جستجوی استاد راهنمای دانشگاهی

Dr. Israat Haque is an Associate Professor in the Faculty of Computer Science at Dalhousie University, Halifax, where she leads the Programmable and Intelligent Networking (PINet) research group. She also holds an adjunct professorship in the Department of Computing Science at the University of Alberta. PhD in Computer Science, University of Alberta NSERC Postdoctoral Fellow, University of California, Riverside MSc in Computer Science, Concordia University Her research focuses on developing high-performance, secure, and dependable distributed and emerging networking systems, with key interests in Software-Defined Networking (SDN), Cyber-physical Systems (CPS), Internet of Things (IoT), 5G/6G technologies, and AI/ML applications in networking. She applies data-driven approaches to solve real-world problems in network programmability, stream processing, and edge/cloud computing. The 15 most recent publications highlight a strong trend in in-network computing, IoT security, AI-driven network reliability, and stream processing. Her work spans from theoretical surveys to practical system implementations, often leveraging programmable data planes (P4), machine learning, and hardware acceleration to address challenges in performance, security, and fairness in distributed systems. Dr. Haque has received numerous prestigious recognitions: IEEE/ACM N2Women Rising Star Award (2021) ACM FAccT Best Paper Award (2023) IEEE WICE Outstanding Mentoring Award (2025) University of Alberta Alumni Honour Award (2024) Digital Nova Scotia Thinking Forward Award (2022) Intel Fast Forward Initiative Winner (2022) President’s Research Excellence Award, Dalhousie (2021) She actively mentors PhD and MSc students and leads externally funded research projects, including a Canada First Research Excellence Fund (CFREF)-supported initiative on Securing Smart Environments. She has served on editorial boards of IEEE Transactions on Vehicular Technology and IEEE Communications Magazine, and on program committees of top conferences such as IEEE ICNP, IEEE NetSoft, and ACM SIGCOMM. Her lab, PINet, fosters innovation in networking systems and produces high-impact research with real-world applicability. Dr. Haque leads the PINet research group, which includes current PhD and MSc students working on cutting-edge topics like post-quantum cryptography, network programmability for security, and large ML model security. The group collaborates with institutions such as the University of Alberta, Laval University, and Concordia, and has strong industry ties with Intel, Meta, Amazon, and Facebook.

• Software-defined Networking (SDN)
• Cyber-physical Systems (CPS)
• Internet of Things (IoT)
• +۱۲ مورد دیگر

Tao Lin is a Tenure-Track Assistant Professor and Principal Investigator of LINs Lab at Westlake University, School of Engineering. He leads cutting-edge research in deep learning optimization, generalization, and robustness, particularly in distributed and federated settings. Prior to this, he was a Ph.D. student at École Polytechnique Fédérale de Lausanne (EPFL), Switzerland, under the supervision of Prof. Martin Jaggi and Prof. Babak Falsafi. Doctor of Science, School of Computer and Communication Sciences, EPFL, Switzerland (2017–2022) Master of Science, School of Computer and Communication Sciences, EPFL, Switzerland (2014–2017) Bachelor of Engineering (with honors), College of Electrical Engineering, Zhejiang University, China (2010–2014) His research focuses on the intersection of optimization and generalization in deep learning, leveraging theoretical and empirical insights into loss landscapes and training dynamics to design efficient and robust learning and inference methods. This includes work on decentralized and federated learning under noisy, heterogeneous, and hardware-constrained environments. His work spans algorithmic innovation, theoretical analysis, and practical system integration. The recent publications from his lab demonstrate a strong trend in advancing federated learning, efficient inference for large language models, multimodal foundation models in pathology, and robust training under distribution shifts. Key themes include communication efficiency, model personalization, gradient tracking, and hardware-aware learning. His group has published at top venues including NeurIPS, ICML, ICLR, CVPR, and ECCV, with several papers receiving oral or spotlight presentations. ECCV Best Paper Candidate, 2024 Top 2% Scientists Worldwide 2024 (Stanford University) Doctoral Program Thesis Distinction Award, EPFL, 2022 Outstanding Performance Bonus, EPFL, 2021–2022 Top Reviewer: NeurIPS, ICML, AISTATS He advises multiple Ph.D. and master’s students, including Yongxin Guo, Futing Wang, Peng Sun, and Yuxuan Sun, whose work has been accepted at premier conferences. He has secured competitive grants as PI and participant, including the National Natural Science Foundation of China for Excellent Young Scientists Fund (Overseas) and the Science and Technology Innovation 2030 – Major Project. He also contributes to the community through service as an area chair (NeurIPS, ICML), reviewer for top journals and conferences, and organizer of workshops and academic events. His open-source contributions, such as Post-local SGD, have been integrated into PyTorch. Tao Lin teaches graduate courses such as Research Methodology of Computer Science and Technology and Deep Learning at Westlake University. He is actively involved in academic governance, serving on committees for student seminars, academic exchange, doctoral studies, and teaching leadership. The LINs Lab runs a regular research seminar on Deep Learning and Optimization, fostering a collaborative and dynamic research environment.

• Deep Learning
• Distributed Machine Learning
• Federated Learning
• +۷ مورد دیگر

Zahra Ebrahimi is a researcher in the field of approximate computing, reconfigurable accelerator design, and embedded systems. She joined the Chair of Embedded Systems at Ruhr University Bochum in April 2024, following her PhD research associate role at the Center for Advancing Electronics Dresden (Cfaed) from 2018 to 2024. Her work focuses on energy-efficient hardware/software co-design for edge-to-cloud computing, with applications in neural networks and bio-signal processing. She leads the BMBF-funded project X-DNet , collaborating with Huawei Research Center Munich. Education : B.Sc. and M.Sc. in Electrical Engineering from Sharif University of Technology, Iran Key Projects : ReAp (DFG), Re-Learning (ESF), X-ReAp (DFG), X-DNet (BMBF) Research Interests : Approximate computing, reconfigurable accelerators, embedded systems, SW/HW co-design, energy-efficient edge/cloud computing. Recent Trends : Zahra’s research emphasizes applying approximation techniques to neural networks for 5G/6G applications and designing specialized hardware like CGRAs for bio-signal processing and distributed computing. Collaborations : Academic-industry partnership with Huawei Research Center Munich.

• Approximate Computing
• Reconfigurable Accelerator Design
• Embedded Systems
• +۲ مورد دیگر

Jianyi Cheng is a Lecturer in Computer Architecture at the School of Informatics, The University of Edinburgh, where he is affiliated with the Institute for Computing Systems Architecture. His work focuses on advancing computing systems through innovative hardware design and optimization. His research interests lie primarily in computer architecture and reconfigurable computing, with emphasis on FPGA-based acceleration, high-level synthesis, and energy-efficient system design. These areas are critical for next-generation computing platforms in domains such as AI, scientific computing, and embedded applications. The trends in his research, though not detailed in specific publications here, align with modern challenges in hardware-software co-design and domain-specific architectures. His work bridges the gap between theoretical algorithms and practical implementations on programmable logic. Jianyi Cheng has not been listed with any scientific awards in the provided text. He is actively involved in research within the Institute for Computing Systems Architecture, contributing to advancements in computing systems. While specific grants or advising roles are not mentioned, his position suggests engagement in research leadership and student supervision.

• Computer Architecture
• FPGA
• Hardware Acceleration
• +۴ مورد دیگر

Jingjie Li is a Lecturer (Assistant Professor) in the School of Informatics at the University of Edinburgh, where he conducts interdisciplinary research at the intersection of computer systems, cybersecurity, and human-computer interaction. He is a member of the Institute for Computing Systems Architecture and the School of Informatics Ethics Committee. Ph.D. in Computer Engineering, University of Wisconsin-Madison (2017–2023) B.Eng. (R&D) with First-Class Honours, Australian National University (2015–2017) B.Sc., Beijing Institute of Technology (2013–2015) His research focuses on user-centric security and privacy , measuring human behavior in digital systems , and efficient human-machine interfaces . He investigates risks in emerging technologies such as smart homes, AR/VR, and AI systems, aiming to make them safer and more human-centric. His work combines technical innovation with behavioral insights to design practical privacy controls, measure digital risks, and build efficient computing platforms. The recent publications highlight a strong trend in privacy transparency , AI explainability , smart home and AR/VR security , and hardware-software co-design . His work frequently appears in top-tier venues including IEEE S&P, USENIX Security, ACM CHI, and ISCA, reflecting a consistent focus on both technical depth and human factors. Notable scientific awards include: ACM CHI Best Paper Award (2019) Facebook Trustworthy Products in AR, VR, and Smart Devices Award (2021) CPS Rising Star, NSF (2022) Generative AI Laboratory Seedcorn Award, University of Edinburgh (2024) Qualcomm Innovation Fellowship Finalist (2019, 2021) Jingjie Li actively supervises PhD students including Jiuming Jiang and Karen Jiamin Zheng, and co-supervises Lawrence Piao and Temima Hrle. He has received research support through fellowships such as the UW–Madison Chancellor’s Opportunity Fellowship and has collaborated globally with institutions including Max Planck Institute, Visa Research, and CSIRO. He serves on the program committees of major conferences like ACM CCS, USENIX Security, and ACM CHI. He leads a dynamic research team focused on systems security and human-centered computing, hosting undergraduate researchers and mentoring students through projects in privacy, AI transparency, and hardware security. His lab fosters interdisciplinary collaboration and real-world impact through community engagement, such as the 'Hack Your Age' workshop with intergenerational participants.

• Computer Systems
• Cybersecurity
• Systems Security
• +۱۲ مورد دیگر

Professor Anestis Terzis serves as a Professor of Digital Systems Design and Head of the Institute for Communication Technology (IKT) at Technische Hochschule Ulm within the Faculty of Electrical Engineering and Information Technology. He coordinates the International Electrical Engineering Program and is responsible for the Vehicle Systems specialization. His office is located in Room W2405 at Albert-Einstein-Allee 53-55, 89081 Ulm, Germany. Professor Terzis specializes in digital system design with focus on FPGA, VHDL, and high-level design methodologies. His research spans Camera Monitor Systems (CMS) for automotive mirror replacement, advanced camera-based driver assistance systems, vehicle electronics, digitalization in laboratory didactics, and autonomous driving technologies. He has pioneered work in digital mirror systems compliant with ISO 16505 standards and has contributed significantly to image processing for automotive applications. His publication record demonstrates consistent contributions to automotive imaging technology, with recent work focusing on image compression impacts on detection quality, CMS image quality parameters, and programmable processing for autonomous vehicles. Professor Terzis has edited the comprehensive Handbook of Camera Monitor Systems published by Springer, establishing himself as a leading authority in this specialized field. As Head of the Institute for Communication Technology, he leads research initiatives connecting digital systems design with automotive applications. He also serves as Coordinator for the Study with In-depth Practice program and is a member of the Institute for Vehicle System Technology (IFS), demonstrating his commitment to both theoretical advancement and practical implementation in automotive electronics.

• Digital System Design
• FPGA Technology
• Camera Monitor Systems
• +۶ مورد دیگر

Yu [Kevin] Cao is the Louis John Schnell Professor in the Department of Electrical and Computer Engineering at the University of Minnesota. His research focuses on microelectronics co-design for energy-efficient computing, spanning integrated circuit design, semiconductor physics, and machine learning methodologies. He leads the Microelectronics Co-design Research Group and actively collaborates with institutions like Georgia Institute of Technology, Sandia National Laboratories, and Notre Dame. His research interests include AI hardware acceleration , in-memory computing , cryogenic CMOS design , and 3D integration of heterogeneous chiplets . Current initiatives explore reconfigurable on-package systems for AI, spiking neural networks on neuromorphic hardware, and low-temperature logic technologies. Recent publications and projects highlight advancements in AI accelerators , RRAM-based compute-in-memory , graph convolutional networks , and 3D integration . His group develops tools like MN-SIM 2.0 for memristor modeling and investigates novel materials for neuromorphic systems. Grants include collaborative NSF funding for chiplet-based AI systems, CoCoSys center funding from SRC, and DOE/Sandia projects on neuromorphic hardware. Future work emphasizes scalable co-design frameworks for intelligent systems and heterogeneous integration challenges.

• Microelectronics
• Computer Engineering
• Semiconductor Technology
• +۵ مورد دیگر

Houpeng Chen is a Research Professor at the Chinese Academy of Sciences, specifically affiliated with the School of Microsystem and Information Technology in the Department of Microelectronics. With over two decades of research experience since the early 2000s, Chen has established himself as a leading expert in memory systems and circuit design, particularly in the areas of Phase Change Memory and neuromorphic computing. Chen's research primarily focuses on advanced memory technologies, with particular emphasis on Phase Change Memory (PCM) systems, neuromorphic computing architectures, and analog circuit design for memory applications. His work spans from fundamental circuit design for memory systems to advanced computing architectures that leverage novel memory technologies. A significant portion of his recent work explores in-memory computing paradigms and brain-inspired computing systems, demonstrating a strategic shift toward next-generation computing architectures that address the limitations of traditional von Neumann systems. Analysis of Chen's publication record shows a clear evolution from traditional circuit design toward more innovative memory-based computing architectures. His recent work demonstrates strong expertise in 3D cross-point memory systems, in-memory computing, and neuromorphic hardware implementations. The research shows consistent quality with publications in top-tier IEEE journals and conferences, indicating strong recognition within the semiconductor and memory research community. As evidenced by the authorship patterns in his publications, Chen has successfully mentored numerous graduate students and junior researchers who have gone on to become first authors on significant publications. His collaborative network includes extensive work with Zhitang Song, Qian Wang, and Xi Li, suggesting a well-established research group with strong internal collaboration.

• Phase Change Memory
• Neuromorphic Computing
• Very Large Scale Integration
• +۴ مورد دیگر

Rafael Pereira Pires is a Lecturer and researcher at École polytechnique fédérale de Lausanne (EPFL) , affiliated with the Scalable Computing Systems Laboratory (SACS) and IC-SIN units. His research focuses on systems solutions at the intersection of privacy, efficiency, and machine learning in distributed environments. Education PhD in Computer Science (2019, University of Neuchâtel, Switzerland) Professional Master in Mechatronics (2014, IFSC, Brazil) Master in Computer Science (2009, UFSC, Brazil) His work explores privacy-preserving decentralized learning , trusted execution environments , and resource-efficient distributed systems . Recent publications address techniques like model fragmentation, approximate caching, and secure aggregation in decentralized learning contexts. Key trends in his 2023-2025 publications include: Advancements in federated learning and Mixture-of-Experts (MoE) models Applications of Trusted Execution Environments (SGX) to decentralized systems Optimization techniques for energy-aware and low-cost learning Scientific recognition includes the 2019 Léon Du Pasquier et Louis Perrier award for his PhD thesis. He has contributed to open-source tools like DecentralizePy and served as reviewer/PC member for top conferences including NeurIPS , Middleware , and ICDCS .

• Distributed Systems
• Machine Learning
• Privacy-Preserving Technologies
• +۳ مورد دیگر

Ariful Azad serves as an Associate Professor in the Department of Computer Science & Engineering at Texas A&M University, where he leads research at the intersection of high-performance computing and graph analytics. His work focuses on developing scalable algorithms for graph machine learning with applications in bioinformatics and security informatics. Educational Background: Ph.D. in Computer Science, Purdue University (2014) B.S. in Computer Science and Engineering, Bangladesh University of Engineering and Technology (2006) Research Focus: Dr. Azad specializes in high-performance graph algorithms , particularly for distributed-memory systems. His pioneering work includes the Combinatorial BLAS library and novel approaches for graph neural networks (GNNs), with emphasis on explainability through Shapley values and optimization of sparse matrix operations. His bioinformatics research tackles large-scale metagenomics challenges through projects like Exabiome. Publication Trends: Recent publications (2023-2025) reveal three dominant themes: (1) Scalable GNN explanation frameworks using distributed Shapley values, (2) High-performance sparse linear algebra for graph embeddings and knowledge graphs, and (3) Bioinformatics applications in metagenomics and network alignment. His work consistently bridges theoretical algorithm development with practical implementations for exascale systems. Scientific Recognition: NSF CAREER Award (2024) for foundational contributions to scalable graph algorithms Indiana University Trustee's Teaching Award (2024) U.S. Department of Energy Early Career Award (2021) Research Leadership: As principal investigator for multiple federal grants, Dr. Azad directs projects advancing graph analytics at extreme scales. His work on Weapons of Mass Destruction knowledge graphs demonstrates applied security research, while Exabiome represents significant contributions to computational biology. He actively develops open-source tools like PLANETALIGN for network analysis benchmarking, fostering reproducibility in computational science.

• High-performance computing
• Graph algorithms
• Graph machine learning
• +۱ مورد دیگر

Sandro Bartolini serves as Associate Professor in the Department of Information Engineering and Mathematical Sciences at the University of Siena, Italy, where he teaches advanced courses in computer architecture and parallel programming while leading cutting-edge research in high-performance computing systems. His academic journey began with a cum laude Laurea in Computer Engineering followed by a PhD in Computer Science and Engineering from Università di Pisa. Education: PhD in Computer Science and Engineering, Università di Pisa Laurea in Computer Engineering (cum laude), Università di Pisa Research Focus: His work centers on photonic interconnects for chip multiprocessors , energy-efficient software optimization for multi-core/GPU architectures, and performance-portable parallel programming models . Current investigations span cryptographic acceleration, blockchain algorithms, and hardware/software co-design for emerging computing paradigms, with strong emphasis on practical implementations bridging theoretical advances and real-world applications. Publication Trends: Recent publications (2019-2023) reveal three dominant threads: (1) Photonic network innovations addressing energy bottlenecks in chip multiprocessors, (2) The PHAST library ecosystem enabling seamless CPU/GPU programming across domains from autonomous vehicles to UAV navigation, and (3) Hardware accelerator designs for convolutional networks and cryptographic workloads. These works consistently target performance-portability challenges in heterogeneous computing environments. Grants and Collaborations: As principal investigator for the Italian Ministry-funded PHOTONICA project, he established international research partnerships with Murcia University, Columbia University, and Hong Kong University of Science and Technology, while securing industry collaborations with STMicroelectronics, Intel Munich, IBM, and IMEC. He has also managed complex IT system deployments for Siemens Italy, RAI (Italian public broadcasting), and SpaceDys. Academic Leadership: Bartolini serves as Associate Editor for the Eurasip Journal of Embedded Computing and actively contributes to the European HiPEAC network. His research group at Siena maintains strong industry ties for technology transfer, particularly in photonic interconnect validation and parallel programming frameworks for next-generation computing systems.

• High-Performance Computing (HPC)
• Computer Architecture
• Embedded Systems
• +۵ مورد دیگر

Dr. Xiaoyan Hong is an Associate Professor in the Department of Computer Science at The University of Alabama's College of Engineering. Her research focuses on mobile/wireless networks, vehicular networks, and delay-tolerant systems. Ph.D., Computer Science, University of California-Los Angeles (2003) M.S., Computer Science, Zhejiang University (2000) Research spans Internet of Things (IoT) , Connected Vehicles , and Underwater Wireless Networks . Key projects include NSF-funded underwater robot communication infrastructure and smart traffic light systems. Recent work explores Named Data Networking (NDN) in vehicular environments, Task Synchronization for autonomous vehicles, and V2I Communication for traffic optimization. NSF Research Experience for Undergraduates (REU) grant recipient $1.5M NSF grant for underwater robotics networking Her research integrates with multiple engineering centers, including the Center for Advanced Vehicle Technologies and Center for Transportation Operations .

• Distributed Systems
• Internet of Things (IoT)
• Wireless Communications
• +۴ مورد دیگر

Dr. Yong Chen is a Professor and Interim Department Chair in the Computer Science Department at Texas Tech University (TTU), where he founded the Data-Intensive Scalable Computing Laboratory (DISCL). He also serves as Co-Director of the NSF Cloud and Autonomic Computing Center (CAC@TTU), focusing on data-intensive computing, high-performance computing (HPC), cloud systems, and parallel/distributed architectures. His research bridges hardware-software co-design for scientific and enterprise applications. Ph.D., Computer Science, Illinois Institute of Technology (2009) M.S., Computer Science, University of Science and Technology of China (2003) B.E., Computer Engineering, University of Science and Technology of China (2000) Dr. Chen's research spans data-intensive computing, HPC, cloud systems, and parallel architectures. He develops scalable solutions for scientific discovery and enterprise computing, emphasizing systems software, storage optimization, and hardware-software co-design. His work addresses challenges in metadata management, 3D-stacked memory, and efficient resource allocation in distributed environments. Recent publications include studies on 3D-stacked memory optimization (IEEE TC), metadata indexing (SC), and parallel file system reliability (ICS). His work is characterized by interdisciplinary collaboration and practical applications in HPC domains. NSF-TCPP Early Adopter Status Award Best Paper Award (IPDPS'21) Outstanding Teaching Assistant, IIT (2006) Dr. Chen advises students through graduate and undergraduate research assistantships at DISCL and CAC@TTU, offering financial support for qualified candidates. He has contributed to major conferences as Program Co-Chair (ICPP) and Committee Member (IPDPS, CCGrid, ISC, HPCAsia). Labs/Teams: Data-Intensive Scalable Computing Laboratory (DISCL), NSF Cloud and Autonomic Computing Center (CAC@TTU).

• Data-Intensive Computing
• Parallel and Distributed Computing
• High-Performance Computing
• +۳ مورد دیگر

Johannes Geier is a Researcher at the Chair of Design Automation at the Technical University of Munich (TUM). His work focuses on electronic design automation, fault injection simulations, and security countermeasures for RISC-V processors. University: Technical University of Munich Department: Chair of Design Automation Email: johannes.geier@tum.de Research Interests Electronic Design Automation (EDA) for analog and digital circuits Fault tolerance and reliability in RISC-V architectures Security analysis of post-quantum cryptographic systems Timing analysis and microfabrication techniques Optical Networks-on-Chip (NoC) and emerging technologies Compiler-assisted hardware security implementations Recent Research Trends Specializes in fault injection methodologies for hardware security validation Develops open-source tools like vRTLmod for RTL simulation acceleration Explores RISC-V vector extensions for post-quantum cryptography Investigates differential fault effect equivalence checks for efficiency Designs compiler-based security countermeasures against instruction skip attacks Works on concurrent multi-node XCP proxy server architectures

• Electronic Design Automation
• Fault Injection Simulation
• RISC-V Processors
• +۷ مورد دیگر

Dr. Ke (Cory) Wang serves as Assistant Professor at the University of North Carolina at Charlotte in the Department of Electrical and Computer Engineering, where he conducts research in computer architecture and parallel systems. Ph.D. in Computer Engineering (2022), George Washington University M.Sc. in Electrical Engineering (2015), Worcester Polytechnic Institute B.Sc. in Computer Science (2013), Peking University His research focuses on machine learning-enabled computer architecture with emphasis on network-on-chip design , domain-specific accelerators , and cross-layer optimization for manycore systems. Specific areas include anomaly detection, graph neural network acceleration, and fault-tolerant communication frameworks. Recent publications demonstrate increasing integration of deep learning techniques with traditional architecture design, particularly for graph convolutional networks and heterogeneous systems . His work addresses multi-objective optimization across performance , energy efficiency , and security dimensions . Key scientific recognitions include: National Science Foundation award (CSR: Small: Cross-layer Design, 2023) National Science Foundation award (CRII: SHF: Flexible Design Framework, 2023) Dr. Wang leads the Intelligent Computer Architecture & Systems Laboratory , which develops advanced frameworks for network-on-chip optimization and AI-driven system design.

• Computer Architecture
• Interconnection Networks
• Machine Learning
• +۸ مورد دیگر