in low-resource language scenarios, language disparities in speech technology, and ethical considerations in real-world deployment. Scientific Awards: Best Paper award at EMNLP 2024 for 'Towards robust speech representation learning for thousands of languages' Best Student Paper Award at ASRU 2023 Best Short Paper Award at CRAC 2021 Top system at WMT-SLT 2023 Karen has successfully advised numerous PhD students and postdoctoral researchers who have gone on to faculty positions at institutions like University of Waterloo, University of Edinburgh, and Stellenbosch University, as well as industry roles at major technology companies including Google, Meta, and NVIDIA. Her research group has secured significant funding for projects including the development of the SLUE benchmark for spoken language understanding and the SUPERB framework for evaluating self-supervised speech models. She has been actively involved in organizing workshops and symposia that bring together researchers in speech and language processing. Karen leads the Speech and Language at TTIC (SL@TTIC) research group, which maintains a strong collaborative relationship with researchers at the University of Chicago and other institutions. The group has been particularly active in advancing sign language processing through projects like ChicagoFSWild and OpenASL, while also making significant contributions to spoken language understanding and multilingual speech recognition. Her team regularly participates in community challenges and benchmarks, helping to push the field forward through open science and collaborative evaluation frameworks.

Roy Dongمشاهده پروفایل

استادیار

• Control Theory
• Cyber-Physical Systems
• Machine Learning
• +۷ مورد دیگر

Roy Dong is an Assistant Professor at the University of Illinois at Urbana-Champaign, affiliated with the Coordinated Science Laboratory. His research bridges Control Theory Economics Statistics Optimization to address challenges in cyber-physical systems and the Internet of Things, focusing on data manipulation, privacy, and strategic behavior in interconnected systems. His academic journey includes a Ph.D. in Electrical Engineering and Computer Sciences from UC Berkeley (2017) and dual B.S. degrees in Economics and Computer Engineering from Michigan State University (2010). At Illinois, he teaches courses ranging from Control Systems to Convex Optimization , with multiple teaching excellence awards. Roy's research explores Closed-loop effects of machine learning Causality in decision systems Incentive design for strategic agents Privacy-utility tradeoff optimization Human behavior modeling with applications in smart grids, transportation networks, and semi-autonomous vehicles. His work formulates privacy-preserving mechanisms as optimization problems, balancing data utility against user privacy in dynamic systems. Article trends show expertise in Game theory for strategic data sources Energy disaggregation techniques Nonlinear basis pursuit algorithms Privacy-aware control systems with a focus on cyber-physical systems and human-in-the-loop applications. Scientific recognition includes 'Teacher Ranked as Excellent' awards (ECE 120, ECE 486, ECE 515) Contributions to smartSDH building control and CPRL compressive sensing Roy leads the Privacy-aware Control Systems research group, collaborating with institutions like UC Berkeley and Michigan State University , and directs projects funded by grants including the New USDA NIFA grant for agricultural robot autonomy .

Pierre Raphaëlمشاهده پروفایل

استاد

• Nonlinear Waves
• Singularity Formation
• Mathematical Physics
• +۳ مورد دیگر

Pierre Raphaël is the Herchel Smith Professor of Pure Mathematics at the Department of Pure Mathematics and Mathematical Statistics (DPMMS) at the University of Cambridge, where he joined in 2019. His academic career spans prestigious institutions including École Polytechnique, University of Cergy-Pontoise, Princeton University, and various research positions in France before his appointment at Cambridge. Professor Raphaël's research lies at the border between physics and pure mathematics, focusing on understanding energy concentration mechanisms and singularity formation during the propagation of non-linear waves. His work is deeply connected to fundamental nonlinear structures occurring in electromagnetism, astrophysics and turbulent fluid flows. He has made significant contributions to the mathematical analysis of nonlinear partial differential equations, particularly in understanding blow-up phenomena and singularity formation. His publications reveal a consistent research trajectory focused on nonlinear wave phenomena, with particular emphasis on energy concentration mechanisms and singularity formation across various mathematical models. His work spans multiple areas including nonlinear Schrödinger equations, heat equations, Korteweg-de Vries equations, and harmonic heat flows, demonstrating his expertise in analyzing critical and supercritical regimes where singularities may form. Grand Prix Alexandre Joannides 2014 from the French Academy of Sciences Royal Society Wolfson Fellowship 2019 Invited Speaker at International Congress of Mathematicians 2014 ERC Advance Grant recipient Professor Raphaël has secured significant research funding including European Research Council grants, demonstrating his leadership in the field. His research group at Cambridge focuses on singularity formation for nonlinear PDEs, as evidenced by the conference he organized at St Catharine's College in September 2024. His work bridges pure mathematics with physical applications, particularly in understanding extreme regimes of nonlinear wave propagation.

• Machine Learning
• Deep Learning
• Graph Analysis
• +۶ مورد دیگر

Michalis Vazirgiannis is a Professor at LIX, École Polytechnique (France) leading the Data Science and Mining (DaSciM) group. With academic backgrounds in Physics (Athens University), AI (Heriot-Watt University), and Informatics (Athens University), he has conducted research at Fraunhofer, Max Planck MPI, and INRIA/FUTURS while teaching at institutions across Greece, France, China, and Spain. His research spans Machine/Deep Learning for Graphs (GNNs, graph kernels, embeddings) Text Mining & NLP (Graph-of-Words, biomedical text analysis) Combinatorial Optimization for pandemic forecasting and energy systems Event/Anomaly Detection in time series and sensory data Industrial collaborations with Airbus, Google, Tencent, and BNP . He has supervised 29 completed PhD theses, published over 250 papers, and received prestigious awards including Marie Curie and Tencent Rhino-Bird Fellowships. His team leads the ANR-HELAS Chair (2020-2025) focusing on heterogeneous data deep learning.

Joachim Weickertمشاهده پروفایل

استاد

• Image Processing
• Computer Vision
• Mathematical Modeling
• +۴ مورد دیگر

Joachim Weickert is a Professor of Mathematics and Computer Science at Saarland University where he heads the Mathematical Image Analysis Group since 2001. He received his diploma and Ph.D. in mathematics from the University of Kaiserslautern (1991, 1996), and a habilitation degree in computer science from the University of Mannheim (2001). Prior to his current position, he worked as a research assistant at the University of Kaiserslautern, as a post-doctoral researcher at the universities of Utrecht and Copenhagen, and as an assistant professor at the University of Mannheim. His research focuses on image processing, computer vision, and scientific computing, with special emphasis on techniques based on partial differential equations, variational principles, wavelets, morphological and nonlocal methods, as well as neuroexplicit approaches. He has developed mathematical models and efficient numerical algorithms for image restoration, enhancement, segmentation, compression, optic flow computation, stereo reconstruction, shape from shading, and signal processing methods for tensor fields. These ideas have been successfully applied in industry, biomedical image analysis, and other fields. Analysis of his recent publications reveals a strong trend toward combining traditional PDE-based methods with modern deep learning approaches, particularly in the areas of image inpainting and compression. His work increasingly explores the connections between numerical algorithms for partial differential equations and neural network architectures, demonstrating how mathematical foundations can inform cutting-edge AI techniques while maintaining strong theoretical guarantees. Gottfried Wilhelm Leibniz Prize (2010), considered the most important research award in Germany ERC Advanced Grant (2017) for "Inpainting-based Compression of Visual Data" Elected member of Academia Europaea - The Academy of Europe Jan Koenderink Prize for Fundamental Contributions in Computer Vision (2014) Multiple DAGM Prizes and Best Paper Awards throughout his career AAIA Fellow (2021) and Highly Ranked Scholar (2024) distinctions Professor Weickert has supervised over 250 bachelor's and master's theses and initiated the Master Programme in Visual Computing at Saarland University, the first of its kind in Germany taught in English. He has established numerous interdisciplinary collaborations with colleagues from medicine, bioinformatics, pharmacy, physics, mechatronics, and mechanical engineering. As Principal Investigator for Visual Computing within the Multimodal Computing and Interaction Cluster of Excellence, and former dean of the Faculty of Mathematics and Computer Science (2008-2010), he has played a significant leadership role in advancing visual computing research and education. He heads the Mathematical Image Analysis Group, which has been at the forefront of developing mathematical methods for image analysis. The group maintains strong connections with both theoretical mathematics and practical applications, bridging the gap between fundamental research and real-world implementation across various domains including medical imaging, industrial inspection, and multimedia processing.