جست‌وجوی استاد و پژوهشگر

Caroline Chanelمشاهده پروفایل

استاد

• Autonomous Systems
• Decision-Making Algorithms
• Robotics
• +۳ مورد دیگر

Caroline Chanel is a Professor at ISAE-SUPAERO (DCAS), holding the CASAC Chair since 2022. She has extensive experience in research and teaching, with roles including Associate Professor (2019-2024), Research Engineer (2013-2019), and HDR accreditation (2023). Her research focuses on embedded decision-making for autonomous systems, including proactive architectures, multi-robot cooperation, and human-machine interaction. She leads and participates in high-profile projects like SAMURAI (AID), CHIP-GT (ANR-DFG), and HAICO (CASAC Chair). She has supervised numerous PhD students and post-doctoral researchers, collaborating with institutions like ONERA, INRAe, and international partners. Education: PhD in Control and Embedded Systems (University of Toulouse 2013), Master's in Automatic Systems (Université Paul Sabatier 2008-2009), and Engineer Diploma from PUCRS (2002-2008). Awards include the Amelia Earhart Fellowship (2011) and ISAE-SUPAERO Foundation PhD Fellowship (2013). Research interests span decision-making algorithms, multi-agent systems, and neuroergonomics. Key projects address UAV navigation, blockchain integration for autonomous systems, and physiological computing for human-robot interaction. Teaching responsibilities include modules on autonomous systems, control systems, and neuroergonomics at ISAE-SUPAERO's engineering and master's programs. She co-leads the DECISIO doctoral team (ISAE-ONERA) and is involved in executive roles for conferences like EDSYS'.

• Requirements Engineering
• Software Engineering
• Cybersecurity
• +۳ مورد دیگر

Prof. Dr. Samuel Fricker is a Lecturer in Requirements Engineering at the FHNW Institute of Interactive Technologies, part of the School of Computer Science at FHNW University of Applied Sciences and Arts Northwestern Switzerland. He leads the Horizon Europe project IMPORTANT and provides advisory services for doctoral studies. His work bridges academia and industry, focusing on software engineering, cybersecurity, and agile methodologies. Samuel’s research interests span Requirements Engineering, Cybersecurity for SMEs, Agile Practices, and Software Product Management. He actively contributes to workshops like REFrame and publishes in leading conferences such as IEEE Requirements Engineering. His recent publications emphasize healthcare systems integration, cybersecurity solutions for SMEs, and educational frameworks in Requirements Engineering. He has collaborated on projects addressing clinical trial recruitment and digital health innovation. Samuel’s professional activities include leading the FHNW Institute’s advisory roles and contributing to international standards through organizations like the International Software Product Management Association.

Frits de Nijsمشاهده پروفایل

پژوهشگر ارشد

• Artificial Intelligence
• Renewable Energy Systems
• Reinforcement Learning
• +۳ مورد دیگر

Frits de Nijs is a Research Fellow in the Department of Data Science & AI at Monash University. His work focuses on advancing artificial intelligence techniques for energy systems optimization, multi-agent decision-making, and sustainable infrastructure. He has contributed to projects such as the Human-in-the-loop Microgrid Project and RACE for 2030 CRC, addressing challenges in renewable energy integration and smart grid management. Key research areas include reinforcement learning applications for energy curtailment, HVAC control systems, and resource-constrained multi-agent planning. His projects often bridge AI methodologies with real-world energy challenges, aiming to enhance grid reliability, carbon efficiency, and fair resource allocation. Notable collaborations involve researchers in machine learning, sustainable energy, and building automation. He has actively participated in 6 major research projects since 2020, including initiatives on AI-ready buildings and probabilistic multi-agent learning environments. His publications span conferences like AAAI, NeurIPS, and IEEE Power & Energy Society events, emphasizing peer-reviewed contributions to energy-AI intersections.

• Edge Computing
• Internet of Things
• Vehicular Networks
• +۵ مورد دیگر

Muhammad Bilal is an Associate Professor at Zhejiang University's School of Computer Science, Department of Computer Science and Technology. His research spans multiple cutting-edge domains in computer science and engineering, with a particular focus on edge computing, Internet of Things, and vehicular networks. His research interests include Edge Computing , Internet of Things , Vehicular Networks , Federated Learning , Digital Twins , and Cloud Computing . His work addresses critical challenges in resource allocation, latency optimization, privacy preservation, and intelligent decision-making in distributed computing environments. He has made significant contributions to developing novel frameworks for service caching, computation offloading, and secure data processing in next-generation networks. His recent publications reveal a strong trend toward integrating artificial intelligence with edge and cloud infrastructure, particularly focusing on privacy-preserving techniques and resource-efficient algorithms. His work spans applications from intelligent transportation systems to healthcare monitoring and meteorological services, demonstrating the versatility and real-world impact of his research. Dr. Bilal has established a prolific publication record with numerous high-impact papers in top-tier journals and conferences, reflecting his active engagement in the academic community. His collaborative approach is evident through extensive co-authorship networks, particularly with researchers in China, Pakistan, and internationally.

• Hardware Security
• Machine Learning for Security
• VLSI Design
• +۷ مورد دیگر

Sai Manoj Pudukotai Dinakarrao is a Professor in the Department of Computer Engineering at Rochester Institute of Technology's College of Engineering. He is an active researcher with numerous publications spanning hardware security, machine learning applications, VLSI design, and IoT security. His work focuses on developing innovative solutions for security challenges in hardware and embedded systems. Dr. Dinakarrao's research interests center around hardware security, machine learning for security applications, VLSI design methodologies, and IoT security frameworks. His work has pioneered approaches to hardware Trojan detection, processing-in-memory architectures, and energy-efficient security mechanisms for resource-constrained devices. He has made significant contributions to the understanding of hardware vulnerabilities and the development of robust countermeasures. His recent publications demonstrate a clear trend toward integrating machine learning techniques with hardware security solutions, particularly focusing on processing-in-memory architectures for efficient security implementations. His work spans both theoretical foundations and practical implementations, with applications in IoT security, malware detection, and hardware verification. The research shows increasing sophistication in addressing security challenges through novel hardware-software co-design approaches. Dr. Dinakarrao has mentored numerous graduate students who have co-authored publications with him, indicating an active research group focused on cutting-edge security and hardware design topics. His collaborations span multiple institutions and research groups, demonstrating his integration within the broader academic community.

• Machine Learning
• Data Systems
• Resource-Aware Computing
• +۵ مورد دیگر

Pinar Tözün is an Associate Professor and Head of the Data, Systems, and Robotics department at the IT University of Copenhagen. She leads multiple research groups, including the Resource-Aware Data Systems and Data-intensive Systems and Applications. Her roles also include academic responsibility for DASYA and involvement in the Center for Climate IT. Her research focuses on resource-aware computing, machine learning systems, heterogeneous hardware optimization, and sustainable data management. Key areas include workload characterization, GPU utilization, and benchmarking frameworks for edge devices and cloud systems. Dr. Tözün has led significant projects such as MOTH (Machine Learning on Tiny Hardware), RAD+ (Resource-Aware Data Science), and DAPHNE (Integrated Data Analysis Pipelines), funded by institutions like the Novo Nordisk Foundation and the European Commission. She has published extensively in top venues, including Proc. ACM Manag. Data and Dagstuhl Reports, with a focus on efficient machine learning pipelines and hardware-aware systems. Her work has been highlighted in media discussions on sustainable hardware and software practices. Tözün also actively participates in academic governance, serving on hiring committees and shaping future faculty recruitment in data-intensive systems.

• Machine Learning
• Optimization
• Computer Vision
• +۵ مورد دیگر

Tom Goldstein is the Volpi-Cupal Endowed Professor of Computer Science at the University of Maryland, with appointments in Applied Mathematics and Electrical and Computer Engineering. His research focuses on AI system development, optimization methods, and their applications in computer vision and signal processing. He emphasizes the intersection of theoretical foundations and practical hardware implementations. Education: PhD in Applied Mathematics from UCLA (2010), BA from Washington University (2006). Prior to UMD, he held postdoctoral positions at Stanford University and Rice University. Research interests include AI security/privacy, algorithmic bias, diffusion models, and large language model robustness. His work addresses challenges in model watermarking, adversarial attacks, and ethical AI deployment. Notable awards include the Sloan Research Fellowship (2017), DARPA Young Faculty Award, and JPMorgan Faculty Research Award. He directs the Maryland Center for Machine Learning and has advised over 20 graduate students in AI-related disciplines. Recent research trends emphasize multimodal systems, generative model analysis, and scalable training techniques. His work on diffusion models explores style similarity and content authenticity, while watermarking studies address anti-plagiarism and data provenance. Labs/Teams: Leads the Maryland Center for Machine Learning, collaborating with UMIACS and ECE departments. Active in open-source AI initiatives and supercomputing applications for large model training.

Il Min Kimمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +۷ مورد دیگر

Il Min Kim is a Professor and Chair of Undergraduate Studies in the Department of Electrical and Computer Engineering at Queen's University, Canada. He is affiliated with the Smith Engineering School and holds cross-appointments as Adjunct Professor at the University of Ottawa's School of Electrical Engineering and Computer Science. His research focuses on AI-driven wireless systems, including edge AI, federated learning, and Geoscience AI. He leads the Ubiquitous Artificial Intelligence Lab (UAI Lab) and is a core member of the Ingenuity Labs Research Institute and Queen's Centre for Security & Privacy. Education: PhD in Electrical Engineering from KAIST (2001), followed by postdoctoral work at MIT and Harvard. He joined Queen's in 2003, progressing from Assistant to Full Professor (2014). Awards include the CVPR 2024 Challenge Prize, KOFST President’s Award (2021), and multiple ECE Best Professor Awards. He has over 150 patents in AI/communications and serves on editorial boards of IEEE journals. Research interests span AI for 6G, V2X communications, and secure machine learning. His work emphasizes practical applications in IoT, energy efficiency, and privacy-preserving techniques.

Peter van Beekمشاهده پروفایل

استاد مدعو

• Artificial Intelligence
• Constraint Programming
• Constraint Satisfaction
• +۳ مورد دیگر

Peter van Beek is an Adjunct Professor and Professor Emeritus in the Cheriton School of Computer Science at the University of Waterloo. He obtained his PhD from the University of Waterloo in 1990 and held a faculty position at the University of Alberta before returning to Waterloo in 2000. His research focuses on Artificial Intelligence, including constraint programming, constraint satisfaction, and applied machine learning. Education: PhD in Computer Science, University of Waterloo (1990). Research Interests: Dr. van Beek's work spans core AI areas such as representation and reasoning, constraint programming, and machine learning applications. He has contributed to foundational algorithms, constraint propagation techniques, and practical applications in scheduling and compiler optimization. Awards: He is a Fellow of AAAI, CAIAC, and AAIA. His co-authored papers have earned multiple awards. Service and Leadership: He served as Editor-in-Chief of the journal Constraints (2005–2009), Co-Director of the Waterloo AI Institute (2018–2021), and held various academic leadership roles at the University of Waterloo. Advising: He has advised numerous graduate and undergraduate students, contributing to over 30 theses and research projects in AI and computer science. His advising spans machine learning, constraint satisfaction, and algorithm design. Collaborations: His work integrates interdisciplinary approaches, with contributions to robotics, digital photography, and environmental data science through projects like water consumption analysis using smart meters.

Christiana Ioannouمشاهده پروفایل

استادیار

• Cybersecurity
• Internet of Things (IoT)
• Intrusion Detection Systems (IDS)
• +۵ مورد دیگر

Dr. Christiana Ioannou is an Assistant Professor in Cybersecurity and Course Leader of the MSc Cybersecurity and MSc Cybersecurity Distance Learning programs at University of Central Lancashire Cyprus (UCLan Cyprus). She holds a PhD in Computer Science from the University of Cyprus (2017) and has extensive experience in academia and industry, including roles as Research Associate at CYENS Center of Excellence and NCC Team Leader at the Digital Security Authority of Cyprus. Education: PhD in Computer Science, University of Cyprus (2017) MSc in Computer Science, Florida Institute of Technology (2006) PGDiploma in Management, Mediterranean Institute of Management (2004) BSc in Computer Science, San Diego State University (2003) Research Interests: Her research focuses on Cybersecurity , Internet of Things (IoT) , and Intrusion Detection Systems (IDS) . She explores secure data management in industrial IoT and WSN environments, attack classification, and decentralized security agents. Her work emphasizes practical applications in constrained networks and industrial settings. Grants & Awards: Recipient of the Onisilos Postdoctoral Fellowship (2022) Winner of the Cyprus Entrepreneurship Competition (3rd Prize, 2021) Recipient of the Cyprus-America Scholarship for Graduate Studies (2004–2006) Professional Activities: She serves on the Technical Program Committees of conferences like WiOpt and MedComNet, and reviews for journals including IEEE Access and Computers & Security. Her work bridges academic research with real-world cybersecurity challenges in IoT and industrial systems. Labs & Collaborations: Active in the CYENS Center of Excellence and collaborates on projects like METACITIES (EU-funded) and BluePark (smart parking system for disabled persons).

Thomas Rauschمشاهده پروفایل

مدرس

• Edge Computing
• Cloud Engineering
• Artificial Intelligence Systems
• +۳ مورد دیگر

Thomas Rausch is an External Researcher and former University Assistant at TU Wien's Distributed Systems Group (Faculty of Informatics). He holds a PhD in Computer Science (2021) and master's degree in Software Engineering from TU Wien (2016). His work focuses on edge computing, cloud engineering, and AI systems, with notable contributions to frameworks like LocalStack and CognitiveXR. Education: M.Sc. in Software Engineering & Internet Computing (2016), Ph.D. in Computer Science (2021 – TU Wien). Research Interests: Edge computing systems, distributed interactive systems, cognitive augmentation, and cloud-edge AI integration. He has pioneered privacy-preserving edge systems and serverless platforms for data-intensive workloads. Publications: Over 20 peer-reviewed articles on edge computing frameworks, privacy-aware systems, and large-scale AI operations. Recent work includes faas-sim (2023) and Mobility-Aware Serverless Adaptions (2022). Awards: SEC 2019 Best Demo Award, 2020 Netidee Grant, Finalist TU Wien Teaching Awards. Grants: Netidee Grant (2020), CPS/IoT Ecosystem collaboration (2019–present). Teams: Founded CognitiveXR (edge-AI platform) and co-founded LocalStack (serverless testing).

Alireza Furutanpeyمشاهده پروفایل

پژوهشگر

• Edge Computing
• Edge Intelligence
• Distributed Inference
• +۳ مورد دیگر

Alireza Furutanpey is a University Assistant (PreDoc Researcher) at TU Wien's Distributed Systems Group within the Institute of Information Systems Engineering, holding a Master's degree with distinction. His academic role combines research in distributed systems with teaching responsibilities for core computer science courses. His educational background includes: Bachelor of Science (BSc) Master of Science (Dipl.-Ing.) with distinction Furutanpey's research centers on Edge Computing and Edge Intelligence, specializing in Distributed Inference, Neural Data Compression, and AI-Systems integration. He pioneers techniques for neural feature compression in satellite/edge environments and develops frameworks for federated learning orchestration under communication constraints. His work bridges theoretical AI with practical system implementation, focusing on resource-constrained scenarios where bandwidth and computational efficiency are critical. Analysis of his 15+ publications reveals dominant trends in neural compression for distributed systems (60%), federated learning optimization (25%), and serverless edge frameworks (15%). Key contributions include solving satellite downlink bottlenecks through feature compression and enabling adaptive inference in heterogeneous edge networks, with methodologies increasingly incorporating generative modeling and robustness against adversarial attacks. Scientific Awards: None documented in available sources. He actively supervises master's theses, guiding students on adversarial machine learning, neural compression, and image retrieval systems. His research is supported through major projects: AloTwin (2023-2025) focusing on edge intelligence, INTEND (2024-2026) on industrial IoT, and TEADAL (2022-2025) on federated learning. He serves as a reviewer for 15+ IEEE/ACM venues including IEEE Transactions on Mobile Computing and ICDCS. Furutanpey operates within TU Wien's Distributed Systems Group, which specializes in edge-cloud continuum research. The team develops tools like faas-sim for serverless edge simulation and explores quantum-classical hybrid architectures, maintaining strong industry collaborations in industrial IoT and satellite communications.

Mahdi Jaliliمشاهده پروفایل

استاد

• Complex Networks
• Dynamical Systems
• Control Systems
• +۳ مورد دیگر

Professor Mahdi Jalili is a faculty member at the School of Engineering, RMIT University, Australia. His research focuses on complex networks, dynamical systems, control systems, graph theory, and machine learning applications. He actively supervises research students in areas such as vehicle-to-home (V2H) technology, AI-driven sustainability solutions, and smart grid optimization. His work integrates theoretical frameworks with practical applications in energy systems, transportation, and data analytics. Education: Not explicitly detailed in the text. Research interests include network control, machine learning for energy analytics, and the integration of renewable energy systems. His recent projects address challenges in electric vehicle infrastructure, grid stability, and distributed energy resource management. Collaborations focus on advancing smart grids and sustainable urban transportation systems. Publications span journals like Expert Systems with Applications and IEEE Transactions , covering topics from graph neural networks to grid-forming inverter placement. His work emphasizes interdisciplinary solutions for modern energy and transportation challenges.

Estrid Heمشاهده پروفایل

مدرس ارشد

• Natural Language Processing
• Data Mining
• Deep Learning
• +۳ مورد دیگر

Dr. Estrid He is a Senior Lecturer at RMIT University's School of Computing Technologies. She obtained her PhD from the University of Melbourne in 2020, where she subsequently served as a postdoctoral research fellow. Her research bridges natural language processing, data mining, and deep learning optimization, with applications spanning healthcare, communications, and algorithmic fairness. Research Focus: Core NLP techniques for knowledge extraction from complex texts (patents, medical records) Enhancing security/efficiency of deep learning models in resource-constrained environments Multimodal learning integrating text, sensor data, and biomedical signals Fairness-aware AI systems for computer vision and graph neural networks Her publication portfolio demonstrates strong cross-disciplinary collaboration, with recent work in: Wireless communications (terahertz signal processing, 6G hardware) Biomedical applications (brain disorder prediction, clinical NLP) Generative models for sensor data and multimodal content Algorithmic fairness in computer vision and graph networks She actively supervises graduate research, with current projects including: Graph learning for brain disorder prediction Multimodal health data mining Privacy-preserving AI for urban sensing Integration of spiking neural networks with LLMs

Sarel Gronumمشاهده پروفایل

استاد

• Management Education
• Innovation
• Business Model Innovation
• +۶ مورد دیگر

Sarel Gronum is a Professor and Director of Teaching and Learning at the University of Queensland Business School, with over 20 years of international experience in managing tertiary education programs. He lectures and supervises in Innovation and Entrepreneurship, while actively researching management education, business model innovation, and SME performance. PhD in Commerce from University of Queensland Postgraduate Diploma from University of Queensland Bachelor (Honours) of Commerce from University of South Australia His research spans SME growth, business model design, innovation management, and technology commercialisation. Google Scholar highlights his work on effectuation theory in crisis scenarios, institutional policy impacts on agri-SMEs, and innovation diversity in tourism sectors. Recent article trends show his focus on crisis-driven business model innovation, policy evaluation for SMEs, and cross-cultural research in African and Sri Lankan contexts. He supervises PhD candidates in topics related to small farm resilience and Boeing Loyal Wingman aircraft sustainability. As UQ Ventures' Academic in Residence and media expert for Business Model Innovation and Entrepreneurship, he bridges academic research with corporate and public sector growth strategies.