Find Academic Supervisors

• Software Engineering
• Empirical Software Engineering
• Mining Software Repositories
• +3 more

Abram Hindle is a Professor in the Department of Computing Science within the Faculty of Science at the University of Alberta. He holds a Ph.D. from the University of Waterloo (2010), an M.Sc. from the University of Victoria (2005), and a B.Sc. (Honours with distinction) from the University of Victoria (2003). His research focuses on evidence-based software development, leveraging techniques from data mining, machine learning, and empirical analysis. Hindle's research spans multiple domains including software repository mining, energy efficiency in software systems, and interdisciplinary applications like computer music and ECG analysis. His work integrates statistical analysis, NLP, and visualization to study software processes, maintenance, and metrics. His recent publications demonstrate a strong focus on healthcare applications of machine learning (particularly ECG-based diagnostics), software defect prediction, container orchestration, and energy-aware development practices. These reflect an ongoing commitment to empirical validation and real-world impact.

• Computer Science Education
• Virtualization
• Embedded Runtime Systems
• +3 more

Rebecca Schreib serves as an Assistant Teaching Professor and Director of Undergraduate Studies in Rice University's Department of Computer Science. She holds a Ph.D., M.S., and B.S. in Computer Science from Rice University (2019, 2015, 2014 respectively). Her research focuses on computer science education, virtualization, and embedded runtime systems. Her educational background includes a doctoral emphasis on designing personalized interactive learning tools for large introductory courses. Key research contributions include MemStep (2024), collaborative computational thinking courses (2020), and automated assessment systems (2016–2017). Her work spans both educational technology and embedded systems, with a focus on bridging theory-practice gaps in programming education while advancing runtime system reliability for constrained environments. She has developed tools that enhance student engagement and automated systems for testing and memory management. While no scientific awards are listed, her publications reflect sustained innovation in educational technology and embedded computing since 2012. Her advising and grant activities are not explicitly documented here. She contributes to Rice's undergraduate program infrastructure through her directorship role.

Steve BlackburnView profile

Professor

• Computer Science
• Programming Languages
• Memory Management
• +5 more

Steve Blackburn is a Professor of Computer Science at the Australian National University (ANU), affiliated with the ANU College of Systems & Society. His research focuses on programming language implementation, garbage collection algorithms, performance analysis, and high-performance runtime systems. He leads key projects like the DaCapo benchmark suite and the MMTk memory management framework. **Education & Background**: No specific education details provided, but his career spans decades with extensive contributions to computer science research. **Research Interests**: Blackburn prioritizes garbage collection innovation, memory management optimization, and empirical performance evaluation. His work bridges theory and practice, addressing challenges in managed runtimes, just-in-time compilation, and hardware-software co-design. He emphasizes reproducible research through shared infrastructure. **Recent Article Trends**: His 2020–2025 publications emphasize low-latency garbage collection, memory management in distributed systems (e.g., Mako collector for datacenters), and novel approaches to Java/JVM performance analysis. He also explores hardware features like transactional memory and their impact on runtime systems. **Awards**: ACM Fellow (2020+). **Grants & Leadership**: Served as Associate Dean for Diversity & Inclusion in ANU’s College of Engineering and Computer Science (2014–2019). Editorial roles include ACM TOPLAS Associate Editor (2017–2020) and program chairs for top conferences like PLDI (2015) and ISMM (2008). **Labs & Projects**: Leads the MMTk and DaCapo initiatives, foundational tools for memory management and benchmarking in academia and industry.

Alberto BacchelliView profile

Associate Professor

• Empirical Software Engineering
• Software Quality
• Code Review
• +3 more

Alberto Bacchelli is an Associate Professor of Empirical Software Engineering at the University of Zurich (UZH), leading the Zurich Empirical Software Engineering Team (ZEST). He joined UZH in 2017, having previously worked as an Assistant Professor at Delft University of Technology (Netherlands), where he earned tenure. His research focuses on improving software quality, developer effectiveness, and code review practices through empirical studies and tool development. Bacchelli holds a PhD from the University of Lugano, with internships at Microsoft Research. He has received prestigious awards, including the MSR Ric Holt Early Career Achievement Award (2020) and the 10-year Most Influential Paper Award from SANER. His work spans code review efficiency, software security, and developer tools. In his personal life, he balances family time with a passion for photography, particularly through his sister Chiara’s wedding photography work. Affiliations: University of Zurich (since 2017), Delft University of Technology (2013–2017), Microsoft Research (internships 2012–2013). Education: PhD in Computer Science (University of Lugano, 2013), Master/BS in Computer Science (University of Bologna), studies at Université Libre de Bruxelles. His research interests center on understanding software engineering challenges and designing tools/methods to enhance practices. Notable projects include studies on code review strategies, developer cognition, and security in collaborative environments. He emphasizes bridging theory and practice, aiming for real-world impact through tools like PyDriller and frameworks for mining software repositories. Awards highlight his contributions to code review and software engineering education. He actively engages in teaching, mentoring, and advancing open-source practices. His team, ZEST, explores topics such as code review dynamics, developer productivity, and empirical methods to improve software processes.

Sabrina M. NeumanView profile

Assistant Professor

• Computer Architecture
• Robotics
• Domain-Specific Accelerators
• +3 more

Sabrina M. Neuman is an Assistant Professor of Computer Science at Boston University, specializing in computer architecture design informed by domain-specific insights, particularly for robotics applications. She holds a PhD in Electrical Engineering and Computer Science from MIT and was a postdoctoral NSF Computing Innovation Fellow at Harvard University. Her research focuses on closing computational gaps in robotics through domain-specific accelerators, leveraging robot morphology for hardware design. She has received honors including the 2021 EECS Rising Star and the Boston University Innovation Career Development Professorship (2023-2026). Education: PhD in Electrical Engineering and Computer Science, MIT M.Eng. in Electrical Engineering and Computer Science, MIT S.B. in Electrical Engineering and Computer Science, MIT Research Interests: Her work emphasizes domain-specific computing for robotics, including automated hardware design flows, FPGA/ASIC acceleration for motion planning, and scalable accelerator deployment across robot platforms. She develops methodologies like Robomorphic Computing, which translates robot morphology into customized hardware accelerators, achieving significant speedups in critical robotics kernels. Awards & Recognition: 2021 EECS Rising Star IEEE Micro Top Picks Honorable Mentions (2022, 2023) 2023-2026 Boston University Innovation Career Development Professorship Grants & Contributions: Developed RobotPerf, an open-source benchmarking suite for robotics computing systems Worked on Grid (GPU-accelerated rigid body dynamics) and RobotCore (ROS 2 hardware acceleration) Labs & Teams: While no specific lab name is mentioned, her work integrates closely with Boston University's robotics and computer architecture groups, emphasizing open collaboration with industry and academia.

Adrian SampsonView profile

Associate Professor

• Computer Architecture
• Programming Languages
• Hardware-Software Co-Design
• +4 more

Adrian Sampson is an Associate Professor in the Department of Computer Science at Cornell University, where he is part of the Computer Systems Laboratory and the Programming Languages Group. He leads the Capra research group, focusing on programming languages, computer architecture, and hardware-software co-design. Previously, he was an Assistant Professor at Cornell (2016–2022) and a researcher at Microsoft and Google. Education: Ph.D. in Computer Science and Engineering from the University of Washington (2015), advised by Luis Ceze and Dan Grossman. Dissertation: Hardware and Software for Approximate Computing . B.S. in Computer Science from Harvey Mudd College (2009). Research interests include approximate computing, compiler optimization, hardware accelerators, and formal methods in systems programming. His work on Geometry Types and Reticle has advanced abstractions for graphics programming and FPGA design. Sampson is known for contributions to compiler infrastructure for accelerators, such as the Timeline Types framework for predictable hardware design. Awards include the Cornell Bowers CIS Research Excellence Award (2024), IEEE TCCA Young Computer Architect Award (2021), NSF CAREER Award (2019), and the Google Ph.D. Fellowship in Computer Architecture (2013–2015). He has advised over 30 graduate and undergraduate students, contributing to projects like compiler-driven accelerator simulation and vectorization techniques. Teaching includes courses on compilers, programming languages, and computer architecture at Cornell. He has served on program committees for major conferences (ASPLOS, PLDI, ISCA) and chairs roles in ISCA and ASPLOS. Sampson’s research is supported by grants from NSF, Google, and industry partnerships.

Stephanie ForrestView profile

Professor

• Computational Immunology
• Computer Security
• Automated Program Repair
• +5 more

Dr. Stephanie Forrest is a Professor at Arizona State University's School of Computing and Augmented Intelligence (SCAI) and serves as Director of the Biodesign Center for Biocomputation, Security and Society . She is also affiliated with the Santa Fe Institute (SFI) as External Faculty and the ASU-SFI Fellowship . Education: B.A. from St. John's College and M.S./Ph.D. in Computer Science from the University of Michigan. Her research spans the biology of computation and computation in biology , including computational immunology , cybersecurity , automated software repair , evolutionary computation , and complex systems . Recent work focuses on GPU code optimization , malware analysis , and privacy-preserving wastewater epidemiology . Her publications (15 most recent) reveal trends in evolutionary algorithms for software repair, cybersecurity frameworks inspired by biological systems, and high-performance computing applications in epidemiology and genetics. Awards include IEEE S&P Test of Time (2020) , ACM/AAAI Allen Newell (2011) , and ICSE Most Influential Paper (2019) . Cybersecurity: Developed anomaly detection, instruction-set randomization, and CRISPR-inspired DoS mitigation. Software Repair: Pioneered biologically inspired methods for bug fixing, vulnerability closure, and energy optimization. Biological Modeling: Agent-based simulations of SARS-CoV-2 spread, cancer evolution, and immune system dynamics.

• Global Program Analysis
• Software Verification
• Debugging
• +5 more

Manuel Hermenegildo is a distinguished professor at the IMDEA Software Institute and a full professor at the Technical University of Madrid (UPM), leading the CLIP Lab. He specializes in program analysis, verification, and optimization, with contributions to logic and constraint programming. His academic roles include founding director of IMDEA and leadership in EU-funded projects. Education: PhD in Electrical and Computer Engineering, University of Texas at Austin (1986) M.S. in Electrical and Computer Engineering, University of Texas at Austin (1984) M.S. in Electrical Engineering, Technical University of Madrid (1981) Research Interests: Focuses on global program analysis, verification, debugging, and optimization for functional/non-functional properties (e.g., time, memory, energy). Explores abstract interpretation, parallelism, constraint/logic/functional programming, and computational logic. His work bridges theoretical foundations and practical tools like Ciao Programming System. Publications & Trends: Over 200+ papers in top venues like Theory and Practice of Logic Programming , POPL , and SAS . Recent work emphasizes static analysis for energy efficiency, formal verification via Horn clauses, and lightweight runtime checks. Contributions span formal methods, compiler optimization, and declarative languages. Awards & Recognition: ACM Fellow (2010) Julio Rey Pastor Prize (2006) – Spain’s top award in mathematics/ICT Academia Europaea Membership (2010) Test of Time Award (ICLP 2017) Google Scholar: 10k+ citations, h-index 60 Advising & Grants: Supervised over 20 PhD students. Principal investigator on major EU projects (e.g., María de Maeztu distinction) and national initiatives. Served as director of the Spanish National Research Directorate, shaping science policy. Labs & Teams: CLIP Lab (UPM-IMDEA), Microsoft-IMDEA Joint Research Center, and leadership in EIT Digital Spain. Collaborates globally with institutions like MIT, INRIA, and Monash University.

Kevin AngstadtView profile

Assistant Professor

• Computer Architecture
• Programming Languages
• Software Engineering
• +3 more

Kevin Angstadt is an Assistant Professor of Computer Science at St. Lawrence University, part of the Department of Mathematics, Computer Science, and Statistics. He holds a Ph.D. from the University of Michigan (2020) and an MCS from the University of Virginia (2016), with undergraduate degrees in Computer Science, Mathematics, and German Studies from St. Lawrence University (2014). His research focuses on the intersection of computer architecture, programming languages, and software engineering, with an emphasis on optimizing programming support for emerging hardware technologies like accelerators and autonomous systems. Education: Ph.D. in Computer Science and Engineering, University of Michigan (2020) M.S. in Computer Science, University of Virginia (2016) B.S. in Computer Science, Mathematics, and German Studies, St. Lawrence University (2014) Research Interests: His work spans programming abstractions for hardware accelerators, fault-tolerant autonomous systems, and tools like MNRL and MNCaRT for automata processing. He also collaborates on projects such as StatKey, a statistical simulation tool used by over one million users. Recent Articles Trends: His publications emphasize hardware-software co-design, resilience in autonomous systems, and debugging support for pattern-matching languages. Recent work includes NSF-funded research on program repair and optimization, and frameworks like LOGI and START for secure autonomous vehicle operation. Awards: NSF Medium Grant ($1.2M, 2022) UVA Teaching and Service Award (2017) Jefferson Scholars Foundation Fellow (2014–2017) Best in Session Award at TECHCON 2016 Advising & Grants: He leads a $1.2M NSF grant and has advised projects on pattern-matching accelerators, autonomous vehicle resilience, and compiler optimization. Collaborates with industry and academic partners on mission-critical systems and software reliability. Labs/Teams: Core contributor to MNRL (automata processing ecosystem), StatKey (statistical tools), and the START project for resilient autonomous systems. Active in open-source development and hardware-accelerator research groups.

• Algorithms
• Theory
• Logic Programming Languages
• +9 more

Thomas Davidson is a Researcher affiliated with the Max Planck Institute for Software Systems (MPI-SWS). His work focuses on foundational areas of computer science including algorithms, theory, programming languages, verification, and cyber-physical systems. He explores topics such as distributed systems, security, privacy, and the intersection of systems research with visualization techniques. His research spans both technical and human-centered aspects of computing, such as improving debugging tools through visualization, analyzing energy metrics in systems, and applying visual analytics to literary studies. Recent work emphasizes the role of cognitive biases in data communication and the challenges of energy efficiency in modern systems. Davidson's publications highlight a blend of theoretical rigor and practical applications, particularly in bridging gaps between system performance analysis and human understanding. His work contributes to both technical communities (e.g., systems researchers) and interdisciplinary fields (e.g., digital humanities).

• High Performance Computing
• MPI Programming
• Parallel Algorithms
• +2 more

Ioannis Vardas is a Researcher in Parallel Computing at TU Wien's Faculty of Informatics. His work focuses on optimizing high-performance computing systems through advanced process mapping, MPI programming, and performance analysis tools. Key research areas: Topology-aware process mapping for HPC MPI library development and optimization Performance profiling of parallel applications Resource allocation in hierarchical architectures Energy-efficient parallel computing He contributes to projects improving MPI performance tools and develops methods for efficient resource utilization in supercomputing environments.

• Distributed Systems
• Cloud Computing
• Tail Latency Optimization
• +3 more

Timothy Zhu is an Associate Professor in Computer Science and Engineering, focusing on distributed systems and cloud computing technologies. His research explores tail latency optimization, resource management in heterogeneous environments, and sustainable computing practices. Current PI for NSF DESC : Extending machine lifetimes to repurpose e-waste Lead investigator for NSF CAREER : Auto-generated experimentation for performance diagnosis Principal Investigator for NSF CNS Core : Tail latency reduction through system heterogeneity Research trends show emphasis on: Latency Management : Tail latency SLOs, admission control, and performance profiling Cloud Efficiency : Autoscaling algorithms, workload characterization, and cost optimization Hardware-aware Systems : DNN performance estimation, SmartNIC utilization, and heterogeneous server architectures Collaborative efforts span institutions including: Co-authorships with researchers at Carnegie Mellon University (CMU) Joint projects with Pennsylvania State University (Penn State) collaborators like Anand Sivasubramaniam

Andrea De LuciaView profile

Professor

• Software Engineering
• Program Comprehension
• Software Security
• +3 more

Andrea De Lucia serves as Full Professor in the Department of Computer Science at the University of Salerno, Italy, maintaining an active research profile with office hours at Fisciano Campus (Building F2, Room 089) and correspondence via adelucia@unisa.it. His scholarly contributions span software engineering with particular emphasis on security, mobile systems, and emerging quantum applications. His research portfolio demonstrates evolving focus through distinct phases: 2018-2020 : Code smell analysis and mobile energy efficiency (e.g., Android energy consumption studies) 2021-2022 : Security vulnerability lifecycle and quantum software engineering foundations 2023-2025 : Ethical AI integration (fairness in ML engineering) and advanced exploit prediction Recent publications reveal strategic expansion into quantum-computing applications and AI ethics, maintaining core software engineering principles while addressing contemporary challenges in secure, reliable systems development. His work consistently bridges theoretical frameworks with empirical validation through large-scale studies. De Lucia actively contributes to the software engineering community as program committee member for premier conferences including ICSE, ASE, and ICSME across multiple years (2018-2026), demonstrating sustained leadership in the field.

• Parallel Computing
• Distributed Systems
• High-Performance Computing (HPC)
• +5 more

Torsten Hoefler is a Professor affiliated with ETH Zurich, leading research in parallel computing, distributed systems, and high-performance computing (HPC). His work bridges theoretical foundations and practical implementations, focusing on optimizing algorithms, network topologies, and hardware-software co-design. Research Interests: His primary areas include parallel algorithms, distributed systems, machine learning infrastructure, and network architectures. He emphasizes scalable solutions for large-scale applications, particularly in data-centric computing and serverless environments. Publications: Recent work highlights include innovations in network topologies (e.g., HammingMesh), serverless benchmarking frameworks (SeBS), and optimizations for large language models (LLMs). His publications often address performance bottlenecks and energy efficiency in HPC and cloud systems. Awards & Grants: While no specific awards are listed here, his prolific publication record and leadership in HPC indicates significant recognition in the field. Active in grant-funded projects related to exascale computing and AI infrastructure. Labs & Teams: Leads the Communication Systems Lab at ETH Zurich, collaborating with industry partners like NVIDIA and IBM on hardware-accelerated computing and cloud-native systems.

Ryan GrantView profile

Assistant Professor

• Cloud Computing
• High Performance Networks
• Low-level Hardware-Software Interfaces
• +6 more

Dr. Ryan Grant is an Assistant Professor in the Department of Electrical and Computer Engineering at Queen’s University, Canada. He leads the Computing at Extreme Scale Advanced Research (CAESAR) lab and is affiliated with the Ingenuity Labs Research Institute. His expertise spans cloud computing, high-performance networks, low-level hardware-software interfaces, and energy-efficient supercomputing systems. Dr. Grant holds a PhD from Queen’s University (2012) and previously worked at Sandia National Laboratories (2012–2021), where he contributed to critical supercomputer communication protocols now deployed globally. He has authored over 80 peer-reviewed articles and received prestigious awards including the R&D100 Award and Queen’s University’s 125th Engineering Alumni Award. His research emphasizes advancing Canada’s supercomputing infrastructure to support AI, climate science, and national security applications. Education: PhD in Computer Engineering, Queen’s University (2012) MSc in Computer Engineering, Queen’s University (2005) BSc in Computer Engineering, Queen’s University (2004) Research Interests: Dr. Grant’s work focuses on optimizing supercomputing architectures for extreme-scale systems, with an emphasis on: High-performance networking and MPI communication protocols Power/energy management in HPC systems AI-driven network traffic prediction and resource disaggregation GPU-accelerated computing and cloud infrastructure integration National sovereignty in supercomputing for sensitive applications (e.g., defense, healthcare) Awards & Recognition: R&D100 Award (Oscars of Research) U.S. Defense Programs Awards Public Good Innovator Award Queen’s University 125th Engineering Alumni Award Grants & Labs: Dr. Grant directs the CAESAR lab, one of the world’s leading supercomputing architecture research groups. His work is supported by grants from Canadian and international agencies, focusing on sovereign supercomputing and HPC-AI convergence. Labs/Teams: CAESAR Lab (Queen’s University) Ingenuity Labs Research Institute