Find Academic Supervisors

Amir RahmatiView profile

Assistant Professor

• System Security
• Privacy
• Internet of Things
• +4 more

Overview Amir Rahmati is an Assistant Professor in the Department of Computer Science at Stony Brook University , where he directs the Ethos Security & Privacy Lab and is a member of the National Security Institute. His work focuses on securing emerging technologies like IoT, AR, and ML systems. Education PhD in Computer Science & Engineering, University of Michigan (2017) Research Focus His research addresses security threats in emerging systems, including: Cyber-Physical System vulnerabilities Adversarial ML attacks Hardware security Privacy-preserving frameworks Notable contributions include work on cryptocurrency scam detection, neural network robustness, and AR system security. Grants & Awards Supported by the Air Force Office of Scientific Research (AFOSR), Office of Naval Research (ONR), Meta, NVIDIA, and IBM. His research has been featured in MIT Technology Review , Washington Post , and Bloomberg . Advising Seeking students with expertise in hardware, software, ML, UX, or network protocols passionate about security/privacy. Apply via the graduate program and fill out his interest form. Labs & Collaborations Leads the Ethos Lab focusing on securing IoT, AR, and ML systems. Collaborates on projects like Erebus (AR access control) and Valve (serverless computing security).

Yuan-Fang LiView profile

Associate Professor

• Knowledge Graphs
• Natural Language Processing
• Multimodality
• +5 more

Yuan-Fang Li is an Associate Professor in the Department of Data Science & AI at Monash University's Faculty of Information Technology. He also serves as Associate Dean International. His research focuses on knowledge graphs, natural language processing, multimodality, and graph representation learning. He holds a PhD from National University of Singapore (2006) and a Bachelor of Computing (Honours) from the same institution (2002). Affiliations: Monash University (since 201?), National University of Singapore (PhD 2002-2006) Key Projects: Leading research on neuro-symbolic systems (HARNESS project), large-scale multimodal knowledge management, and maritime knowledge graphs Teaching: Taught courses including FIT4002, FIT4004, and supervised over 20 PhD students Research interests include complex question answering over knowledge graphs, knowledge extraction from text/images, and structural/temporal graph learning. He has published 152+ works with notable contributions to scene graph generation, event extraction, and LLM-based reasoning. Key awards include the 2020 Best Student Paper Award and 2017 Kurzweil Prize. Grants: ARC Discovery Projects, industry collaborations (e.g., Outotec Oy) Labs/Teams: Active in Monash's Data Science & AI research groups, leading neuro-symbolic AI initiatives

Vivy SuhendraView profile

Associate Professor

• Software Assurance
• Security and Privacy
• Cybersecurity
• +1 more

Vivy Suhendra serves as Associate Professor of Practice and Programme Director for Master Programmes at the National University of Singapore's School of Computing, while also holding the position of Assistant Dean for Graduate Studies. Previously, she led the Singapore Cybersecurity Consortium (SGCSC) as Executive Director from 2016 to 2022, driving collaborative cybersecurity research between academia, industry, and government agencies. Her career at NUS spans over two decades, beginning as a Research Assistant before advancing to her current leadership roles. Her academic credentials include: Ph.D. in Computer Science, National University of Singapore (2009). Thesis: "Memory Optimizations for Time-predictable Embedded Software". Advisors: Abhik Roychoudhury and Tulika Mitra. B.Comp. (Honors) in Computer Science, National University of Singapore (2004). Dr. Suhendra's research integrates Software Assurance, Cybersecurity, Security and Privacy, and Embedded Systems domains. Her work bridges theoretical foundations with practical applications, particularly in national cybersecurity ecosystem development, smart grid security protocols, denial-of-service mitigation techniques, and real-time embedded system optimization. This interdisciplinary approach enables innovative solutions for critical infrastructure protection and time-predictable software execution in multi-core environments. Her 14 selected publications (2004-2020) demonstrate an evolving research trajectory from foundational embedded systems timing analysis to applied cybersecurity solutions. Early work focused on memory optimization for predictable execution in multi-core embedded systems, which naturally transitioned into cybersecurity applications for smart grids, cloud environments, and national infrastructure. This progression highlights her ability to translate low-level system expertise into high-impact security frameworks for complex real-world systems. Scientific recognition includes: Microsoft Research Asia Fellowship (2006) Valedictorian at NUS School of Computing Ph.D. Commencement (2010) While specific graduate student advising details aren't provided, her leadership as SGCSC Executive Director involved extensive mentorship across academic-industry partnerships. She has also contributed significantly to the research community through roles including Conference Chair for ESEC/FSE 2022 and Workshops Committee Member for ICSE 2024. Dr. Suhendra established the Singapore Cybersecurity Consortium as a national platform for collaborative R&D during her directorship (2016-2022). Though no personal laboratory is specified, her research leadership manifests through cross-institutional teams focused on cybersecurity innovation, particularly in critical infrastructure protection and embedded systems security where she maintains active publication records.

Nick HeardView profile

Professor

• Computational Bayesian Inference
• Clustering
• Changepoint Analysis
• +6 more

Nick Heard is a Professor and Chair in Statistics at the Department of Mathematics, Faculty of Natural Sciences, Imperial College London. His research focuses on computational Bayesian inference, clustering, and changepoint analysis applied to dynamic networks (e.g., computer networks, social networks) and bioinformatics. He leads the EPSRC-funded NeST project on Network Stochastic Processes and Time Series, collaborating with universities including Bristol, Oxford, and LSE. His work bridges statistical theory with applied problems in cyber-security and neuroscience. Research Interests - Modelling large dynamic networks - Changepoint analysis and anomaly detection - Statistical methods for cyber-security - Bayesian computation and inference - Spectral clustering and graph embeddings Grants & Collaborations - Co-leads the NeST project on Dynamic graph embeddings: procedures and inference - EPSRC Programme Grant (EP/T004870/1) supporting Network Stochastic Processes and Time Series research Software & Tools - Developed open-source packages for Bayesian changepoint analysis (e.g., changepoints ) - Code for p-value combination methods ( standardised_partial_product )

Simon MooreView profile

Professor

• Computer Architecture
• Computer Security
• Hardware Security
• +3 more

Simon Moore is a Professor of Computer Engineering at the University of Cambridge's Department of Computer Science and Technology. He leads the Computer Architecture research group, focusing on secure processors and subsystems, particularly the CHERI project. His work emphasizes formal verification, hardware-software co-design, and scalable security solutions. He is a Fellow and Director of Studies at Trinity Hall, overseeing undergraduate admissions and mentoring in Computer Science. Research Interests: Moore's primary focus is the CHERI secure processor architecture, integrating RISC-V cores with formal verification. His work spans secure hardware design, memory safety, and embedded systems. Notable contributions include the CHERI-RISC-V microarchitecture, CheriABI, and formal verification frameworks. Key Projects: CHERI, CheriBSD, Morello (ARM collaboration) Recent Achievements: Test of Time Award (IEEE Security & Privacy 2025), finalist for Bhattacharyya Award (2022) Grants: Innovate UK Digital Security by Design, DARPA Mission Oriented Resilient Clouds Publications: Over 200 papers on secure architectures, including influential work on CHERI's capability model, formal verification, and hardware security. Recent focus areas include temporal memory safety, embedded system security, and GPU-based capability systems. Labs/Teams: Directs the Computer Architecture Group and collaborates with industry partners like ARM and Microsoft on CHERI implementations.

• Machine Learning
• Data Mining
• Computer Vision
• +3 more

Professor Xue Li is a faculty member in the School of Electrical Engineering and Computer Science at the University of Queensland. His research focuses on machine learning, data mining, and their applications in healthcare, materials science, and computer vision. He has authored over 300 publications, including seminal works on knowledge graph completion, video quality enhancement, and alloy design using machine learning. His work bridges theoretical advancements with real-world applications, such as clinical diagnosis andTinyML systems. Key research interests include graph representation learning, medical informatics, and efficient algorithms for multimedia data. Notable contributions include developing commonsense-enhanced relation extraction models and frameworks for compressed video reconstruction. His research also addresses challenges in federated learning and privacy-preserving genomics. Prof. Li has collaborated extensively with industry and academia, contributing to projects in RFID systems, electronic nose pattern recognition, and cybersecurity. His work is published in top-tier venues like IEEE Transactions and ACM conferences. Despite no listed awards, his prolific output underscores academic impact.

Marius PortmannView profile

Professor

• Cybersecurity
• Computer Networks
• Internet of Things (IoT)
• +3 more

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.

• Ultra-low power systems
• Microelectronics
• Integrated Circuits
• +11 more

Professor Aniruddha Desai is a Research Professor and Director of the Centre for Technology Infusion (CTI) at La Trobe University. He holds a Bachelor’s in Industrial Electronics, a Master’s in Micro-electronics, and a PhD in Computer Science. His expertise spans microelectronics, AI, IoT, and sensor networks, with a focus on socially impactful applications like transportation, healthcare, and precision agriculture. Research Interests: Ultra-low power systems Micro-nano electronics AI/ML and edge computing IoT and sensor networks Transportation and logistics Major Projects: Led multi-million-dollar R&D programs in areas such as smart cities, energy management, and smart farming. Notable collaborations include the IIT Kanpur - La Trobe University Research Academy and the Asian Smart Cities Research and Innovation Network. Awards: Recipient of the 2016 Vice-Chancellor’s Award for Research Excellence and the 2020 Victorian Tall Poppy Award for Science. Served on advisory panels for the Australian Research Council and provided expert testimony in parliamentary inquiries. Labs/Teams: Directs the CTI, which delivers technology-based innovations to industry and government. Co-founded the Asian Smart Cities network to advance urban technology solutions.

• Artificial Intelligence
• Neurocomputing
• Quantum AI
• +5 more

Dr. Richard Jiang is a Senior Lecturer (Associate Professor) at Lancaster University's School of Computing and Communications. His research focuses on Artificial Intelligence, Neurocomputing, Quantum AI, Privacy Computing, and Medical Computing. He has pioneered secure pattern recognition in encrypted domains and quantum neuromorphic computing. With over £1M in research grants from EPSRC and others, he has authored 100+ publications and supervised over 20 PhD students. Dr. Jiang's work includes the Face2Brain method for neurodegenerative assessment and explainable models for brain aging analysis. He contributes actively to academic committees, editorial boards, and conferences like the World Conference on eXplainable AI. His research spans ethical AI frameworks, quantum algorithms for medical imaging, and privacy-preserving biometric systems.

Ediz CetinView profile

Associate Professor

• Radio Frequency Interference Detection
• Fault-Tolerant Reconfigurable Circuits
• Machine Learning Applications
• +4 more

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.

• Nonlinear analysis
• Scattering theory
• Real and complex analysis
• +7 more

Ronald Coifman is the Sterling Professor of Mathematics and Professor of Computer Science at Yale University. His research focuses on nonlinear analysis, scattering theory, complex analysis, numerical analysis, and their applications in data science, signal processing, and biomedical imaging. He holds the National Medal of Science and is a member of the National Academy of Sciences and the American Academy of Arts and Sciences. Coifman's work bridges pure mathematics and applied sciences, emphasizing harmonic analysis, manifold learning, and data-driven modeling. His contributions include foundational advancements in wavelet theory, diffusion maps, and nonlinear dimensionality reduction techniques. Key innovations include the development of empirical intrinsic geometry for analyzing complex systems and the use of Wasserstein distances in high-dimensional data analysis. His academic portfolio includes over 250 publications since the 1960s, spanning topics from theoretical mathematics to practical medical diagnostics. Notable applications include methods for stroke detection, medical imaging analysis, and anomaly detection in dynamic systems. Coifman collaborates across disciplines, integrating computational methods with domain-specific challenges in biology, chemistry, and engineering. Education: Ph.D. in Mathematics from the University of Geneva (1965) Awards: National Medal of Science (2001), Member of NAS (1993), Member of AAAS (2006) Key Projects: Development of diffusion maps, manifold learning algorithms, and empirical geometry frameworks Coifman's current research explores the intersection of machine learning and mathematical analysis, with recent focus on intrinsic data organization, emergent dynamical models, and scalable computational methods for large datasets.

• Cryptography
• Privacy
• Secure Multiparty Computation (MPC)
• +5 more

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.

Hassan SajjadView profile

Associate Professor

• Natural Language Processing
• Deep Learning
• Safe and Trustworthy AI
• +7 more

Hassan Sajjad is an Associate Professor in the Faculty of Computer Science at Dalhousie University, Halifax, Canada. He is also the Director of HyperMatrix, a research group focused on AI and deep learning. His work centers on Natural Language Processing, Safe and Trustworthy AI, interpretability, and robustness of language models. PhD - University of Stuttgart, Germany (2012) Masters - National University of Computer and Emerging Sciences, Pakistan (2007) BSc - National University of Computer and Emerging Sciences, Pakistan (2005) Hassan Sajjad's research focuses on making AI systems more interpretable, robust, and safe. He investigates how deep learning models, particularly transformers, encode linguistic and conceptual knowledge. His work spans language generation , model editing , interpretability , and generalization . He develops tools like NeuroX and NxPlain to analyze neuron-level behavior in NLP models. His research also extends to crisis informatics and multilingual NLP, especially Arabic and Urdu. The recent publications show a strong trend in analyzing and interpreting deep NLP models, with a focus on neuron interpretation , latent concept discovery , and model robustness . His work appears in top-tier venues like NeurIPS, ICLR, ACL, and EMNLP, indicating high impact in the AI and NLP communities. There is a clear emphasis on developing practical tools and frameworks for model analysis. No formal scientific awards are mentioned in the provided text. Hassan Sajjad mentors students and has fellowship opportunities available. While specific grants are not listed, his extensive publication record and leadership roles suggest active research funding. He advises students in AI, NLP, and deep learning, and collaborates widely across institutions. He leads the HyperMatrix research group at Dalhousie University, which focuses on AI, deep learning, and NLP. The group develops tools for model interpretation and works on safe and trustworthy AI. Collaborations extend to institutions like MBZUAI, NYU Abu Dhabi, and various international research centers.

Wasiu O PopoolaView profile

Professor

• Optical Wireless Communications
• Visible Light Communication (VLC)
• Free-Space Optical Communications (FSO)
• +5 more

Professor Wasiu O Popoola is a Professor of Communications Engineering and Director of Electronics and Electrical Engineering at the School of Engineering, University of Edinburgh . With over 150 publications and a RAEng/Leverhulme Trust Research Fellowship (2022), his work focuses on optical wireless communication systems including VLC/LiFi, FSO, and underwater optical communications. BSc (First Class Hons), MSc (Distinction), PhD in optical communications (Northumbria University) Professional memberships: Fellow of Higher Education Academy (FHEA), Fellow of IET (FIET), Senior Member IEEE Research Interests span Indoor/Outdoor/Underwater Optical Wireless Communication , Modulation Techniques , and LiFi Applications . His 2025 articles explore underwater turbulence mitigation and hybrid RF-water communication systems . Earlier works include Best Poster Award at IEEE ICSAE 2016 and top-downloaded IEEE Xplore article (2008). Scientific Awards include: 2022 RAEng/Leverhulme Trust Research Fellowship 2016 IEEE ICSAE Best Poster Award 2009 'Xcel Best Engineering and Technology Student' (PhD) He contributes to editorial work as Associate Editor (IEEE Access) , Guest Editor (Optik Journal) , and peer reviewer for Physics World . Recent projects involve BOLD (Defence Science and Technology Laboratory) and TITAN Extension (University of Strathclyde) focusing on diffuse optical wireless systems and UAV swarm networks .

Gernot GrabherView profile

Professor

• Economic Geography
• Urban Planning
• Innovation Studies
• +4 more

Gernot Grabher is a Professor at HafenCity University Hamburg, where he is affiliated with the School of Urban Planning. He is a leading scholar in economic geography and urban studies with extensive research on innovation, platform economies, and project ecologies. His work bridges theoretical and empirical approaches to understanding contemporary economic transformations. Grabher's research interests focus on the intersection of economic geography, urban planning, and digital transformation. He explores how platformization reshapes industrial production, how urban futures are made through professional agency, and how project ecologies cope with ignorance and uncertainty. His work combines theoretical innovation with empirical rigor, particularly in studying temporary organizations and their role in economic development. His recent publications reveal a strong focus on platform economies, showing how digital platforms transform industrial production, urban governance, and knowledge creation. The research demonstrates a consistent interest in the spatial dimensions of economic processes, particularly how digital transformations interact with geographical contexts. His work often takes a critical perspective on technological disruption, revealing how new forms of platformization are embedded in existing social and economic structures rather than representing clean breaks from the past. Best Paper Award 2024 of the Project Management Journal Fellow of the Regional Studies Association (FeRSA) Top 2% of the most cited researchers worldwide in his field (Elsevier and Standford University Data Base) Member of the Selection Committee for the Alice Amsden Award (SASE) Most cited economic geographer in German-speaking area (ZitArt 2020 ranking) Professor Grabher has successfully supervised numerous PhD students whose work spans urban biodiversity, former industrial neighborhoods, global garment production, smart city participation, and creative processes. He leads the DFG-Research Training Group 'Urban future-making: Professional agency across time and scales' and frequently organizes international workshops on creativity and innovation. His research has been supported by significant grants including DFG funding for projects on the temporal structuring of creative processes. Grabher is actively involved in major international conferences, regularly hosting special sessions at the Global Conference of Economic Geographers and other venues. He serves on editorial boards and selection committees for prestigious academic organizations, reflecting his standing in the field of economic geography and urban studies.