جست‌وجوی استاد و پژوهشگر

Alexandra Psarrouمشاهده پروفایل

دانشیار

• Deep learning methods for Image Quality Performance
• Explainable learning models
• Reinforcement learning
• +۳ مورد دیگر

Alexandra Psarrou is a Reader in the School of Computer Science and Engineering at the University of Westminster, serving as Head of School from 2002-2019. She holds a BSc and MSc from Queen Mary University of London and a PhD from Queen Mary, London (1996). Her research focuses on dynamic vision, explainable AI, hyperspectral analysis, and image quality metrics. Education: BSc (1987), MSc (1988), PhD (1996) Roles: Reader, former Head of School, member of multiple University committees Research interests include deep learning, reinforcement learning, and applications in cultural heritage. She founded the Computational Vision Research Group (CVRG) in 1997, advancing techniques like recurrent neural networks and kernel PCA models. Awards include Best Paper Awards at BMVC (1999), IMECS (2008), and IS&T (2020). She led major grants like the NOESIS project (€709k) and diARTgnosis (€518k). Supervised 10+ PhD students in areas like gesture recognition and medical imaging. Labs/Teams: Computational Vision and Imaging Technologies Research Group Grants: NOESIS, diARTgnosis, HISTORIA, HEFCE NFF

Hans-Joachim Wunderlichمشاهده پروفایل

استاد

• Computer Architecture
• Hardware Reliability
• Fault Tolerance
• +۷ مورد دیگر

Prof. Hans-Joachim Wunderlich is a Professor at the University of Stuttgart's Institute of Computer Architecture and Computer Engineering, within the Faculty of Computer Science, Electrical Engineering, and Information Technology. His research focuses on hardware reliability, fault tolerance, and testing methodologies for VLSI circuits and embedded systems. He specializes in areas such as delay fault testing under PVT variability, approximate communication, and aging-aware design. Key research interests include robust testing techniques for small delay faults, error-tolerant communication protocols (e.g., RAPPER and Gray code-based approaches), and GPU-accelerated simulation of faults. His work addresses challenges in functional safety, interconnect reliability, and securing reconfigurable architectures against security violations. He explores energy-efficient iterative solvers for approximate computing environments and develops methods for predicting device aging and early life failures. Prof. Wunderlich’s contributions span academic and industrial applications, with a focus on real-time systems and dependable multi-processor systems-on-chip (MPSoCs). His research integrates formal verification, machine learning for defect detection, and hybrid protection schemes for reconfigurable scan networks. Recent work emphasizes stress-aware testing strategies and optimization of sensor data streaming in resource-constrained environments.

Ozlem Kilicمشاهده پروفایل

استاد

• Antennas
• Wave Propagation
• Satellite Communications Systems
• +۶ مورد دیگر

Ozlem Kilic is the Dean of the College of Emerging and Collaborative Studies at the University of Tennessee. Previously, she served as Associate Dean for Academic and Student Affairs in the Tickle College of Engineering and held roles as Professor and Associate Dean at the Catholic University of America. Her academic background includes a DSc in Electrical Engineering from George Washington University (1996), an MS (1991), and a BS from Bogazici University (1989). Kilic has over 25 years of professional experience, including roles as an Electronics Engineer at the U.S. Army Research Laboratory and Senior Engineer/Program Manager at COMSAT Laboratories specializing in satellite communications and antenna systems. Her research focuses on antennas, wave propagation, satellite communications, microwave remote sensing, computational electromagnetics, and radar-based vital sign detection. Notable contributions include developing hybrid numerical electromagnetic tools for defense applications and pioneering non-contact vital sign monitoring systems using UWB radar. She has authored over 150 publications in these areas. Education: DSc in Electrical Engineering, George Washington University (1996) MS in Electrical Engineering, George Washington University (1991) BS in Electrical Engineering, Bogazici University (1989) Kilic's work bridges theory and application, with emphasis on high-performance computing for large-scale electromagnetic problems and bio-inspired optimization techniques. She received the ACES Outstanding Service Award (2017) and became an ACES Fellow (2016). Her lab develops low-cost anechoic chambers and innovative radar systems for healthcare and infrastructure inspection applications. Current research includes: 5G antenna concepts and millimeter-wave systems Non-invasive health monitoring via Doppler radar Compressive sensing for through-wall detection GPU-accelerated electromagnetic simulations Her work has been applied to military antenna design, disaster response sensing, and clinical patient monitoring systems.

• Computer Architecture
• Reliability Engineering
• Cloud Computing
• +۷ مورد دیگر

Professor Gizopoulos Dimitris is a faculty member at the Department of Informatics and Telecommunications, National and Kapodistrian University of Athens. His research focuses on advanced topics in computer architecture, reliability engineering, and hardware security. With a strong emphasis on fault tolerance and computational integrity, his work addresses critical challenges in modern computing systems such as silent data corruptions, GPU reliability, and RISC-V cloud ecosystems. Key research interests include microarchitecture-level analysis, cross-layer system reliability assessment, and energy-efficient computing. His contributions span fault propagation modeling, hardware-software co-design for resilience, and innovative approaches to detecting silent errors in CPUs and GPUs. He actively participates in large-scale projects like NEUROPULS and Vitamin-V, advancing secure neuromorphic architectures and open-source cloud infrastructure. Recent publications highlight trends in silent data corruption quantification, GPU vulnerability analysis, and energy-efficient RISC-V designs. His work bridges theoretical models with practical implementations, emphasizing real-world validation through frameworks like gem5 and fault injection experiments. Despite no listed advisees or grants in current records, his research collaborations and project leadership position him at the forefront of next-generation computing reliability. Labs and teams associated with his work include the Vitamin-V virtual environment development team and the NEUROPULS consortium for secure neuromorphic accelerators. These initiatives reflect his commitment to advancing both theoretical and applied aspects of dependable computing systems.

Karakostas Vasileiosمشاهده پروفایل

استادیار

• Computer Architecture
• Cloud Computing
• RISC-V Processors
• +۵ مورد دیگر

Karakostas Vasileios is an Assistant Professor at the Department of Informatics and Telecommunications of the National and Kapodistrian University of Athens. His research focuses on computer architecture, cloud computing, and energy-efficient hardware systems. He specializes in areas such as RISC-V processors, virtual memory systems, and hardware security. Vasileios leads projects like NEUROPULS (neuromorphic secure accelerators) and Vitamin-V (RISC-V-based cloud infrastructure validation). His work emphasizes resilience analysis, performance optimization, and trustworthy development frameworks. Education and employment details are not explicitly provided in the source text, but his extensive publication record since 2011 demonstrates continuous academic engagement. Key research trends include: Memory systems optimization (e.g., TLB hierarchies, elastic translations) GPU and SoC fault tolerance analysis Cloud resource management (ACTiCLOUD, DAPHNE runtime) Open-source hardware validation (Vitamin-V project) Notable contributions include the Gem5-marvel simulator for heterogeneous architectures and BypassD for SSD access acceleration. His work bridges theoretical computer architecture with practical cloud and embedded system applications.

• Environmental Hydrology
• Subsurface Flow Modeling
• Vadose Zone Hydrology
• +۹ مورد دیگر

Dr. Tissa H. Illangasekare serves as the AMAX Distinguished Chair of Environmental Sciences and Research Professor in the Department of Civil and Environmental Engineering at Colorado School of Mines. He directs the Center for the Experimental Study of Subsurface Environmental Processes, focusing on flow and transport in permeable media. His research spans subsurface hydrology, CO2 sequestration, PFAS contamination, and humanitarian engineering applications. His work includes numerical modeling of saturated/unsaturated flow, environmental impacts of energy development , and wireless sensor networks for monitoring. Recent publications emphasize PFAS transport mechanisms, geologic CO2 storage, and multiscale hydrologic modeling. Notably, he has led studies on climate change impacts and human health risks related to groundwater contamination. Elected foreign member of Academia Europaea (2022) PSIPW International Groundwater Prize (2016) Henry Darcy Medalist by European Geoscience Union (2012) Honorary Doctorate, Uppsala University (2010) As Editor of Frontiers in Water and AGU's Perspectives in Earth and Planetary Sciences , he shapes global hydrologic research discourse. His lab conducts intermediate-scale experiments bridging laboratory and field studies for subsurface process validation.

Alex Chenمشاهده پروفایل

استادیار

• High Energy Astrophysics
• Neutron Stars
• Black Holes
• +۶ مورد دیگر

Alex Chen is an Assistant Professor in the Department of Physics at Washington University in St. Louis. He is affiliated with the McDonnell Center for the Space Sciences and leads a research group focused on astrophysical plasma dynamics. His work involves GPU-based supercomputer simulations to study radiative processes and plasma physics near compact objects like neutron stars and black holes. Alex holds a PhD in Physics from Columbia University (2017) and has held postdoctoral positions at Princeton University’s Department of Astrophysical Sciences and JILA at the University of Colorado Boulder. His research expertise spans multi-wavelength pulsar emission mechanisms, fast radio bursts (FRBs), magnetic reconnection in extreme environments, and gamma-ray flares from active galactic nuclei (AGN). Research interests include: High Energy Astrophysics Neutron Stars and Magnetars Black Hole Magnetospheres Relativistic Plasma Simulations Radiation Physics in Strong Magnetic Fields AI Applications in Medicine and Astronomy Notable grants include a 2023 NSF award co-led with Yuan to simulate pulsar magnetospheres. His recent studies highlight interdisciplinary efforts, such as evaluating AI systems in medicine and developing open-source tools like the CompactObject package for neutron star analysis. Alex also contributes to the HEX-P X-ray probe project targeting magnetars. Labs/Teams: His research group collaborates with the McDonnell Center for the Space Sciences, leveraging advanced computational frameworks like APERTURE and Gemini 1.5 to tackle complex astrophysical problems. He emphasizes mentorship through initiatives like undergraduate research programs.

• Computer Architecture
• Domain-Specific Architectures
• Reconfigurable Computing
• +۴ مورد دیگر

Trevor Mudge is a Professor in the Department of Computer Science and Engineering at the University of Michigan. His work focuses on domain-specific architectures, reconfigurable computing, and energy-efficient processors. He leads a research group emphasizing collaborative innovation, with weekly meetings to discuss progress and foster interdisciplinary collaborations with institutions like MIT and Technion. His research interests include hardware-software co-design, near-memory processing, and accelerators for graph analytics and machine learning. He advocates for student autonomy in research direction, encouraging them to explore novel ideas while providing mentorship through frequent one-on-one interactions and 'extreme editing' sessions for manuscript refinement. Dr. Mudge actively supports student internships aligned with their research, particularly in industry partnerships that enhance their academic work. His publications reflect a strong emphasis on specialized architectures for wireless communication, sparse matrix computations, and low-power embedded systems. His group's approach to advising prioritizes flexibility, with communication channels like email, Slack, and virtual whiteboards enabling continuous feedback. Vacations and time away are accommodated with advance notice, emphasizing student well-being and productivity balance.

Juan David Guerrero Balagueraمشاهده پروفایل

پژوهشگر ارشد

• AI Accelerators
• GPU Reliability
• Fault Tolerance
• +۷ مورد دیگر

Juan David Guerrero Balaguera is a Research Fellow at Politecnico di Torino's Department of Automatic Control and Computer Science (DAUIN), affiliated with the CAD group. He holds a Ph.D. in Computer and Control Engineering from Politecnico di Torino (2024), advised by Prof. Matteo Sonza Reorda and Prof. Ernesto Sanchez. His research focuses on dependable hardware for safety-critical systems, including GPU reliability, fault tolerance, AI accelerators, and functional in-field testing. Prior to his Ph.D., he earned a Master's (2017) and Bachelor's (2013) in Electronics Engineering from Universidad Pedagógica y Tecnológica de Colombia, where he taught digital design, embedded systems, and FPGA-based image processing from 2014 to 2020. His research interests span advanced FPGA design, computational arithmetic for AI, fault effects analysis in GPUs, and reliability assessment of neural networks. Notable contributions include methods for generating self-test libraries (STLs) for GPUs, evaluating fault impacts on TCUs, and enhancing CNN robustness via dropout layer optimization. Education: Ph.D. in Computer and Control Engineering, Politecnico di Torino (2024) M.S. in Electronics Engineering, Universidad Pedagógica y Tecnológica de Colombia (2017) B.S. in Electronics Engineering, Universidad Pedagógica y Tecnológica de Colombia (2013) He has received the Ph.D. Quality Award (2023 and 2024) from Politecnico di Torino and Best Paper recognitions at DATE 2023 and DDECS 2021. His work bridges theoretical fault models with practical GPU testing methodologies, emphasizing real-world applications in AI and edge computing. Current teaching roles include collaborating on GPU programming (Master's level) and computer sciences courses (Automotive Engineering). Research collaborations involve exploring reliability trade-offs in split-computing DNNs and developing fault-aware design flows for AI accelerators.

Dino Distefanoمشاهده پروفایل

استاد

• Software Verification
• Program Analysis
• Static Analysis
• +۳ مورد دیگر

Professor Dino Distefano is a Professor of Software Verification at Queen Mary University of London's School of Electronic Engineering and Computer Science. He is currently on leave at Anthropic, focusing on connections between large language models and program verification. His research centers on automatic program verification, leveraging separation logic and scalable static analysis tools like Infer, which is widely used at Meta and other tech giants. Distefano co-founded Monoidics, acquired by Facebook in 2013, and leads WhatsApp's Dev Infra Org, developing tools to enhance software reliability and security. Education: PhD from the University of Twente, Netherlands. Awards include the IEEE Cybersecurity Award (2021), ACM SIGPLAN POPL 2019 Most Influential Paper Award, and Royal Academy of Engineering Silver Medal (2014). His work spans formal methods, program analysis, and industrial-scale verification tools. Publications focus on compositional verification, dynamic/static analysis integration, and privacy-aware code analysis. He has contributed to over 50 conferences and workshops, including key roles on program committees for VMCAI, ECOOP, and CAV.

Anthony Tzesمشاهده پروفایل

استاد

• Control Applications
• Robotics
• Unmanned Aerial Vehicles (UAVs)
• +۳ مورد دیگر

Anthony Tzes is a Professor of Electrical and Computer Engineering at NYU Tandon School of Engineering and holds the Alan Howard Distinguished Professorship in Artificial Intelligence at NYU Abu Dhabi. He is also a Global Network Professor affiliated with NYU’s engineering programs. His expertise spans control systems, robotics, and mechatronics, with a focus on UAVs, surgical robotics, and cyber-physical systems. Education: PhD in Electrical Engineering from Ohio State University (1990), BEng from University of Patras, Greece (1985). Active roles include Program Head of Electrical Engineering at NYUAD, and leadership in IEEE Control Systems Society and EU-funded projects. Research highlights include pioneering work on autonomous systems, including UAV-based firefighting, surgical robotic tools, and smart sensor networks. He leads the Applied Networked Mechatronics Systems group in Greece and directed the Urban Intelligent Transportation Systems Center in New York. His work is supported by NASA, NSF, ESA, and EU Horizon2020 grants. Awarded over 80 journal/conference publications, including a Best Paper Award (IET Control, 2014) and the ECC 2003 IST Prize. His teaching spans courses like Control Systems Engineering, Robotics, and Embedded Systems. He oversees senior design projects and mentors students in engineering research and innovation. Labs/Teams: Instrumentation and Control Laboratory (NYU Tandon), Applied Networked Mechatronics Systems group (Greece), and collaborations across academia and industry in robotics and automation.

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.

Zhang Tianweiمشاهده پروفایل

دانشیار

• Generative AI Safety
• Deep learning security
• Robotics and autonomous driving security
• +۲ مورد دیگر

Associate Professor at Nanyang Technological University's College of Computing and Data Science, serving as Deputy Director of the Cyber Security Research Centre @ NTU (CYSREN) and Associate Director of the NTU Centre Computational Technologies for Finance (CCTF). His research focuses on building trustworthy, efficient, and intelligent systems with emphasis on security and privacy across AI, robotics, and cloud infrastructures. Educational background: Bachelor of Physics, Peking University, 2011 Ph.D. in Electrical Engineering, Princeton University, 2017 Research spans five core domains: Generative AI Safety (vulnerability identification, safety testing, misuse detection), Deep learning security (adversarial examples, backdoor attacks, privacy protection), Robotics security (perception system attacks, safety testing), Machine learning optimization (workload scheduling, acceleration), and Computer architecture security (side-channel defenses, cloud security). His work bridges theoretical security with practical system implementations across diverse applications. Recent publications (2024-2026) reveal intense focus on securing generative AI systems, particularly text-to-image models and large language models, with significant contributions to red teaming methodologies, backdoor attack mitigation, and multimodal security. Emerging trends show expanding research into autonomous vehicle security and privacy-preserving machine learning with cryptographic techniques. Key awards include: Distinguished Artifact Award (CCS 2024) Stamatis Vassiliadis Best Paper Award Nominee (FPL 2024) Outstanding Paper Award (ACL 2024) Distinguished Artifact Award (Usenix Security 2024) Actively supervises PhD students and research staff while leading multiple high-impact grants: Ongoing: NRF CREATE Quantum Security (2025-2029), Continental NTU Corp Lab Automotive HPC (2025-2028), CRPO EV Charging Security (2025-2027) Completed: MoE AcRF Tier2 IP Protection (2022-2025), NTU S-Lab Efficient GPU Scheduler (2020-2025) Leads research within CYSREN and TAICeN (Trustworthy AI Centre NTU), directing interdisciplinary teams that investigate security threats across AI deployment stacks while developing practical defenses for real-world systems.

Chao Chenمشاهده پروفایل

استادیار

• High performance computing
• Numerical linear algebra
• Numerical PDE
• +۳ مورد دیگر

Chao Chen is an Assistant Professor in the Department of Mathematics at North Carolina State University (NC State), where he has been since 2023. His research focuses on developing efficient algorithms for matrix computations, with applications ranging from solving partial differential equations to analyzing large high-dimensional datasets. Prior to this, he was a postdoctoral researcher at The University of Texas at Austin’s Oden Institute, working with George Biros and Gunnar Martinsson. He earned his PhD in Computational and Mathematical Engineering (ICME) from Stanford University in 2018, advised by Eric Darve. He also holds an MS from Stanford’s ICME (2014) and a BS in Information and Computational Mathematics from Nankai University (2012). Education: PhD in Computational and Mathematical Engineering, Stanford University, 2018 MS in Computational and Mathematical Engineering, Stanford University, 2014 BS in Information and Computational Mathematics, Nankai University, 2012 Research Interests: Chen’s work emphasizes high-performance computing, numerical linear algebra, and randomized algorithms. He develops methods to reduce computational complexity and enhance performance on modern hardware. His applications span computational physics, data science, and machine learning. Notably, he has contributed to fast multipole methods, hierarchical solvers, and preconditioners for large-scale systems. Teaching: Chen teaches courses such as Linear Analysis (MA 303), Mathematics of Scientific Computing (MA 402), Linear Transformations and Matrix Theory (MA 523), and Theory of Matrices (MA 723). His courses emphasize theoretical foundations and practical applications, often incorporating computational tools like MATLAB and LaTeX. Presentations: He has presented at conferences worldwide, including SIAM Annual Meetings, Supercomputing Conferences, and specialized workshops on numerical linear algebra and fast solvers.

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)