Find Academic Supervisors

Xiaoguang WangView profile

Assistant Professor

• Systems and Software Security
• Heterogeneous CPU Architectures
• Secure Software Systems Design
• +4 more

Xiaoguang Wang is an Assistant Professor in the Department of Computer Science at the University of Illinois Chicago. His research spans systems and software security, focusing on heterogeneous CPU architectures, secure software systems, and virtualization-based security frameworks. He actively mentors PhD and Master’s students and offers funded research opportunities for UIC students. University of Illinois Chicago, Department of Computer Science Research: Systems & Software Security, Heterogeneous Architectures, Virtualization His work includes projects like sMVX (multi-variant execution), Dapper (live program rewriting), and DynaCut (dynamic program customization). Recent publications address cross-architecture process migration, Linux kernel security, and using large language models (LLMs) for software security. He teaches advanced courses such as CS 487: Building Secure Computer Systems and CS 594/561: Adv. Linux Kernel Programming , emphasizing hands-on kernel development and security techniques. Grants from the U.S. Office of Naval Research and NSF support his work on secure systems and cross-architecture security solutions.

Shuyin JiaoView profile

Academic

• Computing Education
• Program Analysis
• Code Efficiency Analysis
• +5 more

Shuyin Jiao is an active Associate Teaching Professor in the Department of Computer Science at North Carolina State University's College of Engineering. She serves as course coordinator for CSC 111 Introduction to Computing: Python and teaches multiple core undergraduate courses including CSC 116 (Java) and CSC 246 (Operating Systems), with extensive teaching experience spanning over 9 years at both NC State and the College of William & Mary. Her educational background includes a Ph.D. from the University of Houston (2015), a Diplôme d'Ingénieur from Ecole Central de Lyon, France, and a B.S. from Beihang University, China. She previously held positions as Assistant Teaching Professor (2020-2025) and Lecturer at NCSU, and taught as Lecturer and Adjunct Lecturer at William & Mary from 2015-2020. Dr. Jiao's research focuses on computing education and program analysis, with particular emphasis on developing tools to enhance student learning in computer science. Her work explores pedagogical methods to address diverse learning styles in increasingly large CS classrooms and investigates code inefficiencies through memory profiling and performance analysis. She leads the CERES lab at NCSU and serves as Publications Co-Chair for SIGCSE TS 2026. Her publication record shows consistent output in top venues including SIGCSE, CGO, MobiCom, and ICSE, with recent work spanning from 2025 back to 2012. Her research trajectory demonstrates a clear evolution from materials science (early career) to software engineering and computer science education, with current work heavily focused on educational technology and program analysis tools. Dr. Jiao has secured significant funding including as PI for NSF Grant DUE-#2417469 (2024-2027), NCSU Data Science Academy Seed Grant (2023-2024) as Co-PI, and NCSU GEARS Grants (2023-2024) as PI. She also serves as Co-PI for NSF Grant OAC-#2411136 (2025-2028) focused on computer system research. She actively involves undergraduate students in research through COE REU and CSC 498/499 projects, recruiting at CSC Undergraduate Research Lightning Talks each semester. Her course redesign projects include enhancing operating systems education through multimedia and redesigning CSC 111 with DELTA funding to address challenges in large-class instruction.

• Computer Architecture
• Programming Models
• Parallel Computing
• +5 more

Marc Gonzalez Tallada is a researcher at the Universitat Politècnica de Catalunya (UPC), affiliated with the Department of Computer Architecture within the Barcelona School of Informatics (FIB). He is a member of the MGT Research Group, focusing on Programming Models and high-performance computing systems. His research interests lie in the domain of computer architecture and programming models, with a strong emphasis on parallel and distributed computing, performance optimization, and system-level software. His work contributes to the design and implementation of efficient programming models that exploit modern parallel architectures. The recent publications (2010–2024) reflect a consistent focus on programming models, particularly in the context of high-performance computing. Key themes include task-based programming, runtime systems, compiler optimization, memory management, and performance analysis. The research spans computer architecture, system software, and compiler design, demonstrating a multidisciplinary approach to solving challenges in parallel computing. Scientific Awards and Recognitions: Premi o reconeixement Advising and Grants: While specific student names are not listed, his involvement in competitive R&D+i projects indicates active participation in funded research. These projects suggest leadership or collaboration in grant-supported initiatives related to programming models and computer systems. His role in innovation and research projects highlights contributions to both academic and applied domains. Labs and Research Teams: He is a core member of the MGT (Programming Models) research group at UPC, which focuses on advancing the state-of-the-art in programming models for parallel and distributed systems.

• Software Engineering
• Green Software Development
• Sustainable Computing
• +6 more

Félix García is a prominent professor at the University of Castilla-La Mancha in Ciudad Real, Spain, with extensive contributions to software engineering, sustainable computing, and business process management. His research spans over two decades with 187 publications indexed in dblp, demonstrating consistent scholarly productivity and leadership in multiple research areas. Dr. García's research interests focus on critical contemporary challenges in software development, particularly green software engineering, energy efficiency in computing systems, and sustainable software development practices. His work bridges theoretical foundations with practical applications, addressing how software design, implementation, and maintenance impact environmental sustainability. He has pioneered research connecting software quality attributes with energy consumption, examining how design patterns, code smells, and refactoring techniques affect resource usage. His recent publications (2023-2025) reveal a strong focus on cutting-edge topics including Green AI, quantum computing sustainability, and energy-aware programming language design. These works demonstrate his ability to anticipate and address emerging challenges at the intersection of software engineering and environmental sustainability. Dr. García has received significant recognition through numerous collaborations, particularly with Mario Piattini (133 co-authored papers), Francisco Ruiz (63 papers), and María Ángeles Moraga (30 papers), establishing him as a central figure in his research community. His work has appeared in prestigious venues including IEEE Transactions on Software Engineering, Journal of Systems and Software, and ACM Computing Surveys. He has mentored numerous researchers who have become established scholars in their own right, including Javier Mancebo, Laura Sánchez-González, and César Jesús Pardo Calvache. His contributions to gamification in software engineering education through serious games like GLOBAL-MANAGER demonstrate his commitment to innovative teaching approaches.

Martin SjölundView profile

Associate Professor

• Software Engineering
• Cyber-Physical Systems
• Modelica Compiler Development
• +3 more

Martin Sjölund is an Associate Professor at Linköping University's Department of Computer and Information Science (IDA), part of the Software and Systems (SAS) division. His work focuses on software engineering and cyber-physical systems, with a specialization in Modelica compiler development and open-source simulation tools. He contributes to the OpenModelica project, advancing compiler frameworks, integration with Julia, and standardization efforts. Research interests include compiler design, formal methods, and domain-specific languages. Recent work emphasizes modular compiler architectures, structural variability handling, and interoperability between Modelica and other systems like Julia and Python. He has co-authored over 30 peer-reviewed publications since 2017, with a focus on compiler optimization, co-simulation, and educational applications of Modelica. As part of the SAS division, he collaborates with researchers like Lena Buffoni, Adrian Pop, and Peter Fritzson on projects funded by the MODPROD Center and other initiatives. His contributions to open-source tools have been recognized through conference proceedings and industry partnerships.

Phillip RaffeckView profile

Researcher

• Real-Time Systems
• Embedded Systems
• Energy-Aware Computing
• +7 more

Phillip Raffeck is a researcher at the Department of Computer Science (INF) within the Chair of Computer Science 4 (System Software) at Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU). His work focuses on real-time systems, embedded systems, and energy-aware computing, with a particular emphasis on worst-case execution time (WCET) and energy consumption (WCEC) analysis. He has contributed to projects like the Invasive Run-Time Support System (iRTSS) and tools for energy-neutral system design. Department: Department of Computer Science (INF) University: Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU) Position: Researcher His research spans real-time systems , multi-core optimization , and power-aware computing . Key contributions include migrative synchronization protocols , energy-neutral operating systems , and code metrics for timing analysis . Recent work explores carbon-aware co-design and predictable migration in embedded environments. Publications highlight collaborations with teams across Germany and international symposia like RTSS, WCET, and EMSOFT. His article trends reflect advancements in intermittent execution models , transactional networking , and static analysis toolchains . He serves as a secondary reviewer for conferences including RTAS and ISORC. Phillip supervises graduate theses on topics such as DMA-based OS offloading , dynamic migration , and interrupt latency analysis . He contributes to teaching courses like Betriebsystemtechnik and Systemnahe Programmierung in C at FAU.

Jeffrey K. HollingsworthView profile

Professor

• High Performance Computing (HPC)
• Parallel Programming
• Operating Systems
• +5 more

Jeffrey K. Hollingsworth is a Professor in the Computer Science Department at the University of Maryland and serves as Vice President for Information Technology and Chief Information Officer (CIO) for the university. He holds appointments in CS, UMIACS (University of Maryland Institute for Advanced Computer Studies), and ECE (Electrical and Computer Engineering). His research focuses on High Performance Computing (HPC), parallel programming environments, and system architecture. He received a Ph.D. from the University of Wisconsin at Madison (1994) and a B.S. in Electrical Engineering from UC Berkeley. Hollingsworth leads the university’s IT infrastructure, overseeing critical services like networking, cybersecurity, and support for research and teaching. He has held leadership roles in professional organizations, including past chair of the ACM Special Interest Group on HPC (SIGARCH) and board positions with Internet2 and the Computing Research Association. His awards include IBM Faculty Partnership Awards (2016, 2001), an NSF CAREER Award (1997), and IEEE Senior Member status (2003). Key contributions include advancing HPC education through programs like the SC Student Cluster Competition and developing tools for performance analysis (e.g., PIPER, Chapel profilers). He has authored over 150 papers and has been cited for innovations in auto-tuning, parallel algorithms, and system optimization.

• High-Performance Computing
• Parallel Programming
• Computer Architecture
• +3 more

Prof. Dr. rer. nat. Matthias S. Müller is a Universitätsprofessor and Director of the IT Center at RWTH Aachen University. His research focuses on High-Performance Computing (HPC), parallel programming models, correctness verification, energy-aware computing, and tools for distributed systems. He leads the High-Performance Computing group, contributing to advancements in HPC resource management, runtime systems, and sustainable computing practices. Key areas of expertise include MPI and OpenMP correctness checking, static and dynamic analysis techniques, performance optimization for heterogeneous architectures, and energy footprint modeling. Müller has extensively collaborated on projects like MUST (MPI correctness tool), OMPT tools, and frameworks for analyzing hybrid parallel applications. His work bridges theoretical computer science with practical implementation challenges in large-scale computing environments. Notable contributions include developing methods for data race detection in Remote Memory Access (RMA) programs, latency-aware power management models, and educational frameworks for HPC lab courses. His research often emphasizes tool development, runtime systems, and interdisciplinary applications of HPC across engineering domains. Müller's lab is part of RWTH Aachen's IT Center, which provides infrastructure and expertise for computational research. He actively publishes in top-tier conferences and journals, addressing challenges in parallel programming, energy efficiency, and distributed computing systems.

Anya Helene BaggeView profile

Associate Professor

• Programming Language Design and Implementation
• Software Language Engineering
• Semantics of Programming Languages
• +8 more

Anya Helene Bagge is an Associate Professor in the Department of Informatics at the University of Bergen, where she is a researcher at the Bergen Language Design Laboratory. Her work focuses on programming languages and software language engineering, with strong ties to education and tool development. Research Interests: Programming language design and implementation Software language engineering Semantics of programming languages Domain-specific languages and language extensions Program transformation using Rascal, MPL, and Stratego/XT Aspect-oriented programming, especially Domain-Specific Aspect Languages Her recent publications reflect a consistent focus on software language engineering, refactoring, microservice security, and programming education. Themes across her work include language design, program analysis, and the development of educational tools and methodologies. Scientific Awards: The Realist Committee's Teaching Award 2016/17 Lecturer of the Year in Computer Science (Spring 2015) Anya Bagge actively supervises students, including PhD and Master’s candidates such as Tero Hasu, Kristoffer Haugsbakk, and Nina Andersen. She has been involved in numerous academic activities, including organizing the OOPSLE workshop and serving on program committees for conferences like SLE, HILT, and WCRE. She has also taught core courses including INF101, INF225, and INF328, demonstrating a strong commitment to both research and teaching. Labs and Teams: She is affiliated with the Bergen Language Design Laboratory , collaborating with researchers such as Magne Haveraaen, Eva Burrows, and Tero Hasu.

Milos NikolicView profile

Lecturer

• Databases
• Data Management
• Incremental Computation
• +7 more

Milos Nikolic is a Lecturer in Database Systems at the School of Informatics, University of Edinburgh. He is a member of the Database Group and the Laboratory for Foundations of Computer Science. Prior to joining Edinburgh, he was a Departmental Lecturer in the Department of Computer Science at the University of Oxford. He holds a PhD in Computer Science from EPFL. Research Interests: Milos Nikolic's research focuses on databases and large-scale data management, with emphasis on incremental computation, in-database learning, stream processing, and query compilation. His work explores how complex analytical queries—such as SQL, linear algebra, and machine learning tasks—can be efficiently maintained and evaluated in dynamic and distributed environments. He develops novel techniques in query optimization and compilation to enable real-time analytics over evolving data. His recent publications demonstrate a strong trend in dynamic query evaluation, particularly around conjunctive and hierarchical queries under updates, incremental view maintenance (e.g., F-IVM), and scalable processing of nested and biomedical data. His work often leverages theoretical foundations to achieve worst-case optimal performance, grounded in conjectures like the Online Matrix-Vector Multiplication (OMv) hypothesis. Scientific Awards: Best Paper Award, ICDT 2019 Advising and Grants: Milos is actively seeking PhD students to work on data management topics. While no formal list of advisees is provided, he collaborates extensively with researchers such as Dan Olteanu, Ahmet Kara, and Haozhe Zhang. His projects, including Adaptive Query Processing , Incremental Maintenance of Complex Analytics , and Declarative Data Pipelines (industry-supported), suggest ongoing grant and industry collaborations. Labs and Teams: He is a key member of the Database Group and the Laboratory for Foundations of Computer Science at the University of Edinburgh, contributing to cutting-edge research in foundational and applied database systems.

Sandro BartoliniView profile

Associate Professor

• High-Performance Computing (HPC)
• Computer Architecture
• Embedded Systems
• +5 more

Sandro Bartolini serves as Associate Professor in the Department of Information Engineering and Mathematical Sciences at the University of Siena, Italy, where he teaches advanced courses in computer architecture and parallel programming while leading cutting-edge research in high-performance computing systems. His academic journey began with a cum laude Laurea in Computer Engineering followed by a PhD in Computer Science and Engineering from Università di Pisa. Education: PhD in Computer Science and Engineering, Università di Pisa Laurea in Computer Engineering (cum laude), Università di Pisa Research Focus: His work centers on photonic interconnects for chip multiprocessors , energy-efficient software optimization for multi-core/GPU architectures, and performance-portable parallel programming models . Current investigations span cryptographic acceleration, blockchain algorithms, and hardware/software co-design for emerging computing paradigms, with strong emphasis on practical implementations bridging theoretical advances and real-world applications. Publication Trends: Recent publications (2019-2023) reveal three dominant threads: (1) Photonic network innovations addressing energy bottlenecks in chip multiprocessors, (2) The PHAST library ecosystem enabling seamless CPU/GPU programming across domains from autonomous vehicles to UAV navigation, and (3) Hardware accelerator designs for convolutional networks and cryptographic workloads. These works consistently target performance-portability challenges in heterogeneous computing environments. Grants and Collaborations: As principal investigator for the Italian Ministry-funded PHOTONICA project, he established international research partnerships with Murcia University, Columbia University, and Hong Kong University of Science and Technology, while securing industry collaborations with STMicroelectronics, Intel Munich, IBM, and IMEC. He has also managed complex IT system deployments for Siemens Italy, RAI (Italian public broadcasting), and SpaceDys. Academic Leadership: Bartolini serves as Associate Editor for the Eurasip Journal of Embedded Computing and actively contributes to the European HiPEAC network. His research group at Siena maintains strong industry ties for technology transfer, particularly in photonic interconnect validation and parallel programming frameworks for next-generation computing systems.

• Compilers
• Computer Architecture
• High-Level Synthesis
• +5 more

Scott Mahlke is a Professor and Associate Chair in the Department of Electrical Engineering and Computer Science at the University of Michigan's College of Engineering. He is affiliated with both the Advanced Computer Architecture Laboratory and the Software Systems Laboratory. Dr. Mahlke joined the University of Michigan in 2001 after completing his Ph.D. at the University of Illinois and working at HP Laboratories. Ph.D., University of Illinois Former Researcher, HP Laboratories Dr. Mahlke's research spans compilers, computer architecture, and high-level synthesis, with particular focus on overcoming challenges in performance, power consumption, and reliability for next-generation computer systems. His work integrates hardware and software co-design approaches to address fundamental limitations in modern computing platforms. His research has evolved from traditional compiler and architecture topics toward increasingly incorporating machine learning acceleration, autonomous systems, and reliability engineering. Analysis of his recent publications (2021-2025) reveals a strong trend toward hardware-software co-design for emerging workloads, particularly in autonomous systems, neural network acceleration, and reliability-aware computing. His work demonstrates consistent innovation in bridging compiler technology with architectural innovations to solve real-world performance and efficiency challenges. Dr. Mahlke has received significant recognition for his contributions to the field: National Science Foundation CAREER Award (2003) for "Compiler-Directed Synthesis of Application Specific Processors" Morris Wellman Faculty Development Assistant Professor appointment (2004) ISCA Most Influential Paper Award (2006) for the 1991 paper "IMPACT: An Architectural Framework for Multiple Instruction Issue Processors" Young Alumni Award from the University of Illinois ECE Department (2007) As an educator, Dr. Mahlke has taught core computer systems courses including EECS 370 (Introduction to Computer Organization), EECS 483 (Compiler Construction), and EECS 583 (Advanced Compilers) since joining Michigan. His teaching philosophy follows Yale Patt's 10 commandments for teaching, emphasizing understanding over memorization, genuine respect for students, and taking responsibility for course content. He has received mixed but generally positive student evaluations, with students noting both his deep subject matter expertise and areas for improvement in lecture delivery. Dr. Mahlke maintains active research leadership through his affiliations with the Advanced Computer Architecture Laboratory and Software Systems Laboratory, where his team continues to explore innovative approaches to compiler and architecture challenges in modern computing systems.

• Safety Analysis of Autonomous Systems
• Multi-Agent Systems
• Hazard Analysis Simulation
• +3 more

Rob Alexander is a Senior Lecturer in the Department of Computer Science at the University of York. He holds a BSc from Keele University (2001) and a PhD in Computer Science from the University of York (2008). His research focuses on safety analysis and testing of autonomous robots and multi-agent systems, including simulation methods for hazard analysis and empirical evaluation of safety engineering techniques. He is part of the High Integrity Systems research group. Key research interests include situation coverage-based safety analysis, autonomous systems verification, and safety assurance methodologies. His work spans robotics, autonomous vehicles, and safety-critical systems, with applications in mine environments, healthcare, and transportation. Recent publications emphasize rigorous testing frameworks, risk assessment in complex systems, and bridging theoretical and practical safety engineering challenges. No scientific awards or grants are explicitly listed. He serves as a Union Rep within the department and is involved in academic leadership roles. His research group collaborates on projects such as SCALOFT and SASSI, focusing on safety assurance in autonomous systems.

• Computer Architecture
• Programming Models
• Parallel Computing
• +4 more

Daniel Jimenez Gonzalez is a faculty member at the Universitat Politècnica de Catalunya (UPC), affiliated with the Department of Computer Architecture within the School of Informatics (FIB). He is an active researcher in programming models and high-performance computing, contributing extensively to the field through publications, projects, and academic supervision. Research Interests: His work focuses on Computer Architecture , Programming Models , and Parallel Computing . He investigates task-based programming, runtime systems, compiler optimizations, and performance modeling for multi-core and heterogeneous architectures. His research addresses challenges in scalability, energy efficiency, and resilience in modern computing environments. His recent publications show a consistent trend in task-based programming models , runtime scheduling , and performance optimization across diverse architectures, including NUMA and GPU-accelerated systems. The work spans from theoretical modeling to practical implementation, often targeting real-world HPC applications. Scientific Awards: No awards explicitly mentioned in the provided text. Advising and Grants: While specific students are not listed, his involvement in doctoral theses and R&D projects suggests an active role in mentoring graduate students. He has participated in both competitive and non-competitive R&D+i projects, indicating grant acquisition and project leadership experience in areas related to programming models and computer architecture. Labs and Teams: He is a member of the UPC PM - Programming Models research group, which focuses on the design, analysis, and optimization of modern programming paradigms for high-performance systems.

Guillaume HuardView profile

Associate Professor

• High Performance Computing
• Applications Traces
• Monitoring & Collection Aggregation & Visualization Runtime
• +5 more

Guillaume Huard is an Associate Professor at Grenoble Alpes University, affiliated with the LIG Laboratory and the POLARIS Research Team . His research focuses on high-performance computing (HPC), particularly in tracing, performance analysis, and optimization of parallel applications, compiler optimization, and architecture. PhD in 2002 from École Normale Supérieure de Lyon under Alain Darte His research interests span applications tracing, monitoring and visualization of runtime data, application deployment, scheduling, performance evaluation, resource utilization, and cache/memory footprint optimization. His work often addresses challenges in NUMA architectures and large-scale trace analysis. Guillaume's publications highlight trends in memory trace collection (e.g., Moca system), spatiotemporal data aggregation, NUMA-aware visualization tools (e.g., TABARNAC), and cache utilization control. Key subfields include performance debugging, compiler transformations, runtime environments, and multidimensional data aggregation techniques. Guillaume is actively involved in the POLARIS Bootcamp and DATAMOVE/POLARIS events , contributing to collaborative research and educational initiatives in HPC.