جست‌وجوی استاد و پژوهشگر

• Software and network security
• Operating system design and implementation
• Open-source software development

W. Michael Petullo is an Assistant Professor in the Department of Comp Sci & Comp Engineering at the University of Wisconsin-La Crosse. He holds a Ph.D. in Computer Science from the University of Illinois at Chicago, and prior academic degrees from DePaul University and Drake University. Before academia, he served a 20-year career in the Army, including roles teaching in the Department of Electrical Engineering and Computer Science and leading cyber operations software development. His research focuses on software and network security, operating systems, and open-source software development. Education: Ph.D. in Computer Science, University of Illinois at Chicago M.S. in Computer Science, DePaul University B.S. in Computer Science, Drake University Research Interests: His work emphasizes secure operating system design, network security protocols, and open-source tool development. Recent efforts include the Aquinas Learning System for courseware automation and PivotWall for SDN-based information flow control. He also explores user behavior in cybersecurity contexts and educational applications of cyber defense exercises. Teaching: Currently instructs CS120 (Software Design I), CS410/510 (Open Source Development), and CS455/555 (Fundamentals of Information Security). Past courses include operating systems concepts and secure software development. Labs & Projects: Maintains the Aquinas Learning System project and contributes to Ethos operating system research. Active in developing minimal-latency networking solutions and secure kernel interfaces.

John Guttagمشاهده پروفایل

استاد

• AI for Healthcare and Life Sciences
• Biological and Medical Devices and Systems
• Graphics and Vision
• +۳ مورد دیگر

John Guttag is the Dugald C. Jackson Professor in Electrical Engineering and Computer Science at MIT. His work focuses on AI-driven healthcare solutions, biomedical systems, and advanced computer vision applications. He leads research in medical image analysis, machine learning reliability, and healthcare equity. Guttag's contributions include innovative frameworks like MultiMorph and Scale-Space Hypernetworks, addressing challenges in medical imaging and clinical decision-making. Affiliations: MIT Electrical Engineering & Computer Science Department (EECS) Research emphasizes AI for healthcare, particularly in segmentation, predictive analytics, and ethical algorithm design. Notable projects include real-time fraud detection systems and studies on racial disparities in clinical risk scores. His work bridges computer science with clinical practice through tools like Voxelmorph for medical image registration and ScribblePrompt for interactive biomedical segmentation. Recent publications highlight advancements in uncertainty-aware AI, contrastive learning, and scalable medical data processing. Guttag’s methodologies prioritize practical clinical applications, aiming to improve diagnostics and healthcare workflows. His lab develops open-source tools and frameworks that enhance accessibility to advanced medical imaging technologies.

Leow Wee Khengمشاهده پروفایل

دانشیار

• Medical Image Analysis
• Computer Vision
• 3D Modeling
• +۵ مورد دیگر

Associate Professor Leow Wee Kheng is affiliated with the Department of Computer Science at the School of Computing, National University of Singapore . His career spans over three decades with expertise in medical image analysis , computer vision , and surgical simulation . Ph.D. in Computer Science, University of Texas at Austin (1994) M.Sc. in Computer Science, National University of Singapore (1989) B.Sc. in Computer Science, National University of Singapore (1985) His research focuses on medical image analysis for craniofacial surgery and stroke diagnosis, 3D modeling of anatomical structures, and computer vision techniques like robust PCA and texture analysis . Recent work includes knee joint motion modeling and forearm rotation simulation for clinical applications. Key trends in his 2017-2025 publications involve skull reconstruction algorithms , multi-objective optimization for digital media, subject-specific biomechanical modeling , and low-rank decomposition techniques in visual computing. Collaborations include institutions like Singapore General Hospital and National Taiwan University Hospital . Scientific Awards : CAIP 2017 Best Paper Award 2007 Andrew P. Sage Best Transactions Paper Award Faculty Teaching Excellence Award (AY2015/16) Annual Teaching Excellence Award (AY2015/16) He has mentored numerous students in medical imaging , computer vision , and biomedical modeling . Current and former advisees include Chen Ying , Vineta Lum Lai Fun , and Long Huizhong (Ph.D.).

H.F. Machiel Van der Loosمشاهده پروفایل

دانشیار

• Rehabilitation Robotics
• Roboethics
• Design Methodology
• +۳ مورد دیگر

H.F. Machiel Van der Loos is an Associate Professor and Associate Head – External at the Department of Mechanical Engineering, University of British Columbia. He holds a PhD from Stanford University (1992) in human-robot interaction and a Diplôme d'Ingénieur from École Polytechnique Fédérale de Lausanne (EPFL). As Director of the CARIS Lab, his research focuses on rehabilitation robotics, roboethics, design methodology, and human-robot interaction in industrial contexts. He has authored over 50 peer-reviewed journal articles and 100 conference papers, and serves as Associate Editor for the Journal of Assistive Technology . His teaching includes core design courses such as the Capstone Design Project and cross-disciplinary 'Designing for People' courses spanning Computer Science, Applied Science, and Library Science. Van der Loos organized major conferences including IEEE ICORR 2013 and ICED17 at UBC. His work includes developing assistive technologies like power-assisted wheelchairs, AR interfaces for human-robot collaboration, and rehabilitation robots. The CARIS Lab emphasizes ethical considerations in corporeal robotics and user-centered design principles. Current research projects involve multimodal robot programming through AR, gesture-based interaction, and adaptive wheelchair control systems. His lab has collaborated with industry partners like JDQ Systems Inc. and Tableau to advance practical robotics solutions.

• 5G Networks
• Self-Organizing Networks (SON)
• Long Term Evolution (LTE)
• +۴ مورد دیگر

Prof. Dr.-Ing. Ingo Viering is an Honorary Professor at TU Munich's Chair of Communications Engineering and Co-Founder/CEO of Nomor Research GmbH. He also serves as a consultant at Nokia Bell Labs. His academic role focuses on system aspects in communications, particularly in 5G networks, self-organizing networks (SON), and LTE evolution. Viering holds a PhD from the University of Ulm (2003) and a Diploma in Electrical Engineering from TU Darmstadt (1999). His research spans 5G mobility, heterogeneous networks, antenna concepts (e.g., MIMO), and cognitive radio systems. Over 50+ publications highlight his contributions to radio resource management, network slicing, and SON algorithms. Notable work includes frameworks for slice-aware resource management using AI and beamforming optimizations in 5G. His academic leadership includes guiding diploma theses in MIMO systems and antenna array concepts. Current affiliations include active teaching and research at TU Munich.

• Formal Verification and Control
• Safety-Critical Systems
• Machine Learning
• +۵ مورد دیگر

Dr. Yulong Gao is an Assistant Professor in the Department of Electrical and Electronic Engineering at Imperial College London, affiliated with the Control and Power Research Group. He holds a B.E. in Automation from Beijing Institute of Technology (2013), M.E. in Control Science & Engineering (2016), and a joint Ph.D. in Electrical Engineering from KTH Royal Institute of Technology and Nanyang Technological University (2021). He has held postdoctoral positions at Oxford University and KTH. His research focuses on formal verification and control, machine learning, and their applications to safety-critical systems, including autonomous systems and control synthesis under uncertainty. His work emphasizes robust control strategies, data-driven optimization, and formal methods to ensure safety in dynamic systems. Recent publications address challenges in autonomous vehicle motion planning, risk-aware Bayesian neural networks, and adaptive task planning using temporal logic. He has contributed to stochastic modeling, distributed MPC, and resilient control under cyber-physical threats. Research affiliations include the Control and Power Research Group at Imperial College London, leveraging interdisciplinary collaboration to advance theoretical and applied control systems research.

Elliot J. Crowleyمشاهده پروفایل

مدرس ارشد

• Machine Learning
• Automated Machine Learning
• Low-Resource Deep Learning
• +۳ مورد دیگر

Dr Elliot J. Crowley is a Senior Lecturer in Electronics and Electrical Engineering at the University of Edinburgh, serving as Discipline Programme Manager. He co-leads the Bayesian and Neural Systems research group. His research focuses on simplifying machine learning, automated ML, low-resource deep learning, and engineering applications. He holds an MEng in Engineering Science and a DPhil (PhD) from the University of Oxford, with postdoctoral experience at Edinburgh's School of Informatics. He leads the EPSRC New Investigator Award and participates in the dAIEdge Horizon Network. Notable contributions include foundational work in neural architecture search (NAS), probabilistic methods for model efficiency, and applications in computer vision. His courses, such as the Data Analysis and Machine Learning module, emphasize practical Python-based learning for engineering students. Key awards include an EPSRC grant and recognition through distinguished papers at ASPLOS 2021. His team includes current PhD students (Linus Ericsson, Miguel Espinosa) and former advisees (Chenhongyi Yang at Meta, Jack Turner at Qualcomm). Research spans from NAS algorithms to ethical machine learning practices, with a focus on bridging theoretical advances and real-world engineering challenges.

Miao Xuمشاهده پروفایل

پژوهشگر ارشد

• Data Science
• Machine Learning
• Time Series Analysis
• +۴ مورد دیگر

Dr Miao Xu is a Research Fellow at the University of Queensland (UQ), affiliated with the School of Electrical Engineering and Computer Science within the Faculty of Engineering, Architecture and Information Technology. She holds an Australian Research Council DECRA Fellowship (ARC DECRA), recognizing her early-career research excellence. Her research focuses on machine learning, data science, and time series analysis, with applications in healthcare, materials science, and algorithmic fairness. Dr Xu's work addresses challenges in noisy label handling, unlearning mechanisms, and adaptive modeling for irregular data. Education: She earned a Doctor of Philosophy (PhD) from Nanjing University. She is actively involved in supervising research and contributes to the Centre for Enterprise AI at UQ. Research Interests: Dr Xu’s expertise spans machine learning , time series analysis , deep learning , and unsupervised learning . Her recent work emphasizes robust learning with noisy or incomplete labels, model unlearning, and applications in alloy design and medical informatics. She explores methods like instance-attention GNNs for irregular time series and confidence-guided techniques for adversarial attack detection. Publications: Her recent work includes advancements in GNN-based time series modeling, bias mitigation in text classification, and active learning for alloy design. Key themes include improving generalization, reducing algorithmic bias, and enhancing model transparency. Awards: Her ARC DECRA fellowship (202X–202X) supports her research on data-driven methodologies. Supervision & Grants: Available for PhD supervision in machine learning and data science. Her grants include funding for projects in unlearning mechanisms and spatiotemporal modeling. Labs/Teams: Affiliated with the Centre for Enterprise AI at UQ, collaborating on enterprise-scale AI applications and interdisciplinary research.

Gary Marchioniniمشاهده پروفایل

استاد

• Information Interaction
• Human-Computer Interaction
• Digital Libraries
• +۶ مورد دیگر

Dr. Gary Marchionini is the Cary C. Boshamer Distinguished Professor and Dean of the School of Information and Library Science (SILS) at the University of North Carolina at Chapel Hill. He previously served as a professor at the University of Maryland's College of Library and Information Services and as a member of the Human-Computer Interaction Laboratory. His expertise spans information interaction, human-computer interaction, digital libraries, and information policy. Marchionini holds a PhD in mathematics education from Wayne State University, emphasizing educational computing. His research focuses on interfaces for information seeking, digital video retrieval, and the impact of data science. Notable projects include the Information in Life Video Series (2007), one of the first academic YouTube channels, and leadership in the Open Video Project . He has secured grants from NSF, NASA, Microsoft, and Google, among others. Marchionini's awards include the ASIS&T Award of Merit (2011) and the LITA Kilgour Award (2000). He has served as editor-in-chief of ACM Transactions on Information Systems (2002–2008) and as president of ASIS&T (2010). Current interests include the societal impact of information, personal health records usability, and digital curation strategies. His leadership roles include serving on the iSchools Board of Directors and directing the Center for Information Impact. He has advised numerous grants, including Mellon Foundation-funded initiatives and EPA research library operations. Marchionini's work bridges academic research with real-world applications, emphasizing human-centered design and interdisciplinary collaboration.

Ahmed M. A. Sayedمشاهده پروفایل

مدرس ارشد

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

Dr. Ahmed M. A. Sayed is a Senior Lecturer (equivalent to Associate Professor) and Director of the MSc Big Data Science Programme at Queen Mary University of London's School of Electronic Engineering and Computer Science. He leads the SAYED Systems Group and focuses on distributed systems, federated learning, edge computing, and network optimization. His research bridges system design and machine learning, emphasizing scalability and efficiency. Education: PhD in Computer Science (HKUST, 2017), M.Sc. and B.Sc. (Assiut University, 2012 and 2007). Prior roles include Research Scientist at KAUST and Senior Researcher at Huawei's Future Network Lab. Research Interests: Systems for ML, federated learning, edge/Cloud computing, network congestion control, and IoT. He has secured £730K+ in grants, including a UKRI-EPSRC grant for the KUber project (2024–2027). Awards: 2024 Best Student Paper (IJCAI FL Workshop), Hong Kong PhD Fellowship (2013–2017), and numerous travel grants. Actively supervises PhD/MSc students and postdocs. Grants & Leadership: PI of UKRI-EPSRC KUber project, Co-I in HKRGC and KAUST grants. Organizes workshops at venues like MobiSys and serves on TPC for ICML, EuroSys, and NeurIPS. Labs: Leads SAYED Systems Group, affiliated with Networks Group and DT4SGD Lab at Queen Mary.

Ferrante Neriمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Optimization
• +۵ مورد دیگر

Professor Ferrante Neri is a faculty member at the University of Surrey, holding the positions of Professor of Machine Learning and Artificial Intelligence and Associate Dean (International) for the Faculty of Engineering and Physical Sciences (FEPS). He is affiliated with the Nature Inspired Computing and Engineering Research Group, Surrey Institute for People-Centred AI (PAI), and the Computer Science Research Centre within the School of Computer Science and Electronic Engineering. His research focuses on optimization, explainable AI, and machine learning, with contributions to memetic computing and differential evolution. Since 2010, he has chaired the IEEE Task Force on Memetic Computing. He advises PhD students in topics like dynamic multi-objective optimization and AI-driven applications. His teaching expertise includes mathematical foundations for computer science. He has supervised students such as Aisha E S E Saeid and Pengjin Wu. Notable research areas include evolutionary algorithms, neural architecture search, and applications in robotics and environmental monitoring. Labs and teams include the Nature Inspired Computing group, which explores AI-driven solutions for complex problems. His work bridges theoretical advancements and practical applications in fields like autonomous systems and deep learning.

Paul Franzonمشاهده پروفایل

استاد

• Quantum Information Science
• Machine Learning & AI Hardware
• 3D Integration
• +۴ مورد دیگر

Paul Franzon is the Cirrus Logic Distinguished Professor and Associate Department Head for Graduate Affairs at the Department of Electrical and Computer Engineering, North Carolina State University. He holds a PhD and Bachelor's in Electrical Engineering and a Bachelor's in Physics/Mathematics from the University of Adelaide, Australia. His research focuses on quantum information science, machine learning-driven hardware design, 3D integration, and high-speed systems. Education: PhD in Electrical Engineering, University of Adelaide (1988) Bachelor's in Electrical Engineering, University of Adelaide (1984) Bachelor's in Physics and Mathematics, University of Adelaide (1982) Research Interests: Quantum computing and algorithm optimization AI-driven design automation for 3D integrated circuits High-speed communication systems Hardware security and FPGA acceleration Awards & Honors: IEEE Fellow (2006) Alcoa Foundation Distinguished Engineering Research Award (2005) NC State Alumni Distinguished Undergraduate Professor Award (2003) NSW Australia Expatriate Scientist Award (2003) Advising & Grants: Advised PhD student Priyank Kashyap (2023 graduate) Recipient of NSF Young Investigators Award (1993) Labs & Collaborations: Center for Advanced Electronics Through Machine Learning (CAEML) IEEE EPS Society (Associate Editor)

Sabin Tabircaمشاهده پروفایل

مدرس ارشد

• Artificial Intelligence
• Data Analytics
• Algorithmics
• +۶ مورد دیگر

Dr. Sabin Tabirca is a Senior Lecturer at the School of Computer Science and Information Technology, University College Cork (UCC). He holds a BSc from Bucharest University and a PhD from Brunel University. His research focuses on Artificial Intelligence, Data Analytics, Algorithmics, Interactive Media, and HCI, with applications in computational cancer modeling, mobile health (mHealth), and parallel computing. He coordinates MPT Activities and is affiliated with the CRR Group and BCRI Centre. Notable awards include UCC's President Award for Innovation in Teaching (2007), IT@Cork Leader Award (2008), and UCC Staff Recognition Award (2013). His teaching includes modules like Parallel and Grid Computing, Mobile Application Design, and Graphics for Interactive Media. Dr. Tabirca has supervised over 70 MSc students and numerous PhD candidates, many of whom now hold academic and industry roles globally. His research includes developing mHealth apps for cystic fibrosis patients, 3D cancer visualization tools, and frameworks for mobile parallel computing. Publications span mHealth design pipelines, cancer prediction models, and mobile gaming for health education. He actively engages in interdisciplinary projects, blending computer science with medical and biological applications.

• Urban Computing
• Spatiotemporal Data Management
• Artificial Intelligence
• +۳ مورد دیگر

Associate Professor Ke Deng is affiliated with the School of Computing Technologies at RMIT University. His research focuses on urban computing, spatiotemporal data analysis, and social networks, with expertise in data mining and artificial intelligence. He holds a PhD in Computer Science from The University of Queensland (2007), a Master's in Information and Communication Technology (2001), and a Bachelor's in Electrical Engineering (1994). Previously, he was a postdoctoral researcher at CSIRO ICT Centre and a researcher at Huawei Noah's Ark Lab. His work emphasizes practical applications of AI, such as fairness-aware recommendation systems, traffic scenario modeling, and energy-efficient protocols for IoT. Dr. Deng's career includes roles as an acting lecturer at The University of Queensland and co-supervisor of a PhD student. He has supervised numerous research projects, including advancements in quantum annealing for recommenders, fake news mitigation via reinforcement learning, and smart human sensing using millimeter-wave radar. His research outputs span journals like ACM Transactions on Information Systems and IEEE Transactions on Knowledge and Data Engineering. Education: PhD in Computer Science, The University of Queensland (2007) MSc in Information and Communication Technology (2001) BEng in Electrical Engineering (1994) Key Research Themes: Urban computing and smart cities Mechanisms for fair AI systems Energy-efficient IoT protocols Spatiotemporal data mining Grants and Collaborations: ARC Australian Postdoctoral Fellowship (2010–2012) Research at Huawei Noah's Ark Lab (2013) His recent projects highlight interdisciplinary innovation, such as quantum computing for feature selection and self-supervised networks for traffic scenario clustering. Dr. Deng is actively involved in mentoring PhD/Masters students and contributes to RMIT’s research initiatives in AI and urban informatics.

• Computer Vision
• Autonomous Systems
• Robotics
• +۱۲ مورد دیگر

Dr Lounis Chermak is a Lecturer in Computer Vision and Autonomous Systems at the Centre for Electronic Warfare, Information and Cyber, part of Cranfield Defence and Security at Cranfield University, UK. He leads the Joint Autonomy Lab and is actively involved in research and education in autonomous systems with applications in defence and space. Research Interests: His work focuses on situational awareness in autonomous platforms, with core expertise in computer vision, sensor fusion, artificial intelligence, robotics, and navigation. He investigates perception, decision-making, and mobility across aerial, ground, maritime, and space systems, developing robust solutions for challenging environments including low visibility and extreme illumination. The recent publications reflect a strong trend in autonomous navigation, particularly for space and defence applications, using advanced computer vision techniques such as thermal stereo odometry, HDR imaging, stixel-based scene understanding, and lightweight 3D descriptors. Research also extends to cybersecurity of autonomous systems, including impersonation attack detection and optical countermeasures. Scientific Awards: No scientific awards mentioned in the provided text. Advising and Grants: Dr Chermak leads research activities supported by postdoctoral researchers, PhD, and MSc students. His work is funded and applied in collaboration with major clients including aerospace organizations (ESA, UK Space Agency, Thales Alenia Space), defence agencies (MoD, DSTL, BAE Systems, MBDA), and technology companies (Samsung, Astroscale). He supervises research students in robotics and autonomous systems across civilian and defence domains. Labs and Teams: He leads the Joint Autonomy Laboratory, a 200 m² indoor facility equipped with drone netting, motion capture systems, virtual reality test benches, UAV and ground robot fleets, electric vehicles, and multiple sensors for vision, ranging, and motion. This lab supports both educational and cutting-edge research in autonomous systems.