Find Academic Supervisors

• Cybersecurity and Privacy
• Data Management and Data Science
• Distributed Computing and Systems Software

Professor Jun Zhang is a leading academic in cybersecurity at Swinburne University, Australia, where he directs the Cybersecurity Lab. He has been honored as Australia's top cybersecurity researcher and instrumental in establishing Swinburne as a globally recognized cybersecurity research institution. His work includes high-impact papers and multi-million-dollar R&D projects, culminating in awards like the 2021 'Top Cybersecurity Research Institution' accolade. As course director of the Bachelor of Cyber Security, he pioneered an industry-driven teaching model with Deloitte and CSIRO, significantly boosting course enrollment. His collaborations extend to Adobe's Curriculum Innovation Program and the Australian P-TECH initiative, promoting STEM education and cybersecurity awareness. He supervises doctoral candidates and leads grants focused on AI-driven cybersecurity, smart home security, and blockchain-based edge computing. His research spans vulnerability detection, GAN forensics, IoT security, and privacy preservation in OSNs. Research interests include cybersecurity fundamentals, data science applications, and distributed systems. Notable achievements include the PTFix framework for Java vulnerabilities, the IoTFuzz smart home testing system, and CTI mining methodologies. Awards reflect his mid-career research excellence and industry partnerships. His grants with CSIRO and defense organizations emphasize real-world impact, addressing challenges from malware detection to adversarial machine learning. The Cybersecurity Lab and collaborative projects like Artchain demonstrate his commitment to bridging academia and industry. Professional activities include supervising over 20 HDR students and securing grants totaling millions. His work on blockchain-based edge storage (CSEdge) and SDCCP congestion control highlights innovation in networking. Future directions include advancing AI for design collaboration with CSIRO and enhancing privacy in smart energy technologies. His contributions span technical, educational, and community outreach domains, positioning him as a pivotal figure in cybersecurity's evolution.

Jiang HuView profile

Professor

• VLSI Design Automation
• Hardware Security
• Machine Learning Applications
• +3 more

Jiang Hu is a Professor in the Department of Electrical and Computer Engineering at Texas A&M University, holding the Eric D. Rubin '06 Endowed Professorship. He also serves as Co-Director of Graduate Programs and is affiliated with the Computer Science & Engineering department. His research focuses on VLSI design automation, machine learning applications, and hardware security. He has held roles as editor for IEEE Transactions on CAD and ACM Transactions on Design Automation, and chaired the 2012 ACM International Symposium on Physical Design. Education: B.S. in Optical Engineering (Zhejiang University, 1990), M.S. in Physics (1997), and Ph.D. in Electrical Engineering (University of Minnesota, 2001). He worked at IBM Microelectronics before joining Texas A&M in 2002. Research interests include energy-efficient VLSI circuits, on-chip communication fabrics, analog layout automation, and AI-driven EDA. Recent work emphasizes machine learning for design closure, privacy-preserving frameworks, and systolic array-based architectures. Awards: IEEE Fellow (2016) Humboldt Research Fellowship (2012) Multiple best paper awards at DAC, ICCAD, and ASPDAC Advising and grants: Leads initiatives like the SLICE project, NSF workshops on ML-EDA infrastructure, and serves as Editor-in-Chief of ACM TODAES since 2024. His work bridges academic research and industry applications in EDA and semiconductor design. Labs/Teams: Active contributor to open-source tools like ALIGN for analog layout generation and collaborations on machine learning for EDA commons.

• Machine Learning
• Energy Harvesting
• Low Latency Systems
• +3 more

Kiwan Maeng is an Assistant Professor in Computer Science and Engineering, focusing on the intersection of machine learning systems, privacy-preserving techniques, and low-latency computing architectures. His research emphasizes algorithm-system co-design for scalable and secure AI implementations. Research Trends : His recent work explores retrieval-augmented generation systems, low-latency diffusion models, privacy-preserving federated learning, and energy-harvesting intermittent computing frameworks. Publications highlight collaborations across machine learning, cryptography, and hardware-software co-design. Key Projects : He leads a 3-year NSF SaTC grant (2024-2027) addressing privacy-preserving data embedding for untrusted ML services, and contributes to serverless video analytics frameworks (SVDE) and VR streaming optimization (PIRATE). Technical Contributions : His scholarship spans 17 conference contributions and 3 journal articles since 2007, with notable work on memory encryption for edge devices, secure MPC-based inference, and sustainable AI systems. Current research focuses on balancing privacy guarantees with model utility while optimizing for environmental efficiency.

• Software Engineering
• Graph Transformation Systems
• Model-Based Development
• +5 more

Reiko Heckel is a Professor of Software Engineering at the University of Leicester, serving as Director of Postgraduate Teaching for Computing degrees and Data Analytics Lead at the Leicester Innovation Hub. She previously held academic roles at the Technical Universities of Dresden and Berlin before joining Leicester in 2004. Her research focuses on graph transformation systems, model-based development, stochastic modeling, and formal methods in software engineering. She earned her PhD (Dr.-Ing.) in Computer Science from TU Berlin in 1998. Her research interests span software engineering pedagogy, formal specification techniques, and applications of graph grammars in system modeling. Recent work explores stochastic graph transformations for social networks, transparency engineering in AI systems, and blockchain-based smart contract frameworks. Her contributions bridge theoretical foundations with practical applications in cybersecurity, data integration, and human-centric systems design. Key contributions include advancements in automated test case generation via graph transformations, visual contracts for software reverse engineering, and formal methods for complex system analysis. Her work frequently intersects with industry through collaborations via the Leicester Innovation Hub, emphasizing data analytics and technology transfer. Education: MSc Computer Science, Technical University of Dresden PhD (Dr.-Ing.), Computer Science, TU Berlin (1998) Leadership Roles: Head of Department (2014-2018) Director of Postgraduate Teaching (Ongoing) Research Themes: Model-Based Development Stochastic Systems Analysis Graph Neural Networks Trustworthy AI Her publications reflect a focus on formal methods, with recent trends in applying graph transformation techniques to social network modeling, blockchain smart contracts, and educational pedagogy.

David I. AugustView profile

Professor

• Compilers
• Computer Architecture
• Parallel Processing
• +4 more

David I. August is a Professor of Computer Science at Princeton University, affiliated with the Department of Electrical Engineering. He earned his Ph.D. from the University of Illinois at Urbana-Champaign in 2000. His research focuses on compilers and computer architecture, emphasizing synergistic design between compilers and microarchitecture. He leads the Liberty Research Group, which explores topics such as automatic parallelization, memory profiling, and speculative execution. August joined Princeton in 1999 as a lecturer, advancing to full professor in 2012. He has served as program chair for MICRO 2009 and on committees for ISCA, PLDI, and ASPLOS. His notable accolades include the IEEE Fellow designation, Best Paper Awards at PLDI and CGO, and teaching awards from Princeton's School of Engineering. His work spans compiler optimizations, hardware-software co-design, and security architectures like TrustGuard. Recent research includes GPU scheduling (GhOST), memory profiling frameworks (PROMPT), and instruction prefetching (PDIP). He advises over 20 graduate students, many now leading roles at tech companies and academia. August teaches courses such as COS-126 (Intro to CS), COS-375 (Computer Architecture), and graduate seminars. His projects often bridge theory and practice, with tools like NOELLE and Liberty Research Group initiatives advancing compiler infrastructure and parallelism extraction.

Jeremy GibbonsView profile

Professor

• Functional Programming
• Programming Languages
• Software Engineering
• +3 more

Jeremy Gibbons is a Professor of Computing at the University of Oxford, affiliated with the Department of Computer Science within the Faculty of Computer Science. He serves as Director of the Professional Programmes, overseeing part-time postgraduate degrees in Software Engineering. His roles include Chair of the Faculty of Computer Science (2012–2016), Director of the Software Engineering Programme, and Fellow of Kellogg College. Gibbons' research focuses on programming methodologies, particularly functional and object-oriented languages, with an emphasis on program calculation, design patterns, and bidirectional transformations. He leads the Algebra of Programming research group and is Editor-in-Chief of the Journal of Functional Programming and The Art, Science, and Engineering of Programming . Education includes a D.Phil. from Oxford University. His work spans formal methods, domain-specific modeling for clinical trials (e.g., CancerGrid project), and semantic frameworks for software systems. He has advised numerous students and contributed to open-access initiatives in publishing. Key collaborations include roles in ACM SIGPLAN and IFIP Working Groups 2.1 and 2.11. Research interests emphasize foundational aspects like profunctor optics, categorical programming, and algorithm design. Notable projects include datatype-generic programming and metadata-driven engineering for clinical trials. His work bridges theoretical computer science with practical applications in software architecture and system design.

Michael SchaubView profile

Assistant Professor

• Network Science
• Dynamical Systems
• Machine Learning
• +3 more

Michael Schaub is a tenure-track Assistant Professor in the Department of Computer Science at RWTH Aachen University, specializing in Computational Network Science. His research focuses on analyzing complex systems through network and graph models, integrating dynamical systems, control theory, and machine learning. He leads the Computational Network Science group, advancing methodologies for higher-order network models like simplicial complexes and hypergraphs. Schaub holds a PhD from Imperial College London and has held postdoctoral positions at MIT and Oxford. He is an ERC Starting Grant recipient (2022) and a Marie Curie Fellow, recognized for contributions to network dynamics and topological data analysis. Education: PhD in Mathematics, Imperial College London (2011-2015) MSc in Biomedical Engineering, Imperial College London (2010) BSc in Electrical Engineering, ETH Zurich (2007-2010) Research Interests: Schaub’s work spans interdisciplinary applications of network science, including biological systems, social networks, and technical infrastructures. Key areas include: Higher-order network models (hypergraphs, simplicial complexes) Graph signal processing and dynamics on networks Community detection and dynamical systems analysis Topological data analysis and machine learning Grants & Awards: ERC Starting Grant (2022): HIGH-HOPeS project Marie Skłodowska-Curie Fellowship (2017-2019) Junior Fellow, German Informatics Society (GI) Member of Junges Kolleg (North Rhine-Westphalia Academy) Labs & Teams: Leads the Computational Network Science Lab at RWTH Aachen, collaborating internationally on projects like the ELLIS Society and the European Laboratory for Learning and Intelligent Systems (ELLIS). Active in organizing workshops (e.g., Toponets, SIAM MDS).

Stephen W. KecklerView profile

Adjunct Professor

• Computer Architecture
• Deep Learning Accelerators
• GPU Computing
• +4 more

Stephen W. Keckler is an Adjunct Professor at the Department of Computer Science , The University of Texas at Austin , and serves as Vice President of Architecture Research at NVIDIA . He is an ACM Fellow , IEEE Fellow , and Sloan Foundation Research Fellow . Education: BS in Electrical Engineering, Stanford University (1990) SM in Computer Science, Massachusetts Institute of Technology (1992) PhD in Computer Science, MIT (1998) Research Interests focus on computer architecture for deep learning , GPU computing , and energy-efficient systems . His work explores memory compression , network-on-chip designs , and heterogeneous computing . Publication Trends highlight advancements in deep learning accelerators , GPU memory systems , and energy-efficient architectures . Notable themes include sparsity exploitation , multi-chip modules , and fault-tolerant GPU pipelines . Scientific Recognition : ACM Fellow IEEE Fellow Sloan Foundation Research Fellow Best Paper Awards at ASPLOS 2009 and ISPASS 2011 Laboratory Affiliations : Computer Architecture and Technology Laboratory (CART) TRIPS Project (Tera-Op Reliable Intelligently adaptive Processing System) NVIDIA Research

Hyosang LeeView profile

Assistant Professor

• Tactile Sensing
• Robotics
• Soft Robotics
• +4 more

Hyosang Lee is an Assistant Professor in the Robotics Section of the Mechanical Engineering Department at Eindhoven University of Technology (TU/e). He holds a PhD from KAIST and has held research positions at the Max Planck Institute and University of Stuttgart. His work focuses on tactile sensing technologies, including artificial skin development, soft robotics, and integration of sensory systems with AI. Bachelor's: Mechanical Engineering, Korea University Master's: Robotics and Mechanical Engineering (double major) PhD: Mechanical Engineering, KAIST (2017) Research interests span tactile sensor design, electrical impedance tomography (EIT), and human-robot interaction. His group emphasizes creating scalable, flexible tactile systems for robots. Recent work includes air pressure sensing for force estimation and biomimetic skin materials. Publications highlight innovations in multi-directional force sensing, soft component technologies, and haptic interfaces for autism therapy. He teaches 'Dynamics and Control of Robotic Systems' and serves on the editorial board of npj Robotics . No formal student advisees are listed, though his lab, the Tactile Sensing and Robotic Skin Group , likely involves graduate researchers. His research contributes to UN Sustainable Development Goals related to health and technology.

Andrew WarfieldView profile

Adjunct Professor

• Computer Systems Software
• Virtualization
• Distributed Storage
• +3 more

Andrew Warfield is an Adjunct Professor in the Department of Computer Science at the University of British Columbia (UBC) and a Senior Principal Engineer at Amazon. His research focuses on computer systems software, including virtualization, distributed storage, security, and high-availability systems. He previously held roles as Associate Professor at UBC, CTO at Coho Data, and Technical Director at Citrix Systems. His work has led to projects like Remus (high-availability replication), Tralfamadore (execution analysis), and secure hypervisor development with Xen. Warfield's education includes a PhD from the University of Cambridge's Computer Laboratory, where he researched I/O device virtualization under Steven Hand. He has held visiting roles at Intel Research Cambridge and internships at AT&T Research and Nortel Networks. Research Grants: Supported by Intel Research, NSERC, Network Appliance, and the Communications Security Establishment. Professional Activities: Technical Advisory Board Member at Teradici, and involvement in program committees for conferences like HotOS, EuroSys, and FAST. His research emphasizes practical systems, aiming to bridge the gap between theoretical computer science and real-world applications. Notable contributions include innovations in storage for virtualized environments, secure hypervisor architectures, and disaster-tolerant systems like SecondSite. Warfield is affiliated with UBC's Department of Computer Science and maintains active collaboration with industry partners. Though currently not actively recruiting students, his prior mentorship has influenced many in systems research.

Juergen DingelView profile

Professor

• Software Engineering
• Model-Driven Engineering
• Formal Methods
• +7 more

Juergen Dingel is a Professor in the School of Computing at Queen's University, Canada. He joined the faculty in 2000 and holds a PhD in Computer Science from Carnegie Mellon University (1999). His research focuses on software modeling, model-driven engineering, formal methods, and formal verification, with applications in real-time systems and embedded systems. He leads the Modeling and Analysis in Software Engineering (MASE) research group. Education: PhD in Computer Science, Carnegie Mellon University (1999) M.Sc. in Pure and Applied Logic, Berlin University of Technology (1994) M.Sc. in Computer Science, Berlin University of Technology (1992) Research Interests: Model-driven engineering and transformation Formal specification and verification Automated testing and debugging Real-time and embedded systems Service composition and distributed systems His work emphasizes practical tools like Papyrus-RT and MDebugger , integrating formal methods into software development. Grants & Collaborations: Funded by NSERC, OCE, and industry partners (IBM, GM, Ericsson) Focus on automotive systems, IoT, and safety-critical applications Service: Editorial board member for SoSyM , STTT , and JOT Former chair of the MODELS Steering Committee (2016–2018) PC co-chair for MODELS 2014 and FMOODS/FORTE 2011 Labs & Teams: Leads the MASE group, which develops open-source tools for model-driven engineering. Collaborates with industry on automotive and IoT projects.

Vyas SekarView profile

Professor

• Networking
• Cybersecurity
• Network Security
• +9 more

Vyas Sekar is the Tan Family Professor of Electrical and Computer Engineering at Carnegie Mellon University (CMU), with a courtesy appointment in the Computer Science Department. He is affiliated with CyLab and co-directs the Future of Enterprise Security initiative. His research focuses on networking, cybersecurity, distributed systems, and IoT security, with an emphasis on data-driven approaches and network verification. Education: Ph.D. in Computer Science (2010) from CMU; B.Tech. from IIT Madras (President of India Gold Medal recipient). Professional roles include Chief Scientist at Conviva and co-founder of Rockfish Data. Research Interests: Cybersecurity, network security, software-defined networking (SDN), IoT security, DDoS defense, privacy-preserving data sharing, and network performance optimization. Recent work includes developing tools like Pigasus (FPGA-accelerated intrusion detection), Nomad (cloud side-channel mitigation), and frameworks for anomaly detection in IoT networks. Articles Trends: Recent publications address advanced threats like LLM-driven network attacks, stealthy automotive network exploits (CANDid), and optical-layer DDoS defenses. Emphasis on practical solutions (e.g., SketchPlan for telemetry, Pryde for firewall evasion detection). Awards: ACM SIGCOMM Test of Time Award (2022), IIT Madras Young Alumni Achiever Award (2022), Intel Outstanding Researcher Award (2021), and NSF CAREER Award (2016). Recognized for contributions to intrusion prevention, network security, and IoT resilience. Grants & Projects: Led NSF-funded ONSET project (optical-layer DDoS defense), CyLab's Secure IoT Initiative, and collaborations with industry partners like Intel, Facebook, and Nokia Bell Labs. Advises graduate students in cybersecurity and networking. Labs & Teams: Active contributor to CyLab, co-developer of frameworks like Lumos (hidden IoT device detection) and KalKi (IoT security platform). Engages in interdisciplinary research across CMU’s Robotics Institute and Software Engineering Institute.

Eric SaxView profile

Professor

• Automotive Systems
• Autonomous Driving
• Cybersecurity
• +7 more

Prof. Dr.-Ing. Eric Sax is a Professor of Electronic Systems Engineering and Management at the Karlsruhe Institute of Technology (KIT), serving as Dean of the Department of Electrical Engineering and Information Technology (ETIT). He leads the Institut für Technik der Informationsverarbeitung (ITIV) and directs the Forschungszentrum Informatik ESS division . As Program Director of the Electronic Systems Engineering & Management (ESEM) master's program at the HECTOR School, he focuses on integrating academic and professional education. His research spans automotive systems engineering , self-learning functions , cybersecurity , and data-driven validation . Key themes include over-the-air updates, scenario-based testing, and the synergy between machine learning and automotive systems. His work addresses challenges in autonomous driving validation, software-defined mobility, and cyber-physical system security. Prof. Sax's contributions include frameworks for automotive software partitioning, cloud-enabled vehicle architectures, and methodologies for quantifying data quality impacts on perception systems. He actively collaborates with industry partners to bridge academic research with industrial application. His recent projects include OptiCAM (cloud/edge function offloading), Drive4C (autonomous driving benchmarking), and UNCOVER (data-driven security monitoring). He holds leadership roles in both KIT and the HECTOR School's technology business programs.

Tom SchrijversView profile

Professor

• Programming Languages
• Functional Programming
• Type Systems
• +6 more

Tom Schrijvers is a Professor at the Department of Computer Science in the Faculty of Engineering Science at KU Leuven, Belgium. He leads the Programming Languages Group within the Declarative Languages and Artificial Intelligence (DTAI) research group. His research focuses on programming languages, particularly functional and logic programming, with special emphasis on Haskell, type systems, and algebraic effects. His research interests include: Functional Programming, especially Haskell Type Systems and Type Theory Algebraic Effects and Handlers Logic Programming, particularly Prolog Constraint Programming Domain-Specific Languages Programming Language Theory Prof. Schrijvers' recent research has focused on effect systems, staged programming, and language composition. His work on algebraic effect handlers has been particularly influential, providing new insights into how effects can be modularly composed and handled in functional languages. He has also made significant contributions to the understanding of type classes and their implementation in Haskell. His publications demonstrate a consistent focus on practical applications of programming language theory, with work spanning from foundational type theory to applied domain-specific languages for areas like fluorescence microscopy. His research often bridges the gap between theoretical programming language concepts and practical implementation concerns. Prof. Schrijvers has supervised numerous PhD students to completion, including Pieter Wuille, Benoit Desouter, George Karachalias, Steven Keuchel, Amr Saleh, Alexander Vandenbroucke, and Ruben Pieters. He currently supervises PhD students Klara Mardirosian, César Santos, Gert-Jan Bottu, Koen Pauwels, Birthe van den Berg, and Roger Bosman. His research group has received funding from various sources including EU projects like GRACeFUL. The Programming Languages Group at KU Leuven, which he leads, focuses on functional (Haskell) and logic (Prolog, Datalog, CLP) programming languages, as well as general programming language theory. The group has been active in numerous research projects and collaborations across Europe.

• Autonomous Underwater Vehicles (AUVs)
• Multi-Agent Systems
• Robotics
• +6 more

Özer Özkahraman is a postdoctoral researcher at the Division of Robotics, Perception and Learning (RPL) at KTH Royal Institute of Technology. He works under Ivan Stenius and John Folkesson, focusing on underwater mission planning, simulation, and integration of autonomous systems. His email is ozero@kth.se . He completed his PhD at KTH under Petter Ögren, researching large-scale multi-agent coverage planning for autonomous underwater vehicles (AUVs). Current projects include the SMaRCSim multi-domain simulation platform and development of underwater vehicles like LoLo, SAM, and Evolo. Research interests span autonomous underwater systems, multi-agent coordination, control systems, and simulation infrastructure. He emphasizes modular, accessible frameworks for vehicle testing and real-world deployment. His work bridges theoretical methods (e.g., control barrier functions) with practical applications in marine robotics. Publications focus on AUV navigation, environmental sensing, and adaptive control. Projects like Real2Sim aim to align simulation with real-world vehicle dynamics using motion capture data. He collaborates internationally on topics like data-driven damage detection and model compression for resource-constrained robots. No academic awards are explicitly mentioned. He actively seeks collaborators for projects in sonar simulation, flow field modeling, and cyber-physical system integration.