جست‌وجوی استاد و پژوهشگر

• Formal Methods
• Program Analysis
• Automata Theory
• +۵ مورد دیگر

Ondřej Lengál is an Associate Professor at the Department of Intelligent Systems, Faculty of Information Technology, Brno University of Technology. His academic career spans over a decade with continuous research funding and publication output, progressing from doctoral work to leading multiple significant research projects. His research interests include: Formal Methods and Verification Automata Theory and Applications Program Analysis and Static Verification Theoretical Foundations of Computer Science Model Checking Techniques Software Engineering with Formal Guarantees Dr. Lengál's publication record demonstrates a consistent research trajectory in automata-based verification techniques. His recent work (2021-2023) has focused on Büchi automata complementation, with significant contributions to rank-based algorithms and tighter complexity bounds. He has also made substantial contributions to forest automata for shape analysis and heap verification, as well as symbolic techniques for program analysis. His research bridges theoretical computer science with practical applications in software verification. Dr. Lengál leads and participates in multiple research projects funded by GACR (Czech Science Foundation), EU Horizon programs, and internal university grants. His current projects include QUAK: Quantum Program Analysis using Automata Toolkit (2025-2027), Verification and Analysis for Safety and Security of Applications in Life (2024-2027), and Reliable, Secure, and Intelligent Computer Systems (2023-2026). As an educator, Dr. Lengál teaches several advanced courses including Formal Program Analysis, Advanced Mathematics, Operating Systems, and Theoretical Computer Science, demonstrating his ability to connect theoretical foundations with practical applications.

• Numerical Analysis
• Scientific Computing
• Computational Fluid Dynamics
• +۶ مورد دیگر

Yulong Xing is a Professor and Vice Chair for Graduate Studies in the Department of Mathematics at The Ohio State University. His research focuses on numerical analysis and scientific computing, particularly in high-order numerical methods for partial differential equations, computational fluid dynamics, and multiscale modeling. He holds a PhD from Brown University (2006) and has authored numerous papers on discontinuous Galerkin methods, well-balanced schemes, and computational approaches for fluid dynamics and geophysical flows. Key research areas include: Discontinuous Galerkin (DG) methods for hyperbolic conservation laws, radiative transfer, and wave propagation Well-balanced schemes for geophysical and astrophysical flows High-order adaptive algorithms for phase field models and nonlinear PDEs Structure-preserving numerical methods for Hamiltonian systems and energy conservation Recent work emphasizes asymptotic preserving methods for multiscale problems, positivity-preserving techniques, and applications to relativistic radiation transport and shallow water equations. Articles often address stability, error analysis, and computational efficiency in complex physical systems. His contributions include pioneering work on DG methods for the Euler equations with gravitational fields and innovative treatments of discontinuous bottom topography in shallow water modeling. Despite extensive publications, no specific awards or grants are explicitly listed in the provided text.

Arvindمشاهده پروفایل

استاد

• Computer Architecture
• Parallel Computing
• Memory Systems
• +۳ مورد دیگر

Arvind is the Johnson Professor of Computer Science and Engineering at MIT and a member of CSAIL (Computer Science and Artificial Intelligence Laboratory). He holds a B.Tech. from IIT Kanpur (1969), M.S. and Ph.D. from the University of Minnesota (1972-1973). His research focuses on computer architecture, parallel computing, memory models, and hardware synthesis. He pioneered dataflow architectures and developed the pH programming language. Notable projects include the Monsoon dataflow machine and Sandburst, a semiconductor company for 10G-bit Ethernet routers. Arvind has received prestigious awards like the IEEE Harry H. Goode Memorial Award (2012) and ACM Fellow (2007). He co-founded Bluespec Inc. and managed collaborations like Nokia-CSAIL (2006-2010). His work spans academia and industry, emphasizing scalable systems and secure computing. Research interests include synthesis/verification of digital systems, graph algorithms, and weak memory models. Current projects explore next-gen Graph AI systems and financial security applications.

• Atmospheric Science
• Meteorology
• Climate Dynamics
• +۳ مورد دیگر

Dr. Chimene Daleu is a researcher at the University of Reading, specializing in atmospheric science and meteorology. Her work focuses on convection parameterization, climate dynamics, and numerical modeling of atmospheric processes. She has contributed to studies on the diurnal cycle of deep convection, the impact of surface heterogeneity, and the evaluation of convective parameterization schemes like CoMorph-A. Her research employs idealized simulations and single-column models to assess model performance in representing tropical and midlatitude precipitation patterns. Collaborations with institutions globally highlight her interdisciplinary approach to understanding climate change impacts and model uncertainties. Key research interests include the interplay between convection and large-scale circulation, weak-temperature gradient approximations, and model intercomparison studies. Her findings advance the representation of convective processes in weather and climate models, with applications to improving prediction accuracy in tropical and midlatitude systems. Despite no explicit awards listed, her extensive publication record reflects sustained contributions to atmospheric science.

Christopher Kulpمشاهده پروفایل

استاد

• Nonlinear Dynamics
• Complex Systems
• Machine Learning
• +۷ مورد دیگر

Christopher Kulp is the John P. Graham Teaching Professor of Physics in the Department of Astronomy and Physics at Lycoming College. He holds a B.A. from McDaniel College, and M.S. and Ph.D. from The College of William & Mary. Since joining Lycoming College in 2008, he has taught physics at all levels, along with courses in machine learning, data analysis, and mathematical modeling. Education: B.A., McDaniel College M.S., The College of William & Mary Ph.D., The College of William & Mary His research centers on nonlinear and complex systems, with a focus on nonlinear time series analysis, machine learning, and mathematical modeling. He applies these techniques to data from diverse fields including astrophysics, climatology, medicine, economics, biology, and combustion. His group emphasizes undergraduate research, and he has published over 30 peer-reviewed articles, book chapters, and conference papers. The recent publications highlight a strong trend toward applying machine learning and agent-based modeling to socioeconomic systems, particularly in understanding wealth distribution and taxation impacts. Other works explore symbolic dynamics in chaotic systems, reflecting a deep engagement with nonlinear time series analysis and complex behavior characterization. Scientific Awards: Mike Resnick Memorial Award for Best Science Fiction Short Story by a New Author Kulp has secured research support enabling sustained undergraduate involvement in computational and theoretical projects. His advising emphasizes hands-on experience in data science and modeling. He is also a co-author of influential textbooks, including Classical Mechanics: A Computational Approach (2020) and Mathematical Methods using Python (2024), which integrate programming into physics education. His interdisciplinary approach bridges scientific rigor with creative expression. He leads an active research group focused on complex systems and maintains a strong commitment to pedagogy and innovation in STEM education. His work often involves network representations of time series and intelligent agent design, fostering collaboration across disciplines.

• Optimization Theory
• Numerical Optimization
• Newton-Type Methods
• +۵ مورد دیگر

Mikhail V. Solodov is a Researcher at the Institute for Pure and Applied Mathematics (IMPA) in Rio de Janeiro, Brazil, where he conducts cutting-edge research in optimization theory and algorithms. His work spans theoretical foundations and practical applications, with significant contributions to Newton-type methods, augmented Lagrangian techniques, and equilibrium problems. Dr. Solodov's educational background includes: Ph.D. in Optimization/Computer Sciences from University of Wisconsin-Madison (1995) M.S. in Computer Sciences from University of Wisconsin-Madison (1992) Diploma (with Honors) in Applied Mathematics from Moscow State University (1991) His research focuses on developing and analyzing optimization algorithms, particularly Newton and Newton-related methods for optimization and variational problems under weakened assumptions, augmented Lagrangian techniques, decomposition methods, and nonsmooth optimization (especially bundle methods). He has made significant contributions to understanding optimization problems with degenerate constraints and has developed relaxed regularity concepts for these challenging problems. Dr. Solodov's recent publications (2022-2025) show a strong trend toward applications in energy markets while maintaining theoretical rigor. His work bridges mathematical theory with practical implementation, demonstrating how optimization theory can solve real-world problems in energy systems and economic equilibrium modeling. The publications reveal increasing focus on equilibrium problems with applications to energy markets, while continuing to advance theoretical understanding of optimization algorithms. His recognition in the field is evident through his editorial appointments: Mathematical Programming, Series A (since 2005) Optimization Methods and Software (since 2002) SIAM Journal on Optimization (2009-2024) Dr. Solodov has also contributed to special journal issues dedicated to prominent figures in optimization, including a special issue on Hierarchical Optimization in Mathematical Programming (2023) and volumes dedicated to the memory of Prof. Naum Shor and Olvi Mangasarian.

João Pedro Faria Mendonça Barretoمشاهده پروفایل

دانشیار

• Persistent Memory
• Exascale Computing
• Transactional Memory
• +۵ مورد دیگر

João Pedro Faria Mendonça Barreto is an Associate Professor at Instituto Superior Técnico (University of Lisbon) and a researcher in the Distributed Systems Group at INESC-ID. His work focuses on system support for persistent memory, exascale computing, transactional memory, and blockchain consensus protocols, with significant contributions to heterogeneous memory systems and NUMA optimization.

Lyman Alexander Page Jr.مشاهده پروفایل

استاد

• Observational Cosmology
• Astrophysics
• Cosmic Microwave Background
• +۵ مورد دیگر

Lyman Alexander Page Jr. is a Professor of Physics at Princeton University and a leading observational cosmologist. He co-led the Wilkinson Microwave Anisotropy Probe (WMAP) project, which revolutionized our understanding of the cosmic microwave background (CMB) radiation and the early universe. Page is also the founding director of the Atacama Cosmology Telescope project and currently serves on the Simons Observatory executive board and as an advisor at the Canadian Institute for Advanced Research. PhD in Physics from MIT (1989) Bachelor's degree from Bowdoin College His research focuses on the CMB, dark energy, and galaxy clusters, using advanced observational techniques to study the universe's origin and composition. Over decades, his work has involved precise measurements of the universe’s expansion, structure growth, and thermodynamic properties of cosmic systems. Notable trends in his recent publications include multi-instrument cosmological analyses (ACT, SPT, Planck), atmospheric modeling for telescopes, and studies of the thermal and kinematic Sunyaev-Zel'dovich effects. His articles often address cosmic birefringence, dark matter interactions, and galaxy evolution. Marc Aaronson Memorial Lectureship (2004) Shaw Prize in Astronomy (2010) Gruber Prize in Cosmology (2015) Breakthrough Prize in Fundamental Physics (2018) Marcel Grossman Award (2018) Fellow of the American Physical Society (2013) Page has held leadership roles, including Chair of Princeton's Physics Department (2011–2017), and contributes to global collaborations in cosmology and astrophysics.

George Papadimitriouمشاهده پروفایل

استادیار

• Computer Architecture
• Energy-Efficient Architectures
• Microprocessor Reliability
• +۷ مورد دیگر

George Papadimitriou is an Assistant Professor in the Computer Engineering & Informatics Department at the University of Patras , Greece, hosted within the School of Engineering . His primary affiliation lies with the Computer Hardware and Architecture division, where he leads research and teaching activities focused on dependable, energy-efficient computer architectures. Education: PhD in Computer Science, Department of Informatics & Telecommunications, National and Kapodistrian University of Athens (2019) Post-doctoral researcher, Computer Architecture Lab, National and Kapodistrian University of Athens Research Interests: Dr Papadimitriou’s research lies at the intersection of computer architecture , energy efficiency , and microprocessor reliability . His work specifically targets: Robust and energy-efficient CPU/GPU/accelerator architectures Post-silicon validation techniques for catching elusive hardware bugs Silent data corruption detection and mitigation across the compute stack Characterization of voltage margins and power consumption in modern microprocessors Modeling and simulation of domain-specific accelerators for low-power, dependable operation More recently, his team has been extending these methodologies to RISC-V and neuromorphic photonic accelerators within large European consortia. Scientific Awards & Recognition: Eight HiPEAC Paper Awards for top-tier conference publications (MICRO, HPCA, ISCA) between 2017–2024 IEEE Transactions on Computers 2022 Best Paper Award for the article “Anatomy of On-Chip Memory Hardware Fault Effects Across the Layers” TTTC/ITC Gerald W. Gordon Student Award 2023 Research Funding & Projects: Dr Papadimitriou is principal investigator or key technical contributor in multiple Horizon Europe and industry-backed projects that collectively exceed €50 M in funding. Current leadership roles include: DARE (Digital Autonomy for RISC-V in Europe) NEUROPULS (Neuromorphic Energy-Efficient Secure Accelerators) REBECCA (Reconfigurable Heterogeneous Highly Parallel Processing Platform) Vitamin-V (Virtual Environment & Tool-boxing for Trustworthy RISC-V Cloud Services) Intel, IBM, and Thales bilateral research contracts on energy-efficient and resilient microarchitectures Laboratory & Team: He leads the Energy-Efficient and Dependable Architectures (EEDA) research group at University of Patras, operating laboratory facilities for silicon measurement, FPGA emulation, and full-system simulation (gem5, MARSS, custom tools). The team currently comprises 3 PhD candidates, 2 post-docs, and several MSc thesis students collaborating with European and US partners.

Dan Fuمشاهده پروفایل

استادیار

• Machine Learning
• Computer Systems
• Efficient Algorithms
• +۳ مورد دیگر

Dan Fu is an Assistant Professor at the University of California, San Diego (UCSD) in the Computer Science and Engineering Department and a Distinguished Research Scientist at Together AI. He leads the SandyResearch Lab and is affiliated with the MLSys group, focusing on making machine learning models faster and more efficient through hardware-aware algorithms and subquadratic architectures. Research interests include: Efficient ML architectures (Chipmunk, Hyena, Monarch Mixer) Hardware-aware systems algorithms (ThunderKittens, FlashAttention) Long-sequence modeling and GPU optimization His recent work spans training-free Transformer acceleration (Chipmunk), convolutional language models (Hyena), and hardware-aware attention optimizations (FlashAttention). Projects are deployed in production at Together AI and integrated with frameworks like PyTorch. Scientific awards include: NDSEG Fellowship (2025) Best Paper at ICML Hardware Aware Workshop (2022) Best Student Paper Runner Up at UAI (2022) Best Poster at ENLSP Workshop (NeurIPS 2023) He contributes to open-source projects (Safari repository, FlyingSquid) and teaches courses on machine learning systems (Stanford CS 324/528, Harvard CS 61/152).

Jean-Michel ZAKOIANمشاهده پروفایل

استاد

• Financial Econometrics
• Time Series
• Dynamic Risk Measures
• +۷ مورد دیگر

Jean-Michel ZAKOIAN is a Full Professor at ENSAE Paris-Saclay and Professor of Applied Mathematics at Université de Lille, currently on secondment at ENSAE since 2008. He leads the Finance-Insurance lab at CREST and serves as Associate Editor for Econometric Theory , Scandinavian Journal of Statistics , and Journal of Time Series Analysis . Co-author of GARCH models. Structure, Statistical Inference and Financial Applications (2nd ed., 2019) Published over 80 peer-reviewed works in top journals like Econometrica and Journal of the American Statistical Association Research Interests : Specializing in Financial Econometrics and Time Series , ZAKOIAN's work focuses on GARCH-type models, dynamic risk measures, estimation risk, unit roots, explosive processes, and quasi-maximum likelihood (QML) estimation. His recent publications address challenges in volatility modeling without moment constraints and systemic risk inference. Academic Contributions : As a supervisor of 13 PhD theses in Applied Mathematics and Economics, he has significantly shaped emerging researchers in econometric theory. His editorial roles across four journals reflect his standing in advancing methodological rigor in time series analysis.

Matthias Löweمشاهده پروفایل

استاد

• Probability Theory
• Statistical Physics
• Random Matrix Theory
• +۳ مورد دیگر

Matthias Löwe is a Professor at the Institute of Stochastics within the Faculty of Mathematics and Computer Science at the University of Münster. His academic career spans several decades with continuous contributions to probability theory and statistical physics. His research focuses on deep theoretical investigations of complex stochastic systems, particularly in the context of disordered systems and random structures. Löwe's research interests center on Probability Theory , Statistical Physics , and Random Matrix Theory . His work examines phase transitions in spin systems, fluctuation phenomena in random graphs, and the mathematical foundations of neural network models. His research program consistently bridges theoretical probability with applications in statistical physics, particularly in understanding the behavior of complex interacting particle systems at critical points. His publication record shows consistent output in top probability journals including Annals of Probability , Electronic Journal of Probability , and Annales de l'Institut Henri Poincaré . His most recent work (2020-2023) focuses on propagation of chaos in mean-field models, fluctuations in Ising models on random graphs, and exact recovery problems in block spin systems. Löwe frequently collaborates with researchers including Zakhar Kabluchko, Kristina Schubert, and Jonas Jalowy. Löwe has supervised multiple doctoral students including Raphael Meiners, Mirko Ebbers, Jens Ameskamp, and Sarah Behrens. His research group has included postdoctoral researchers and doctoral candidates working on various aspects of probability theory and its applications.

Benjamin Delawareمشاهده پروفایل

استادیار

• Programming Languages
• Formal Methods
• Program Verification
• +۵ مورد دیگر

Benjamin Delaware is an Assistant Professor in the Department of Computer Science at Purdue University since 2016. He earned his Ph.D. in Computer Science from the University of Texas at Austin in 2013 under William Cook and Don Batory, following an M.Sc. in Computer Science from Washington University in St. Louis (2007) and a dual B.S. in Computer Science and B.A. in Russian from Truman State University (2005). Research focuses on program synthesis , formal verification , and relational program properties . Key contributions include KestRel (relational verification), PALM (LLM-assisted proof automation), and Clotho (distributed system testing). His publications span top venues like OOPSLA, PLDI, and POPL, with recent work (2025) on coverage-type-guided synthesis and LLM-integrated proof repair . He has received multiple scientific awards , including a SIGPLAN Distinguished Paper Award (2023) and CRII Award from NSF (2018). Ben advises a research group producing graduates such as Qianchuan Ye (now at SUNY Buffalo) and Kia Rahmani (UT Austin postdoc). He teaches graduate courses on program reasoning (CS560) and programming language design (CS456/CS565), with earlier teaching experience at UT Austin. Active in academic service , he served as Program Committee Co-Chair for RocqPL 2026 and CoqPL 2025 , and as Diversity, Equity, and Inclusion Chair at POPL 2023. His grants include NSF funding for input generator verification (2023-2026) and Cisco research on privacy-preserving computation (2022-2023).

Soham Chakrabortyمشاهده پروفایل

استادیار

• Programming Languages
• Compilers
• Computer Architecture
• +۳ مورد دیگر

Soham Chakraborty is an Assistant Professor at the Programming Languages Group, TU Delft since 2021. He previously served as an Assistant Professor at the CSE Department, IIT Delhi before joining TU Delft. His academic journey includes a PhD from Max Planck Institute for Software Systems (MPI-SWS) in 2019 and a Master's degree from CSE Department, IIT Kharagpur in 2008. Affiliation: TU Delft, IIT Delhi, MPI-SWS Research Interests: Programming Languages, Compilers, Architecture, Verification, Concurrency His research focuses on formal semantics, compiler correctness, architecture-to-architecture translation, and verification of weak memory models. He has contributed to major conferences including POPL, PLDI, CGO, and ASPLOS. His work addresses challenges in heterogeneous concurrency, relaxed memory concurrency, and binary translation for weak memory architectures. Recent works include How Hard is Weak-Memory Testing? (POPL'24) and Toast: A Heterogeneous Memory Management System (PACT'24). He also received the Distinguished Paper Award at DATE'25 and Distinguished Artifact Award at PODC'22 . Teaching: Courses like Concepts of Programming Languages (CS2120), Parallel and Concurrent Programming (CS4555), Compiler Construction (CS4555), Analysis of Concurrent and Distributed Programs (CS4405), Software Fundamentals (Rust), and Seminar Programming Languages (COL 380) Students: PhD student Dennis Sprokholt, MSc students Michal Raczkiewicz, Alexandru Dumitriu, Huixuan Wu, and alumni including Pieter van den Ham, Mingyu Gao, Jeroen Kloppenburg, and others. Service: Served on program committees for POPL, PLDI, CGO, VMCAI, APLAS, and Eurosys workshops.

Mijail D. Serruyaمشاهده پروفایل

استادیار

• Neurotechnology
• Neurological Rehabilitation
• Biomedical Engineering
• +۳ مورد دیگر

Mijail D. Serruya, MD, PhD, is an Assistant Professor in the Department of Neurology at the College of Life Sciences. His primary base is at 901 Walnut Street, Suite 400, Philadelphia, PA 19107. Dr. Serruya's research aims to pioneer neurotechnology solutions for paralysis and weakness stemming from stroke or neurological injuries. Key areas include: Development of wearable/implantable neural devices Biological constructs like 'living electrodes' and 'neonerves' Therapeutic interventions for adult and pediatric rehabilitation Integration of engineering and neuroscience for clinical applications His publications emphasize neural signal processing, brain-computer interfaces, and spinal stimulation, consistently targeting translational neuroengineering for rehabilitation. Dr. Serruya leads a dedicated research lab focused on neurotechnology innovation. Further details are available through his lab website.