Find Academic Supervisors

• Distributed Computing
• High-Performance Computing
• Distributed Shared Memory
• +7 more

Tore Brox-Larsen is an Associate Professor in the Department of Informatics at UiT The Arctic University of Norway. His work centers on distributed computing, high-performance systems, and large-scale visualization technologies. He is actively engaged in research involving sensor networks, Arctic observatories, and remote visualization of scientific data. His research interests span distributed shared memory systems, MPI performance, tiled display walls, and networked visualization. He has made significant contributions to improving communication efficiency in cluster computing and enabling scalable interactive visualization environments. His work bridges computer systems engineering with applications in genomics and environmental monitoring. The most recent publications reflect a strong trend toward real-world deployment of large-scale systems, particularly in Arctic observation and healthcare informatics. His work emphasizes practical system design, latency optimization, and cross-platform interoperability in distributed environments. Tore Brox-Larsen has collaborated extensively with researchers such as Otto Anshus, John Markus Bjørndalen, and Brian Vinter. While no formal advising or grant history is listed, his sustained publication record indicates active research leadership and team-based scientific inquiry. He has contributed to major projects including the development of a large-scale Arctic observatory sensor system and interactive tiled display walls. His work supports both academic research and societal applications in health and environmental science.

• Theoretical Computer Science
• Statistics
• High-Dimensional Estimation
• +7 more

Tselil Schramm is an Assistant Professor in the Department of Statistics at Stanford University, with courtesy appointments in Computer Science and Mathematics. She is actively engaged in research and teaching in theoretical computer science and statistics. Department: Department of Statistics School: School of Humanities and Sciences University: Stanford University Office: CoDa E254 Email: tselil@stanford.edu She earned her PhD from UC Berkeley under Prasad Raghavendra and Satish Rao, followed by postdoctoral work at Harvard and MIT with Boaz Barak, Jon Kelner, Ankur Moitra, and Pablo Parrilo. Her research lies at the intersection of theoretical computer science and statistics, focusing on high-dimensional estimation, information-computation tradeoffs, sum-of-squares algorithms, and random graph theory. She develops algorithms for statistical problems and investigates the boundaries between what is statistically possible and what is computationally feasible. Her recent publications span topics including the overlap-gap property, discrepancy algorithms, robust message passing, semidefinite programming, spectral clustering, and random geometric graphs, appearing in top venues such as STOC, FOCS, COLT, NeurIPS, and The Annals of Statistics. She teaches a range of courses, including Introduction to Statistics (STATS 60), Theory of Statistics II (STATS 300B), and Machine Learning Theory (STATS 214 / CS 228M), reflecting her expertise in both foundational and advanced statistical theory. Runner-up for Best Paper at COLT 2021 Invited to STOC 2022 special issue of SICOMP Invited to SODA 2016 special issue of ACM Transactions on Algorithms Invited to CCC 2019 special issue of Theory of Computing Tselil Schramm advises and collaborates with numerous students and researchers, including Shuangping Li, Misha Ivkov, and Siqi Liu. She has been involved in multiple research grants and projects, particularly in the areas of high-dimensional inference and algorithmic robustness. Her work often bridges theoretical guarantees with practical algorithmic design. She is affiliated with Stanford’s theoretical computer science and statistics research groups, contributing to a vibrant academic environment. Her future work is expected to further explore the limits of efficient computation in statistical settings, with potential applications in machine learning, signal processing, and network analysis.

Ivan AvramovicView profile

Associate Professor

• Randomized Algorithms
• Combinatorics
• Information Dissemination Algorithms
• +4 more

Ivan Avramovic is an Associate Professor in the Department of Computer Science at George Mason University, where he has been a full-time faculty member since 2018. He previously served as a term instructor, adjunct faculty member, and graduate teaching assistant in both Computer Science and Health Informatics programs. He currently holds the position of Graduate Teaching Assistant (GTA) Coordinator for the department. Education: • PhD in Computer Science, George Mason University • MS in Computer Science, George Mason University • BS in Electrical Engineering, University of Illinois at Urbana-Champaign Research Interests: Dr. Avramovic's research focuses on theoretical and applied aspects of computer science, including randomized algorithms, combinatorics, information dissemination methods, and network communication systems. His work bridges foundational computer science with practical applications in health informatics and educational technology, particularly exploring AI integration in computational pedagogy. Awards: Multiple Distinguished Teaching Awards for educational excellence Teaching & Advising: He has taught diverse courses including Analysis of Algorithms, Computer Systems Programming, Formal Methods, and Object-Oriented Programming. As GTA Coordinator, he oversees graduate teaching assistant training and curriculum development. No specific student advisees are listed. Professional Background: Prior to academia, he worked on programming and simulation projects at Science Applications International Corporation (SAIC) and maintains research collaborations with health informatics initiatives.

Simon KranzerView profile

Senior Lecturer

• Knowledge Transfer
• Cognitive Manufacturing
• Digital Twins
• +7 more

Simon Kranzer is a Senior Lecturer and Head of Research Group at the Department of Information Technologies and Digitalisation, FH Salzburg. His work bridges academic research and practical application in digital transformation. Location: Campus Urstein, Room 425 Contact: simon.kranzer@fh-salzburg.ac.at | +43-50-2211-1316 Research Focus: Digital Twins for industrial systems Collaborative Robotics (Co-Bots) in retail Knowledge Transfer between academia and industry Operational Technology (OT) Security Data Acquisition and Visualization Programming Language Applications in industrial contexts Research Trends: Recent publications show expertise in retail automation (service robots, customer behavior analysis), industrial digital twins, and OT security. Earlier work spans GIS-SCADA integration, medical software implementation, and 3D microstructure analysis. Collaborative Projects: Active in interdisciplinary living labs, smart factory bootcamps, and 5G-based robotics exploration.

Marco CarboneView profile

Professor

• Theoretical Computer Science
• Session Types
• Concurrency Theory
• +7 more

Marco Carbone is a Professor of Theoretical Computer Science at IT University of Copenhagen. His research focuses on session types , concurrency theory , structured communication , and formal verification of distributed systems. He leads the Center for Information Security and Trust and serves as Head of Education for the Master of Science in Computer Science program. Research Areas: Session Types, Concurrency, Security Protocols, Trust Management, Programming Logic Projects: GAINER (2023-2024), MECHANIST (2021-2025), PROBABILIST (2025-2028), BeHApi (2018-2023) Scientific Awards: International Prize (2018) His recent work explores probabilistic choreographies, asynchronous session subtyping, and mechanized proofs for session type systems. He has published extensively in venues like Logical Methods in Computer Science and Lecture Notes in Computer Science .

Juan Carlos Pichel CamposView profile

Professor

• parallel and distributed computing
• Big Data technologies
• programming models
• +7 more

Juan Carlos Pichel Campos is a Full Professor at the University of Santiago de Compostela (USC) specializing in high performance computing and language technologies. His research spans quantum computing, distributed systems, and Big Data technologies with a focus on practical applications in health informatics and computational physics. He received his B.Sc. and M.Sc. in Physics from University of Santiago de Compostela (Spain) and completed his Ph.D. there in 2006. He conducted postdoctoral research at University Carlos III de Madrid and University of Illinois at Urbana-Champaign, and worked as a researcher and project manager at Galicia Supercomputing Center. Professor Pichel's research interests include parallel and distributed computing, Big Data technologies, programming models, and software optimization techniques for emerging architectures. His recent work has focused on bridging quantum computing with classical high performance computing systems, developing efficient algorithms for processing massive biological datasets, and creating tools for health-related information retrieval and misinformation detection. His interdisciplinary approach combines techniques from computer science, physics, and biomedical informatics to solve complex computational problems. Analysis of his recent publications reveals a strong trend toward quantum computing applications and integration with classical HPC systems (accounting for approximately 40% of his recent work), followed by health informatics and natural language processing (about 30%), and bioinformatics and computational physics (about 30%). His research demonstrates a consistent focus on developing practical tools and frameworks that address real-world computational challenges across multiple domains. rePowerSiC: High-Efficiency High-Power Laser Beaming In-Space Systems Based On Sic (2024-2028) C3HS: Content curation for consumer health search - Search and misinformation detection (2023-2026) Big-eRisk: Early Prediction of Personal Risks on Massive Data (2021-2024) eRISK: Technologies for the early prediction of signs related with psychological disorders (2019-2021) BigNLP: Approaching High Performance Computing to Big Data Technologies: Natural Language Processing as Case Study (2015-2018) Professor Pichel has established strong collaborations with research groups across Europe and the United States, particularly in the fields of quantum computing and biomedical informatics. He is actively involved with CiTIUS (Centro singular de investigación en tecnoloxías da información e da comunicación de USC), contributing to its mission of advancing information and communication technologies through interdisciplinary research.

Andrew LumsdaineView profile

Academic

• High Performance Computing
• Scalable Graph Algorithms
• Computational Photography
• +5 more

Andrew Lumsdaine is the Chief Scientist at the Northwest Institute for Advanced Computing , a dual appointee between the University of Washington and the Pacific Northwest National Laboratory (PNNL). He holds the title of Affiliate Professor in the Paul G. Allen School of Computer Science and Engineering at UW and serves as a Laboratory Fellow in PNNL's Applied Mathematics, Computing, and Data Division. His research spans foundational and applied aspects of High Performance Computing , focusing on scalable graph algorithms, computational photography, and runtime systems for distributed-memory architectures. Education : Not explicitly stated in the provided text. Lumsdaine's work addresses critical challenges in parallel and distributed computing , including synchronization avoidance, communication optimization, and domain-specific language design for graph analytics. He has led projects like GraphPack (NSF-sponsored) and contributed to DARPA's HIVE program through the HAGGLE software development kit. His publications highlight innovations in light field imaging , GPU programming models , and graph algorithm abstractions . Notable collaborations include the GraphBLAS standardization effort and development of tools for checkpoint/restart fault tolerance. His research has been presented at leading conferences like SC , IPDPS , and Eurographics . Lumsdaine actively seeks collaborators and advises students/postdocs through projects listed on his research page.

• High Performance Computing
• Parallel Computing
• Message Passing Interface
• +3 more

William D Gropp is a Professor of Computer Science at the University of Illinois at Urbana-Champaign holding the Thomas M. Siebel Chair, and serves as Acting Director and Chief Scientist of the National Center for Supercomputing Applications (NCSA). His career spans foundational contributions to parallel computing infrastructure and scientific computation. Gropp pioneered the Message Passing Interface (MPI) standard and developed MPICH—the reference implementation that became the global lingua franca for parallel programming. His work enables portable scientific applications across supercomputing platforms, driving advances in fluid dynamics, nanosimulations, biology, and fusion research through tools like the Portable, Extensible Toolkit for Scientific Computation (PETSc). His major honors include: ACM Software System Award (2024) for MPICH's 30-year impact ACM-IEEE CS Ken Kennedy Award (2016) for programmability contributions SIAM/ACM Prize (2015) for computational science engineering ACM, IEEE, and SIAM Fellowships Election to the National Academy of Engineering Gropp co-founded ACM SIGHPC in 2011 and authored 187+ publications including the seminal book Using MPI . His NCSA leadership advances supercomputing infrastructure for nationwide scientific discovery.

Eran YahavView profile

Associate Professor

• Program Analysis
• Program Synthesis
• Program Verification
• +4 more

Eran Yahav is an Associate Professor in the Computer Science Department at the Technion - Israel Institute of Technology. He previously served as a research staff member at IBM T.J. Watson Research Center from 2004 to 2010. His academic journey began with a B.Sc. from the Technion in 1996, followed by a Ph.D. from Tel Aviv University in 2005. Yahav's research focuses on program analysis, program synthesis, program verification, and machine learning for programming. His work bridges theoretical foundations with practical applications, particularly in developing techniques that help programmers work more effectively with complex frameworks and APIs. He has pioneered approaches that combine static analysis with machine learning to address challenges in code search, completion, and understanding. His recent work heavily intersects with neural network applications to programming tasks, demonstrating how deep learning can enhance traditional program analysis techniques. His publication record shows a clear evolution from traditional program analysis and verification toward integrating machine learning with programming language processing. The most recent articles reveal a strong focus on neural methods for code understanding, including structural language models, adversarial examples for code models, and neural approaches to binary analysis and program synthesis. This represents a significant shift toward leveraging AI techniques to solve longstanding problems in programming languages and software engineering. Yahav has received numerous accolades including the prestigious Alon Fellowship for Outstanding Young Researchers, the Andre Deloro Career Advancement Chair in Engineering, and an ERC Consolidator Grant. He also earned best paper awards at ISSTA 2006 and 2007. As an advisor, Yahav has mentored numerous Ph.D. and Master's students who have gone on to make significant contributions in academia and industry. His research has been supported by substantial grants, including the ERC Consolidator Grant. He also serves as CTO at Tabnine, demonstrating the practical impact of his research. Yahav leads multiple research projects including PRIME (Programming with Millions of Examples), Fender (Preserving Correctness under Weak Memory Models), Saint (Synthesis using Abstract Interpretation), and several others focused on program analysis, verification, and synthesis. His work often involves building practical tools that translate theoretical advances into usable software engineering solutions.

• Computer Software
• Computation Theory
• Pure Mathematics
• +3 more

Martin Berger is an Associate Professor in Foundations of Computation at the University of Sussex's School of Engineering and Informatics. His research bridges theoretical computer science and practical systems engineering, with focus areas including: Formal methods and program verification Programming language design and semantics Hardware security vulnerabilities and mitigations GPU-accelerated computation and program synthesis Automated reasoning and logic-based systems With 51 publications, his recent work demonstrates strong emphasis on: Developing high-performance simulation tools (Pydrofoil) GPU-based acceleration of formal methods Security analysis of programming language ecosystems Automated inference systems for regular expressions He maintains active research collaborations across Europe and welcomes inquiries via his institutional email.

Michał WójcikView profile

Lecturer

• Software Engineering
• High Performance Computing
• Environmental Informatics
• +3 more

Michał Wójcik serves as Assistant Lecturer in the Department of Computer Systems Architecture within the Faculty of Electronics, Telecommunications and Informatics at Gdańsk University of Technology. Concurrently, he holds a technical role as Senior Specialist – Design Programmer in the Team for Development and Maintenance of Central University Systems (CUI), operating from Building A of the Faculty (room 523) with contact number 583471867. His research bridges computer science with environmental and business applications, focusing on Software Engineering (RESTful systems, Jakarta EE), High Performance Computing (parallel application simulation), and Environmental Informatics (marine forecasting, benthic habitat mapping). Key projects include the FindFISH numerical forecasting platform for fisheries and MERPSYS simulation environment for HPC systems, demonstrating strong interdisciplinary collaboration. Analysis of his 2017-2024 publications reveals evolving expertise from core HPC simulation toward applied environmental informatics, with recent work emphasizing open innovation competence profiling and marine ecosystem modeling. His publications consistently address real-world problems through computational solutions, particularly in Baltic Sea environmental monitoring and multi-partner innovation ecosystems.

Felix StutzView profile

Researcher

• Formal Methods
• Distributed Systems
• Security Protocols
• +3 more

Felix Stutz is a postdoctoral researcher in the Security and Trust of Software Systems (SaToSS) group at the University of Luxembourg. His research focuses on formal methods for concurrent and distributed systems, as well as security protocols. He previously held positions as a PhD student at the Max Planck Institute for Software Systems (MPI-SWS) and a half-year research intern at MIT CSAIL (with Nikos Vasilakis) and Imperial College London (with Emanuele D’Osualdo and Philippa Gardner). MSc in Computer Science, University of Saarland PhD, Max Planck Institute for Software Systems (MPI-SWS), co-advised by Rupak Majumdar and Damien Zufferey Research interests include formal verification of software systems, algorithmic analysis of message-passing programs, and depth-bounded protocol verification. His work has produced tools like Lemma9 for cryptographic protocol verification and contributions to the PaSh parallelization framework. He has collaborated with institutions including MIT CSAIL, Imperial College London, and the University of Saarland. Felix is actively involved in software development and is available for asynchronous communication via email.

Andrew K. HirschView profile

Assistant Professor

• Choreographic Programming
• Information-Flow Security
• Computational Effects
• +3 more

Andrew K. Hirsch is an Assistant Professor at the University at Buffalo, SUNY , Department of Computer Science and Engineering. He leads the Databases and Programming Languages group and focuses on programming languages for decentralized systems, particularly choreographic programming and information-flow security. Education: Ph.D. in Computer Science (2019) from Cornell University, supervised by Ross Tate on computational effects. B.S. in Computer Science and Pure Mathematics from The George Washington University. Research Interests: His work centers on choreographic programming, a paradigm ensuring deadlock-free concurrent systems, and information-flow security for decentralized applications. He also explores computational effects and type systems in programming language theory. Publications: Recent work includes advancements in process polymorphism (OOPSLA 2025), type-level polymorphism (PLACES 2025), and security definitions for higher-order declassification (OOPSLA 2023). Students: Doctoral: Michael Piskozub, Keith Allen Mason Masters: Alexander Bohosian, Gianna Bossoreale Undergraduate: Alex Doyoon Kim, Julia Montouri Recent Alumni: Ethan Canton, Tiffany Cai, Vamsi Krishna Bellam, Vincent Chan, Frank (Feng-Mao) Tsai Projects: Leads initiatives such as Choret (open choreographies) and The Pirouette Language and Compiler , which translate choreographic programs into concurrent system implementations.

• Performance Analysis and Engineering
• Energy Efficient Computing
• Parallel and Distributed Computer Architectures
• +7 more

Vladimir Getov is a Professor at the University of Westminster with extensive international affiliations including Honorary Professor at Technical University of Sofia and Visiting Lab Fellow at Pacific Northwest National Laboratory. His leadership roles span the IEEE Computer Society (Governor since 2015), CoreGRID/ERCIM Working Group (Topic Leader 2009-2015), and multiple editorial positions including Area Editor for High Performance Computing at IEEE Computer. His research centers on Performance Analysis and Engineering, Energy Efficient Computing, and Parallel and Distributed Architectures with significant contributions to cloud computing, wireless sensor networks, and component-oriented design. Recent work focuses on energy-drain attack prevention in MAC protocols and application-specific thermal modeling for multi-core processors. Key awards include the Bulgarian National Scientific Award 'Pythagoras' (2009), IEEE Computer Society Certificates of Appreciation (2008, 2010), and CoreGRID Outstanding Contribution Award (2008). His publication record spans 35+ years with over 100 papers including the 2024 IEEE ICAI conference paper on electronic digital computing history. As leader of the Distributed and Intelligent Systems Research Group since 1996, he has supervised 7 PhD students to completion with frameworks like AGOCS cloud simulator and TunableMAC protocol. His EU project leadership includes CoreGRID (2004-2009) and GridCOMP (2006-2010), securing sustained research funding including Daiwa Foundation support (2017) for low-energy co-design methodology.

Mehdi BagherzadehView profile

Assistant Professor

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

Mehdi Bagherzadeh is an Assistant Professor in the Department of Computer Science and Engineering at Oakland University. His professional profile shows consistent involvement in major software engineering conferences including ASE, SPLASH, and ICSE, where he has served in various leadership roles such as Workshops Co-Chair for SPLASH (2021-2023) and Late Breaking Results Co-Chair for ASE (2022). Dr. Bagherzadeh's research focuses on the correct construction of concurrent and big data software, situated at the intersection of Software Engineering and Programming Languages. His work particularly addresses challenges in deep learning program optimization, actor concurrency models, and automated refactoring techniques. His research trajectory shows a clear evolution from foundational work on concurrent programming models (Panini, Capsule) to current applications in deep learning systems. Analysis of his publication history reveals a strong focus on practical software engineering challenges in concurrent systems, with recent work concentrating on the migration and optimization of deep learning programs. His research combines empirical studies with formal methods, addressing both theoretical foundations and practical implementation concerns in modern software systems. Through his committee service across multiple top-tier conferences including ASE, SPLASH, ICSE, and ESEC/FSE, Dr. Bagherzadeh has established himself as an active contributor to the software engineering research community. His service roles have ranged from program committee membership to organizational leadership positions.