جستجوی استاد راهنمای دانشگاهی

Tobias Klausمشاهده پروفایل

پژوهشگر

• Real-Time Systems
• Distributed Systems
• Operating Systems
• +۳ مورد دیگر

Tobias Klaus is a Researcher at the Department of Computer Science 4 (Distributed Systems and Operating Systems) at Friedrich-Alexander-Universität Erlangen-Nürnberg. His work focuses on real-time systems, distributed systems, and operating systems, with a particular emphasis on embedded real-time control and multicore processing. He has contributed to projects such as qron OS, AORTA, and the RTSC compiler framework. His research addresses challenges in real-time scheduling, job-level dependencies, and quality-aware system design. Education and Background: No explicit education details provided in the text. Research Interests: Klaus’s research spans real-time systems, distributed architectures, and embedded control systems. He explores topics such as deadline-driven scheduling, static analysis for job migration optimization, and tool-supported design of time-triggered systems. His work often bridges theoretical foundations with practical implementation in embedded and robotics domains. Grants and Funding: No specific grants or funding opportunities are mentioned in the text. Awards: No scientific awards explicitly listed. Teaching and Advising: Klaus has been involved in teaching courses such as Real-Time Systems, System Programming, and Distributed Systems. He has advised multiple theses, including projects on distributed real-time systems, WCET analysis, and multicore resource protocols. Notable advisees include Hausmann Jannis, Florian Güthlein, Thomas Reichinger, and Felix Bräunling. Labs and Teams: He contributes to interdisciplinary projects like the I4 Copter quadrocopter system and collaborates on frameworks such as RTSC and AORTA. His work often involves cross-disciplinary efforts in real-time control and embedded systems.

Keith D. Cooperمشاهده پروفایل

استاد

• Compiler Construction
• Program Analysis and Optimization
• Parallel Computing
• +۵ مورد دیگر

Keith D. Cooper is the L. John and Ann H. Doerr Professor in Computational Engineering and Professor of Computer Science at Rice University. He holds a courtesy appointment in the Department of Electrical and Computer Engineering. His research focuses on program analysis, optimization, and compiler construction. He has authored influential textbooks like Engineering a Compiler and has produced 18 Ph.D. students. Cooper has served in key administrative roles, including Chair of Computational and Applied Mathematics (2019–2020), Associate Dean for Research in the Brown School of Engineering (2012–2018), and Co-Director of the Ken Kennedy Institute for Information Technology (2015–2019). Education: Ph.D. (1983), M.A. (1982), and B.S. (1978) in Mathematical Sciences and Electrical Engineering from Rice University. Research Interests: Program analysis and optimization, compiler design, parallel computing, adaptive compilation, and memory hierarchy optimization. His work includes foundational contributions to interprocedural analysis, register allocation (Chaitin-Briggs algorithm), and compiler frameworks like ParaScope. Awards: ACM Fellow, George R. Brown Award for Superior Teaching (2019), and the L. John and Ann H. Doerr Chair (2019). Grants & Leadership: Played a pivotal role in Rice’s Data Science Initiative and the design of Duncan Hall. Advised over 25 students and contributed to numerous grants in compiler research and high-performance computing. Labs & Teams: Key contributor to the Rice Compiler Group and the Ken Kennedy Institute, advancing research in compilers, parallel computing, and computational science.

• Computer Science Education
• Virtualization
• Embedded Runtime Systems
• +۳ مورد دیگر

Rebecca Schreib serves as an Assistant Teaching Professor and Director of Undergraduate Studies in Rice University's Department of Computer Science. She holds a Ph.D., M.S., and B.S. in Computer Science from Rice University (2019, 2015, 2014 respectively). Her research focuses on computer science education, virtualization, and embedded runtime systems. Her educational background includes a doctoral emphasis on designing personalized interactive learning tools for large introductory courses. Key research contributions include MemStep (2024), collaborative computational thinking courses (2020), and automated assessment systems (2016–2017). Her work spans both educational technology and embedded systems, with a focus on bridging theory-practice gaps in programming education while advancing runtime system reliability for constrained environments. She has developed tools that enhance student engagement and automated systems for testing and memory management. While no scientific awards are listed, her publications reflect sustained innovation in educational technology and embedded computing since 2012. Her advising and grant activities are not explicitly documented here. She contributes to Rice's undergraduate program infrastructure through her directorship role.

Yuke Wangمشاهده پروفایل

استادیار

• Deep Learning Systems
• High-Performance Computing
• Parallel Programming
• +۳ مورد دیگر

Yuke Wang is an incoming Assistant Professor at Rice University's Department of Computer Science starting Fall 2025. He earned his Ph.D. in Computer Science from the University of California, Santa Barbara (2024) and B.E. in Software Engineering from the University of Electronic Science and Technology of China (2018). His research focuses on optimizing deep learning systems through compiler and hardware co-design, with expertise in GPU acceleration, parallel computing, and distributed training. Education: Ph.D., UC Santa Barbara (2024); B.E., UESTC (2018). Professional experience includes postdoctoral research at Amazon AWS AI and internships at NVIDIA, Microsoft Research, and Alibaba DAMO Academy. Research interests include accelerating graph neural networks (GNNs), recommendation systems, and large language models (LLMs). He has developed frameworks like GNNAdvisor, MGG, and ZEN to enhance efficiency and scalability in deep learning workloads. His work has been recognized with awards such as the NVIDIA Graduate Fellowship and ACM PACT Student Research Competition. Awards include NVIDIA Graduate Fellowship (2022-2023), UCSB Dissertation Fellowship (2023), and multiple best paper nominations. He actively serves on program committees for top conferences like OSDI, ASPLOS, and ICS. Current hiring: Seeking graduate/undergraduate students for projects in deep learning systems, compiler optimization, and GPU acceleration. Collaborates closely with industry partners including NVIDIA and Amazon.

• Computer Architecture
• Computer Security
• Quantum Computing
• +۵ مورد دیگر

Assoc Prof Anupam Chattopadhyay is an Associate Professor in the College of Computing & Data Science at Nanyang Technological University (NTU), Singapore. He also holds a courtesy appointment in the School of Physical & Mathematical Sciences. His research focuses on computer architecture, security, design automation, and quantum computing. Anupam received his PhD from RWTH Aachen University in 2008, followed by roles at CoWare R&D and RWTH Aachen as a Junior Professor before joining NTU in 2014. Education: B.E., Jadavpur University, India MSc, ALaRI, Switzerland PhD, RWTH Aachen University, Germany Research Interests: Anupam’s work spans quantum computing, hardware security, AI security, post-quantum cryptography, and emerging technologies. His contributions include novel high-level synthesis techniques for cryptography, reliability estimation flows for embedded processors, and exploration of coarse-grained reconfigurable architectures. His research has been featured in major outlets like The Economist and Asian Scientist. Awards: Borcher's plaque (2008) for outstanding doctoral dissertation Nomination for Best IP Award (ACM/IEEE DATE 2016) Nomination for Best Paper Award (International Conference on VLSI Design 2018) Advising & Grants: Anupam leads a team of over 20 researchers, overseeing projects funded by various grants. He has advised numerous doctoral students and collaborates with institutions like Temasek Labs and the Indian Statistical Institute. Labs & Teams: His research group focuses on advanced topics in secure computing, quantum-resistant systems, and hardware-software co-design, leveraging cutting-edge tools and platforms for next-generation technologies.

Harry Xuمشاهده پروفایل

استاد

• Computer Systems
• Programming Languages
• Compilers
• +۷ مورد دیگر

Harry Xu is a Professor in the Computer Science Department at the Samueli School of Engineering , University of California, Los Angeles . His research spans computer systems, programming languages, compilers, and AI infrastructure. He co-founded BreezeML for GenAI risk management and has held visiting roles at Microsoft Research and IBM Watson Research Center. Current research focuses on user-defined clouds and AI application infrastructures Founded BreezeML and contributed to Niijima (SOSP'19) and Yak GC (OSDI'16) Developed VQPy, integrated into Cisco's DeepVision Scientific Awards 2018 Dahl-Nygaard Junior Prize ACM Distinguished Scientist Advising and Collaborations Current students: Shan Yu, Zhenting Zhu, Shu Anzai, Yicheng Liu Alumni: Shi Liu (Databricks), Jiyuan Wang (AWS), Haoran Ma (ByteDance AI Infra), Yifan Qiao (UC Berkeley), Christian Navasca (BreezeML), Pengzhan Zhao (BreezeML co-founder)

• Proof Assistants
• Formal Verification
• Inductive and Coinductive Datatypes
• +۱۲ مورد دیگر

Andrei Popescu is a Senior Lecturer (Associate Professor level) in the Department of Computer Science at the University of Sheffield, where he conducts research in formal methods, proof assistants, and information flow security. He previously held academic positions at Middlesex University and TU Munich. University: University of Sheffield Department: Department of Computer Science Previous Affiliations: Middlesex University, TU Munich His research focuses on the logical foundations and practical applications of proof assistants, particularly Isabelle/HOL. He has made foundational contributions to inductive and coinductive datatypes, syntax with bindings, higher-order logic, and the formal verification of secure systems. His work bridges theoretical logic with real-world systems such as conference management (CoCon) and social media platforms (CoSMeDis). The recent publications highlight a strong trend in formalizing deep logical results (e.g., Gödel’s incompleteness theorems), advancing datatype theory, verifying complex security properties, and applying formal methods to practical systems. His work consistently appears in top-tier venues such as POPL, CAV, ITP, and CSF. Distinguished Paper Award at POPL 2025 Distinguished Paper Award at POPL 2024 Distinguished Paper Award at POPL 2023 RS 3 Best Paper Award for 2012–2013 He has advised PhD students including Lorenzo Gheri and has been actively involved in organizing major academic events such as the Midlands Graduate School, CPP, ITP, and TABLEAUX conferences. He has served on numerous program committees including POPL, ITP, CSF, and CAV, and has led research projects funded by VeTSS and industrial partners. He is a key contributor to the Isabelle proof assistant ecosystem, particularly in the development of the (co)datatype package and foundational consistency results. His work combines deep theoretical insight with practical implementation, making significant impacts in both academia and applied security.

Michael E. Cotterellمشاهده پروفایل

مدرس ارشد

• Computer Science Education
• Active Learning
• Big Data Analytics
• +۳ مورد دیگر

Michael E. Cotterell is a Senior Lecturer of Computer Science and Undergraduate Coordinator at the University of Georgia's School of Computing. He holds a B.S. (2011) and Ph.D. (2017) in Computer Science from UGA. His roles include directing the Computer Science Undergraduate Assistant (CSUA) and UGAHacks experiential learning programs. He chairs the Undergraduate Program & Curriculum Committee and serves on UGA’s University Council. Dr. Cotterell’s teaching career began in 2012 with Software Development courses, transitioning to full-time instructor in 2015. He has won multiple teaching awards, including the Teaching Excellence in Computer Science Award (2018, 2020) and was promoted to Senior Lecturer in 2021. He has also served as a UGA Online Learning Fellow, Writing Fellow, and Teaching Academy Fellow. His research interests span computational education, active learning methodologies, and technical fields like big data analytics, ontology-based semantics, and energy informatics. His work bridges pedagogical innovation with applied computational science. Dr. Cotterell’s publications focus on improving educational practices, predictive analytics, and interdisciplinary applications of computer science. His most recent work emphasizes student sentiment analysis in active learning environments and scalable energy systems modeling. Awards include the 2016 Outstanding Faculty Teaching Award and multiple fellowships recognizing his contributions to pedagogy and online education. He oversees undergraduate program development and coordinates experiential learning initiatives, emphasizing hands-on learning through hackathons and assistantship programs.

Mae Milanoمشاهده پروفایل

استادیار

• Programming Languages
• Distributed Systems
• Systems & Networking
• +۳ مورد دیگر

Mae Milano is an Assistant Professor in the Department of Computer Science at Princeton University. She joined in 2024 after earning her Ph.D. from Cornell University in 2020. Her research focuses on designing programming languages to address challenges in distributed systems, with particular emphasis on concurrency safety, language interoperability, and formal verification. Education: Ph.D. in Programming Languages and Systems, Cornell University, 2020 Research Interests: Milano's work bridges programming languages and distributed systems. She develops type systems for safe concurrency (e.g., fearless concurrency ), designs languages for distributed state management (e.g., Gallifrey, Hydro), and explores compiler techniques for interoperability between systems. Her projects include: Fearless Concurrency – Safe shared-memory programming Gallifrey – Language for geographically distributed applications Hydro – Cloud-native programming models Grants & Collaborations: Milano collaborates with researchers like Alvin Cheung (University of Washington) and Joseph M. Hellerstein (UC Berkeley). Her work has been supported by projects such as the Hydro Framework and the Gallifrey language initiative. Labs & Teams: She leads the Princeton Systems Group and collaborates with the Programming Languages Group , fostering interdisciplinary research between PL and distributed systems.

Hubie Chenمشاهده پروفایل

مدرس ارشد

• Database theory
• Logic in computer science
• Computational complexity
• +۳ مورد دیگر

Hubie Chen is a Senior Lecturer in the Department of Informatics at King's College London, part of the Faculty of Natural, Mathematical & Engineering Sciences. Prior to working in the UK, he was an Ikerbasque Research Professor in Donostia-San Sebastian, Spain. He has held invited positions at institutions such as École Polytechnique, Humboldt-Universität zu Berlin, and Universität Wien. Dr. Chen received his PhD and BS degrees in computer science and mathematics from Cornell University and Stanford University, respectively. His academic career spans multiple prestigious institutions across Europe and the United States, demonstrating his international recognition in theoretical computer science. Dr. Chen's research focuses on the theoretical foundations of computer science, with particular expertise in database theory, logic in computer science, computational complexity, and constraint satisfaction. His work bridges theoretical computer science with practical database applications, exploring the fundamental limits and possibilities of computation and data management. His recent book "Computability and Complexity" published by MIT Press provides a comprehensive introduction to automata theory, computability theory, and complexity theory, including parameterized complexity. Dr. Chen's publication record demonstrates a consistent contribution to top-tier theoretical computer science venues. His recent work shows a strong focus on constraint satisfaction problems, database query theory, and computational complexity. He has made significant contributions to understanding the theoretical foundations of database queries, constraint satisfaction, and computational complexity, with publications in prestigious venues such as Computational Complexity, ACM Transactions on Computation Theory, and proceedings of ICDT, PODS, and LICS. Dr. Chen has received recognition for his work, including the ICDT 2017 Best Paper Award. His research has been supported by the Engineering and Physical Sciences Research Council (EPSRC), with active projects on "Algebraic methods for Quantified Constraints" (2024-2026) and "Query Evaluation" (2023-2026). Within King's College London, Dr. Chen is affiliated with the Software Systems Group and contributes to the broader research ecosystem of the Department of Informatics, which includes hubs for Trusted Autonomous Systems, Finance (FinTech), Algorithms and Data Analysis, and Security.

• Biomedical Engineering
• Biomedical Signal Processing
• Entropy Analysis
• +۷ مورد دیگر

Georgios Manis is an Associate Professor in the Department of Computer Science and Engineering at the School of Engineering, University of Ioannina, Greece. He holds a PhD from the National Technical University of Athens and has been a faculty member at the University of Ioannina since 2002, progressing from Lecturer to Associate Professor in 2018. He has also served as temporary teaching staff at the University of Patras, University of Crete, and University of Ioannina in the late 1990s and early 2000s. Education: B.Sc. in Computer Engineering (Diploma), National Technical University of Athens (NTUA), 1987–1992 MSc in Advanced Methods in Computer Science (Distributed and Parallel Systems), Queen Mary, University of London, 1992–1993 PhD in Computer Engineering, NTUA, School of Electrical and Computer Engineering, 1993–1997 His research interests lie at the intersection of Biomedical Engineering and Computing Systems , with a strong emphasis on Biomedical Signal Processing , Entropy Analysis , and Machine Learning . He has pioneered work in Bubble Entropy —a parameter-free entropy measure—and developed fast algorithms for entropy computation. His work also extends to compiler design and parallel computing, particularly in the automatic parallelization of recursive functions and loops. The trends in his recent publications reflect a dual focus: (1) biomedical applications involving entropy, heart rate analysis, and disease diagnosis using machine learning (especially Random Forests and SVMs), and (2) high-performance computing, including parallelization techniques and compiler optimizations for multi-core and SVP architectures. His research is highly interdisciplinary, combining signal processing, algorithm design, and clinical applications. Scientific Leadership and Recognition: Guest Editor, Special Issue on “Entropy in Biomedical Engineering”, Entropy (MDPI) Member of the IPAN Laboratory, University of Ioannina Active contributor to IEEE, Elsevier, and MDPI journals He has supervised several graduate students and is involved in funded research projects such as Palimpsest and Homore , focusing on smart systems for cultural interaction and elderly monitoring. His advising contributions are evident in co-authored papers with students like Evanthia Tripoliti and Aristeidis Mastoras. He teaches both undergraduate and postgraduate courses, including Compilers I/II and Biomedical Data Analysis . Laboratories and Teams: He is a member of the IPAN lab at the University of Ioannina, which supports interdisciplinary research in informatics and biomedical applications. His collaborative network includes researchers from Greece and abroad, particularly in the fields of biomedical signal analysis and entropy-based methods.

• Analytical Chemistry
• Green Analytical Chemistry
• Environmental Analysis
• +۶ مورد دیگر

Salvador Garrigues Mateo is a Professor at the University of Valencia in the Faculty of Chemistry and Department of Analytical Chemistry . He leads the research group SOLINQUIANA (Solutions & Innovation in Analytical Chemistry) , focusing on green analytical chemistry and innovative spectroscopic methods. Education: PhD in Analytical Chemistry from the University of Valencia (1993) with a thesis on Quantitative analysis by Fourier Transform Infrared Spectrometry . Research Interests: Green Analytical Chemistry, Fourier Transform Infrared Spectroscopy (FTIR), Ion Mobility Spectrometry (IMS), environmental and biomedical analysis, sample preparation techniques, and forensic chemistry. His recent publications highlight trends in environmental analysis (wastewater drug monitoring, bioaerosol assessment), forensic chemistry (illicit drug detection, synthetic cannabinoids), and green sample preparation (natural sorbents, microextraction techniques). He also contributes to food analysis (oil degradation, essential oil authentication) and biomedical applications (urinalysis for drug exposure, serum diagnostics). His work emphasizes green chemistry principles , focusing on reducing solvent consumption, developing non-invasive testing protocols, and applying chemometrics for improved analytical accuracy. He has published extensively on spectroscopic methods for both environmental and clinical applications, often combining FTIR with ion mobility spectrometry for enhanced detection capabilities. Contact: salvador.garrigues@uv.es

Alan William Doughertyمشاهده پروفایل

مدرس

• Computer Vision
• Machine Learning
• Medical Robotics
• +۳ مورد دیگر

Dr. Alan William Dougherty is a Lecturer at the School of Computing and Data Science, University of Hong Kong. He holds an MEng in Electronic Engineering from King’s College London (2013) and a PhD from Hong Kong Polytechnic University (2019), where he received the prestigious Hong Kong PhD Fellowship. His professional background includes engineering roles in compiler design for edge AI on FPGAs, autonomous robotic systems, and computer vision applications such as human emotion classification and 3D mesh generation from single images. Affiliations: School of Computing and Data Science (HKU) Research Interests: Computer Vision, Machine Learning, Medical Robotics, Edge AI, Autonomous Systems His research spans interdisciplinary areas including immersive online learning systems, human pose estimation using transformers, and computational material science for energy applications. Notable contributions include the SAILS platform for young learners and advanced catalyst discovery methodologies. He integrates practical engineering challenges with theoretical research to address real-world problems. Dr. Dougherty has been recognized for his work with awards such as the Hong Kong PhD Fellowship. His teaching philosophy emphasizes bridging theory and practice, fostering critical thinking in emerging tech fields.

• High Performance Computing
• Parallel Computing
• Heterogeneous Computing
• +۱۲ مورد دیگر

Anne C. Elster is a Professor in the Department of Computer Science at the Norwegian University of Science and Technology (NTNU), within the Faculty of Information Technology and Electrical Engineering. She is the founder and director of the HPC-Lab, a leading research group in heterogeneous and parallel computing. She also maintains a long-standing affiliation with the Oden Institute at the University of Texas at Austin as a Senior Visiting Scientist until Summer 2025. Research Interests: Her work spans high-performance computing (HPC), GPU computing, parallel algorithms, auto-tuning, performance optimization, and machine learning applications in scientific computing. She leads research in heterogeneous architectures and has contributed significantly to compiler and runtime systems for GPUs and accelerators. Publications Trends: Her recent publications (2021–2024) focus on GPU acceleration, auto-tuning frameworks (e.g., BAT, LS-CAT), performance modeling (Roofline), machine learning integration in HPC, and applications in geophysical and scientific computing. There is a strong emphasis on empirical evaluation, benchmarking, and practical optimization techniques. Scientific Awards and Recognition: IEEE Senior Member (2000) IEEE Computer Society Distinguished Contributor Charter member, NTNU's Board (2021) Distinguished Speaker, IEEE Computer Society (2019–2022) Advising and Grants: She has advised over 100 master’s students and several PhD students. She has led major funded projects including the RCN SFI Centre for Geophysical Forecasting, EU H2020 CloudLightning and TICOH, and NFR FRINATEK on Computational Microscopy. She has served on numerous international program committees and evaluation boards. Labs and Teams: She leads the HPC-Lab at NTNU, which includes postdocs, PhDs, and master’s students, and collaborates with international researchers. The lab is a hub for innovation in GPU computing, auto-tuning, and HPC applications.

Louis-Noël Pouchetمشاهده پروفایل

دانشیار

• polyhedral compilation
• iterative and adaptive compilation
• auto-tuning compilers
• +۱۵ مورد دیگر

Louis-Noël Pouchet is an Associate Professor in the Department of Computer Science at Colorado State University, with a joint appointment in the Electrical and Computer Engineering department. He leads research in high-performance computing, focusing on polyhedral compilation, performance portability, and hardware-software co-design. His research interests include polyhedral compilation, iterative and adaptive compilation, machine learning for compilers, performance-oriented domain-specific languages, energy-aware program optimization, and high-level synthesis. He develops compiler technologies to optimize and parallelize code for heterogeneous platforms, with applications in scientific computing and embedded systems. The 15 most recent publications highlight a strong focus on compiler optimization for high-performance systems, particularly using the polyhedral model. Key themes include data locality, parallelization, vectorization, memory access optimization, and performance modeling. His work spans both theoretical advances in program transformation and practical implementations in tools like PoCC and PolyOpt. Member, Center for Domain-Specific Computing (NSF) Member, DSL Technology for Exascale Computing (DoE) Lead, Polyhedral Compilation Research (NSF and Intel ISRA) Former member, Platform-Aware Compilation Environment (DARPA) He teaches courses on polyhedral compilation and has developed widely used software tools such as PoCC, PolyBench/C, and PolyOpt/C. His research is supported by major funding agencies including NSF, DoE, and Intel.