جستجوی استاد راهنمای دانشگاهی

• Power electronics
• Renewable energy
• Nonlinear dynamics and chaos
• +۷ مورد دیگر

Professor Herbert Ho Ching Iu is a distinguished academic at The University of Western Australia, serving in the School of Engineering within the Department of Electrical, Electronic and Computer Engineering. With an impressive research portfolio of over 500 publications and an h-index of 61, Prof. Iu has established himself as a leading authority in power electronics and nonlinear systems research. Prof. Iu received his BEng(Hons) in Electrical and Electronic Engineering from The University of Hong Kong in 1997, followed by a PhD in Electronic and Information Engineering from The Hong Kong Polytechnic University in 2000. After a brief research fellowship at HKPU, he joined The University of Western Australia in 2002 as a Lecturer and has since risen to the rank of full Professor. His primary research focuses on power electronics , renewable energy systems , nonlinear dynamics and chaos , current sensing techniques , and memristive systems . Prof. Iu's work uniquely bridges theoretical exploration with practical implementations, particularly in energy conversion, secure communications, and neuromorphic computing. His research has significant implications for DC microgrids, advanced encryption techniques, and next-generation computing paradigms. Analysis of Prof. Iu's recent publications reveals a strong interdisciplinary trajectory combining memristive systems with chaotic dynamics for applications in image encryption and secure communications . There's a notable emphasis on machine learning techniques applied to power electronics and energy systems , particularly for DC microgrids and battery management. His work demonstrates consistent progression from fundamental research in nonlinear systems to practical engineering solutions with real-world impact. Prof. Iu's significant contributions have been recognized with several prestigious awards: Vice-Chancellor's Award for HDR Supervision (2024) School of Engineering Award for Research Mentorship (2023) Vice Chancellor's Award in Research Mentorship (2023) With 18 supervised research students and leadership on 16 research grants, Prof. Iu has built a robust research program at the forefront of power systems innovation. His grant portfolio includes major projects like 'Mine Electrification' and 'Microgrid Battery Deployment' through the CRC for Future Battery Industry, as well as collaborations with Western Power on 'Project Symphony.' These initiatives demonstrate his ability to secure substantial funding and translate theoretical concepts into practical engineering solutions for industry. Prof. Iu leads a dynamic research team that specializes in hardware implementation of advanced theoretical concepts, particularly in memristive systems and chaotic circuits. The laboratory maintains strong industry connections, especially with energy and mining sectors, ensuring research has tangible real-world applications. Current work emphasizes DC microgrid technologies, advanced battery systems for electrified transportation, and novel applications of chaotic systems in security contexts, positioning the team at the cutting edge of power electronics research.

Franz Fuchsمشاهده پروفایل

پژوهشگر ارشد

• Computer Architecture
• Cybersecurity
• Hardware Security
• +۳ مورد دیگر

Franz Fuchs is a Research Fellow at the Department of Computer Science and Technology (University of Cambridge), working on cutting-edge topics in computer architecture and security. His research focuses on hardware security mechanisms, processor architecture design, and formal verification of microarchitectures. He is affiliated with the Computer Architecture Group and contributes to projects such as the Toooba out-of-order CHERI-RISC-V microprocessor. Fuchs collaborates on developing novel approaches to mitigate transient-execution attacks and improve CPU testing methodologies. His work bridges theoretical computer science principles with practical hardware implementations. He holds a position in the University’s William Gates Building and can be reached at franz.fuchs@cl.cam.ac.uk . His research aligns with the department’s initiatives in secure computing and advanced microprocessor design.

Christina Delimitrouمشاهده پروفایل

دانشیار

• Computer Architecture
• Cloud Computing
• Systems & Networking
• +۶ مورد دیگر

Christina Delimitrou is an Associate Professor at MIT's Department of Electrical Engineering and Computer Science (EECS) and a Principal Investigator at the Computer Science and Artificial Intelligence Laboratory (CSAIL). Her research focuses on optimizing cloud computing systems, with a strong emphasis on resource management, sustainability, and machine learning-driven solutions. Delimitrou leads projects on carbon-aware scheduling, efficient datacenter operations, and serverless computing frameworks like Ursa and Ditto. Her work bridges theoretical system design with practical deployment challenges, addressing topics such as microservices orchestration, approximation techniques for resource efficiency, and security implications of multi-tenancy in shared cloud environments. Notably, she received the Presidential Early Career Award for her contributions to improving datacenter efficiency through innovative scheduling and resource allocation strategies. Delimitrou's research group develops tools like Sage (ML-driven performance debugging) and Seer (big data analytics for cloud systems), emphasizing reproducibility and scalability. Her lab also explores edge computing, swarm robotics coordination (e.g., Hivemind), and hardware-software co-design for next-generation systems. Her academic affiliations include MIT CSAIL's Systems Community of Research, where she collaborates on large-scale software systems. Key themes in her work include QoS-aware resource management, sustainable computing practices, and leveraging approximation to enhance cloud resource utilization.

Hossein Sayadiمشاهده پروفایل

استادیار

• Cybersecurity
• Hardware Security and Trust
• Applied Machine Learning
• +۷ مورد دیگر

Dr. Hossein Sayadi is an Assistant Professor and Associate Chair in the Department of Computer Engineering and Computer Science at California State University, Long Beach (CSULB). He holds a Ph.D. in Electrical and Computer Engineering from George Mason University, an M.S. from Sharif University of Technology, and a B.S. from K. N. Toosi University of Technology. His research focuses on hardware security , AI/ML applications , cybersecurity , and computer architecture . He leads the iSEC Lab , exploring topics like hardware trust, malware detection, and edge computing security. His work is supported by NSF grants and CSU awards, including the 2024-25 CSU STEM-NET Faculty Fellowship. Education: Ph.D., Electrical and Computer Engineering (George Mason University) M.S., Computer Engineering (Sharif University of Technology) B.S., Computer Engineering (K. N. Toosi University of Technology) His publications span conferences like IEEE ISQED, ISCAS, and DATE. He serves as Technical Program Committee Chair for IEEE ISQED (2024–2025). Awards include NSF ERI grants ($195,305) and the 2023 Multidisciplinary Research Grant. Research opportunities are available for students in machine learning , hardware security , and cybersecurity education .

Khandaker Mamun Ahmedمشاهده پروفایل

استادیار

• Computer Vision
• Federated Learning
• Cybersecurity
• +۴ مورد دیگر

Dr. Khandaker Mamun Ahmed is an Assistant Professor at The Beacom College of Computer & Cyber Sciences, Dakota State University. He teaches undergraduate and graduate courses in artificial intelligence, algorithms, and data structures. He holds a Ph.D. in Computer Science from Florida International University (2024), an M.Sc. from the same institution (2023), and a B.Sc. in Software Engineering from the University of Dhaka (2016). His research focuses on computer vision, federated learning, cybersecurity, explainable AI, vision-language models, and optimization algorithms. He has contributed to peer-reviewed publications and conference presentations, with notable work in federated learning for IoT, anomaly detection in videos, and AI applications in healthcare and agriculture. Recent articles highlight advancements in federated learning frameworks, AI-driven healthcare systems, and real-time object detection using neural networks. His work also addresses cybersecurity challenges in DevOps pipelines and generative AI for educational datasets. Recipient of the 'Best graduate student in research award' (2022), Dr. Ahmed advises on AI ethics and mentors students through academic-industry collaborations. His research bridges theoretical computer science with practical applications in agriculture, healthcare, and infrastructure monitoring.

Robert Peharzمشاهده پروفایل

استادیار

• Probabilistic Machine Learning
• Tractable Probabilistic Models
• Artificial Intelligence
• +۶ مورد دیگر

Robert Peharz is an Assistant Professor at Graz University of Technology, where he leads research at the Institute of Machine Learning and Neural Computation. His work focuses on probabilistic machine learning, with particular emphasis on tractable probabilistic models, causality, and neurosymbolic AI. Education and Career PhD from TU Graz (Austria) in 2015 Postdoc at Medical University of Graz Postdoc and Marie-Curie Individual Fellow at University of Cambridge (2017-2019) Assistant Professor at Eindhoven University of Technology (2019-2021) Current: Assistant Professor at Graz University of Technology Research Interests Peharz's research spans multiple areas of artificial intelligence with a focus on making probabilistic reasoning both theoretically sound and practically efficient. His work addresses fundamental challenges in tractable probabilistic inference and learning, probabilistic circuits as a unified framework for deep generative models, Bayesian causal inference, and neurosymbolic AI combining sub-symbolic and symbolic approaches. His research has applications in cybersecurity, healthcare, and energy systems. Research Projects VENTUS (2024-present): Physics-informed, probabilistic and causal machine learning for wind energy systems NEO DNA (2023-present): DNA-based data storage systems using computer vision and probabilistic ML VanillaFlow (2023-present): AI-guided development of novel vanillin-based molecules for redox flow batteries Bilateral AI : Cluster of Excellence focused on Broad AI combining sub-symbolic and symbolic AI approaches Awards and Recognition Finalist for TUG's Excellent Teaching Award (2023) for all 3 of his courses Marie-Curie Individual Fellow at University of Cambridge Academic Service Peharz is actively involved in the academic community through conference organization and reviewing: Area Chair: UAI (2022), ECML/PKDD (2022) Senior Committee Member: UAI (2021), IJCAI (2019, 2020) Reviewer for major conferences including ICML, NeurIPS, AAAI, IJCAI-ECAI Teaching and Mentorship Peharz supervises multiple PhD students working on diverse projects at the intersection of machine learning, causality, and neurosymbolic AI. His current advisees include Sepideh Adamiat, Irina Dobrianski, Johannes Exenberger, Giacomo Di Gobbi, Tim d'Hondt, Christian Toth, and Thomas Wedenig. Previous students include Alvaro Correia, Martin Trapp, and David Montalvan.

Mehdi Sadiمشاهده پروفایل

استادیار

• Secure and Reliable System-on-Chip Design
• AI and Neuromorphic Hardware
• Safety-critical Hardware
• +۲ مورد دیگر

Mehdi Sadi is an Assistant Professor of Electrical and Computer Engineering at Auburn University's College of Engineering. He holds a Ph.D. from the University of Florida, an M.S. from the University of California-Riverside, and a B.S. from Bangladesh University of Engineering and Technology. His research focuses on secure and reliable system-on-chip design, AI/ML-driven VLSI CAD/EDA, neuromorphic hardware, and emerging post-CMOS computing technologies. Notable achievements include earning the NSF CAREER Award for chiplet-based design optimization and a $175k NSF grant for magnetic RAM research. His work integrates machine learning with hardware co-design to enhance AI accelerators' performance, energy efficiency, and security. Recent projects include adversarial attack mitigation on AI hardware and reliability analysis of neuromorphic systems. Dr. Sadi's contributions span chiplet architecture, memory systems (e.g., STT-MRAM/SOT-MRAM), and fault-tolerant computing. He actively publishes on topics like skyrmion logic gates and TRNG implementations using MRAM. His work bridges theoretical machine learning advancements with practical hardware implementations, addressing critical challenges in next-generation computing systems.

• Computer Operating Systems
• Computer Systems Analysis
• Distributed Systems
• +۵ مورد دیگر

Liuba Shrira is a Professor of Computer Science at Brandeis University, affiliated with the Michtom School of Computer Science and the Benjamin and Mae Volen National Center for Complex Systems. Her research focuses on distributed systems, storage systems, blockchain technology, concurrent programming, and system architectures. She holds a Ph.D., M.S., and B.S. from the Technion – Israel Institute of Technology. Her work emphasizes reliable and highly available systems, including innovations in snapshot management, transactional memory, and adversarial cross-chain commerce. She has been recognized with awards such as the ACM Distinguished Scientist (2009), Lady Davis Fellowship (2010-2011), and a Best Paper Award (2020). Her research has been supported by grants from the National Science Foundation and other institutions. Recent publications highlight advancements in optimistic concurrency control, blockchain interoperability, and modular past-state systems. Shrira has also contributed to middleware design and distributed computing frameworks, with applications in both academic and industry settings.

Justin Marshallمشاهده پروفایل

استاد

• Digital Design
• Ceramics
• Craft Practices
• +۳ مورد دیگر

Professor Justin Marshall is a Professor at Northumbria University’s School of Design, specializing in digital craft and interdisciplinary research. He holds a PhD in architectural ceramics and digital technologies, and has pioneered work in integrating digital tools into craft practices, co-founding the ‘Autonomatic’ research group. His research emphasizes a ‘Digital Craft Ethos’ to explore ethical and aesthetic dimensions of technology use in craft. Education: PhD in architectural ceramics and digital technologies (2000), BA and MA in fine art, ceramics, design, and craft. Qualifications include an Associate Fellowship of the Higher Education Academy (AFHEA). Research Interests: Craft ethics in digital making Interdisciplinary collaboration (e.g., with computer scientists, social scientists) IoT ethics and ‘healthy’ internet development (hiCraft project) Community-centered design and public engagement Grants and Projects: Principal Investigator for the AHRC-funded ‘hiCraft: Crafting a Healthier Internet’ project (2024–), exploring IoT trust and transparency via craft practices. Previously led the RCUK Digital Economy ‘Bespoke’ project (2011–2014), focusing on community innovation through bespoke devices. Exhibitions and Awards: Exhibited nationally/internationally (e.g., ‘LabCraft’ UK touring show, Design Museum Barcelona). Awards include Fellow of the RSA and AHRC Peer Review College membership (2012–2018). Labs/Teams: Leads the hiCraft project team and collaborates with diverse disciplines across Northumbria and global partners.

• Blockchain Technology
• Distributed Systems
• Consensus Protocols
• +۶ مورد دیگر

Mohammad Sadoghi is a Professor at the University of California, Davis, with former affiliations at Purdue University, IBM T.J. Watson Research Center, and the University of Toronto. His research focuses on distributed systems, blockchain technologies, consensus protocols, and fault-tolerant computing. He has contributed extensively to transaction processing, stream processing architectures, and the integration of edge-cloud systems with blockchain frameworks. Current Affiliation: University of California, Davis Former Affiliations: Purdue University, IBM, University of Toronto Research Interests include consensus algorithms, Byzantine fault tolerance, distributed ledger technologies, and scalable data processing. He has pioneered systems like ResilientDB and ByShard, addressing challenges in global-scale distributed systems and blockchain fabrics. His work bridges theoretical foundations with practical implementations, emphasizing real-world applications in edge computing and hybrid cloud-edge environments. Key publications highlight advancements in consensus protocols, blockchain scalability, and fault-tolerant architectures. Recent trends in his work focus on concurrent consensus mechanisms, DAG-based systems, and secure geo-replication. Contributions span both academic publications and industry-oriented solutions, such as the Bedrock platform for BFT protocol analysis. Grants and advising roles are implied through his extensive research output, though specific grants are not detailed in the provided text. His collaborations include projects on self-curating databases (e.g., L-Store) and systems like SplitJoin for stream processing.

Eric Wustrowمشاهده پروفایل

دانشیار

• Network Security
• Internet Censorship
• Cybersecurity
• +۸ مورد دیگر

Eric Wustrow is an Associate Professor in the Department of Electrical, Computer & Energy Engineering at the University of Colorado Boulder , affiliated with the College of Engineering and Applied Science . His research focuses on network security, internet censorship, and cryptographic protocols. Contact: ewust@colorado.edu , Phone: 734-330-8702, Office: ECCR 1B13. Academic Rank: Associate Professor Department: Electrical, Computer & Energy Engineering University: University of Colorado Boulder Eric Wustrow's research explores methods to measure and circumvent internet censorship, analyze network vulnerabilities, and enhance privacy in digital communications. His work includes studying the Great Firewall of China, DNS and TLS security, and developing anticensorship tools like ShadowTLS and WATER. He also investigates cryptographic weaknesses in network devices and secure hardware implementations. Recent publications examine the effectiveness of anticensorship tools, the impact of IPv6 on information controls, and vulnerabilities in encrypted protocols. His studies include speculative execution attacks, DNS censorship measurement, and techniques to exploit transient errors in TLS for key compromise. These works highlight evolving network threats and countermeasures. Eric's research has been supported by grants such as the CAREER: Combating Censorship from within the Network (2022) and SaTC: CORE: Medium: Collaborative: Studying the Impact of IPv6 on Information Controls and Censorship Circumvention (2020). These projects focus on developing network-level solutions to censorship and security challenges.

Wenwen Wangمشاهده پروفایل

دانشیار

• Computer Systems
• Compiler Design
• Computer Architecture
• +۴ مورد دیگر

Wenwen Wang is an Associate Professor in the School of Computing at the University of Georgia's Franklin College of Arts & Sciences. His research focuses on computer systems, compiler design, and embedded systems security. He holds a Ph.D. in Computer Science from the University of Chinese Academy of Sciences (2014). Education: Ph.D., Computer Science, University of Chinese Academy of Sciences, 2014 His research emphasizes dynamic binary translation, compiler optimization, and secure embedded systems. Notable contributions include frameworks like JavART (JIT compiler optimization) and BSan (memory error detection). He received the 2021 M. G. Michael Award for Sciences from the Franklin College. Wang has secured two NSF grants totaling $1.2 million, including CSR: Small grants for FALCON (2023–2027) and Modernizing Dynamic Binary Translation Systems (2023–2027). He advises three graduate students: Ruili Fang, Yage Hu, and Boyang Yi. His work addresses challenges in cross-architecture virtualization, GPU-based graph computing, and hardware-triggered security mechanisms. Recent projects include Liberator (GPU graph processing) and InvisiGuard (embedded device integrity).

• Security of Deep Learning Systems
• Side-Channel Attacks
• Privacy-Preserving Machine Learning
• +۳ مورد دیگر

Dr. Yuanyuan Yuan is a Researcher at the Department of Computer Science, ETH Zurich, Switzerland, based at CNB H 104.1, Universitätstrasse 6, 8092 Zurich. Her work bridges computer security and machine learning with a focus on practical vulnerabilities in deployed AI systems. Her research centers on exposing and mitigating security flaws in deep learning deployments, particularly targeting trusted execution environments (TEEs) and on-device inference systems. Key contributions include pioneering side-channel attacks against TEE-shielded neural networks (CipherSteal, HyperTheft), bit-flip attack surfaces in DNN executables (BitShield), and novel testing methodologies for neural network robustness. She investigates cache/timing side channels, ciphertext analysis, privacy leakage in partitioned ML, and concept-based explainability. Analysis of her 2023-2025 publications reveals a cohesive focus on offensive security research for AI infrastructures, with consistent contributions to top venues in security and machine learning. Her work demonstrates expertise in low-level system interactions (memory, cryptography) applied to ML security, spanning attack vectors, defensive mechanisms, and validation frameworks. The research trajectory shows increasing sophistication in exploiting hardware-software interfaces while developing practical hardening techniques for real-world deployments.

Anton Akusokمشاهده پروفایل

مدرس

• Machine Learning
• Big Data Analytics
• Mobile Edge Computing
• +۳ مورد دیگر

Anton Akusok is a Part-time Lecturer in the Big Data Analytics Master's program at Arcada University of Applied Sciences. He holds a BSc in IT from Moscow (2011), MSc in ML and Data from Aalto University (2014), and a DSc in ML from the University of Iowa, USA (2016). His research focuses on Extreme Learning Machines (ELM), hardware acceleration for ML on mobile devices, and real-time geospatial predictions. He has developed libraries like HPELM and Scikit-ELM, and created the HaSuRiski app for acid sulfate soil prediction in Finland. Research Interests: ELM applications in environmental modeling, federated learning security, mobile edge computing, and geospatial visualization. Key projects include real-time mapping apps with iOS integration and open-source ML tools. Publications (2021-2024) highlight work on federated learning privacy, acid sulfate soil detection, signature verification, and distributed ELM algorithms.

• Reliable Hardware Design
• Electronic Design Automation
• Edge Computing
• +۴ مورد دیگر

Dr. Abusaleh Jabir is a University Reader at the School of Engineering, Computing and Mathematics, Oxford Brookes University. He holds a DPhil in Computing from the University of Oxford and leads the Advanced Reliable Computer Systems (ARCoS) group. His research focuses on reliable hardware design, memristive nanotechnology, edge computing, and secure authentication systems. He has over 80 peer-reviewed publications and multiple patents, including innovations in error-tolerant circuits and memristive architectures. **Education**: DPhil in Computing (University of Oxford). **Research Interests**: Reliable hardware design, electronic design automation, sensing at the edge, physical uncloneable authentication, and emerging memristor technologies. His work addresses challenges in IoT, edge computing, and cybersecurity through innovative electronic systems. **Funding & Projects**: Current projects include the Leverhulme Trust-funded MONITOR gas sensor array initiative. His research has been supported by the UK Ministry of Defence, EPSRC, and Finance South East. **Awards & Patents**: Multiple patents granted, including EU 17706875.6 (memristive logic) and GB 1914221.5 (reconfigurable memristive logic). Recognized with best paper awards. **Advising & Impact**: Supervised PhD students now leading semiconductor and automotive industries (e.g., Infineon Technologies, Continental Teves AG). Collaborates with academic and industrial partners globally. **Labs & Teams**: Leads the ARCoS group within the Artificial Intelligence, Data Analysis and Systems (AIDAS) Institute, fostering interdisciplinary innovation in secure and reliable electronics.