جست‌وجوی استاد و پژوهشگر

Rika Antonovaمشاهده پروفایل

دانشیار

• Robotics
• Reinforcement Learning
• Machine Learning
• +۷ مورد دیگر

Dr. Rika Antonova is an Associate Professor at the Department of Computer Science and Technology, University of Cambridge , UK. Her research focuses on robot learning , data-efficient reinforcement learning , and simulation-to-reality transfer . She offers fully funded PhD positions in robot learning, novel robot hardware design, and reinforcement learning (RL) at Cambridge, where she also teaches an RL course. Educational Background: PhD in Robotics from KTH Royal Institute of Technology, Sweden (advisor: Danica Kragic). MSc in Robotics from Carnegie Mellon University (advisor: Emma Brunskill). Research Interests: Antonova's work bridges Bayesian optimization , deformable object manipulation , and multimodal sensor design . Her recent projects include equivariant diffusion policies and GPU-accelerated control frameworks . She has contributed to data-efficient RL algorithms and topological representations for deformable objects . Recent Publications: Antonova's 2024 articles on EquiBot (CoRL) and Runtime Monitoring (CoRL) highlight her focus on generalizable policies and safe AI. Earlier works include DiffCloud (IROS 2022) for real-to-sim rendering and Bayesian Optimization in Variational Latent Spaces (CoRL 2020). Scientific Awards: NSF/CRA Computing Innovation Fellowship (postdoctoral). Research Community Involvement: Antonova serves on the Board of Directors of the Robot Learning Foundation and has been an Associate Editor for ICRA (2024), Chair for CoRL Demos (2024), and reviewer for JMLR, Nature Machine Intelligence, and top robotics conferences.

Sreepathi Paiمشاهده پروفایل

دانشیار

• Compilers
• Heterogeneous Architectures
• GPU Algorithms
• +۳ مورد دیگر

Sreepathi Pai is an Associate Professor of Computer Science at the Hajim School of Engineering & Applied Sciences, University of Rochester. He holds a PhD from the Indian Institute of Science. His research focuses on experimental computer systems, particularly the performance of computer programs, with emphasis on heterogeneous architectures combining CPUs and GPUs. He develops optimizing compilers for irregular/graph algorithms on GPUs and creates performance models for these systems. Key research interests include compilers, heterogeneous architectures, GPU algorithms, and performance modeling. His work addresses challenges in large-scale automata processing, graph processing frameworks like Groute, and modular GPU emulation (Horus). He collaborates on projects such as TriForce for multiphysics simulations and explores efficient electromagnetic algorithms for plasma simulations. Recent projects include formal methods for scientific computing (DANUBE), lightweight GPU performance simulation, and autotuning for volunteer computing. His research also spans statistical caching, compiler-assisted memory management, and parallel triangle counting algorithms. His work bridges theory and practice, with contributions to frameworks like PLASMA for heterogeneous accelerators and tools for analyzing memory utilization in shared-memory systems. He has published extensively on topics ranging from tensor core modeling to preemptive thread block scheduling in GPUs.

Ray Zirui Zhangمشاهده پروفایل

استادیار مهمان

• Scientific Machine Learning
• Numerical Analysis
• Biophysics
• +۳ مورد دیگر

Ray Zirui Zhang is a Visiting Assistant Professor in the Department of Mathematics at the University of California, Irvine, mentored by John Lowengrub. His research focuses on scientific machine learning, numerical analysis, and their applications in biophysics and cancer research. He holds a Ph.D. in Mathematics from UC San Diego, advised by Li-Tien Cheng and Bo Li. Education: Ph.D. in Mathematics, UC San Diego (2017-2022) M.Sc. in Computational Science, UC San Diego (2017-2019) Bachelor of Engineering, University of Hong Kong (2012-2016) Exchange student at University of Wisconsin, Madison (2014-2015) Research Interests: Scientific machine learning, numerical analysis, biophysics, cancer research, computational mathematics, and medical imaging. His work bridges mathematical modeling with clinical applications, particularly in glioblastoma treatment planning and biomolecular simulations. Awards: Keynote speaker at WCCM 2024 for cancer progression modeling UCI Systems Biology Opportunity Award (2024) Rising Star speaker at UCI Center for Complex Biological Systems Retreat (2024) NSF Data Science Conference travel grant (2023) Powell Dissertation Award (UCSD, 2022) Grants & Collaborations: Involved in interdisciplinary projects combining physics-informed neural networks, numerical methods, and clinical data for personalized cancer treatment. Active in collaborations with institutions like École Polytechnique and Bjoern Menze's lab at UCI. Labs & Teams: Leads computational projects in mathematical oncology and biomolecular modeling, leveraging tools like Physics-Informed Neural Networks (PINNs) and level set methods for interface problems.

Kevin Harkinsمشاهده پروفایل

عضو هیئت علمی

• Magnetic Resonance Imaging
• Computational Modeling
• Optimization
• +۶ مورد دیگر

Kevin Harkins is a Research Assistant Professor in Biomedical Engineering and Radiology at Vanderbilt University’s School of Engineering. His research focuses on advancing magnetic resonance imaging (MRI) techniques, computational modeling, and optimization for biomedical applications. Key areas include diffusion MRI methodologies, microstructural analysis, and radiomic features for clinical diagnostics. He leads efforts in developing tools like the MATI toolbox, enhancing MRI data processing and simulation. His work bridges engineering and medicine, addressing challenges in musculoskeletal imaging, tumor microstructure analysis, and long-term post-traumatic outcomes. Research interests emphasize MRI advancements for preclinical and clinical studies, particularly in ex vivo imaging, in vivo small-animal models, and radiomics for osteoarthritis and knee injury assessment. His contributions include optimizing MRI protocols for reproducibility and applying AI-driven methods like reinforcement learning for gradient waveform optimization. Publications highlight interdisciplinary applications, such as MRI-derived bone analysis for fracture risk, bioresorbable implant design, and multi-site cohort studies. While no awards or grants are explicitly mentioned, his work aligns with Vanderbilt’s commitment to translational biomedical research. Collaborations likely span engineering, radiology, and orthopedics departments.

• Structural Biology
• Biomolecular Simulations
• Molecular Dynamics
• +۳ مورد دیگر

Mu Yuguang serves as Associate Professor at Nanyang Technological University's School of Biological Sciences since 2006, following his Lee Kuan Yew Fellowship (2003-2006) at the same institution. His interdisciplinary work bridges Biomedical Sciences, Chemistry, Computer Science, and Physics with core expertise in Structural Biology and computational modeling. His research centers on biomolecular simulations and molecular dynamics, specifically investigating membrane biophysics (including lipid bilayer responses to solvents), protein dynamics, and computational drug design. He develops simulation methodologies to analyze microbial membrane interactions and viral protein targets, with strong emphasis on translating computational findings into biomedical applications. Recent publications (2018-2019) reveal consistent application of molecular dynamics to membrane-related challenges—from butanol-induced disorder in microbial membranes to conjugated oligoelectrolyte interactions—and antiviral drug discovery targeting dengue virus. This work demonstrates integration of physics-based modeling with biological problem-solving across multiple scales. Scientific recognition includes: Lee Kuan Yew Postdoctoral Fellowship (2003-2006) Alexander von Humboldt Research Fellowship (1999-2000)

Jesús Fernández-Villaverdeمشاهده پروفایل

استاد

• Macroeconomics
• Econometrics
• DSGE Models
• +۴ مورد دیگر

Jesús Fernández-Villaverde is the Howard Marks Presidential Professor of Economics at the University of Pennsylvania's Department of Economics, part of the School of Arts and Sciences. His research focuses on macroeconomics and econometrics, particularly dynamic stochastic general equilibrium (DSGE) models. He holds affiliations with institutions like the National Bureau of Economic Research (NBER) and the Centre for Economic Policy Research. He has authored over 100 publications, including influential works on fiscal policy, monetary policy, and computational economics. His academic career includes roles as Director of the Penn Initiative for the Study of the Markets and Co-Director of the Business, Economic, and Financial History Project. He has advised numerous PhD students and received prestigious awards such as the Fellow of the Econometric Society (2020) and the Kravis Teaching Award (2004, 2019). His teaching materials span macroeconomic dynamics, global economic history, and political economy. He has also been a visiting scholar at institutions globally, including the Federal Reserve Banks and universities like Oxford and Harvard. Education: Ph.D. in Economics, University of Minnesota, 2001 B.Sc. in Economics and Management, ICADE, Spain, 1996 LL.B., ICADE, Spain, 1995 Research Interests: Fernández-Villaverde specializes in computational and empirical macroeconomics, with a focus on DSGE models. His work addresses fiscal and monetary policy, uncertainty shocks, and the integration of machine learning and quantum computing in economic modeling. He has pioneered methods for estimating complex models, including the pruned state-space system and GPU-based simulations. Articles Overview: His recent articles explore topics like central bank digital currencies, climate policy implications, and the economic effects of lockdowns. These contributions bridge theory and application, emphasizing policy-relevant insights and methodological innovation. Awards and Grants: Fellow of the Econometric Society (2020) National Science Foundation grants (e.g., 'Solution and Estimation Methods for DSGE Models') Peter Singer Foundation grant (2022-2024) Richard Stone Prize in Applied Econometrics (2004/2005, 2006) Advising and Teams: As thesis advisor to over 30 students, he has mentored leading researchers now in academia and policy institutions. He collaborates with teams at the Penn Initiative for the Study of the Markets and the Wharton Financial Policy and Regulation Initiative.

Nicholas Durrمشاهده پروفایل

دانشیار

• Optics
• Medical Imaging
• Deep Learning
• +۴ مورد دیگر

Nicholas Durr is an Associate Professor in the Department of Biomedical Engineering at Johns Hopkins University's Whiting School of Engineering, with secondary appointments in Electrical and Computer Engineering and Ophthalmology at the Wilmer Eye Institute. He co-directs the undergraduate Design Team program and leads the Durr Computational Biophotonics Lab, focusing on creating optical technologies to improve diagnostics and healthcare accessibility. His work integrates optical engineering, machine learning, and biodesign to develop tools like QuickSee (via his co-founded company PlenOptika), smart endoscopy systems, and non-invasive blood analysis devices. Education: B.S. in Electrical Engineering and Computer Science from UC Berkeley (2003), M.S. and Ph.D. in Biomedical Engineering from UT Austin (2007, 2010). Postdoctoral training at Harvard Medical School and MIT's M+Visión program. Joined Johns Hopkins faculty in 2016. Research interests include computational biophotonics, medical imaging, and accessible healthcare technologies. His lab emphasizes low-cost innovations for global health, such as diffuser-based fundus cameras and carbon dioxide cryotherapy systems. Awards include the NIH Trailblazer Award and Johns Hopkins mentoring accolades. Collaborations span industry partners like Google, Olympus, and Under Armour, and he actively mentors via MedHacks and the FLI Network for first-generation students. His work has been published in journals like Nature BME and IEEE Transactions on Medical Imaging.

Tom Spinkمشاهده پروفایل

مدرس

• Dynamic Binary Translation
• Hardware Virtualisation
• Operating Systems
• +۳ مورد دیگر

Dr. Tom Spink is a Lecturer at the School of Computer Science, University of St Andrews. His research focuses on efficient cross-architecture hardware virtualization, leveraging Dynamic Binary Translation (DBT) and hardware acceleration to enhance virtualized system performance. He teaches Operating Systems (CS3104) and Computer Architecture (CS4202), and serves as the First-level CS Coordinator. Dr. Spink holds a PhD in computer systems architecture from the University of Edinburgh and is a Fellow of the British Computer Society (BCS). His research interests span operating systems, virtualization, compilers, and runtime systems. Notable contributions include work on DBT hypervisors, weak memory model architectures (Risotto/Lasagne), and embedded systems security. He has been awarded the Best Paper Award in 2019 for his work on retargetable DBT hypervisors. Dr. Spink advises PhD student Ferdia McKeogh and collaborates on tools like Risotto and Lasagne. His work addresses challenges in cross-platform execution, IoT virtualization, and compiler optimizations for embedded systems. He actively engages in academic activities, including organizing conferences and delivering invited talks on hardware acceleration and virtualization techniques.

• Cloud Computing
• Cybersecurity
• Internet of Things (IoT)
• +۷ مورد دیگر

Gokop Goteng is a Senior Lecturer at Queen Mary University of London's School of Electronic Engineering and Computer Science, specializing in cloud computing, IoT security, and middleware systems. He holds a PhD in Grid Computing from Cranfield University (2009) and has held postdoctoral roles at Qatar Computing Research Institute (2012-2014) and King Abdullah University of Science and Technology (2012-2014). Currently, he leads the BUPT joint program's Cloud Computing and Middleware modules, focusing on industry-relevant skills in distributed systems and software engineering. His research integrates cloud technologies, cybersecurity, and AI to address challenges in smart cities, energy systems, and digital healthcare. Notable projects include malware detection in IoT devices and optimizing cloud service latency via deep learning. He is an Oracle Certified Professional (OCP), Java Certified Programmer (JCP), and active member of the British Computer Society (BCS) and IEEE. Teaching responsibilities include Cloud Computing (BUPT joint programme) Middleware for IoT Software Engineering , emphasizing hands-on programming with APIs, secure middleware design, and team-based software development. His work emphasizes industry collaboration, as seen in AWS Academy partnerships to enhance student employability. He maintains a research website at gotengphdstudents for doctoral supervision and project updates.

• Quantum Field Theory
• Elementary Particle Physics
• Scientific Computing
• +۳ مورد دیگر

Claudio Pica is a Professor of Computational Science and Head of Section at the University of Southern Denmark (SDU), affiliated with the Department of Mathematics and Computer Science (IMADA) and CP3-Origins. He holds leadership roles as CEO of DeiC National HPC Center and Head of the SDU eScience Center. With a PhD in Physics from the University of Pisa (2005) and a Laurea cum Laude in Theoretical Physics from the Scuola Normale Superiore (2001), his expertise spans quantum field theory, lattice gauge simulations, and high-performance computing. His research focuses on fundamental particle physics, including composite Higgs models, walking technicolor theories, and lattice QCD. He leads large-scale computational projects, such as scaling SU(2) simulations to 1000 GPUs and optimizing algorithms for supercomputers. His outreach initiatives include award-winning educational projects like 'KvanteBanditter' (Lovie Awards 2018) and the SDU Supercomputing Challenge. He has secured € millions in grants, including the €4M European ITN ETN 'EuroPLEx' (2018-2022) and Danish Lundbeckfonden funding (2013-2018). Pica has organized major conferences like the 'Origin of Mass' series and the 'Odense Winter School on Geometry and Theoretical Physics.' His work bridges theoretical physics with computational innovation, addressing challenges in conformal dynamics, dark matter, and supercomputing infrastructure. He is a frequent speaker at international forums and a reviewer for prestigious journals like Physical Review D and European Physical Journal C.

John Keyserمشاهده پروفایل

استاد

• Computer Science
• Graphics and visualization
• Physically-based simulation
• +۵ مورد دیگر

John Keyser is a Professor in the Department of Computer Science & Engineering at Texas A&M University, serving as Graduate Advisor. He holds a Ph.D. from the University of North Carolina at Chapel Hill and multiple B.S. degrees from Abilene Christian University. His research focuses on geometric computing, physically-based simulation, graphics, and modeling, with notable contributions to 3D visualization and computational geometry. Keyser's educational background includes advanced degrees in Computer Science, Applied Mathematics, and Engineering Physics. He has received prestigious awards such as the William Keeler Faculty Fellowship and teaching accolades from the Association of Former Students and Tenneco. His research interests span robust geometric modeling, physically-based simulation, brain networks, and graphics. Keyser's work bridges theoretical foundations with applied systems, including innovations in particle-based fluids, mesh rendering, and filament tracking in microscopy. Awards: William Keeler Faculty Fellow (2009-2010), Teaching Excellence Awards (2007), Montague Scholar (2003-2004). Advising: Graduate Advisor role with expertise in guiding students through complex computational projects. Keyser collaborates on advanced imaging techniques like the Knife-Edge Scanning Microscope (KESM) for brain tissue analysis and contributes to GPU-accelerated algorithms for real-time simulations. His research trends emphasize interdisciplinary applications of geometric computing in both academic and industrial contexts.

Eike Muellerمشاهده پروفایل

دانشیار

• Numerical Analysis
• Parallel Computing
• Partial Differential Equations
• +۲ مورد دیگر

Dr. Eike Mueller is a Reader (Associate Professor) in the Department of Mathematical Sciences at the University of Bath. His research focuses on developing fast numerical algorithms for solving physical problems across scales, from atmospheric models to subatomic particles. As part of the Numerical Analysis group, he specializes in parallel computing implementations using frameworks like DUNE and Firedrake/PyOP2. His work is characterized by close collaboration with the Met Office on weather and climate forecasting models. Research interests center on numerical techniques for complex systems, emphasizing parallel computing architectures including GPU clusters. Key areas include massively parallel solvers for PDEs, multilevel Monte Carlo methods for atmospheric dispersion, and performance-portable frameworks for physics simulations. Recent work explores machine learning integration with traditional numerical methods for enhanced computational efficiency. Publications demonstrate consistent focus on accelerating scientific computation through novel algorithms. Recent articles emphasize the application of multigrid methods to climate modeling, neural networks in numerical integration, and Bayesian inference techniques. A significant portion of work targets performance optimization on modern hardware architectures including GPU acceleration. Research leadership includes principal investigator roles for major EPSRC projects: 'MGHyPE: An ExaHyPE version with a multigrid solver' and 'IAA – Accelerating climate- and weather-forecasts with faster multigrid solvers'. Collaboration with the Met Office has resulted in operational improvements to weather prediction models through bespoke solver development.

Masao Sakoمشاهده پروفایل

استاد

• Observational Astronomy
• Cosmology
• Dark Energy
• +۳ مورد دیگر

Masao Sako is the Arifa Hasan Ahmad and Nada Al Shoaibi Presidential Professor of Physics and Astronomy at the University of Pennsylvania. He has held academic positions including Professor (2020–present), Associate Professor (2012–2020), and Assistant Professor (2006–2012) at UPenn. His research focuses on observational cosmology using Type Ia supernovae to study dark energy and the universe's expansion, leveraging large-scale surveys like DES, LSST, and Roman. He also develops machine learning/deep learning methods for astronomical data analysis and GPU-accelerated image processing. His educational background includes a B.S. from Columbia University (1995), and M.A., M.Phil., and Ph.D. in Physics from Columbia (1997–2001). Postdoctoral training included Chandra and KIPAC fellowships at Caltech and Stanford. Research interests emphasize cosmological parameter estimation via supernova surveys, systematic uncertainty mitigation, and interdisciplinary applications of AI/ML. Recent work includes Hubble constant measurements, dark energy constraints, and astrometric redshift techniques. Key contributions include the Dark Energy Survey's cosmological results, photometric classification with SCONE, and collaborations with LSST/ Rubin Observatory. His team's machine learning tools enhance transient detection and photometric redshift estimation.

• Computer Architecture
• Machine Learning
• Data Storage Systems
• +۵ مورد دیگر

Youtao Zhang is a Professor at the Swanson School of Engineering, University of Pittsburgh. His research focuses on computer architecture, machine learning, quantum computing, and data storage systems. He has contributed to advancements in secure non-volatile memories, GPU optimization, and healthcare informatics through deep learning applications. His work addresses challenges in hardware security, memory management, and efficient quantum circuit simulation. Key research areas include: Hardware Security and Covert Channels Quantum Computing Algorithms and Hardware Storage Systems Optimization GPU Acceleration and Parallel Computing Medical Imaging and Healthcare AI Recent publications emphasize innovations in hybrid storage deduplication, quantum circuit evaluation, and adversarial defense in vision transformers. His work bridges theoretical computer science with practical applications in emerging technologies.

Xulong Tangمشاهده پروفایل

استادیار

• High-Performance Computing
• Parallel Computer Architectures
• GPU Optimization
• +۴ مورد دیگر

Xulong Tang is an Assistant Professor in the Department of Computer Science at the University of Pittsburgh. He holds a PhD in Computer Science from Penn State (2019), an MS from the University of Science and Technology of China (2013), and a BS from Harbin Institute of Technology (2010). His research focuses on high-performance computing, parallel architectures, and compiler optimization for heterogeneous systems. Research interests include GPU optimization, compiler-assisted parallel computing, quantum computing systems, and efficient machine learning frameworks. He advises multiple PhD students and has published extensively in top conferences like ASPLOS, ISCA, and MICRO. His work addresses challenges in multi-GPU systems, quantum circuit simulation, and adversarial machine learning. Tang has collaborated on projects involving quantum computing optimization, photonic quantum systems, and edge device acceleration. Notable contributions include frameworks for efficient training of temporal graph neural networks and compiler-driven hardware-software co-design. He serves as a program committee member for ASPLOS and IEEE Micro Top Picks.