Find Academic Supervisors

• Learning and Teaching
• Computer Education
• Serious Games
• +7 more

Dr. Julian Dermoudy is an Associate Professor and Associate Head (Learning and Teaching) in the School of Information and Communication Technology at the University of Tasmania. With over 25 years of teaching experience, he has received national and institutional recognition for his pedagogical contributions. His roles have included Head of School, Associate Dean, and Course Coordinator, reflecting his leadership in academic administration. His teaching spans the Bachelor of ICT and Master of ITS programs. Research Interests: Learning and Teaching Innovations Computing Education and Curriculum Design Serious Games and Gamification for Behavior Change Multimodal Analysis and Verification Parallelism and Software Engineering Funded Projects: UTAS Internal Funding: Embedding Indigenous Knowledge in Curriculum (2021-2021) Advising: Supervised 11 doctoral candidates across domains like biometric authentication, ICT curriculum design, and mobile technology impact. Recent students include Simon Stanton (Cooperative Intent) and Shahan Chowdhury (Collaborative In-Store Shopping). Labs/Teams: Active in curriculum redesign initiatives and educational technology innovation, particularly in VR and gamification applications.

José ProençaView profile

Assistant Professor

• Formal Methods
• Concurrency
• Coordination Models
• +5 more

José Proença is an Assistant Professor at the Faculty of Sciences of the University of Porto (FCUP) and a Researcher at CISTER Research Centre, collaborating with INESC TEC. He specializes in formal methods for component-based systems, concurrency, and cyber-physical systems. His research focuses on coordination models, embedded systems, and software product lines, with contributions to frameworks like Reo and LooCI. Education: PhD in Concurrent & Distributed Computing (Leiden University, 2011), BSc+MSc in Mathematics & Computer Science (University of Minho, 2005) Research interests include formal verification, runtime verification for real-time systems, and hybrid systems programming. He coordinates projects like Ibex (FCT-funded) and has been involved in ECSEL projects like VALU3S. Supervised numerous MSc and PhD students, including work on CPS monitors, hybrid simulations, and variability analysis. Teaching includes Concurrent Programming, Algorithms, and Software Verification at the MSc level. Active in program committees for conferences like COORDINATION and SAFECOMP, and steering committee roles. Tools developed include Lince and RebeCaos for system simulation and formal analysis.

Tobias GrosserView profile

Associate Professor

• Compilers
• Programming Languages
• Performance Programming
• +7 more

Tobias Grosser is an Associate Professor in the Department of Computer Science and Technology at the University of Cambridge. His research focuses on rethinking performance programming by bridging the gap between developers and compilers. He holds a PhD from École Normale Supérieure Paris and has held positions including Reader at the University of Edinburgh and Ambizione Fellow at ETH Zurich. His research interests span compilers, programming language design, static/dynamic analysis, and the integration of machine learning into compiler development. He emphasizes making compilation more modular, automatic, and trustworthy, with applications in quantum computing, climate science, and open-source hardware. Key projects include xDSL (a Python-native compiler framework), LoopOpt, and the Open Earth Compiler for climate simulations. Recent publications highlight advancements in multi-level intermediate representations (IR), formal verification in MLIR, and performance optimization for GPUs and FPGAs. His work often addresses barriers between programmers and compilers, aiming for intuitive collaboration between developers and automated systems. Tobias mentors a dynamic team of PhD students, postdocs, and researchers, including notable contributors like Siddharth Bhat, Arjun Pitchanathan, and Mathieu Fehr. His lab focuses on compiler toolchains for domain-specific hardware accelerators, quantum computing ecosystems, and verified compilation techniques.

• Formal Verification
• Parallel and Distributed Systems
• GPU Programming
• +4 more

GANESH GOPALAKRISHNAN is a Professor of Computer Science at the University of Utah's School of Computing. His work focuses on formal verification of parallel/distributed systems, GPU programming, and numerical error analysis. He has contributed to tools like ISP for MPI verification, ARCHER for OpenMP race detection, and FLiT for floating-point consistency testing. His research spans theoretical foundations (e.g., concurrency models) and practical applications (e.g., GPU error analysis). Recent work includes advancing formal methods for mixed-precision computing and resilience in exascale systems. Notable projects include rigorous error estimation for floating-point operations and compiler-assisted verification techniques. Research Interests: Formal Verification of Parallel Systems | GPU & HPC Correctness | Floating-Point Numerical Analysis | Concurrency Bugs | Tools for Distributed Systems. Current work emphasizes hybrid approaches combining formal methods with dynamic analysis to address emerging challenges in heterogeneous computing architectures. Articles Trends: Recent publications (2020–2025) emphasize GPU verification (data races, error analysis), mixed-precision computing (matrix operations, tensor cores), and resilience in HPC systems. Tools like FPDetect and BinFPE highlight practical contributions to error detection in production runs. Workshops (DOE/NSF) indicate leadership in defining correctness strategies for exascale computing. Labs/Teams: Leads research groups focused on formal methods for parallel computing and numerical system reliability. Collaborations include Argonne National Lab, NVIDIA, and LLNL on verification tools and HPC correctness frameworks.

Benjamin Lee GreenmanView profile

Assistant Professor

• Programming Languages
• Gradual/Migratory Type Systems
• Language Interoperability
• +4 more

Benjamin Lee Greenman is an Assistant Professor at the Kahlert School of Computing , part of the John and Maria Price College of Engineering at the University of Utah. His research focuses on programming languages, gradual/migratory type systems, formal methods, and human factors in software development. He holds a Ph.D. from Northeastern University (2020), a CIFellows postdoc at Brown University (2020–2022), and degrees from Cornell University (B.S. in ILR, M.Eng. in CS). Key projects include: Forge: A tool for teaching formal methods with lightweight model finding. FlowFPX: Tools for debugging floating-point exceptions in scientific computing. LTL Tutor: An adaptive learning system addressing temporal logic misconceptions. Gradual Typing Benchmarks: Evaluating performance and guarantees of type systems. His work emphasizes rigorous methods for language design, including empirical studies, performance evaluation, and human-centered approaches. Recent contributions include exploring misconceptions in LTL education and advancing type system interoperability between typed and untyped code. Teaching roles include courses on compilers, software verification, and programming languages. He advocates for practical tools like Rhombus (Python-like syntax with Lisp macros) and Static Python ’s sound gradual typing system.

Dustin RichmondView profile

Assistant Professor

• Computer Architecture
• FPGA
• RISC-V
• +4 more

Dustin Richmond is an Assistant Professor in the Department of Computer Science and Engineering at the Baskin School of Engineering, University of California, Santa Cruz. His work focuses on secure, usable hardware systems with applications in FPGA acceleration, RISC-V architectures, and side-channel analysis. Email: drichmond@ucsc Office: Engineering 2, Room 221 Research Interests: Secure hardware systems FPGA-based computing Manycore processors High-level synthesis Side-channel vulnerabilities Notable Article Trends: Recent publications emphasize cloud FPGA security, manycore design optimization, and hardware security. Earlier works focus on RISC-V acceleration, OpenCL compiler enhancements, and heterogeneous computing systems. GitHub Contributions: Maintains open-source projects like RISC-V-On-PYNQ and PYNQ-HLS, addressing FPGA programming challenges and RISC-V integration. Active in resolving community issues related to toolchain compatibility and hardware-software interfaces.

Matthias TichyView profile

Professor

• Domain Specific Languages
• Model-Driven Software Engineering
• Self-Adaptive Systems
• +4 more

Prof. Dr. Matthias Tichy is a Full Professor and head of the Institute of Software Engineering and Programming Languages at Ulm University, Germany, since 2015. His research focuses on domain-specific languages (DSLs), model-driven engineering (MDE), self-adaptive software , and cyber-physical systems , with an emphasis on safety-critical applications and graph transformation formalisms. He employs empirical research methods to evaluate technical contributions and human factors in software engineering. University: Ulm University Role: Professor & Institute Head Research Interests span domain-specific languages for mechatronic systems, collaborative modeling , performance prediction in model transformations, and software evolution in industrial contexts. His work often bridges graph transformations and safety assurance for self-adaptive systems. Recent Publications highlight trends in model versioning (e.g., operation-based caching), DSL design (e.g., flowR for R code analysis), and automotive software testing (e.g., clustering test case specifications). He frequently collaborates with international institutions on topics like cyber-physical systems and IoT resilience . Key Collaborations include projects with Chalmers University, University of Gothenburg, and industrial partners like dSPACE GmbH. His grants and industry partnerships focus on automotive software , robotics , and self-healing systems .

Armin AlaghiView profile

Assistant Professor

• Embedded Systems
• Digital Circuits
• Mathematics
• +7 more

Armin Alaghi serves as a Research Scientist at Oculus Research (Redmond, WA) and holds an Affiliate Assistant Professor position at the University of Washington. His dual affiliation enables valuable knowledge transfer between cutting-edge industrial research and academic pursuits in computer systems engineering. Dr. Alaghi's research spans the intersection of embedded systems, digital circuits, and mathematics. His primary focus involves building low-power augmented reality (AR) and virtual reality (VR) systems while developing novel computation methods for unreliable beyond-CMOS technologies. His previous research contributions include significant work in stochastic computing (where he developed the STRAUSS synthesis methodology), reliable Network on chip (NoC) design, FPGA testing methodologies, NoC testing techniques, artificial neural networks implementations, asynchronous circuit design, and multi-valued logic systems. He has made his spectral-transform-based synthesis tool publicly available on GitHub, demonstrating commitment to open research. Analysis of Dr. Alaghi's publication record reveals a clear research trajectory from foundational circuit-level work toward practical applications in AR/VR systems. His publications from 2020-2025 demonstrate expertise spanning computer architecture, security for immersive technologies, neural network compression techniques, and homomorphic encryption methods. A recurring theme throughout his work is the exploration of quality-energy tradeoffs and error-resilient computing approaches, with increasing focus on security aspects of AR/VR systems in his most recent work. Dr. Alaghi maintains active connections with the broader research community, as evidenced by his Erdős number of 3 (Armin Alaghi John P. Hayes Frank Harary Paul Erdős) and his ongoing contributions to open-source research tools. His GitHub repository for stochastic computing synthesis shows community engagement with multiple contributors. At Oculus Research, Dr. Alaghi applies his theoretical expertise to practical challenges in next-generation AR/VR system development. His work bridges academic research with real-world product development, particularly in addressing energy efficiency challenges for wearable computing platforms through innovative circuit design approaches.

• Functional Programming
• Type Theory
• Program Verification
• +5 more

Dr. Wouter Swierstra is an Associate Professor in the Software Technology group within the Faculty of Science at Utrecht University. His research focuses on functional programming, type theory, and program verification, with particular expertise in dependently typed programming languages like Agda. His research interests span several key areas in programming language theory and implementation: Functional programming language design and implementation Dependent types and formal verification Program transformation and derivation Generic programming techniques Formal methods for blockchain and smart contracts Hardware verification using domain-specific languages Swierstra's recent work shows a strong trend toward applying formal methods to practical problems, particularly in the areas of blockchain technology and hardware verification. His publications demonstrate expertise in both theoretical foundations of programming languages and their practical applications. He has made significant contributions to the understanding of data structures in functional settings, program derivation techniques, and verification methodologies. Dr. Swierstra teaches courses related to functional programming, including "Advanced functional programming" and "Logic for Computer Science," helping to train the next generation of programming language researchers and practitioners.

• Programming Languages
• Software Engineering
• Artificial Intelligence
• +7 more

Pedro Manuel Rangel Santos Henriques is a Full Professor at the College of Engineering of Universidade do Minho , where he has been a teacher/researcher since 1981. He holds a PhD in Computer Science (1993) and a Habilitation in Informatics (2012). Research Focus : Programming Languages, Software Analysis, Augmented Reality for Education Teaching : Programming Languages, Compilers, Software Transformation His recent 15 publications (2022-2025) show expertise in: Augmented/Virtual Reality for Special Education Computational Thinking Development Biomedical Sensor Integration Program Analysis & Code Quality He has supervised 20 PhD students (17 completed) and over 95 MSc theses, while leading major international collaborations with institutions in Serbia, Argentina, Spain, and Czech Republic.

Xiaokang QiuView profile

Associate Professor

• Programming Languages
• Software Engineering
• Program Verification
• +6 more

Xiaokang Qiu is an Associate Professor at the Elmore Family School of Electrical and Computer Engineering , Purdue University , with a Ph.D. in Computer Science from University of Illinois at Urbana-Champaign (2013). His research focuses on Programming Languages and Software Engineering , particularly theories, algorithms, and tools for program synthesis, verification, and logic-based analysis. Research Interests His work addresses: Formal methods for heap-manipulating programs using separation logic Automated deduction and decision procedures for data structures Syntax-guided synthesis of concurrent and bit-vector programs Integration of machine learning with formal verification Scalable verification of hardware memory consistency Network protocol optimization through program synthesis Recent Publications Recent work includes PLDI 2025 on concurrent string synthesis, POPL 2024 on bit-vector synthesis, and POPL 2023 on comparative network design. His tools like STRAND and VCDryad enable automated verification of complex data-structure manipulations. Grants & Awards Recipient of: NSF SHF Small Award (2024, co-PI, $593K) NSF FMitF Award (2023, PI, $750K) Tenure at Purdue (2023) Professional Service Active in program committees for PLDI , POPL , CAV , and ATVA conferences. Developed tools like DryadSynth (PLDI 2020), ImpSynt (OOPSLA 2017), and JSketch (ESEC/FSE 2015).

Paolo G. GiarrussoView profile

Researcher

• Programming Language Theory
• Domain-Specific Languages
• Distributed Systems
• +5 more

Paolo G. Giarrusso is a researcher at the Institute for Programming Languages and Software Engineering within the Faculty of Informatics at the University of Tübingen . Previously, he was a Ph.D. student at the University of Marburg , where he defended his thesis Optimizing and Incrementalizing Collection Queries by AST Transformation in January 2018.

Yufei DingView profile

Associate Professor

• Compiler Technology
• Machine Learning
• Quantum Computing
• +3 more

Yufei Ding is an Associate Professor in the Computer Science & Engineering Department at the University of California, San Diego (UCSD), where she leads the PICASSO Lab. Her research spans domain-specific language design, architecture and compiler optimization, and hardware acceleration, with current focus on developing high-performance, energy-efficient, and high-fidelity programming frameworks for quantum computing and machine learning. Dr. Ding received her Ph.D. in Computer Science from North Carolina State University and a B.S. in Physics from the University of Science and Technology of China. Her interdisciplinary background bridges physics and computer science, enabling her to tackle challenges in emerging computing paradigms. Her research interests focus on Compiler Technology, Machine Learning, and Quantum Computing , with specific expertise in domain-specific language design, architecture and compiler optimization, and hardware acceleration. Dr. Ding's work addresses critical challenges in programming frameworks for emerging technologies, particularly in making quantum computing more accessible and efficient through innovative compiler techniques and runtime systems. Dr. Ding's scientific contributions have been recognized with prestigious awards including the NSF CAREER Award (2020) and the IEEE Computer Society TCHPC Early Career Researchers Award for Excellence in High-Performance Computing (2019) . As an active researcher and educator, Dr. Ding serves on program committees for major conferences including PLDI, PPoPP, and SPLASH. She currently has Ph.D. openings in quantum computing and machine learning systems research, as well as a postdoc position in quantum computing for physics Ph.D. candidates with relevant background. Dr. Ding founded and leads the PICASSO Lab at UCSD, which focuses on developing innovative solutions for programming emerging computing technologies. The lab's work bridges theoretical foundations with practical implementations to address real-world challenges in high-performance computing.

Arjun GuhaView profile

Associate Professor

• Programming Languages
• Program Synthesis
• Low-Resource Programming Languages
• +6 more

Arjun Guha is an Associate Professor at Northeastern University's Khoury College of Computer Sciences, where he also serves as the Area Chair for Software. He conducts research in programming languages with a focus on program synthesis for low-resource programming languages and understanding how computer science education is impacted by large language models. His work spans multiple domains including WebAssembly, software-defined networking, and serverless computing. Guha's research interests center around programming language design, implementation, and application. He has made significant contributions to understanding JavaScript through formal semantics (LambdaJS), developing functional reactive programming for web applications (Flapjax), and creating tools for software-defined networks (NetKAT, Frenetic). His recent work focuses on leveraging large language models for code generation in specialized programming languages and understanding their impact on programming education. His publications reveal a strong trend toward applying AI and machine learning techniques to programming language problems, particularly in code generation and understanding. The research spans from foundational programming language theory to practical applications in education and software development tools, with increasing focus on the intersection of programming languages and large language models. Guha has received several prestigious awards including the OOPSLA Most Influential Paper Award in 2019 for his work on Flapjax, an ACM SIGPLAN Research Highlight for his work on NetKAT, and a Best Student Paper Award. His research has been recognized for its foundational contributions to programming language theory and practical impact on software development. OOPSLA Most Influential Paper Award (2019) for Flapjax ACM SIGPLAN Research Highlight for A Fast Compiler for NetKAT Best Student Paper Award for Flapjax paper Guha advises numerous PhD, MS, and undergraduate students, with several alumni now at leading tech companies and academic institutions. His research has been supported by the National Science Foundation, the Department of Energy, the Office of Naval Research, and industry partners including Google, JPMorgan Chase, MathWorks, Meta, Oracle, and Roblox. He leads the Programming Research Laboratory at Northeastern and is actively involved in major research collaborations like the BigCode Project. Guha is a member of the Programming Research Laboratory at Northeastern and leads several major research initiatives including the BigCode Project's evaluation working group. His lab develops practical software systems like MultiPL-E (a polyglot benchmark for Code LLMs) and WasmFX (bringing effect handlers to WebAssembly), with applications in education, software development, and high-performance computing.

Gilbert BernsteinView profile

Assistant Professor

• Computer Graphics
• Programming Languages
• High-Performance DSLs
• +7 more

Gilbert Bernstein is an Assistant Professor in the Computer Science & Engineering department within the College of Engineering at the University of Washington. His research bridges computer graphics and programming languages, with a focus on high-performance domain-specific languages. Previously, he was a post-doctoral scholar at UC Berkeley and MIT working with Jonathan Ragan-Kelley, and received his PhD from Stanford University under Pat Hanrahan. His research interests span Computer Graphics, Programming Languages, High-Performance DSLs, Physical Simulation, Geometry & Topology, Differentiable Programming, Hardware DSLs, Tools for Artists, Fabrication, and Human-Computer Interaction. Bernstein develops languages and compilers that enable efficient computation for creative applications, physical simulations, and graphics rendering systems. His recent publications reveal strong trends in differentiable programming for graphics applications, domain-specific languages for hardware acceleration, and computational approaches to traditional crafts like quilting and knitting. His work consistently combines formal language theory with practical applications in graphics and fabrication. Bernstein actively mentors students across multiple institutions including current advisees Felix Hahnlein (UW Postdoc), Ryan Zambrotta (UW PhD), Haoran Peng (UW PhD), and previous students including Alex Reinking (UC Berkeley PhD 2022, now at Qualcomm) and MacKenzie Leake (Stanford PhD 2021, now at Adobe Research). His lab works on diverse projects including debugging CAD programs, compilers for finite element methods, semantics for knitting machines, algebraic scheduling of tensor programs, and exocompilers for hardware accelerators. Bernstein also collaborates on DSLs for networking, Counterstrike bots, gradient-based optimization, memory management, hardware design, and garment design tools.