Find Academic Supervisors

Matthew J. ParkinsonView profile

Researcher

• Verification
• Runtimes
• Compilers
• +6 more

Matthew J. Parkinson is a Principal Researcher at Microsoft Azure Research in Cambridge, UK. His work focuses on memory and concurrency safety , with significant contributions to formal verification , systems programming , and language design . He actively participates in academic communities as a session chair and committee member for conferences like OOPSLA , PLDI , and ISMM . Research Trends : His recent publications (2023-2025) emphasize dynamic region ownership , wait-free reference counting , and allocator optimization for concurrent systems. These works span systems programming , formal verification , and domain-specific language tools , reflecting his interdisciplinary approach. Conference Contributions : Parkinson has served as session chair for tracks including MPLR Session 2 (2023) and PLDI: Memory Models & Program Logics (2023). He has also contributed to program committees for POPL , IWACO , and The Future of Weak Memory (2024).

• Data Management
• Computer Systems
• Database Optimization
• +3 more

Prof. Dr. Jana Giceva is a Professor for Database Systems at the TUM School of Computation, Information and Technology since 2020. Her research bridges database systems with modern computer architecture, focusing on hardware-aware data processing, operating system integration, and efficient execution of big data workloads. She previously held roles at Imperial College London, Microsoft Research, and Oracle Labs. Education: PhD in Computer Science from ETH Zurich (2017) Awards: ERC Starting Grant (2024), ETH Medal (2018), VMware Early Career Faculty Award (2019), Google PhD Fellowship (2014) Her work explores database/operating system co-design , chiplet-aware scheduling , and disaggregated systems programming , with publications covering query optimization, graph data structures, and hardware acceleration. Collaborations with institutions like Imperial College London and ETH Zurich highlight her cross-disciplinary impact. Key Research Themes: Hardware-Software Integration High-Performance Query Execution Asynchronous I/O Optimization Adaptive Runtime Systems

Sam MaddenView profile

Professor

• Database Systems
• Data Management
• Machine Learning
• +5 more

Sam Madden is a Professor of Electrical Engineering and Computer Science at MIT, leading the Data Systems Group (DSG) and the Data Science and AI Lab (DSAIL). He holds the MIT College of Computing Distinguished Professor title. Madden's research focuses on database systems, including learned database systems, query processing, and data management for video and sensor networks. He received his Ph.D. from UC Berkeley (2003) and MEng/BS from MIT (1999). His work spans systems for sensor networks (TinyDB), columnar databases (C-Store), and video analytics (VaaS). Notable projects include Starling (cloud query optimization), BRAD (cloud data infrastructure), and Aurum (data discovery). Madden has advised numerous students and received prestigious awards like the ACM Fellow (2020) and SIGMOD Codd Innovations Award (2024). Education: Ph.D., University of California, Berkeley (2003) M.Eng., MIT (1999) B.S., MIT (1999) Awards: MIT TR35 (2005) SIGMOD Test of Time Award (2013, 2017) ACM Fellow (2020) Teaching: Courses include Database Systems, Software Systems for Data Science, and Computer Systems. Companies: Co-founder of Vertica (acquired by HP), Chief Scientist at Cambridge Mobile Telematics.

• Database Management
• Transaction Management
• Replication
• +4 more

Alan Fekete is a Professor of Enterprise Software Systems in the School of Computer Science at the University of Sydney. He holds a BSc (Hons) from the University of Sydney and a PhD from Harvard University's mathematics department. With over 30 years at the University of Sydney, he has made significant contributions to database systems, distributed computing, and computing education. His research focuses on improving software systems' state-of-practice, particularly in transaction management, replication, and cloud computing. Education background: BSc(Hons) from the University of Sydney, PhD in Mathematics from Harvard University. Research interests include database management (transaction systems, replication), cloud computing, distributed systems, software engineering, and computing education. His teaching spans enterprise software architecture, data models, and systems analysis. He has supervised 13 doctoral students and contributed to curriculum development, notably in Java programming and cloud workforce training. Recent publications explore transactional frameworks, database benchmarking, and concurrency control. He is a Distinguished Scientist of ACM and a member of IEEE and Australian Computer Society. His work on robust rolling-upgrades and cloud storage benchmarking addresses modern system challenges. Fekete's teaching innovations include data-centric curricula and frameworks for self-assessment in programming education.

Chris StoneView profile

Professor

• Formal Methods
• Programming Languages
• Natural Language Processing
• +3 more

Chris Stone is a Professor specializing in formal methods and the intersection of natural language processing with mathematical proofs. He co-directs the Math+CS Clinic Program at his institution alongside Prof. Talithia Williams (Math) and Prof. Ben Wiedermann (CS). His research focuses on improving the readability and accessibility of computer-verified proofs, analyzing language patterns in academic mathematical proofs, and developing tools like the ProofLang Corpus. He collaborates extensively with undergraduate researchers and has contributed to foundational work in proof assistants such as Coq and Andromeda. His work bridges theoretical computer science and practical applications, including tools like RZ for computable mathematics and studies in multithreading performance optimization. He explores how imperative language structures in proofs affect formal verification systems, retraining NLP models to better parse mathematical texts. Key contributions include analyzing over 3.7M proofs from arXiv.org to identify linguistic patterns and collocations, discovering that phrases like 'without loss of generality' exhibit predictable syntactic structures. His projects also span curriculum analysis (CS Curricula: https://cscurricula.fyi) and explorations of non-classical logics. Labs/Teams: Principal investigator of the ProofLang Corpus project and core member of the Math+CS Clinic program. Current research directions include making proof assistants more user-friendly for mathematicians and improving cross-disciplinary collaboration through language-aware verification systems.

• Parallel Algorithms
• Parallel Computing
• High-Performance Computing (HPC)
• +4 more

Prof. Jesper Larsson Träff is a Full Professor and Head of the Research Unit in Parallel Computing at Technische Universität Wien (TU Wien). His academic role is centered within the Department of Computer Engineering. He holds an MSc and PhD, and has contributed extensively to the field of parallel computing through his research and leadership in international projects. His research focuses on parallel algorithms, scheduling, and optimization of the Message Passing Interface (MPI), with emphasis on high-performance computing (HPC) systems and distributed architectures. Key projects include the Process Mapping initiative (2019–2024), Autotune (2021–2025), and contributions to Exascale Programming Models. He has also led efforts in evaluating MPI performance tools and their reproducibility challenges. Research Areas: Parallel Algorithms, MPI Optimization, HPC, Process Mapping, Collective Communication, and Memory Models. Notable Achievements: Awarded the Best Paper at EuroMPI 2014 and recognized by the Innovation Radar for his work on PGAS-based MPI interoperability in 2018. Teaching: Teaches courses such as Advanced Multiprocessor Programming, Bachelor/Master Thesis supervision, and seminars in Computer Engineering and Theoretical Computer Science. Prof. Träff’s advising includes students researching topics like lock-free data structures, MPI datatype optimization, and task scheduling. His grants span Austrian and EU funding bodies, addressing challenges in exascale computing and reproducible experimental research. He is affiliated with the Vienna Mapping and Sparse Quadratic Assignment (Vienna Mapping) project and actively contributes to international workshops and conferences.

Graeme SmithView profile

Associate Professor

• Formal Methods
• Software Engineering
• Concurrent Systems
• +4 more

Associate Professor Graeme Smith is a faculty member at the School of Electrical Engineering and Computer Science, The University of Queensland. He leads research in formal methods, focusing on the design and analysis of software systems with applications in telecommunications, railways, and defense sectors. His work includes formal object-oriented modeling, real-time embedded systems, and concurrency. He has held academic positions at institutions in Australia, Germany, and France. Currently, he leads a research cell under the Defence Science and Technology Group, addressing formal security analysis of concurrent code. Education: Bachelor (Honours) of Engineering, The University of Queensland Doctor of Philosophy, The University of Queensland Research Interests: Formal methods for functional correctness and security of concurrent programs, including refinement techniques, information flow security, and distributed systems like multi-agent systems. His work emphasizes weak memory models, hardware-software interaction, and formal verification. Articles Trends: Recent publications emphasize formal verification of concurrent systems, weak memory models, and security protocols. Key areas include compositional reasoning, vulnerability detection, and applications in ARM/POWER architectures. Grants & Funding: ARC Discovery Grants on fault-tolerant systems, distributed autonomous systems, and lock-free algorithms Defence Science and Technology Group collaboration on formal security analysis Labs/Teams: Leads a Defence-funded research cell focusing on formal security analysis of concurrent code.

Monika KorteView profile

Research Professor

• Geomagnetism
• Paleomagnetism
• Geophysical Modeling
• +7 more

Dr. Monika Korte is the Acting Head of the Geomagnetism Section (Section 2.3) at the GFZ German Research Centre for Geosciences. She leads the working group on the 'Evolution of the Earth's Magnetic Field' and has been instrumental in advancing research on geomagnetic field reversals, secular variation, and space weather effects. Her work integrates paleomagnetic data, satellite observations, and computational modeling to understand Earth's core dynamics and their climatic impacts. Education & Career: PhD in Geophysics, Free University of Berlin (1999) Diploma in Geophysics, Ludwig Maximilian University (1996) Acting Head of Section 2.3 since 2021 Leadership roles in international projects, including the ERC Synergy Grant GERACLE (2024–2030) Research Interests: Geomagnetic field reversals/excursions Global and regional magnetic field modeling Geomagnetic observatory networks Cosmogenic isotope analysis Machine learning applications in geophysics Awards & Grants: ERC Synergy Grant (2024): €10M for GERACLE project GFZ Research Award (2003) Feodor-Lynen Fellowship (2001) Labs & Collaborations: Leading the Niemegk Geomagnetic Observatory Coordinating the GERACLE international consortium Member of IAGA Executive Committee and AGU editorial boards

Ludmil ZikatanovView profile

Professor

• Numerical Analysis
• Computational Mathematics
• Multilevel Methods
• +1 more

Ludmil Zikatanov is a Professor of Mathematics at Pennsylvania State University's Eberly College of Science. He received his PhD from Sofia University and leads research in computational mathematics. His work develops numerical methods for PDEs, multilevel algorithms , and scientific computing techniques, with applications in hydrogeology, physics, and resource economics. Current projects include adaptive Markov Chain Monte Carlo methods and modeling invasive species migration. Zikatanov's extensive publication record focuses on multigrid methods , preconditioning techniques , and finite element frameworks . He co-developed the HAZmath finite element library and maintains an active research group working on numerical PDEs and scientific computing applications.

Scott MartinView profile

Assistant Professor

• GPS Software Receiver Design
• Real Time Kinematic Positioning
• Sensor Fusion for Navigation
• +3 more

Scott Martin is an Assistant Professor in the Department of Mechanical Engineering at Auburn University. His research focuses on advanced navigation systems, particularly in GPS software receiver design, real-time kinematic positioning, and control of unmanned systems. He leads the GPS and Vehicle Dynamics Laboratory and is involved in Auburn's Autonomous Vehicle Research facility at the National Center for Asphalt Technology (NCAT). His expertise includes robust positioning in degraded environments, anti-spoofing techniques, and sensor fusion for autonomous systems. Education: Ph.D. Mechanical Engineering (Auburn University), M.S. Mechanical Engineering (Auburn University), B.S. Systems Engineering (East Carolina University). Research Interests: Dr. Martin’s work addresses challenges in navigation and positioning for autonomous vehicles, including cybersecurity for GNSS systems, cooperative vector tracking, and integration of GPS with inertial and cellular signals. Key areas include spoofing detection, multi-sensor fusion, and navigation in signal-degraded environments. His recent projects include platooning studies with the American Center for Mobility and leveraging maritime buoys as signals of opportunity. Labs/Teams: Directs the GPS and Vehicle Dynamics Laboratory and collaborates with the National Center for Asphalt Technology on autonomous vehicle testing.

Gael ThomasView profile

Professor

• Distributed Systems
• Operating Systems
• Virtualization
• +4 more

Prof. Gael Thomas is a Professor at Telecom SudParis, focusing on distributed systems, operating systems, and high-performance computing. His work emphasizes virtualization, concurrency control, and secure execution environments. He has contributed extensively to projects like VMKit, I-JVM, and J-NVM, addressing challenges in garbage collection, system scalability, and trusted computing. His research interests include: Virtualization techniques for improved system isolation Optimizing garbage collectors for multicore architectures Trusted execution environments using SGX and HTM Performance analysis of MPI/OpenMP applications Notable contributions: Developed PALLAS trace format for HPC analysis (2025) Created NVCache for NVMM-based I/O optimization (2021) Pioneered secure code partitioning techniques (2023-2024) His work has been presented at top conferences including EuroSys, Middleware, and DSN.

• Optical Atomic Clocks
• Metrology
• Spectroscopy
• +6 more

Piotr Ablewski is an Assistant Professor at the Department of Applied Informatics , Institute of Technical Sciences , within the Faculty of Physics, Astronomy and Applied Computer Science at Nicolaus Copernicus University. His research focuses on optical atomic clocks, precision metrology, and dark matter detection through global clock networks. Research Areas: Optical clocks, spectroscopy, quantum metrology, dark matter detection, frequency standards, and atmospheric science. Key Collaborations: Member of the "Biophysics at the Nanoscale" research team. Participates in multi-center work on clock development and dark matter studies. Publications: 31 documented publications with a total impact factor of 33,493 and ministerial score of 577. Instrumentation: Works on VIPA spectrometer calibration, frequency comb spectroscopy, and fiber-optic time/frequency delivery systems. Email: piotra@umk.pl

• Ultrasonics
• Nondestructive Testing
• Robotics
• +3 more

Morteza Tabatabaeipour is a Lecturer in Advanced Manufacturing at the School of Engineering, Ulster University . His research focuses on ultrasonic nondestructive testing, guided wave inspection, and robotic applications in industrial analysis. Institution: Ulster University Academic Rank: Lecturer Research Areas: Ultrasonics, Robotics, Composite Materials, Guided Wave Technology His work includes developing flexible ultrasonic arrays, mitigating biofouling in marine environments, and integrating machine vision with ultrasonic testing. Recent projects explore wall thickness quantification using shear horizontal waves and calibration-free guided wave analysis. Email: m.tabatabaeipour@ulster.ac.uk

Davor RunjeView profile

Lecturer

• Artificial Intelligence
• Theoretical Computer Science
• Software Engineering
• +4 more

Davor Runje is a lecturer at Algebra University with extensive industry experience as a software engineer, computer scientist, and serial entrepreneur. He has co-founded multiple technology ventures including AIRT (an AI startup), DRAP (a digital agency acquired by Imago Ogilvy in 2018), and PlayMedia Systems (1997), which became a global leader in AMP MP3 playback technology. His research interests span artificial intelligence, theoretical computer science, parallel and distributed computing, and cryptography. Runje has made significant contributions to programming for multiprocessor/multicore systems, with work that later became the Task Parallel Library in the .NET framework during his time at Microsoft Research. His publication portfolio shows a clear evolution from theoretical computer science and concurrency research (2003-2009) toward machine learning and AI (2019-2025), with particular focus on neural networks, model interpretability, and security aspects of large language models. His recent work demonstrates expertise in constrained monotonic neural networks, attention mechanisms for tabular time-series, and vulnerabilities in large language models. His notable achievements include: Microsoft Research SSCLI and Phoenix 2005 awards Two US patents 20 publications in theoretical computer science and artificial intelligence Chairing the Croatian Independent Software Exporters board (2020-2024) As an active industry leader, Runje has participated in numerous legal and tax reform initiatives aimed at increasing the competitiveness of the Croatian IT sector in global markets, representing approximately one-third of the Croatian IT industry through his leadership role.

Mario BadrView profile

Assistant Professor

• Computer Architecture
• Parallel Computing
• Heterogeneous Computing
• +3 more

Mario Badr is an Assistant Professor (Teaching Stream) in the Department of Computer Science at the University of Toronto, part of the Faculty of Arts and Science. His research focuses on computer architecture, with an emphasis on simulation tools for evaluating multi-core, heterogeneous, and energy-harvesting systems. He has contributed to methodologies for analyzing memory behavior, synchronization performance, and cache coherence in modern architectures. Key research areas include parallel computing, performance analysis of GPU offloading, and energy-efficient computing models like the EH framework. His work often involves developing synthetic traffic models (e.g., SynFull) to assess architectural designs under realistic conditions. These tools enable early-stage exploration of trade-offs in processor architectures, particularly for mobile and embedded systems. Dr. Badr’s publications span topics such as cache behavior modeling, multi-core scalability, and intermittent computing. His research bridges theoretical analysis and practical implementation, with applications in both academic and industry contexts. While no specific awards or grants are listed, his contributions to architecture evaluation frameworks reflect a strong focus on advancing computational efficiency and system design.