جست‌وجوی استاد و پژوهشگر

Zhiqiang Linمشاهده پروفایل

دانشیار

• Software Security
• Cloud Computing and Virtualization
• Memory Data Analysis
• +۳ مورد دیگر

Dr. Zhiqiang Lin is an Associate Professor in the Computer Science Department at the University of Texas at Dallas (UT Dallas), affiliated with the Erik Jonsson School of Engineering and Computer Science. He holds a PhD in Computer Science from Purdue University (2011). His research focuses on software security, cloud computing security, and memory data analysis, with applications in vulnerability discovery, malware analysis, and virtualization security. He has received prestigious awards including the NSF CAREER Award and Air Force YIP Award. Education: PhD in Computer Science, Purdue University, 2011 Research Interests: Software Security: Binary code analysis, kernel malware detection, and vulnerability discovery. Cloud Computing: Virtual machine introspection, cloud security mechanisms, and data protection in distributed systems. Memory Analysis: Data structure identification in memory/disk, forensic recovery, and semantic data extraction. Teaching: CS 4393: Computer and Network Security (Spring 2013) CS 6324: Information Security (Fall 2012) CS 6V81: Systems Security/Binary Code Analysis (Spring 2012) CS 6V81: Advanced Digital Forensics (Fall 2011) Grants & Projects: Lead researcher on DARPA-funded project to transition legacy system data to secure platforms (collaboration with Purdue University). Developed "space travel" technique for cross-VM monitoring, enhancing cloud security. Air Force-funded framework to protect computer cores from advanced threats. Service & Leadership: NSF proposal review panel member (2012+) Publication Chair for IEEE IPCCC (2012) TPC member for ICDCS, AsiaCCS, CCGrid, and other conferences.

Mohammad Javad Latifiمشاهده پروفایل

پژوهشگر

• Mathematical Physics
• Geometry
• Applied Mathematics
• +۳ مورد دیگر

Mohammad Javad Latifi is a Researcher at Dartmouth College's Department of Mathematics. He holds a PhD in Mathematics from the University of Arizona and focuses on Mathematical Physics, Geometry, Applied Mathematics, and Data Science. His research bridges theoretical work in quantum field theory and dynamical systems with applied areas like machine learning and numerical modeling of physical systems. Research Highlights: Developed kernel smoothing techniques for sea-ice dynamics modeling. Advanced the theory of star transforms and V-line tomography in imaging and inverse problems. Contributed to tensor network approximations of Koopman operators in nonlinear dynamics. Collaborated on multi-level graph spanners and optimization algorithms. Teaching: Taught undergraduate courses including Differential Equations (Math 23) and Linear Algebra (Math 22). Designed visualizations for vector calculus and ODEs, emphasizing conceptual understanding and real-world applications. Software & Projects: Developed KSPoly , a Python package for smooth field approximations in polygonal geometries. Created a sound visualization tool exploring numerical sequences as musical patterns. Contributed to Lunewave's radar and autonomous vehicle software, implementing C++ algorithms for object tracking and data analysis.

• Mathematical Physics
• Quantum Field Theory
• Spectral Geometry
• +۳ مورد دیگر

Prof. Dr. Alexander Strohmaier is a Professor at the Institute of Analysis, part of the Faculty of Mathematics and Physics at Leibniz University Hannover. He serves on the Executive Board of the university, the Riemann Center for Geometry and Physics, and various committees such as the Faculty Council and Admissions Board for Mathematics. His research focuses on mathematical physics, quantum field theory, spectral geometry, and partial differential equations, with a particular emphasis on scattering theory and geometric analysis. He has contributed significantly to topics like Casimir energy computation, microlocal analysis, and the application of spectral methods in curved spacetimes. Prof. Strohmaier’s work bridges theoretical physics and mathematics, addressing foundational questions in quantum field theory on curved spacetimes and the interplay between geometry and analysis. His recent articles explore trace formulas in electromagnetic scattering, Lorentzian manifolds, and numerical methods for spectral problems. While no specific awards are listed, his prolific publication record reflects his influence in these fields. He is actively involved in academic governance and mentorship within the university.

Binoy Ravindranمشاهده پروفایل

استاد

• Computer Systems
• Software Verification
• Concurrency
• +۷ مورد دیگر

Binoy Ravindran is a Professor at Virginia Tech’s College of Engineering, Department of Electrical and Computer Engineering, leading the Systems Software Research Group (SSRG). His research focuses on computer systems, emphasizing security, performance, concurrency, distributed systems, and real-time computing, with recent work in software verification and heterogeneous-ISA platforms. Key projects: Low-level Reasoning Machine (LLRM), Popcorn Linux, LibrettOS, Hyflow, HermiTux, SlimGuard, HydraVM, KairosVM. He has co-authored 15+ papers from 2025 to 2022, spanning venues like ASPLOS, POPL, PLDI, VEE, PPoPP, and MIDDLEWARE, with awards including ACM Distinguished Scientist and eight Best Paper Awards. Service roles: Editorial Boards (IEEE Transactions on Cloud Computing, ACM TECS), Program Co-Chair (ACM Systor 2025), Committee memberships across ASPLOS, PLDI, and more.

Dominic Edelmannمشاهده پروفایل

پژوهشگر

• Distance Correlation
• Survival Analysis
• High-Dimensional Molecular Data
• +۷ مورد دیگر

Dominic Edelmann is a researcher at Heidelberg University, Germany, specializing in mathematical statistics and its applications in biostatistics and high-dimensional molecular data. His work bridges theoretical statistics and biomedical research, particularly in developing and applying distance-based dependence measures. Research Interests: His research centers on distance correlation , survival analysis for high-dimensional data , epigenetic data analysis , and machine learning . He investigates nonlinear relationships in complex datasets, with applications in oncology and molecular biology. The recent publications show a strong trend in extending distance correlation methods to survival and competing risks data, as well as time series and high-dimensional settings. His work combines rigorous mathematical foundations with practical applications in biomedicine. Scientific Funding: DFG Grant "dCortools: Distanzkorrelationsverfahren zur Erkennung Nichtlinearer Zusammenhänge in Hochdimensionalen Molekularen Daten" (2019–present) Academic Supervision: He has co-supervised Master’s theses on bias correction in distance correlation and regression models for bounded responses in DNA methylation studies, indicating active involvement in training the next generation of statisticians. He holds a Dr. rer. nat. in Mathematics from Heidelberg University (2015) and was a research assistant there during his doctoral studies. His work continues to be centered at Heidelberg University, contributing to both theoretical and applied statistical science.

• High Performance Computing
• Performance Engineering
• Performance Modeling
• +۷ مورد دیگر

Gerhard Wellein is a Professor for High Performance Computing at the Department of Computer Science of Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). He is the head of NHR@FAU (Erlangen National Center for High Performance Computing) and a member of the board of directors of the German NHR-Alliance. Since 2024, he has also served as a Visiting Professor for HPC at the Delft Institute of Applied Mathematics, Delft University of Technology. He holds a PhD in theoretical physics from the University of Bayreuth and has over two decades of experience in HPC education and research. Research Interests: His research focuses on performance modeling and engineering, architecture-specific code optimization, novel parallelization techniques, and the development of hardware-efficient building blocks for sparse linear algebra and stencil solvers. His work bridges computer science, applied mathematics, and computational physics, aiming to maximize efficiency on current and future HPC architectures, including exascale systems. Publication Trends: His recent publications emphasize analytical performance modeling (e.g., Roofline, oscillator models), energy efficiency, GPU optimization, and scalable linear algebra. They reflect a strong focus on both theoretical modeling and practical implementation, with applications in CFD, quantum physics, and molecular dynamics. Scientific Awards: 2011 Informatics Europe Curriculum Best Practices Award (shared with Jan Treibig and Georg Hager) for outstanding teaching contributions in HPC. Grants and Advising: He has led numerous third-party funded projects from the EU, BMBF, and DFG, including EoCoE-III, ESSEX, EXASTEEL, and ProPE. These projects focus on exascale software, performance engineering, fault tolerance, and multiscale simulation. He has mentored multiple researchers and students, contributing to the development of tools such as LIKWID, ClusterCockpit, and GEOPM. Labs and Teams: He leads the HPC research group at FAU and is deeply involved in national and international HPC initiatives. His team collaborates extensively on open-source HPC software and performance tools, fostering a strong community-driven approach to performance engineering.

• Computational Optimization
• Global Optimization
• Mixed-Integer Nonlinear Programming
• +۹ مورد دیگر

Dr. Ruth Misener is a Professor in the Department of Computing at Imperial College London and holds the BASF/RAEng Research Chair in Data-Driven Optimization (2022-27). Her research focuses on computational optimization, including global optimization of mixed-integer nonlinear programs, optimization under uncertainty, and integration with machine learning. She leads the Computational Optimization Group, which develops open-source tools like GALINI, and collaborates with industry partners such as Royal Mail for logistics optimization. Her research interests span optimization challenges in engineering, bioprocess design, and healthcare, including bioreactor optimization and disease trajectory modeling for leukemia. She has been awarded the Royal Academy of Engineering Research Fellowship, supporting her work on hybrid computational/experimental platforms for biomedical applications. Recent projects include scheduling optimization under uncertainty, Bayesian methods for experimental design, and robust optimization for heat recovery networks. Her work emphasizes explainable optimization and safety-critical decision-making, with applications in energy systems, pharmaceuticals, and manufacturing. Ruth’s contributions include over 50 publications, with a focus on algorithm development and industrial impact. She actively participates in conferences and chairs workshops on optimization, such as the Mixed Integer Programming Workshop and the British-French-German Conference on Optimization.

• Uncertainty Quantification
• Bayesian Inverse Problems
• Data Science
• +۷ مورد دیگر

Dr. Aretha Teckentrup is a Lecturer in the Mathematics of Data Science at the University of Edinburgh's School of Mathematics. Her research focuses on integrating mathematical models with observational data, particularly in areas like uncertainty quantification, Bayesian inverse problems, and computational methods for partial differential equations (PDEs). She holds a PhD in Mathematics from the University of Bath and has held postdoctoral positions internationally, including in Florida. Her work emphasizes interdisciplinary approaches, blending statistics, numerical analysis, and applied mathematics. Notably, she has contributed to advancing Gaussian processes, multilevel Monte Carlo techniques, and sparse grid methods for high-dimensional problems. Her academic journey reflects a strong commitment to bridging theoretical foundations with practical applications. She has published extensively on topics such as probabilistic numerical methods, error estimation in Bayesian inference, and adaptive sampling strategies. Dr. Teckentrup is an active member of the SIAM community, having received the prestigious SIAG/UQ Early Career Prize in recognition of her contributions to uncertainty quantification. Her research continues to explore innovative solutions for data-driven modeling challenges in science and engineering. Dr. Teckentrup’s work often addresses the growing importance of combining data with physical models, exemplified by her development of numerical methods for weather prediction and stochastic dispersion modeling. She advocates for collaboration across disciplines and emphasizes the transformative potential of integrating computational tools with real-world data. Her career trajectory underscores the dynamic and collaborative nature of modern academic research in applied mathematics and data science.

Rüdiger Kapitzaمشاهده پروفایل

استاد

• Systems Software
• Trusted Execution Environments
• Byzantine Fault Tolerance
• +۵ مورد دیگر

Prof. Dr.-Ing. Rüdiger Kapitza serves as a Professor and Chair of Computer Science 4 (Systems Software) at Friedrich Alexander University Erlangen-Nuremberg (FAU). His extensive research portfolio spans trusted execution environments, Byzantine fault tolerance, blockchain technology, and secure systems. Kapitza actively contributes to the academic community through program committee roles for major conferences including OSDI, Middleware, EuroSys, and DSN. His research interests focus on creating secure, reliable systems through the integration of hardware security features with distributed systems. Kapitza investigates how trusted computing can enhance system security while maintaining performance, particularly for critical applications. His work bridges theoretical foundations with practical implementations across domains including cloud infrastructure, edge computing, and transportation systems. Analysis of his recent publications reveals consistent innovation in trusted execution environments, distributed consensus protocols, and secure virtualization. His research demonstrates a clear trajectory from early work on adaptive services to current focus on hardware-assisted security solutions. Key themes include making distributed systems more robust through trusted components, optimizing energy efficiency in heterogeneous systems, and developing practical security solutions for real-world applications. Prof. Kapitza's standing in the systems research community is evidenced by his service on program committees for top-tier conferences. His work has been published in prestigious venues including OSDI, EuroSys, Middleware, and DSN, reflecting significant impact in both academic and industrial contexts. He leads the Systems Software research group at FAU, mentoring students and collaborating with researchers worldwide. The group addresses fundamental challenges in systems security, reliability, and performance, with applications ranging from drone data recording to railway systems and secure cloud services. His team develops practical tools and frameworks that advance the state of the art in systems research.

Toby Murrayمشاهده پروفایل

استاد

• Cybersecurity
• Formal Methods
• Secure Systems Engineering
• +۵ مورد دیگر

Toby Murray is a Professor in the School of Computing and Information Systems at the University of Melbourne, where he serves as Director of the Defence Science Institute and Co-Lead of the Computer Science Research Group. His work bridges formal methods, cybersecurity, and practical system security, with significant contributions to verified security and vulnerability detection. Murray's research focuses on building highly secure computing systems cost-effectively, with expertise in formal verification, information flow security, and vulnerability detection. His current research projects include Verisimilar (Verified, Secure Machine Learning), EDEFuzz (Detecting excessive data exposure in web applications), COVERN (Proving information flow security of concurrent programs), and Time Protection (Proving timing channel freedom for seL4). His work combines theoretical rigor with practical implementation, resulting in multiple open-source tools including SecC, Legion, and Underflow. Murray's recent publications demonstrate a consistent focus on verified security properties across diverse domains, from neural networks to concurrent systems. His work often bridges the gap between formal methods and practical security concerns, with increasing attention to machine learning security and policy implications of technical security measures. His publications span top venues in security, formal methods, and software engineering. Distinguished Paper Award at ICSE 2024 for EDEFuzz work on detecting excessive data exposure in web applications Extensive media commentary on cybersecurity issues including CrowdStrike outage analysis and social media regulation Regular contributions to The Conversation and Pursuit on cybersecurity policy matters Murray has advised numerous PhD students to completion, including Lianglu Pan (EDEFuzz), Zhiyuan Zhang, Mo Zhang, and Renlord Yang. He currently supervises multiple PhD students working on security verification, machine learning security, and web application security. His service includes being Program Chair for CSF'25, Associate Editor for IEEE Security & Privacy and ACM TOPS, and membership in IFIP's WG 1.7 and WG 2.3. His research group has developed multiple significant software tools including SecC (Verified Security for Concurrent C Programs), Legion (Principled Automatic Test Case Generation), and Underflow (Compositional Vulnerability Detection for C Programs), all available under open source licenses. Murray's work often involves discovering and reporting bugs in security analysis tools during his research, demonstrating the practical impact of his verification approaches.

Massimiliano Pontilمشاهده پروفایل

استاد

• Machine Learning
• Dynamical Systems
• Optimal Transport
• +۵ مورد دیگر

Massimiliano Pontil is a Professor at University College London's Department of Computer Science within the Faculty of Mathematical & Physical Sciences, with additional affiliation at the Italian Institute of Technology in Genoa. He leads cutting-edge research at the intersection of machine learning and dynamical systems theory. His research focuses on developing theoretical frameworks for learning transfer operators of stochastic dynamical systems using kernel methods. Pontil's work provides rigorous spectral learning bounds and addresses representation learning challenges in modeling time-evolving phenomena. His research spans both theoretical foundations and practical applications in data-driven science and engineering. Pontil's recent publications reveal a strong emphasis on Koopman operator theory, with particular attention to spectral analysis, error bounds, and efficient algorithms for large-scale dynamical systems. His work connects kernel methods with dynamical systems theory to create mathematically grounded approaches for forecasting and understanding temporal phenomena. His scientific contributions include multiple publications in top-tier venues including NeurIPS and ICLR, with recent work on invariant representations, long-term forecasting, and randomized algorithms for operator regression. Pontil collaborates extensively with researchers including Vladimir Kostic, Karim Lounici, and Pietro Novelli, leading a productive research group in this specialized area of machine learning theory.

Stephen Siegelمشاهده پروفایل

دانشیار

• Formal methods for software verification
• Verification of parallel and scientific software
• Concurrency in software systems
• +۳ مورد دیگر

Stephen Siegel is an Associate Professor at the University of Delaware with a joint appointment in the Department of Computer and Information Sciences and the Department of Mathematical Sciences . Holding a PhD in Mathematics from the University of Chicago (1993), he transitioned from finite group theory research to formal methods in computer science, focusing on verification of parallel and scientific software. His research centers on the Verified Software Laboratory (VSL) and the CIVL Model Checker for HPC program verification. Recent work includes formal verification of PETSc components at CAV 2025 and collective contract frameworks for message-passing programs. Research Interests Formal methods for software verification Parallel and HPC software reliability Model checking techniques Application of mathematical logic to computing Academic Service Highlights Program Committee & Publication Chair, CAV 2025 Co-organizer, International Workshop on Verification of Scientific Software (VSS 2025) Chair, VerifyThis competition (2023) Editorial service at IEEE Transactions on Software Engineering (2015-2019) Teaching Portfolio CISC 404/604: Logic in Computer Science CISC 414/614: Formal Methods in Software Engineering CISC 372: Parallel Computing (MPI/OpenMP/CUDA instruction) Advanced Topics courses: Model Checking, Abstract Interpretation

Sherry Flint-Garciaمشاهده پروفایل

استاد مدعو

• Genetics and Genomics
• Plant Biology
• Maize Genetics
• +۷ مورد دیگر

Dr. Sherry Flint-Garcia is a Research Geneticist and Lead Scientist at the USDA-ARS and an Adjunct Professor in the Division of Plant Sciences within the College of Agriculture, Food and Natural Resources at the University of Missouri. Her work bridges agricultural research and academia, focusing on the genetic foundations of maize domestication, breeding, and kernel traits. Research Interests: Her lab investigates the genetic diversity of maize, particularly how artificial selection during domestication and modern breeding has shaped traits related to adaptation, inbreeding, and kernel composition (protein, oil, starch, amino acids). A major goal is to reintegrate lost genetic variation from landraces and teosinte into modern breeding programs to enhance food quality, flavor, aroma, and texture in corn. Research Resources: The lab utilizes advanced germplasm populations including the maize Nested Association Mapping (NAM) population (5,000 RILs), ten teoNIL populations (~900 NILs), and doubled haploid lines from Zea Synthetic to conduct QTL mapping, association studies, and selection analyses. Scientific Awards: L. Stadler Mid-Career Maize Genetics Award (2025) David Dunn Award in Plant Biology, University of Missouri (2023) Advising and Grants: While specific students are not listed, Dr. Flint-Garcia leads an active research lab supported by federal and institutional funding, mentoring graduate students and researchers in plant genetics and breeding. Her projects integrate genomics, quantitative genetics, and breeding to develop novel food corn varieties with improved sensory and nutritional qualities. Labs and Teams: She leads the Flint-Garcia Lab, which is affiliated with the USDA-ARS, the Interdisciplinary Plant Group, the Missouri Maize Center, and the Maize Genetics Cooperation. These collaborations enhance interdisciplinary research in plant science and maize genetics.

Salvatore Ivan Trapassoمشاهده پروفایل

استادیار

• Applied harmonic analysis
• Fourier analysis
• Machine learning
• +۵ مورد دیگر

Salvatore Ivan Trapasso is a Fixed-term Assistant Professor at the Department of Mathematical Sciences "GL Lagrange" (DISMA) , Polytechnic University of Turin , and a member of the SmartData@PoliTO - Big Data and Data Science Laboratory . His research focuses on Applied Harmonic Analysis , Fourier Analysis , Machine Learning , and Quantum Theory , with expertise in Mathematical Analysis (MATH-03/A) and Theoretical PDEs (PE1_11). Education : Implied PhD in Mathematics. Research Areas : Phase Space Analysis, Time-Frequency Methods, and Applications to Quantum Mechanics. His recent publications investigate phase space techniques for Feynman Path Integrals , Twisted Laplacian , Compressed Sensing , and Stability of Scattering Transforms . His work bridges Harmonic Analysis with Machine Learning and Quantum Dynamics . Notable scientific awards include the Axioms Young Investigator Award (2022) , Best Paper Award (ICGF 2020) , and the Quality Award 2019 from Polytechnic University of Turin. He serves on the Editorial Board of Advances in Operator Theory and as Associate Editor for University Texts in the Mathematical Sciences . Teaching roles include Lecturer for Mathematical Principles in the College of Architecture and Design , Collaborator for Mathematical Analysis I/II in Biomedical and Aerospace Engineering, and Contributor to advanced mathematical methods in Computer Science Engineering.

MARIA ALONSO PENAمشاهده پروفایل

استادیار

• Nonparametric Statistics
• Circular Regression
• Statistical Modeling
• +۳ مورد دیگر

María Alonso-Peña is an Assistant Professor at the University of Santiago de Compostela , affiliated with the Faculty of Biology and the Department of Statistics, Mathematical Analysis, and Optimization . Her research focuses on nonparametric statistical methods, particularly in circular regression models and their applications in neuroscience and animal behavior analysis. She holds a PhD in Statistics from the University of Santiago de Compostela (2022), with a thesis titled New approaches to nonparametric circular regression models . Education: PhD in Statistics (2022), University of Santiago de Compostela Research Interests: Nonparametric statistics, circular regression, optimization, and statistical modeling in neuroscience and ecology Her work bridges theoretical statistics with applied problems, such as analyzing neuronal spike counts and animal escape behavior using advanced circular regression techniques. She has collaborated with institutions like KU Leuven and Universidad de Granada. Recent research includes a grant from the Xunta de Galicia (ED481A-2019/139) for neuroscientific applications. Key contributions include frameworks for circular local likelihood regression and parametrically guided kernel density estimators for spherical data. These methods address challenges in modeling directional and multimodal datasets, with implications for fields like neuroscience and environmental science.