جست‌وجوی استاد و پژوهشگر

• Security
• Privacy
• Cybersecurity
• +۴ مورد دیگر

Frank Stajano is a Full Professor of Security and Privacy at the University of Cambridge, leading the Academic Centre of Excellence in Cyber Security Research. He is a Fellow of Trinity College, where he collaborates with Nobel laureates. His research focuses on security, privacy, and usability in ubiquitous computing, IoT, and digital finance. He holds a PhD from Cambridge and a Laurea in Electronic Engineering from La Sapienza University. Frank's work includes the Pico project (ERC-funded), aiming to replace passwords with secure, usable alternatives. He co-founded Cambridge Cyber, offering cybersecurity consulting. Notable achievements include the Resurrecting Duckling authentication protocol and contributions to location privacy (Mix Zones). He has authored over 50 refereed publications and a research monograph on ubiquitous computing security. His awards include the Toshiba Fellowship (2000) and an ERC Starting Grant (2017). Frank teaches cybersecurity and lectures globally via his YouTube channel. Beyond academia, he is a 5th-dan kendo practitioner and a scholar of Disney comics.

Marius Portmannمشاهده پروفایل

استاد

• Cybersecurity
• Computer Networks
• Internet of Things (IoT)
• +۳ مورد دیگر

Professor Marius Portmann is the UQ-Cisco Chair of Network Security at the School of Electrical Engineering and Computer Science (EECS), University of Queensland. His expertise spans Cybersecurity, IoT, and Applied AI. He holds a PhD from ETH Zurich (2003) and has led research in Software Defined Networking (SDN), blockchain, and energy-harvesting IoT systems. Education: PhD in Electrical Engineering from Swiss Federal Institute of Technology (ETH Zurich), 2003. Research focuses on securing IoT networks, AI-driven intrusion detection, and sustainable sensor systems. He has pioneered self-powered IoT systems using energy harvesters and developed frameworks like FlowTransformer for network analysis. His work bridges theoretical advancements with practical applications in smart tourism, energy efficiency, and edge computing. Recent publications highlight innovations in DDoS detection (P4-Secure), sensor-based environmental monitoring (EcoShower), and graph-based anomaly detection (XG-BoT). His datasets (e.g., NF-ToN-IoT-v3) are widely used in ML-based cybersecurity research. Collaborations include industry partners like Cisco and institutions like RMIT. Grants and leadership roles in interdisciplinary projects underscore his impact. He advises on IoT security standards and contributes to open-source tools for network research. Current projects explore edge-AI integration and sustainable sensor networks.

Annie Antónمشاهده پروفایل

استاد

• Privacy
• Cybersecurity
• Software Engineering
• +۴ مورد دیگر

Annie Antón is a Professor in the School of Interactive Computing at Georgia Institute of Technology. Former Chair of her school, she specializes in privacy, security, software engineering, and public policy. Dr. Antón advises national defense and intelligence entities, including roles in the IDA/DARPA Defense Science Study Group (2005-2006) and the Obama-appointed 2016 Commission on Enhancing Cybersecurity for the Nation. She serves on numerous boards, including NIST Information Security & Privacy Advisory Board, Future of Privacy Forum, and former roles in U.S. DHS Data Privacy Committee and NSF Advisory Council. Her research focuses on aligning software systems with federal privacy/security regulations, emphasizing legal compliance, secure development practices, and IoT privacy challenges. Notable projects include UCON_LEGAL for HIPAA compliance and frameworks for analyzing regulatory requirements. She has contributed to policy implementations via empirical studies on user privacy perceptions and legal text mining. Dr. Antón's work bridges technical and policy domains, addressing ethical AI education, cross-border data governance, and cybersecurity preparedness. Her leadership spans academia, government, and industry through advisory roles at Intel, Microsoft Research, and the Georgia Tech Alumni Board.

Yu Huangمشاهده پروفایل

استادیار

• Software Engineering
• Human-Centered AI
• Human Factors in Programming
• +۵ مورد دیگر

Yu Huang is an Assistant Professor of Computer Science at Vanderbilt University with a secondary appointment in the Department of Teaching and Learning at the Peabody School of Education. She is affiliated with the Institute for Software Integrated Systems, the Frist Center for Autism and Innovation, the Vanderbilt Lab for Immersive AI Translation (VALIANT), and the Vanderbilt LIVE Learning Innovation Incubator. Her research focuses on human-centered AI for software engineering, combining human cognition with machine intelligence to enhance software development processes. Educated at the University of Michigan (PhD, 2021), University of Virginia (MS, 2015), and Harbin Institute of Technology (BS, 2011), her work spans software engineering, human factors, AI, and medical imaging. Key projects include the MIND Lab, studying programmer expertise and cognitive processes, and the HumanAISE workshop on Human-Centered AI for Software Engineering. Huang has received significant recognition, including the 2025 ICPC Vaclav Rajlich Early Career Achievement Award and three ACM SIGSOFT Distinguished Paper Awards. Her research is supported by NSF, GitHub, and Vanderbilt initiatives. She advises numerous graduate and undergraduate students, emphasizing diversity and innovation in programming education.

Damian Arellanesمشاهده پروفایل

مدرس

• Theoretical Computer Science
• Algebraic Composition
• Models of High-Level Computation
• +۵ مورد دیگر

Dr. Damian Arellanes is a Lecturer (Assistant Professor) in Computer Science at Lancaster University, UK, affiliated with the Software Engineering Group and Lancaster Centre for Intelligent, Robotic and Autonomous Systems (LIRA). He holds a PhD from The University of Manchester (2020) and a Postgraduate Certificate in Academic Practice from Lancaster University (2023). His research focuses on theoretical foundations of algebraic composition for high-level computation models, including emergent/self-organising systems and software composition. He has contributed to areas such as category theory, control-flow separation, and compositional programming for IoT systems. Education: PhD in Computer Science, University of Manchester (2020) Postgraduate Certificate in Academic Practice, Lancaster University (2023) MSc in Computer Science, supported by CONACYT (2012–2014) BEng in Computer Engineering, supported by PRONABES (2009–2012) Research Interests: Damian’s work emphasizes algebraic semantics, compositional models for software, and theoretical computer science principles. He explores how abstract mathematical frameworks (e.g., category theory) can formalize computational systems and enable scalable IoT solutions. Publications: Damian has published extensively on algebraic composition, IoT systems, and formal methods. Key themes include compositional programming, self-organizing software, and scalable service architectures. Awards: Official Nominator for the VinFuture Prize (2024) Honourable Mention for Most Outstanding Mexican Student in STEM (2021) Nick Sanders Kickstarter Fund (2019) Outstanding Doctoral Paper Award (2019) Best MSc Thesis in AI (2015) Advising & Grants: Damian supervises PhD students, such as Mina Yavari, and actively reviews for journals like IEEE TSC and conferences like TASE. He has secured scholarships and fellowships from CONACYT and the Mexican government. Labs/Teams: Member of LIRA’s Fundamentals Section and the Software Engineering Group at Lancaster University.

Ediz Cetinمشاهده پروفایل

دانشیار

• Radio Frequency Interference Detection
• Fault-Tolerant Reconfigurable Circuits
• Machine Learning Applications
• +۴ مورد دیگر

Ediz Cetin is an Associate Professor in Digital Electronics Engineering at Macquarie University's School of Engineering and a member of the Astrophysics and Space Technologies Research Centre. He serves as Course Director for the MEng Electronics Engineering program and Chair of the School's Postgraduate Coursework Committee. His research focuses on radio frequency interference mitigation, fault-tolerant reconfigurable circuits for space applications, machine learning in RF signal analysis, and low-power digital circuit design. Education: PhD in Signal Processing (Unsupervised Adaptive Signal Processing Techniques for Wireless Receivers) B.Eng. (Hons.) in Control and Computer Engineering Research Interests: RF interference detection and localization GNSS anti-jamming and spoofing detection FPGA-based reconfigurable systems Space instrumentation and CubeSat technologies Machine learning for signal processing Awards: Excellence in Learning Innovation (FSE Teaching Award, 2022) Highly Commended Finalist – Vice-Chancellor’s Award for Learning Innovation (2022) Innovative Approaches – Highly Commended (FSE Teaching Award, 2020) Key Projects: SmartSat CRC (2020–2026): Smart Satellite Technologies and Analytics Spacecraft Innovation Lab (2021–2022) CubeSat Biological Payload (2019–2022) Teaching Contributions: Led the 'Improving Student Engagement with Anywhere and Any-time Laboratory Access' initiative (2019–2020), enhancing remote lab accessibility for students.

Glen Tianمشاهده پروفایل

استاد

• Big Data Computing
• Cloud Computing
• Computer Networks
• +۱۱ مورد دیگر

Professor Glen Tian is a Professor at the School of Computer Science , Queensland University of Technology . He holds two PhDs: one in computer and software engineering from the University of Sydney (2009) and another in industrial automation from Zhejiang University (1993) . His academic career spans institutions including Hong Kong University of Science and Technology, Curtin University, and the University of Maryland at College Park. Editor-in-Chief of the Handbook of Real-Time Computing (Springer) Associate Editor for Information Sciences (Elsevier) and Asia-Pacific Journal of Chemical Engineering (Wiley) His research focuses on big data computing , cloud computing , computer networks , smart grid communication and control , networked control systems , and cyber-physical system security . Applications include power systems , medical big data , vehicular networks , and transport systems . Recent publications highlight advancements in smart grid communications , distributed optimization , secure multi-agent systems , and medical imaging analysis . He has led QUT's Big Data Lab and served as Leader of QUT's Networks and Communications Discipline . Scientific achievements include Over 20 research grants totaling >$6M 6 Australian Research Council (ARC) grants 1 MRFF-TTRA grant ($745,623) 1 ATN-DAAD Australia-Germany Collaborative Grant 1 DEST International Science Linkage grant He supervises PhD students in big data bioinformatics , smart grid optimization , and cyber-physical security , while mentoring 30+ postdocs and research fellows. Current projects include mitigating cyberattacks on power systems and developing AI-based atheroma diagnostic tools .

Siddharth Gargمشاهده پروفایل

دانشیار

• Machine Learning
• Cybersecurity
• Computer Hardware Design
• +۴ مورد دیگر

Siddharth Garg is the Institute Associate Professor of Electrical and Computer Engineering at NYU Tandon School of Engineering, leading the EnSuRe Research Group. He holds a Ph.D. from Carnegie Mellon University (2009) and a B.Tech. from IIT Madras. His research focuses on secure and energy-efficient computing systems, integrating machine learning, cybersecurity, and hardware design. He previously held roles as Assistant Professor at NYU Tandon (2014-2020) and the University of Waterloo (2010-2014). Key affiliations include NYU Center for Cybersecurity (CCS), NYU Wireless, and the Center for Advanced Technology in Telecommunications. His work has been recognized with prestigious awards like the NSF CAREER Award (2015) and inclusion in Popular Science’s 'Brilliant 10' (2016). Notable research includes private inference optimization, secure hardware IP protection, and adversarial machine learning defenses. Publications highlight advancements in zero-knowledge proofs, AI-driven chip design, and mitigating backdoor attacks in neural networks. His grants include funding from NYU Wireless and NSF initiatives like the Chips4All project. The EnSuRe group emphasizes bridging software and hardware design gaps using AI and fostering cybersecurity education.

Rajeev Sahayمشاهده پروفایل

استاد آموزشی

• Machine Learning
• Wireless Communications
• Networking
• +۴ مورد دیگر

Rajeev Sahay is an Assistant Teaching Professor in the Department of Electrical and Computer Engineering at the University of California San Diego (UCSD), affiliated with the Jacobs School of Engineering. He holds a Ph.D. and M.S. from Purdue University (2022 and 2021) and a B.S. from the University of Utah (2018). Prior to UCSD, he worked as a Senior Machine Learning Software Engineer at Saab, Inc., and taught courses at Purdue, earning the Purdue Engineering Dean’s Teaching Fellow award in 2021. His research bridges machine learning and networking, focusing on social learning networks (SLNs) for personalized education and robust wireless communications in adversarial environments. Key areas include federated learning for privacy preservation, adversarial attack mitigation, and next-generation 6G wireless systems. His work emphasizes data-driven methodologies to enhance learning outcomes and secure communication networks against vulnerabilities. Dr. Sahay’s teaching spans undergraduate and graduate courses in machine learning, data science, and programming languages (C/C++/Python), with a passion for tailoring curricula to student needs. His publications reflect expertise in federated learning, adversarial defense mechanisms, and signal processing, with recent contributions to AI-assisted education and robust wireless systems. Scientific Awards: Purdue Engineering Dean’s Teaching Fellow (2021) Teaching and Research Interests: Machine Learning Pedagogy Adversarial Robustness in ML Systems Federated Learning Applications Social Learning Network Analysis 6G Wireless Communication Security

Céline Castets-Renardمشاهده پروفایل

استاد

• Artificial Intelligence Law
• Digital Governance
• Data Protection Law
• +۴ مورد دیگر

Dr. Céline Castets-Renard is a Full Professor in the Civil Law Section of the Faculty of Law at the University of Ottawa and holds the Canada Research Chair in International and Comparative Law of Artificial Intelligence. She also serves as the University Research Chair on Accountable AI in a Global Context (2020-2024) and is an affiliated researcher at ANITI (Toulouse) and Yale Law School's Internet Society Project. She previously held senior academic roles at Université Toulouse Capitole and was a visiting scholar at institutions including Fordham Law School and Osaka University. Her research focuses on digital technology and AI governance from an international and comparative law perspective, emphasizing EU, Canadian, and US frameworks. Key areas include AI law, data protection, digital copyright, platform regulation, and the intersection of technology with human rights, equity, and gender issues. She has authored over 100 articles, 2 monographs, and edited 4 books. Dr. Castets-Renard advises on EU initiatives such as the AI Office’s General-Purpose AI Code of Practice and the Digital Markets Act. Her work bridges legal scholarship with practical challenges in AI accountability, privacy, and transnational governance. Awards include the Honorary Junior Membership of the Institut Universitaire de France (2015-2019).

Kenneth Edwin Barkerمشاهده پروفایل

استاد

• Privacy Preserving Data Repositories
• Database Management Systems
• Data Privacy and Security
• +۴ مورد دیگر

Dr. Kenneth Edwin Barker is a Professor in the Department of Computer Science within the Faculty of Science at the University of Calgary, where he also serves as Director of the Institute for Security, Privacy and Information Assurance (ISPIA). His academic career spans several decades with significant contributions to database systems and privacy research. Dr. Barker earned his B.S. and M.S. in Computer Science from the University of Calgary in 1982 and 1984 respectively, followed by a Ph.D. in Computer Science from the University of Alberta in 1990. His educational background established the foundation for his extensive research career in database systems and information security. His primary research interests focus on Privacy Preserving Data Repositories , with specific attention to protecting privacy in mobile applications, understanding privacy's impact on data analytics, and architecting database management systems that inherently respect user privacy. His work also extends to distributed database environments, integration of legacy systems, and multidatabase environments. Dr. Barker's research bridges theoretical foundations with practical applications, making significant contributions to how privacy is implemented in real-world systems. An analysis of his recent publications reveals a strong trend toward practical privacy-preserving techniques for cloud data, social networks, and location-based services. His work consistently addresses the tension between data utility and privacy protection, developing innovative methods to maintain data value while safeguarding personal information. The publications span multiple subfields including encrypted search, graph privacy, high-dimensional data privacy, and privacy metrics. Best Paper Award at DBSec 2012 Best Paper Award at CODASPY 2012 Best Paper at BNCOD 2009 Dr. Barker has been instrumental in establishing privacy research infrastructure at the University of Calgary through his leadership of ISPIA. His research has attracted significant funding from various sources supporting privacy and security initiatives. While specific grant details aren't provided in the text, his extensive publication record indicates sustained research funding throughout his career. He has collaborated extensively with researchers both within and outside the University of Calgary, particularly with R. Alhajj and other colleagues on numerous projects. As Director of ISPIA, Dr. Barker oversees a research environment focused on advancing security and privacy technologies. The institute serves as a hub for interdisciplinary research, bringing together computer scientists, social scientists, and legal experts to address complex privacy challenges. His leadership has positioned the University of Calgary as a significant player in privacy research within Canada.

Pablo Aragónمشاهده پروفایل

استاد مدعو

• Computational Social Science
• Social Computing
• Wikipedia Studies
• +۴ مورد دیگر

Pablo Aragón is a Research Scientist at the Wikimedia Foundation and an Adjunct Professor at Universitat Pompeu Fabra. His work bridges computational social science, civic technology, and technopolitics, with a focus on Wikipedia's governance, digital democracy tools, and participatory systems. He co-founded the Democratic Innovation Lab in Barcelona and the DatAnalysis15M research network. Key research interests include analyzing knowledge integrity in Wikipedia, configuring digital participatory budgeting systems, and studying platform effects in civic technologies. He has led projects like DECODE (decentralized citizen engagement) and contributed to platforms like Decidim, which empower participatory democracy in cities like Barcelona. Recent conference engagements include KDD 2024 (data mining), ICWSM 2024 (social media analysis), and Wikimedia CEE Meeting 2024. His work emphasizes cross-cultural collaboration, with studies published in ACM Transactions on Computer-Human Interaction and peer-reviewed conferences like CIKM and ACM SIGKDD. Professional affiliations include the Decidim association, Amnistía Internacional España, and the open knowledge advocacy group Civio. His research often intersects with open science, free culture movements, and gender equity in urban mobility.

Xi Zhangمشاهده پروفایل

استاد

• Wireless Networks
• Quality of Service (QoS) Theory
• 6G/Next-Generation Mobile Networks
• +۷ مورد دیگر

Xi Zhang is a Full Professor in the Department of Electrical and Computer Engineering at Texas A&M University . He is also the Founding Director of the Networking and Information Systems Laboratory. His academic career includes research fellowships at the University of Technology Sydney and James Cook University, as well as prior roles at AT&T Bell Laboratories and AT&T Laboratories Research. Education: B.S. and M.S. in Electrical Engineering & Computer Science, Xidian University, China M.S. in Electrical Engineering & Computer Science, Lehigh University, USA Ph.D. in Electrical Engineering-Systems, University of Michigan, USA Research Interests: His work focuses on Quality-of-Service (QoS) theory, 6G/Next-Generation Wireless Networks , Massive MIMO , Integrated Sensing and Communications (ISAC) , and Network Function Virtualization (NFV) . He has pioneered advancements in statistical delay/error-rate bounded QoS , AI-driven 6G architectures , and mURLLC (massive ultra-reliable low-latency communications) . Awards & Honors: IEEE Fellow (2014) for contributions to QoS theory in mobile wireless networks NSF Early Career Award (2004) Multiple Best Paper Awards (IEEE GLOBECOM, WCNC, ICC) Outstanding Faculty Award from Texas A&M (2020) Leadership Roles: He has held key positions as Technical Program Committee (TPC) Chair for major conferences (e.g., IEEE GLOBECOM 2011, IEEE ICDCS 2026) and serves as Editor for top-tier journals like IEEE Transactions on Communications and IEEE Transactions on Wireless Communications . Labs & Teams: He leads the Networking and Information Systems Laboratory , focusing on 6G mobile networks, ISAC systems, and AI-driven network architectures.

Sandeep Kumarمشاهده پروفایل

دانشیار

• Computer Security
• Computer Networking
• Operating Systems
• +۳ مورد دیگر

Sandeep Kumar is an Associate Professor in the Department of Computer Science and Engineering at Texas A&M University, College Station. He holds a PhD in Computer Science from Purdue University (1995) and a B.Tech in Electrical Engineering from the Indian Institute of Technology, New Delhi (1985). His research focuses on computer security, networking, and system-level programming. Prior to academia, he worked in industry roles including at VMware in Palo Alto, CA. He currently teaches courses such as CSCE 313 (Introduction to Computer Systems) and CSCE 222 (Discrete Mathematics), emphasizing system software, networking, and cybersecurity. His teaching philosophy incorporates modern tools like GCP and Docker for practical learning. He advises students on technical projects but notes his non-tenure track role limits formal research supervision. Professional interests include curriculum design, educational technology, and bridging industry-academia gaps in cybersecurity. Education: Ph.D., Computer Science, Purdue University, 1995 M.S., Computer Science, University of Tennessee, 1987 B.Tech, Electrical Engineering, IIT Delhi, 1985 Research Interests: Computer Security, Networking, Operating Systems Teaching: CSCE 313 (Computer Systems), CSCE 222 (Discrete Math), CSCE 111 (Java Programming) Industry Experience: VMware (Networking & Security), Former Googler Awards: Hagler Fellow (2023), Google GCP Educational Grants Dr. Kumar’s work emphasizes practical system-level programming and security, with contributions to intrusion detection systems and secure enterprise networks. His courses integrate modern tools like RustRover and Docker, reflecting industry standards. He actively engages with educational technology, including LaTeX-based lecture materials and Gradescope integration.

• Cryptography
• Privacy
• Secure Multiparty Computation (MPC)
• +۵ مورد دیگر

Gabe Kaptchuk is an Assistant Professor in the Computer Science Department at the University of Maryland, College Park (UMD), focusing on cryptography, privacy, and their social implications. He is affiliated with UMD's MC² (Maryland Cybersecurity Center) and UMIACS (Institute for Advanced Computer Studies). Previously, he was research faculty at Boston University and earned his PhD from Johns Hopkins University under advisors Avi Rubin and Matt Green. Research Interests: Kaptchuk's work bridges theoretical cryptography and social applications, emphasizing secure multiparty computation (MPC), zero-knowledge proofs, and steganography. He also explores the intersection of cryptography with law, policy, and human-centered design. Notable projects include Meteor (secure steganography using generative models) and Pulsar (MPC for dynamic participants). Teaching: Courses include Governing Algorithms (UMD) and Law and Algorithms (BU), focusing on algorithmic governance, privacy, and legal implications of technology. He has taught computer security and networks at Johns Hopkins and BU. Grants & Funding: Recent NSF awards include Collaborative Research: ReDDDoT Phase 2 (2024) for participatory privacy protections in AI training data. Active in deploying MPC for social good, such as analyses for Boston Women’s Workforce Council. Labs/Teams: Collaborates with researchers at Georgetown, Columbia, and institutions like the Wikimedia Foundation. Co-authors include Rachel Cummings, Elissa Redmiles, and Matthew Green on privacy and usability topics.