جست‌وجوی استاد و پژوهشگر

Marco Guarnieriمشاهده پروفایل

دانشیار

• Security and Privacy
• Program Verification
• Programming Languages
• +۳ مورد دیگر

Marco Guarnieri is an Associate Professor at the IMDEA Software Institute in Spain, where he leads research at the intersection of security, programming languages, and computer architecture. Prior to his current position, he completed his Ph.D. in Computer Science at the Institute of Information Security at ETH Zurich in 2017. His educational background includes: PhD in Computer Science, 2017, ETH Zurich MSc in Computer Engineering, 2012, Università degli Studi di Bergamo BSc in Computer Engineering, 2010, Università degli Studi di Bergamo Dr. Guarnieri's research spans multiple security domains with particular focus on: Security at the hardware-software boundary Microarchitectural side-channel attacks and countermeasures Database security and access control Formal methods for security verification Secure compilation and information flow control His publication record shows consistent contributions to microarchitectural security, especially regarding speculative execution vulnerabilities and hardware-software leakage contracts, combining theoretical foundations with practical implementations that have advanced secure systems design. Dr. Guarnieri has received significant recognition for his research: Distinguished paper award at CCS 2024 Distinguished paper award at CCS 2023 Distinguished paper award at CCS 2022 Best paper award at S&P 2021 He actively contributes to the research community through conference organization and program committees for PLDI, POPL, and other top venues. Dr. Guarnieri maintains an active research group at IMDEA Software Institute and is currently seeking talented researchers to work on security at the hardware-software interface.

• Advanced Vehicle Technologies
• Energy Storage Systems
• Automated Driving Systems
• +۷ مورد دیگر

Jeffrey Wishart is an adjunct professor and faculty associate at Arizona State University's Ira A. Fulton Schools of Engineering, specializing in automotive systems. His work focuses on advanced transportation technologies, energy systems, and automated driving validation. Education: Ph.D. in Mechanical Engineering from University of Victoria Specialties: Vehicle electrification, fuel cell systems, automated driving verification Dr. Wishart's research spans energy storage, vehicle-to-infrastructure communication, and smart grid integration. He chairs the V&V Task Force under SAE's On-Road Automated Driving Committee. His recent publications emphasize automated vehicle safety assessment, sensor fusion, and operational validation frameworks. Key tools include Autonomie and MATLAB/Simulink for powertrain optimization.

• Software Model Checking
• Formal Methods
• Software Verification
• +۶ مورد دیگر

Dirk Beyer is a Full Professor and Head of Research Chair at the Department of Computer Science, Ludwig-Maximilians-Universität München (LMU Munich), where he leads the Software and Computational Systems Lab. His research focuses on developing models, algorithms, and tools for constructing and analyzing reliable software systems, with emphasis on software verification, model checking, and static analysis. Professor Beyer's research spans multiple critical areas in software engineering and formal methods. He has made significant contributions to software model checking through tools like CPAchecker and BLAST, structure analysis of large systems using CrocoPat and CCVisu, and formal verification of real-time systems with Rabbit. His work on interfaces for component-based design (Chic) has advanced modular software development approaches. Beyer has pioneered methodologies in benchmarking and reliable experimental evaluation through BenchExec, which has become a standard in tool competitions. Analysis of his recent publications reveals a strong focus on advancing software verification techniques, particularly in transferring knowledge between hardware and software verification domains, decomposing verification tasks for parallel processing, and improving the effectiveness of verification witnesses. His work consistently bridges theoretical foundations with practical tool implementations, with a growing emphasis on comparative evaluation and competition frameworks that drive the field forward. ACM SIGSOFT Distinguished Paper Award (FSE 2024) ACM SIGSOFT Best Artifact Award (FSE 2024) As a principal investigator of the DFG Research Training Group ConVeY, Beyer has secured significant funding for advancing verification techniques. He has played leadership roles in numerous software verification competitions including SV-COMP and Test-Comp, serving as PC Chair for major conferences like TACAS 2018 and VMCAI 2020. His service contributions extend to chairing ETAPS 2022 and organizing multiple workshops on CPAchecker. Professor Beyer leads the Software and Computational Systems Lab at LMU Munich, which has developed numerous influential verification tools including CPAchecker (configurable software verification), BenchExec (reliable benchmarking), and CCVisu (software structure visualization). The lab maintains active collaborations with research groups worldwide and contributes significantly to the international verification community through competitions, benchmarks, and open-source tool development.

Federico Holikمشاهده پروفایل

پژوهشگر ارشد

• Foundations of Quantum Mechanics
• Quantum Information Theory
• Quantum Logic
• +۳ مورد دیگر

Federico Holik serves as a Research Fellow at Argentina's National Scientific and Technical Research Council (CONICET) with primary affiliation to Vrije Universiteit Brussel (VUB) in Belgium. His institutional presence is anchored through VUB's CRIS profile and publications portal, reflecting active engagement in quantum research despite the absence of specified departmental or school affiliations within the university structure. His research program critically examines the logical, algebraic, and geometrical frameworks underpinning quantum mechanics, with concentrated efforts in quantum information theory and foundational probability interpretations. Key investigations include quantum resource management for NISQ-era devices, ontological indistinguishability of quantum entities, and the development of quantum mereology to address part-whole relationships in quantum systems. His interdisciplinary reach extends to quantum-inspired AI through quasi-set theory and epidemiological applications via information quantifiers in pandemic data analysis. Analysis of his 2023-2025 publications reveals two dominant trajectories: (1) practical quantum computing challenges centered on resource optimization, error mitigation, and software engineering frameworks for noisy hardware, and (2) deep foundational inquiries into quantum ontology, probability structures, and mereological paradoxes. This dual focus bridges theoretical rigor with emerging quantum technologies while maintaining strong connections to philosophical questions about quantum identity and agency.

Michael Plevinمشاهده پروفایل

مدرس ارشد

• Biomolecular Recognition
• Protein Engineering
• Liquid-Liquid Phase Separation
• +۳ مورد دیگر

Dr. Michael Plevin is a Senior Lecturer in Molecular Biophysics and Royal Society Industry Fellow at the University of York's Department of Biology. His research focuses on biomolecular recognition mechanisms, protein engineering for industrial applications, and liquid-liquid phase separation. He employs structural biology tools like X-ray crystallography, cryoEM, and NMR spectroscopy. Current collaborations include Eluceda Ltd, Syngenta, and Oxford Nanopore Technologies. Dr. Plevin also explores aromatic interactions in proteins and promotes positive research culture through initiatives like the Enhancing Research Culture working group. His teaching emphasizes biophysical techniques and protein engineering, including modules on biomacromolecule structure determination. He supervises PhD students in protein engineering, molecular recognition, and sequence-structure-function relationships. Dr. Plevin has held grants from UKRI, The Royal Society, and industry partners. His work bridges academic and industrial challenges, with projects on non-antibody scaffolds, DNA helicases, and nanopores. Key research themes include phase separation dynamics in cellular organization, weak transient interactions, and Rubisco's role in algal photosynthesis. He collaborates with experts in algal biology and single-molecule biophysics. His contributions to isotopic labeling methods for NMR have advanced structural studies of large proteins.

• Computer Networking
• Network Security
• Software-Defined Networking
• +۴ مورد دیگر

Laurent Vanbever is an Associate Professor at ETH Zürich's Department of Information Technology and Electrical Engineering and heads the Computer Engineering and Networks Lab. His research focuses on networking systems, with a strong emphasis on network security, routing protocols, software-defined networking (SDN), and distributed systems. He explores topics such as BGP convergence, energy-efficient router designs, and mitigating routing attacks on cryptocurrencies. His work bridges theoretical advancements with practical implementations, addressing challenges in network scalability, resilience, and sustainability. Key research areas include network verification, programmable packet processing (P4), and the security of decentralized systems like blockchain. Recent projects analyze transient forwarding anomalies, SDN-based network optimizations, and energy consumption in ISP networks. His contributions to network measurement and control plane innovations have significant implications for Internet architecture and operational practices. Vanbever actively publishes in top-tier conferences/journals and collaborates on open-source networking tools. His lab develops frameworks to enhance network performance and security, such as verifying link loads and mitigating BGP hijacks. Current efforts include exploring energy-efficient network designs and safeguarding cryptocurrency infrastructure against routing vulnerabilities.

Wei Huمشاهده پروفایل

استادیار

• Deep Learning Theory
• Machine Learning
• Neural Networks
• +۲ مورد دیگر

Wei Hu is an Assistant Professor in the Department of Computer Science and Engineering at the University of Michigan's College of Engineering. His research focuses on uncovering the theoretical and scientific foundations of deep learning, aiming to open the black box of neural networks through a combination of theoretical and empirical approaches. Dr. Hu received his PhD in Computer Science from Princeton University, where he was advised by Sanjeev Arora. Prior to his PhD, he completed his undergraduate studies at Tsinghua University as a member of the prestigious Yao Class. He also served as a FODSI postdoc at UC Berkeley before joining the University of Michigan faculty. His research interests center around understanding the fundamental mechanisms of deep learning, particularly focusing on training dynamics, generalization properties, and the theoretical underpinnings of neural networks. His work spans both clean, controlled problems and complex real-world models, with recent emphasis on transformer architectures, grokking phenomena, and the implicit biases in neural network training. Analysis of his recent publications reveals a strong focus on theoretical deep learning with particular attention to transformer models, grokking phenomena, and generalization theory. His work bridges the gap between theoretical understanding and practical deep learning applications, with significant contributions to understanding abrupt learning transitions, representation learning, and the dynamics of neural network training. AAAI New Faculty Highlights, 2024 Google Research Scholar Award, 2023 Siebel Scholar, 2021 Best paper award at ICML Workshop on Modern Trends in Nonconvex Optimization for Machine Learning, 2018 Gordon Y.S. Wu Fellowship, 2016 Gold medal (1st place), The 27th Chinese Mathematical Olympiad, 2012 Dr. Hu currently advises three PhD students: Pulkit Gopalani, Zhiwei Xu (co-advised with Yixin Wang), and Yongyi Yang. His research group has received substantial funding through awards including the Google Research Scholar Award. He teaches courses including Introduction to Machine Learning (EECS 445) and specialized topics in machine learning theory and large language models (EECS 598/CSE 598). His research group maintains an active presence in top machine learning conferences, with publications appearing regularly in venues such as NeurIPS, ICML, ICLR, and others. The group's work has gained significant recognition in the theoretical machine learning community for its rigorous approach to understanding deep learning phenomena.

Haiping Luمشاهده پروفایل

استاد

• Machine Learning
• Multimodal AI
• Healthcare AI
• +۷ مورد دیگر

Professor Haiping Lu is the Professor of Machine Learning and Head of AI Research Engineering at the University of Sheffield's School of Computer Science. He leads the UK Open Multimodal AI Network (UKOMAIN), a national initiative funded by EPSRC. His research focuses on translational multimodal AI for healthcare and scientific discovery, including applications in medical imaging, protein engineering, and drug discovery. Lu holds a PhD from the University of Toronto and has received awards such as the Turing Network Development Award and Wellcome Trust Innovator Award. Education: BEng and MEng from Nanyang Technological University (2001-2004), PhD in Electrical and Computer Engineering from the University of Toronto (2008). Research Interests: Multimodal AI foundations, generative AI, domain adaptation, healthcare AI (brain/cardiac imaging, cancer diagnosis), and scientific discovery (protein engineering, drug/materials discovery). He developed the open-source PyKale library embedded in PyTorch. Awards: Listed above, including grant leadership for projects like the £446k Cancer Data Driven Detection (CRUK) and £2M UKOMAIN network. Grants include EPSRC-funded UKOMAIN, Wellcome Trust AI in Health, and Amazon Research Awards. His team collaborates with institutions globally, focusing on AI ethics and clinical translation. Labs/Teams: Leads the KALE Group (Knowledge-Aware Machine Learning) with 15+ current researchers. Collaborations span medicine, bioinformatics, and materials science.

Tirado Franciscoمشاهده پروفایل

استاد

• Power Aware Computing
• Heterogeneous Computing
• Applications optimization
• +۴ مورد دیگر

Francisco Tirado is a Full Professor of Computer Architecture at Complutense University of Madrid since 1986. He has held leadership roles including Vice-Chancellor for Research (2012-2015), Dean of the Faculty of Physics (1994-2002), and Director of the Complutense Supercomputing Center (2002-2007). He is a founding member of the Spanish Society for Computer Science (SCIE) and Spanish Society for Computer Architecture (SARTECO). His research focuses on power-aware computing, heterogeneous systems, bioinformatics, and hardware design automation. He has authored over 242 publications and supervised 15 PhD theses. His professional service includes chairing 138 international conferences and serving on numerous committees for EUROMICRO, IEEE, and national research agencies. Education: No explicit details provided, but has been active at Complutense University since 1978. Affiliations: Member of COSCE Executive Board (2012), IEEE Senior Member (2005), and EUROMICRO Board (1991-2004). Research Interests: - Optimizing energy efficiency in high-performance computing systems - Design of heterogeneous architectures and accelerators - Bioinformatics applications through hardware/software co-design - Parallel programming models for emerging architectures - Automation tools for FPGA and embedded systems development Conference Contributions: Played multiple roles (General Chair, Program Chair) for major events like ParCo, HPCA, and EUROMICRO PDP conferences. Delivered 64 keynote/plenary lectures globally. Scientific Awards: Doctor 'Honoris Causa' from universities in Spain, Paraguay, and Peru 2013 National Computer Science Award IEEE Senior Membership Advising: Supervised 15 PhD students. Active in technology transfer contracts with industry. Labs/Teams: Founder and leader of the ArTeCS research group at Complutense University, specializing in computer architecture and high-performance computing innovations.

Mark Eramianمشاهده پروفایل

استاد

• Image Processing
• Medical Image Analysis
• Image Segmentation
• +۵ مورد دیگر

Mark Eramian is a Professor in the Department of Computer Science at the University of Saskatchewan. His research focuses on image processing, medical image analysis, and computer vision, with significant contributions to plant phenotyping and medical diagnostics. He leads a lab emphasizing equity, diversity, and inclusivity, outlined in the Lab Code of Conduct . His work spans applications in agriculture, such as automated plant trait analysis using UAV and multispectral imaging, and medical imaging, including thyroid nodule classification and ovarian follicle segmentation. He collaborates with organizations like the Canadian Food Inspection Agency and is part of the Plant Phenotyping and Imaging Research Centre (P2IRC), funded by the Canada First Research Excellence Fund. Eramian teaches courses in image processing, computer vision, and algorithms. His lab actively develops open-source tools for image analysis, including texture enhancement methods and segmentation algorithms. Current projects include self-supervised learning for phenotyping and semi-automated seed classification. Prospective students are encouraged to apply through the university's graduate program, selecting 'Computer Vision and Image Processing' as their focus area. His lab emphasizes collaboration, learning, and ethical conduct, with ongoing work in phenomics and medical imaging technologies.

Julie R. Williamsonمشاهده پروفایل

مدرس

• Human-Computer Interaction (HCI)
• Virtual Reality (VR)
• Social Signal Processing
• +۶ مورد دیگر

Dr. Julie R. Williamson is a Lecturer in Human-Computer Interaction (HCI) at the School of Computing Science, University of Glasgow. She leads the FUSION ERC Consolidator Grant (€1.98M), focusing on stabilizing social interactions in Extended Reality (XR). Her work bridges social signal processing, immersive technologies, and ethical design. Key contributions include research on social XR, digital proxemics, and VR acceptance in public spaces. She has authored over 60 publications, including influential papers on VR user awareness, mixed reality in transit, and inclusive metaverse design. Williamson is a vocal advocate for open science, sharing rejected grant proposals to highlight iterative research processes. She holds an ACM Senior Member title and chairs ACM SIGCHI’s Publications Board. Her lab, SoCShop, supports electronics prototyping for interdisciplinary research. Education & Grants: ERC Consolidator Grant (2023), EPSRC Open Fellowship (2023), EPSRC First Grant Scheme (prior success). Research spans HCI, VR ethics, and public display systems. Advises PhD/MSc students on immersive tech and social computing. Awards: ERC Consolidator Grant (2023), ACM Senior Member (2019), multiple grant proposals rejected but iterated into successful frameworks. Grants & Labs: FUSION Grant focuses on XR social signal fusion. SoCShop lab provides prototyping resources for computing science research. Collaborates with CWI, Toyota Research Institute, and Northeastern University. Research Trends: Articles emphasize stable interpersonal realities, inclusive VR design, and ethical challenges in immersive tech. Recent work analyzes user acceptance of EMS and thermal security threats in hardware.

• Formal Verification
• Software Engineering
• Computer-Aided Verification
• +۵ مورد دیگر

Zvonimir Rakamaric is a Professor in the Department of Computer Science at the University of Utah. His primary research focuses on formal methods, software verification, and numerical analysis, with an emphasis on ensuring correctness and reliability in software systems. He leads efforts in developing tools and techniques for verifying complex systems, including formal verification of floating-point arithmetic, compiler optimizations, and concurrent/distributed systems. Key Roles: Formal Methods Researcher, Software Verification Specialist, Floating-Point Arithmetic Expert Affiliations: University of Utah School of Computing Research interests span formal verification tools (e.g., SMACK, Dafny), static analysis, and numerical error analysis. His work addresses challenges in safety-critical systems, including embedded software, high-performance computing, and compiler optimizations. Collaborations include institutions like Sandia National Labs and Microsoft Research. Publications emphasize rigorous methods for analyzing floating-point precision, equivalence checking, and tool development for formal verification. His work bridges theoretical foundations with practical tool implementations, contributing to open-source verification frameworks widely adopted in academia and industry.

Michael Rietzlerمشاهده پروفایل

پژوهشگر

• Human-Computer Interaction (HCI)
• Virtual Reality
• Interaction Design
• +۴ مورد دیگر

Dr. Michael Rietzler is a Researcher at Ulm University's Department of Informatics, specializing in Human-Computer Interaction (HCI), Virtual Reality (VR), and tracking systems. He earned his PhD in 2019 with a thesis on cross-reality interaction and has been actively involved in research since 2015. His work focuses on VR navigation, haptic feedback, and open-source tracking tools like FusionKit. Education - Bachelor's and Master's in Media Informatics (Ulm University, completed 2015) - PhD in Informatics (Ulm University, 2019) Research Interests His research spans VR/AR interaction, motion tracking, user experience evaluation, and ubiquitous computing. Notable projects include Telewalk (endless VR walking) and INTERACT (factory automation tools). Publications Recent work addresses VR presence perception, energy-saving appliance adoption, and voice assistant design. His studies often bridge hardware innovation with user-centric design. Teaching Lectured on HCI and media informatics topics, including courses like 'Research Trends in Media Informatics.' Labs/Teams Part of the Human-Computer Interaction research group at Ulm, contributing to projects like FusionKit and VRSpinning rotation platform.

Martin Jaggiمشاهده پروفایل

دانشیار

• Machine Learning
• Optimization
• Artificial Intelligence
• +۳ مورد دیگر

Martin Jaggi is an Associate Professor at EPFL, leading the Machine Learning and Optimization Laboratory (MLO). He holds academic positions within the School of Computer and Communication Sciences (IC), including roles in the SIN, SSC, and EDIC departments. His research focuses on machine learning optimization, federated learning, and large language models. He earned his PhD in Machine Learning and Optimization from ETH Zurich (2011) and a MSc in Mathematics from the same institution. Education: PhD in Machine Learning and Optimization, ETH Zurich (2011) MSc in Mathematics, ETH Zurich Research Interests: Dr. Jaggi's work integrates optimization theory with machine learning applications, emphasizing scalable algorithms and ethical AI. He explores topics like distributed learning systems, privacy-preserving techniques (e.g., federated learning), and model interpretability. His lab develops foundational methods for large language models (LLMs) and their ethical deployment. Teaching & Courses: Optimization for Machine Learning Topics in Machine Learning Systems Advising & Grants: He currently advises 9 PhD students and has supervised 11 past students. His work is supported by grants focusing on federated learning, optimization algorithms, and medical AI applications like Meditron-70b. Collaborations include projects on respiratory disease detection and mental health modeling. Labs & Teams: Academic Director of RCP-GE and oversees MLO lab, which develops open-source tools like FLamby for federated learning benchmarks and Meditron for clinical AI. The lab also advances techniques in distributed optimization and low-precision training.

Peter M. Chenمشاهده پروفایل

استاد

• Operating Systems
• Distributed Systems
• Virtualization
• +۵ مورد دیگر

Peter M. Chen is the Arthur F. Thurnau Professor in the Department of Electrical Engineering and Computer Science (EECS) at the University of Michigan, part of the College of Engineering. He leads research in operating systems, distributed systems, and persistent memory technologies. His work includes foundational contributions to virtualization (e.g., ReVirt), reliable memory systems (Rio), and non-volatile memory semantics. He is a member of the Software Systems Lab and collaborates with the Computer Engineering Lab. Education and affiliations: Ph.D. in Computer Science (implied through career trajectory), affiliated with EECS and multiple labs at U-M. His research interests span speculative execution, security in distributed systems, and high-performance storage. He has advised over 20 graduate students, many of whom have contributed to seminal papers in systems research. Awards include the Arthur F. Thurnau Professorship and multiple best paper awards at top conferences like OSDI and SOSP. His work on ReVirt pioneered virtual machine logging for intrusion analysis, and Rio revolutionized reliable memory caching. Current projects focus on persistent memory programming models and wear management in NVM technologies. Key grants and teams: Active in NSF-funded projects on persistent memory systems and security. Collaborates with industry through partnerships in cloud computing and mobile systems optimization. His lab develops open-source tools like Rio and Vista, emphasizing practical system implementations alongside theoretical contributions.