Find Academic Supervisors

Miriam LeeserView profile

Professor

• Computer Architecture
• Reconfigurable Computing
• Hardware Acceleration
• +4 more

Miriam Leeser is Professor of Electrical and Computer Engineering at Northeastern University, where she has served since 1996 as head of the Reconfigurable Computing Laboratory and member of the Computer Engineering group. She holds senior membership in ACM, IEEE, and SWE. Education: BS in Electrical Engineering, Cornell University Diploma in Computer Science, Cambridge University PhD in Computer Science, Cambridge University Her research pioneers hardware acceleration using FPGAs and GPUs, with breakthroughs in floating point implementations, medical imaging algorithms, and privacy-preserving data processing frameworks. She actively bridges theoretical computer architecture with practical applications in unsupervised learning systems and embedded computing solutions. Scientific Awards: NSF Young Investigator Award Fulbright Scholar Award Professor Leeser has secured continuous research funding from DARPA, NSF, Google, MathWorks, and Microsoft. As an active academic mentor, she guides graduate researchers through her laboratory while serving as Section Editor for PeerJ Computer Science in computer architecture and distributed systems. Her editorial contributions span emerging areas including neural network accelerators and IoT security. She directs the Reconfigurable Computing Laboratory (RCL), which develops innovative FPGA/GPU architectures for high-performance computing applications across medical imaging and machine learning domains.

• Formal Verification
• Software Engineering
• Computer-Aided Verification
• +5 more

Zvonimir Rakamaric is a Professor in the Department of Computer Science at the University of Utah. His primary research focuses on formal methods, software verification, and numerical analysis, with an emphasis on ensuring correctness and reliability in software systems. He leads efforts in developing tools and techniques for verifying complex systems, including formal verification of floating-point arithmetic, compiler optimizations, and concurrent/distributed systems. Key Roles: Formal Methods Researcher, Software Verification Specialist, Floating-Point Arithmetic Expert Affiliations: University of Utah School of Computing Research interests span formal verification tools (e.g., SMACK, Dafny), static analysis, and numerical error analysis. His work addresses challenges in safety-critical systems, including embedded software, high-performance computing, and compiler optimizations. Collaborations include institutions like Sandia National Labs and Microsoft Research. Publications emphasize rigorous methods for analyzing floating-point precision, equivalence checking, and tool development for formal verification. His work bridges theoretical foundations with practical tool implementations, contributing to open-source verification frameworks widely adopted in academia and industry.

• Cryptographic Hardware Design
• Homomorphic Encryption
• Approximate Computing
• +4 more

Dr. Sunwoong 'Sunny' Kim is an Assistant Professor at the Rochester Institute of Technology (RIT), affiliated with the Kate Gleason College of Engineering and the Global Cybersecurity Institute. Previously, he held roles at the University of Washington Bothell (2019–2023) and completed postdoctoral research at the University of Illinois Urbana-Champaign (2017–2019) and Seoul National University (2016–2017). His expertise spans cryptographic hardware design, homomorphic encryption, and privacy-preserving systems, with a focus on FPGA-based accelerators. Education: Ph.D. (2016), M.S. (2012), B.S. (2010) in Electrical Engineering and Computer Science from Seoul National University, advised by Prof. Hyuk-Jae Lee. Research interests include custom hardware accelerators for cryptography, machine learning, and image/video compression. He explores approximate computing and privacy-preserving applications in embedded systems. Recent trends in his articles emphasize homomorphic encryption implementations, efficient cryptographic primitives, and FPGA-based solutions for secure computing. Scientific Awards: NSF CRII Grant (2021), RIT SURF (2024), RIT FEAD (2024), UW RRF Grant (2020), and multiple student scholarships. Advising & Grants: Supervised students including Wei He, Grant Bosworth, and James. Active in securing grants as PI/Co-PI (e.g., IITP Grant, FEAD Grant). Collaborates with labs like CryptoLab in South Korea. Labs/Teams: Leads research in cryptographic hardware acceleration and cybersecurity at RIT, with collaborations on FPGA-based secure systems and privacy-preserving AI.

Christian HeidornView profile

Researcher

• Deep Learning
• Hardware-Software Co-Design
• Neural Networks
• +5 more

Christian Heidorn is a Researcher at the Chair of Computer Science 12 (Hardware-Software Co-Design) within the Department of Computer Science at Friedrich-Alexander University Erlangen-Nürnberg (FAU), Germany. He has held this position since 2018 and teaches "Fundamentals of Computer Engineering" regularly across multiple semesters. His educational background includes an M.Sc. in Medical Engineering (2015-2018) and a B.Sc. in Medical Engineering (2012-2016), both from FAU. Born in 1989 in Dachau, Germany, he maintains an office in Room 02.128 at Cauerstr. 11, Erlangen. Dr. Heidorn's research focuses on the Application of Deep Learning on Tightly Coupled Processor Arrays and Invasive Computing . His work bridges medical engineering with computer architecture, particularly emphasizing neural network deployment on specialized hardware. His research projects include OpTC and KISS Invasive Computing, which optimize neural networks for embedded systems and processor arrays. His recent publications demonstrate a strong trend toward efficient neural network deployment on embedded systems, with emphasis on automotive applications (AURIX microcontrollers), hardware-aware neural network pruning, and processor array optimization. His work spans both theoretical neural architecture search and practical implementations for real-world applications. His notable scientific contributions include: Development of the OpTC toolchain for neural network deployment on automotive microcontrollers Hardware-aware evolutionary filter pruning techniques for CNNs ALPACA: An accelerator chip design for nested loop programs Efficient mapping of CNNs onto tightly coupled processor arrays Dr. Heidorn has supervised numerous Master's and Bachelor's theses on topics ranging from neural network compression to robotic hand control using EMG data and processor array optimization. His advising reflects his dual expertise in medical engineering and computer architecture, with many projects focusing on practical embedded applications of deep learning. He is actively involved in research projects related to invasive computing and tightly coupled processor arrays, with a particular focus on making deep learning more accessible on resource-constrained devices for automotive and medical applications.

James GarlandView profile

Lecturer

• Machine Learning
• Embedded Systems
• FPGA/ASIC Design
• +4 more

Dr. James Garland is a Lecturer in the Department of Electronic Engineering and Communications at South-East Technological University (SETU), part of the engCORE academic unit. He holds a PhD in Computer Science from Trinity College Dublin (2022). His research focuses on micro-architectural implementations of deep learning algorithms in FPGA/ASIC/embedded systems, with applications in robotics and aerospace engineering. Key areas include defect detection via unmanned aircraft systems, image classification, and GAN-based enhancement for license plate recognition. Education: PhD in Computer Science (Trinity College Dublin, 2022). Research interests span machine learning optimization, embedded systems design, and aerospace robotics. His work emphasizes low-complexity hardware solutions for high-performance computing and energy efficiency. Recent articles explore topics like super-resolution GANs, zero-shot learning in pre-trained models, and spatial mapping for aircraft defect localization. He actively contributes to peer-reviewed journals, including Journal of Universal Computer Science and IEEE Access . Media coverage includes collaborations with the Irish Department of Aerospace and Mechanical Engineering. Teaching responsibilities include courses on artificial intelligence, control systems, and avionics fundamentals.

Ben JonesView profile

Associate Professor

• Computer Graphics
• Physical Simulation
• Cybersecurity
• +3 more

Ben Jones is an Associate Professor and Lecturer at the Kahlert School of Computing, University of Utah. His research focuses on computer graphics, physical simulation, and cybersecurity, with notable contributions to DNS censorship analysis and network reliability. He is affiliated with the Kahlert School of Computing and actively publishes in interdisciplinary domains. Research Interests: His primary areas include computer graphics, physical simulation, and cybersecurity. Notable work spans internet governance via DNS censorship studies and medical pharmacology through drug efficacy analyses. His physics-based animation research has advanced rigid body dynamics and fracture mechanics modeling. Articles Trends: Recent work (2023) addresses global censorship techniques and medical treatment protocols, while earlier studies (2016-2017) focused on computational physics and environmental contamination. A recurring theme is interdisciplinary research bridging computer science, medicine, and environmental science. No scientific awards explicitly mentioned No advising/grant information available No lab/teams information provided

• Numerical Analysis
• Linear Algebra
• Optimization
• +7 more

Michael L. Overton is a Silver Professor of Computer Science and Mathematics at the Courant Institute of Mathematical Sciences, New York University. His research focuses on numerical analysis, optimization, linear algebra, and mathematical programming. He holds a Ph.D. in Computer Science from Stanford University (1979) and has been a faculty member at NYU since then. Education: B.Sc. (Honors) in Computer Science from the University of British Columbia (1974), M.S. and Ph.D. in Computer Science from Stanford University (1977, 1979). Research Interests: Overton's work centers on numerical algorithms, eigenvalue optimization, nonsmooth optimization, and semidefinite programming. He has contributed to software tools like HIFOO and GRANSO for optimization and control problems. Awards: Fellow of SIAM and Fellow of the IMA. Advising: Supervised numerous Ph.D. and M.S. students, including Azam Asl, Tim Mitchell, and Mert Gürbüzbalaban. Grants: Research supported by the National Science Foundation from 1981 to 2024. Labs/Teams: Active in software development for optimization, control, and linear algebra. Involved in editorial roles for journals like SIAM, Numerische Mathematik, and Foundations of Computational Mathematics.

• Automated Reasoning
• Automated Verification
• Formal Methods
• +12 more

Dr Martin Nyx Brain is a researcher at City St George's, University of London, affiliated with the Department of Computing within the College of Engineering, Science and Technology. His work focuses on advancing automated reasoning and formal verification techniques for ensuring software safety and security, particularly in critical low-level and embedded systems. His research spans core areas in formal methods, including the development and application of SAT and SMT solvers. He is a co-author of the SMT-LIB standard for floating-point arithmetic and has contributed to the CVC4 solver's floating-point theory, demonstrating deep expertise in logical foundations and solver technology. He applies abstract interpretation, symbolic execution, and deductive verification to analyze systems written in C, C++, and Ada, using tools such as SPARK and CBMC/CPROVER. Dr Brain’s work bridges theoretical logic and practical software verification, with strong relevance to cybersecurity and safety-critical systems. His research interests include formal verification, static analysis, programming language semantics, and automated theorem proving. While no specific publications or awards are listed in the provided text, his technical contributions suggest an active research trajectory in the intersection of programming languages, logic, and software reliability. He is involved in tool development and standardization efforts that are influential in the formal methods community. There is no mention of student supervision, grants, or educational background in the available information. However, his role implies engagement with research teams or labs focused on software verification and automated reasoning, potentially contributing to collaborative projects in formal methods and secure systems engineering.

Cesar MunozView profile

Researcher

• Formal Methods
• Theorem Proving
• Programming Languages
• +2 more

Cesar Munoz is a research scientist at the National Aeronautics and Space Administration (NASA), specifically affiliated with NASA Langley Research Center as indicated by his institutional profile at shemesh.larc.nasa.gov. He maintains an active research profile in formal methods with significant contributions to programming languages and verification communities. His research focuses on: Formal Methods for system verification Theorem proving and logical frameworks Programming language semantics and type theory Analysis of numerical programs and floating-point arithmetic Munoz's recent publications demonstrate expertise in temporal logic formalizations, runtime assurance frameworks, and floating-point error analysis. His work bridges theoretical computer science with practical applications in safety-critical aerospace systems where correctness is paramount. He has served on program committees for prestigious conferences including: CPP (Certified Programs and Proofs) - 2025, 2024, 2020 NASA Formal Methods (NFM) - 2024, 2023 Principles of Programming Languages (POPL) - multiple years Munoz maintains active contributions to open-source formal methods tools through GitHub repositories related to theorem proving and verification frameworks, demonstrating commitment to both theoretical advances and practical tool development in the formal methods community.

Yu WangView profile

Researcher

• Compiler Optimization
• Concurrency Analysis
• Static Analysis
• +6 more

Yu Wang is affiliated with Nanjing University (China). The person actively contributes to programming language and software engineering research through publications and committee roles. Published papers on compiler optimization, concurrency analysis, and AI-driven program analysis Served on program committees for SPLASH, ICSME, and PLDI Research focuses on compiler verification , static analysis , and deep learning applications in software engineering . Key trends in their publications include: Algorithmic optimization for floating-point constraints Concurrency modeling in Go programming Adversarial robustness in code analysis models Yu Wang's committee memberships demonstrate leadership in academic peer review processes across multiple conferences.

Michal OutrataView profile

Assistant Professor

• Numerical Linear Algebra
• Krylov Methods
• Domain Decomposition Methods
• +5 more

Michal Outrata is an Assistant Professor in the Department of Numerical Mathematics at Charles University's Faculty of Mathematics and Physics in Prague, Czech Republic. He began his position in fall 2024 after completing a postdoctoral fellowship at Virginia Tech with Prof. Eric de Sturler and earning his PhD under Prof. Martin Gander at the University of Geneva, where his thesis was awarded the Henri Fehr Prize in 2023. His academic journey started with undergraduate studies in Prague under Prof. Zdeněk Strakoš (bachelor) and Prof. Miroslav Tůma (master). Dr. Outrata's research focuses on understanding why certain numerical methods work for specific problem classes, with emphasis on numerical linear algebra , Krylov subspace methods , and domain decomposition techniques . His work bridges theoretical analysis with practical algorithm development, particularly in preconditioning strategies for iterative solvers. Current projects include the Primus Research Programme (2025-2028) titled 'Divide, Conquer and Optimize: Domain Decomposition Methods in Scientific Computing' which explores hierarchical matrix formats and mixed precision computations for optimizing domain decomposition methods. His publication record demonstrates consistent contributions to top journals including SIAM Journal on Scientific Computing and Linear Algebra with Applications. His research shows progression from foundational work on GMRES convergence to sophisticated analyses of block Runge-Kutta preconditioners and optimized Schwarz methods with data-sparse transmission conditions. His recent work increasingly integrates hierarchical matrix formats and explores mixed precision computing approaches. Swiss Government Excellence Scholarship (3-year award) Henri Fehr Prize for best PhD thesis in mathematics (2023) Primus Research Programme grant (2025-2028) Dr. Outrata actively mentors students and postdocs, currently supervising Marouan Handa and Lenka Ptáčková (PostDocs) along with undergraduate researchers. He teaches courses including Numerical Analysis and Introduction to Numerical Mathematics at Charles University. His collaborative network spans international institutions including University of Geneva, Virginia Tech, and various European research centers. He is also involved in organizing major conferences including DD29 and GAMM95.

• Cyber-Physical Systems
• Interval Analysis
• Robotics
• +5 more

Alexandre Chapoutot is a Lecturer and researcher at ENSTA Paris within the Computer Science and Systems Engineering Unit (U2IS). He leads the Semantics of Hybrid Systems (SSH) research group and serves as Head of the 2nd year specialization of the engineering cycle in Computer Science. His academic work bridges theoretical computer science with practical engineering applications, focusing on the rigorous analysis and verification of complex systems. Chapoutot's research interests span the verification of cyber-physical systems, with a strong emphasis on interval analysis and mobile robotics. He has made significant contributions to the static analysis of programs through abstract interpretation, particularly for improving the accuracy of floating-point arithmetic calculations. His work bridges theoretical computer science with practical engineering applications, developing methods that ensure the reliability and safety of autonomous systems. The research has direct applications in robotics, control systems, and safety-critical embedded software where numerical precision and system correctness are paramount. His recent publications reveal a strong trend toward the integration of formal methods with practical robotics applications. The work spans theoretical foundations in hybrid systems verification, practical implementations for robotics navigation and control, and innovative approaches to combining different verification techniques. A significant portion of his recent work focuses on Signal Temporal Logic applications, trajectory optimization, and the development of verified algorithms for autonomous systems. The publications demonstrate a consistent progression from theoretical foundations to practical implementations with real-world robotics applications. Chapoutot actively supervises multiple PhD students working on cutting-edge research topics at the intersection of formal methods and robotics. His research group collaborates extensively with other institutions and researchers, as evidenced by the numerous co-authored publications across various domains. He contributes to the development of the DynIbex software library, which provides validated solutions for ordinary differential equations using Runge-Kutta methods. His laboratory work centers around the Semantics of Hybrid Systems (SSH) group, which develops theoretical frameworks and practical tools for the analysis and verification of cyber-physical systems. The group's research spans formal methods, numerical analysis, and robotics, with a strong emphasis on developing mathematically rigorous approaches that can be applied to real-world engineering problems.

Guy EvenView profile

Researcher

• Computer Science
• Mathematics
• Approximation Algorithms
• +4 more

Guy Even is a prominent researcher in Computer Science and Mathematics, with significant contributions to approximation algorithms, graph theory, and network design. His work spans theoretical and applied domains, including algorithmic complexity, Steiner tree problems, and IEEE floating-point arithmetic optimization. Research Interests: His research focuses on combinatorial optimization, generalized connectivity in graphs, and efficient algorithm design. He has advanced methodologies for divide-and-conquer strategies, feedback set approximation, and spreading metrics in undirected and directed graph problems. Key Article Trends: His publications from 1992 to 2013 highlight advancements in approximation algorithms for NP-hard problems, with a particular emphasis on Steiner networks (undirected/directed), Boolean network observability, and hardware-efficient rounding techniques for floating-point arithmetic. These works bridge theoretical insights with practical applications in computing. Collaborations: Guy has collaborated with notable researchers such as Chandra Chekuri, Anupam Gupta, and Danny Segev. His co-authorship network includes Moti Medina (71 publications), Nissim Halabi (14 publications), and other leading scholars in algorithmic and computational fields. Notable Affiliations: While specific institutions are not explicitly stated in the provided text, his work is cited in prestigious venues like ACM, IEEE, and Springer, indicating strong ties to academia and research communities.

Martin NowackView profile

Researcher

• Symbolic Execution
• Software Testing
• Program Analysis
• +3 more

Martin Nowack is a researcher at Imperial College London specializing in symbolic execution and software testing. He has been actively contributing to the field through conference publications and program committee service since 2019. His research focuses on advancing symbolic execution techniques, particularly through the KLEE framework. His work addresses key challenges in making symbolic execution practical for real-world applications, including memory management, handling of floating-point operations, and deterministic state-space exploration. His contributions have appeared in top software engineering venues including ASE, ISSTA, and ECOOP. Dr. Nowack has served in various organizational roles at major conferences, including as Program Committee member for ASE 2024 and ASE 2025, co-chair for KLEE 2024, and session chair for multiple conference tracks. His recent work shows a consistent focus on improving the effectiveness and practicality of symbolic execution for software verification and testing.

Jay P. LimView profile

Lecturer

• Computer Science
• Numerical Analysis
• Floating Point Arithmetic
• +3 more

Jay P. Lim is a Lecturer in the Department of Computer Science at Yale University. His research focuses on numerical methods for high-precision mathematical computations, particularly in the design and implementation of correctly rounded math libraries for various floating-point representations. He specializes in polynomial approximation techniques, error detection in numerical computations, and compiler optimizations for cryptographic libraries. His work emphasizes cross-platform compatibility, algorithm efficiency, and formal verification of mathematical functions. Research areas include floating-point arithmetic standards, posit number systems, and the application of CORDIC algorithms for trigonometric functions. Notable contributions include RLIBM-Prog and RLIBM-ALL frameworks for generating high-performance math libraries, as well as tools for debugging numerical errors in posit-based computations. His publications span topics from 32-bit floating-point optimizations to compiler-level security verifications. Currently affiliated with Yale's Computer Science Department, his office is located at Dunham 427, 10 Hillhouse Avenue, New Haven, CT. No specific lab or team affiliations are explicitly mentioned in the provided materials.