جست‌وجوی استاد و پژوهشگر

• Mobile Sensing
• Pervasive Computing
• TinyML
• +۱۱ مورد دیگر

Eiman Kanjo is a Professor of Pervasive Computing and Head of the Smart Sensing Lab at Nottingham Trent University (NTU), and a Provost’s Visiting Professor in Pervasive Sensing and tinyML at Imperial College London (2023–2024). She leads initiatives such as Smart Nottingham and Smart Campus , focusing on impactful mobile sensing solutions. Her research integrates AI, IoT, and TinyML for applications in environmental monitoring, wearable devices, and well-being. Awards & Recognition: Recognized as one of the Top 50 Women in Engineering (2023), recipient of the Turing Network Development Awards 2022 , and head of NTU’s Outstanding Researcher Team (2020/2021). She also serves on the Royal Society’s Newton International Fellowships Committee . Grants & Collaborations: Secured over £10M in grants from funders like DCMS (e.g., 5G Connected Forest ), InnovateUK, and EPSRC. Collaborates with industry, charities, and local authorities on projects such as TagWithMe (well-being social prescribing) and air quality monitoring in Nottingham. Labs & Networks: Leads NTU’s Turing University Network , the Green+ network (EPSRC-funded), and chairs the tinyML EMEA steering committee . Her work is published in journals like the Data Centric Engineering Journal .

Mukul Shirvaikarمشاهده پروفایل

استاد

• Real Time Systems
• Image and Signal Processing
• Deep Learning and Neural Networks
• +۵ مورد دیگر

Dr. Mukul Shirvaikar is a Professor of Electrical Engineering and Undergraduate Coordinator at the University of Texas at Tyler, affiliated with the College of Engineering. He holds a Ph.D. from the University of Tennessee (1993), M.Sc. from the University of Maine (198?), and B.Tech. from IIT Banaras Hindu University (India). A Senior IEEE Member, he serves on editorial boards for journals like Journal of Real Time Image Processing and Journal of Medical Imaging and Health Informatics , and participates in ABET accreditation processes. His research focuses on real-time systems, image/signal processing, deep learning, medical imaging, and engineering education. He pioneered the 'Back-To-Basics' student retention program (2005–2008) and developed an electrical engineering freshman experience course. Dr. Shirvaikar has two patents and authored a textbook on real-time systems (2017). Industrial experience includes roles at Texas Instruments (real-time imaging systems) and Pressco Technology (ultra-high-speed inspection systems). Key contributions include FPGA-based biomedical image analysis, embedded AI systems, and curriculum innovations integrating FPGA technology. His recent work emphasizes real-time video analysis for infrastructure monitoring, crop disease detection via smartphone apps, and hardware-aware neural architecture search.

Kanad Ghoseمشاهده پروفایل

استاد

• Computer Architecture
• Power-Aware Systems
• High-Performance Computing
• +۶ مورد دیگر

Kanad Ghose is a Distinguished Professor and Director of the Center for Energy-Smart Electronic Systems (ES2) at SUNY Binghamton's School of Computing. His research focuses on computer architecture, power-aware systems, high-performance computing, and cybersecurity. He leads projects in data center energy management, secure processor design, and flexible electronics for wearable health monitoring. Education: BTech/M.Tech from University of Calcutta; MS/PhD from Iowa State University Awards: SUNY Chancellor's Award, IBM Faculty Award, National Science Talent Search Research interests include energy-efficient microarchitectures, cybersecurity at the hardware level, and autonomous systems. Notable projects involve data center cooling optimization, secure execution validation, and wearable ECG sensors. His lab collaborates with industry partners like Intel, Lockheed Martin, and UC Berkeley's Arias group. Current projects include DC-powered data centers, FPGA accelerators, and flexible electronics for clinical-grade health monitoring. Past work includes the MARSSx86 simulator and ARINC-compliant avionics systems.

Ramesh Sitaramanمشاهده پروفایل

استاد

• Networking and Distributed Systems
• Theoretical Computer Science
• Internet-scale Systems
• +۵ مورد دیگر

Ramesh Sitaraman is a Distinguished Professor and Associate Dean for Educational Programs and Teaching at the Manning College of Information and Computer Sciences (CICS), University of Massachusetts Amherst. He is a world-renowned expert in Internet-scale distributed systems and content delivery networks (CDNs). His contributions include pioneering the first major CDN (Akamai) and foundational work in edge computing and video streaming technologies. He holds part-time roles as Akamai’s Chief Consulting Scientist and directs the Laboratory for Internet-Scale Distributed Systems (LIDS). Education: PhD in Computer Science, Princeton University B.Tech. in Electrical Engineering, Indian Institute of Technology, Madras Research Interests: His work spans distributed systems, networking, energy efficiency, and sustainable computing. Key areas include: CDNs and edge computing architectures Adaptive bitrate algorithms (e.g., BOLA) Energy-aware systems and decarbonization Algorithmic design for scalability Awards and Honors: AAAS Fellow (2023) IEEE William R. Bennett Prize (2023) ACM SIGCOMM Networking Systems Award (2018) ACM and IEEE Fellowships (2019) University Distinguished Teaching Award (2020) Teaching and Leadership: As founding director of the Informatics Program, he bridges computer science with interdisciplinary fields. He has received top teaching awards and advocates for curiosity-driven research and broad undergraduate education. Labs and Initiatives: Directs LIDS and collaborates with the Center for Data Science. Research focuses on sustainable internet infrastructure and next-generation distributed systems.

Lieven Eeckhoutمشاهده پروفایل

استاد

• Computer Architecture
• Performance Evaluation
• Performance Modeling
• +۴ مورد دیگر

Lieven Eeckhout is a Professor at Ghent University, Belgium, in the Department of Electronics and Information Systems (ELIS), where he leads the PerformanceLab research group. His work spans computer architecture, performance evaluation and modeling, workload characterization, dynamic resource management, CPU/GPU microarchitecture, and sustainability. He has supervised a dozen PhD students and postdocs, with alumni now in academia (e.g., assistant professors at TU Eindhoven, ANU) and industry (e.g., Intel, Huawei). Education : PhD in Computer Science (Ghent University, 2002) Awards : ACM Fellow (2021), IEEE Fellow (2018), 2017 ACM SIGARCH Maurice Wilkes Award, 2017 OOPSLA Most Influential Paper Award, 2024 IEEE CAL Best Paper Award, Distinguished Artifact Evaluation Award at ASPLOS 2024, and multiple Hall of Fame/Top Pick recognitions. Research focuses on computer architecture and hardware/software interface , with recent emphasis on sustainability and processor design . Key projects include the Sniper multi-core simulator and ERC-funded initiatives like LSC (Load Slice Core) and DPMP (Dependable Performance Management). Articles highlight trends in GPU memory systems , vector runahead , and carbon-aware architectural models . Scientific honors include 2024 IEEE CAL Best Paper Award Distinguished Artifact Evaluation at ASPLOS 2024 IEEE/ACM MICRO 2023 Best Paper Award ISCA@50 25-Year Retrospective Selection ACM Fellow (2021) IEEE Fellow (2018) Maurice Wilkes Award (2017) IBM Belgium Prize for Informatics (2003) As a research advisor , he has mentored numerous PhD students, including Benyamin Eslami, Jaime Roelandts, and Hossein SeyyedAghaei. His grants include ERC Starting (2011-2017) and Advanced Grants (2018-2023). The PerformanceLab group explores topics like multi-core simulation , GPU architecture , and energy-proportional systems , with collaborations across institutions (e.g., TU Eindhoven, Intel, ANU).

• System Security
• Fault Injection
• Fuzzing
• +۵ مورد دیگر

Erik van der Kouwe is an Assistant Professor at the Faculty of Science, Vrije Universiteit Amsterdam, holding dual appointments in the Computer Systems department and the Network Institute. His research focuses on system security, fault injection, fuzzing, and embedded systems security. He contributes to both theoretical advancements and practical implementations of security mechanisms. Key research areas include mitigating vulnerabilities through hardware-software co-design, exposing compiler optimization flaws that evade sanitizers, and developing efficient fuzzing frameworks for malware analysis. His work often bridges academic research with real-world applications in embedded systems and operating systems. Recent publications highlight innovations like InvisiGuard for microcontroller security, hwdbg for hardware debugging frameworks, and Enviral for environment-aware malware analysis. His research has been published in top-tier venues such as IEEE Transactions, ACM conferences, and workshops like EuroSEC. Teaching responsibilities include Advanced Operating Systems, Secure Programming, and Software Security courses. Collaborations span international teams working on topics like compiler hardening, embedded system resilience, and vulnerability benchmarking.

• Artificial Intelligence Ethics
• Digitalization and Democracy
• Online Hate Speech
• +۳ مورد دیگر

Dr. João Gonçalves is an Associate Professor at the Department of Media and Communication, Erasmus University Rotterdam. He leads research on AI ethics, digitalization impacts, and online discrimination. His work includes developing the open-source Erasmian Language Model and co-leading the Horizon Europe SEISMEC project (€10M) with 31 international partners. He also holds a VENI grant (€280k) for AI data quality research. Research Clusters: REACT (Responsible Engagement with AI), Hum-Andr010d (Digital Humanities) Key Projects: SEISMEC, Erasmian Language Model Grants: VENI Talent Program (2023), Horizon Europe (2024) His research spans algorithmic bias analysis, content moderation fairness, and AI-driven social science methods. Recent work critiques gender/race biases in GPT-3 and explores ethical AI architecture design. Awards: ESHCC Societal Engagement Award (2024) Public Engagement: Keynote speaker on generative AI ethics, media commentator on digital society issues

• Computer Science
• Model Checking
• Performance Evaluation
• +۴ مورد دیگر

Professor Boudewijn R.H.M. Haverkort is affiliated with the University of Twente's Digital Society Institute and Faculty of Electrical Engineering, Mathematics, and Computer Science. His research focuses on formal methods, performance evaluation, and thermal management in computing systems. He has contributed to advancing model checking techniques, data center workload modeling, and temperature-aware scheduling algorithms. His work intersects computer science, embedded systems, and cyber-physical systems. Research interests include: - Model Checking and Formal Verification - Performance Evaluation of Distributed Systems - Thermal Management in Real-Time Systems - Machine Learning Applications for System Workload Analysis Notable contributions include developing statistical model checking frameworks and temperature prediction methods recognized by the Harvey Rosten Award in 2020. He has supervised 27 research projects, though student names are not listed here. His collaborations span thermal investigations in ICs, cyber-physical system design, and digital communication systems in conflict resolution contexts. Labs/Teams: Active in the Digital Society Institute and MESA+ Institute, collaborating with TechMed Centre on interdisciplinary projects.

Piotr Sankowskiمشاهده پروفایل

استاد

• Algorithms
• Machine Learning
• Graph Theory
• +۵ مورد دیگر

Piotr Sankowski is a Professor at the Institute of Informatics, University of Warsaw, specializing in algorithms and theoretical computer science with applications in economics and machine learning. He is also CEO of MIM Solutions, deploying ML solutions, and heads the IDEAS NCBR center for AI and Digital Economy. His research focuses on algorithm design, computational complexity, and interdisciplinary applications in physics and economics. Education & Affiliations: Professor at University of Warsaw's Faculty of Mathematics, Informatics, and Mechanics (MIM) Head of IDEAS NCBR Center (Artificial Intelligence & Digital Economy) CEO of MIM Solutions (AI/ML consulting) Research Interests: Piotr's work bridges theoretical computer science and practical applications, with key areas: Algorithms for matching problems, graph theory, and optimization Machine learning explainability and feature importance Quantum tunneling phenomena in semiconductor nanostructures (physics) Algorithmic game theory and network design His ERC grants highlight contributions to algorithmic foundations and interdisciplinary research. Grants & Awards: ERC Starting (2010), Proof of Concept (2015), and Consolidator (2017) Grants First Pole to secure three ERC grants Fellow of the Institute of Informatics at University of Warsaw Publications: Over 80 papers spanning CS (algorithms, ML) and physics (solid-state theory). Recent work focuses on online algorithms, dynamic graph problems, and scalable ML systems. Labs & Projects: Leads the IGAFIT initiative promoting algorithms in Europe, and the EXALT project for explainable optimization tools. Active in the IDEAS NCBR center for AI innovation.

Mei Yangمشاهده پروفایل

استاد

• Computer architectures
• Interconnection networks
• Machine learning
• +۵ مورد دیگر

Mei Yang is a Professor and Chair in the Department of Electrical and Computer Engineering at the University of Nevada, Las Vegas (UNLV). Her research focuses on computer architectures, interconnection networks, machine learning, embedded systems, and wireless sensor networks. She leads the Networking and System Integration Laboratory (NSIL), advancing interdisciplinary projects at the intersection of hardware design and intelligent systems. Education Ph.D., Computer Science, University of Texas at Dallas, 2003 Research Interests Dr. Yang's work spans network-on-chip (NoC) architectures, thermal-aware task management, and deep learning applications in microscopy and environmental monitoring. She explores energy-efficient computing, cybersecurity in many-core systems, and educational strategies to engage girls in STEM through hands-on projects like IoT and embedded systems. Articles Trends Her recent publications emphasize hardware security (e.g., thermal covert channels), AI-driven biomedical image analysis, and optimizing multi-chiplet systems. She bridges theoretical contributions with practical implementations, such as FPGA-based NoC emulators and GAN-based protein localization tools. Labs & Teams Hosts the NSIL lab, fostering collaborations in photonic networks-on-chip, embedded systems, and cybersecurity. Active in IEEE and ACM conferences, organizing special sessions on high-performance computing and network coding.

• Network Science
• Evolutionary Dynamics
• Complex Systems
• +۵ مورد دیگر

Dr. Wei Zhang is a Researcher at the Complex Systems and Data Science group within The University of Sydney. Her work focuses on network science, evolutionary dynamics, and complex systems, with an emphasis on modeling social systems and analyzing meso-scale structures in social networks. She combines theoretical approaches, data-driven modeling, and web-based experimental methods to study emergent collective phenomena. Dr. Zhang holds a PhD in Network Science from ETH Zurich. Her research spans interdisciplinary topics including data science applications to complex systems and the detection of structural patterns in social networks. Recent projects include exploring domain adaptation techniques in machine learning and developing methods for 3D object detection and model compression. Her publications reflect expertise in computer vision, machine learning, and environmental science. Key themes include domain adaptation networks, 3D reconstruction, and LiDAR-based forestry analysis. While no specific awards are mentioned, her work demonstrates significant contributions to theoretical and applied data science. Dr. Zhang collaborates on projects involving interdisciplinary teams, though specific grants or lab affiliations are not detailed in the provided text. Her current research continues to bridge complex systems theory with modern data-driven methodologies.

• Data center networking
• Optical circuit switching
• Distributed systems
• +۳ مورد دیگر

Paolo Costa is a Senior Principal Research Manager at Microsoft Research Cambridge and Honorary Lecturer at Imperial College London. His research bridges distributed systems and networking with focus on optical technologies for next-generation data centers. Key innovations include Sirius nanosecond optical switching architecture, soliton microcomb-based circuit switching, and PANAMA in-network aggregation for machine learning clusters. Research explores programmable switches, hardware acceleration, and AI infrastructure optimization. Projects include 'CamCube' and 'Predictable Datacenters' enhancing application-network integration. Recent publications examine memory systems for AI, stateful in-network computing, and lite-GPU clusters for scalable AI infrastructure.

Yoon Kimمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۳ مورد دیگر

Yoon Kim is an Assistant Professor at MIT, affiliated with the departments of Computer Science (CS), Electrical Engineering (EE), and the AI+D initiative within the School of Engineering. His research focuses on artificial intelligence, machine learning, natural language processing, and speech processing, with notable work on vision-language models and large language models (LLMs). He was named an AI2050 Early Career Fellow in 2024. His recent studies address critical issues like LLMs' failure to handle negation in high-stakes contexts and the ethical implications of retrieval-augmented systems in medical communication. Kim’s work bridges theoretical advancements and practical applications, including optimizing transformer architectures, improving model robustness, and exploring semantic representations across languages and modalities. His publications often emphasize hardware-efficient training and inference techniques, reflecting his interest in scalable AI systems. Collaborative projects with interdisciplinary teams highlight his commitment to advancing both technical and societal aspects of AI.

Darren Danceyمشاهده پروفایل

استاد

• Artificial Intelligence
• Deep Learning
• Neural Networks
• +۳ مورد دیگر

Professor Darren Dancey is the Head of Department for Computing and Mathematics at Manchester Metropolitan University (MMU). His academic background includes a PhD in Artificial Neural Networks and expertise in Artificial Intelligence, Deep Learning, and Software Development. He has led major research projects funded by Innovate UK, the European Research Council, and the Digital R&D Fund for the Arts, focusing on collaborations between academia and industry SMEs. Education and Research Interests: Professor Dancey's research spans neural networks, medical imaging applications, cybersecurity, and robotics. He has pioneered interdisciplinary projects such as a biologically inspired deep learning model for vegetation recognition and a spike transformer network for event-based vision systems. His work emphasizes explainable AI and practical industry applications. Grants and Collaborations: He has secured significant funding for initiatives like malware detection frameworks, hyperspectral image processing, and IoT security solutions. His projects often bridge academic innovation with real-world SME needs. Professional Engagement: He actively contributes to academic communities through roles such as organizing the Manchester Raspberry Pi Jam and serving on the BCS Manchester branch committee. His teaching focuses on software development, algorithms, and applied programming courses.

Annabel Lathamمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence in Education
• Ethics and Trust of AI
• Computational Intelligence
• +۸ مورد دیگر

Dr. Annabel Latham is a Senior Lecturer in Computer Science at Manchester Metropolitan University within the Faculty of Science and Engineering’s Computing and Mathematics department. She holds a PhD in Artificial Intelligence, an MSc in Computing, a Postgraduate Certificate in Academic Practice, a CIM Diploma in Marketing, and a BSc(Hons) in Computation. As a Fellow of the Higher Education Academy (FHEA) and Senior Member of IEEE (SMIEEE), she contributes extensively to AI research and education. PhD in Artificial Intelligence MSc in Computing PGC Academic Practice CIM Diploma in Marketing BSc(Hons) Computation FHEA (2015) SMIEEE (2018) Research Interests include Artificial Intelligence in Education , Ethics of AI , and Computational Intelligence . She specializes in conversational agents, intelligent tutoring systems, and public trust in AI. Her work addresses fairness, accountability, and accessibility in AI-driven education, leveraging technologies like large language models and fuzzy logic. Research Outputs span 15 years, with a focus on explainable AI, educational applications of conversational agents, and ethical frameworks. Recent work (2024-2025) explores postdigital citizen science, trustworthy AI implementation, and XAI usability for non-specialists. 2023 IEEE Region 8 Outstanding Women in Engineering Volunteer Award 2019 IEEE Region 8 Outstanding Women in Engineering Affinity Group of the Year 2018 Outstanding Peer Reviewer, Elsevier: Computers & Education Senior Member IEEE (2018) FHEA (2015) Teaching and Supervision includes undergraduate Databases, postgraduate units in Information Systems and AI Ethics. She supervises MSc and PhD students in areas like explainability-aware machine learning, data responsibility, and AI trustworthiness. Her grants involve collaborations with international funding bodies such as NAFOSTED (Vietnam) and Croatia’s National Council for Science. Labs and Groups include the Computational Intelligence Lab, Machine Intelligence research group, and the Data and AI Ethics research group, where she co-leads initiatives on ethical AI in education.