Find Academic Supervisors

• Numerical Relativity
• Gravitational Wave Astronomy
• Computational Astrophysics
• +3 more

Zachariah Etienne is an Associate Professor in the Department of Physics at the University of Idaho , where he conducts cutting-edge research in Numerical Relativity and Gravitational Wave Astronomy . He holds a PhD in Physics from the University of Illinois (2009) and dual BS degrees in Mathematics and Physics from Indiana University (2003) . Etienne is actively involved in developing computational tools like NRPy+ and GRoovy to enhance simulations of compact binary systems and relativistic astrophysics phenomena. Education PhD, Physics, University of Illinois (2009) BS, Mathematics, Indiana University (2003) BS, Physics, Indiana University (2003) His research spans Numerical Relativity , General Relativity , and High-Performance Computing , with a focus on gravitational wave sources such as binary black holes , neutron star mergers , and direct collapse black holes . Etienne has contributed to the Einstein Toolkit ecosystem and projects like BlackHoles@Home , which aim to democratize access to numerical relativity simulations through open-source frameworks and consumer-grade hardware. Etienne’s work emphasizes code development, algorithmic efficiency, and collaborative research in numerical relativity. Projects like NRPyElliptic and superB highlight his efforts in solving elliptic equations and simulating high mass ratio black hole binaries, while his studies on debris disks and bar-mode instabilities explore astrophysical phenomena through computational models.

• Smart Textiles
• Occupational Safety
• Ergonomics
• +5 more

George Priniotakis is a Professor in the Department of Industrial Production Systems at the University of West Attica. He specializes in innovative textile technologies for multifunctional clothing, with a focus on medical and military applications, wearable sensors, and sustainable materials. His work bridges engineering, ergonomics, and environmental stewardship. University of Leeds – M.Sc. in Fiber Science and Technology (1992) University of Ghent – PhD in Conductive Textile Materials His research interests include: Smart systems using conductive textiles Risk management via wearable sensors Recyclable yarn development Innovative educational tools for engineering Professor Priniotakis has led numerous research projects as a scientific director and coordinator, participated in over 100 international conferences, and maintains an H-index of 12. He serves as President of the Organizing Committee for the biennial AITAE conference.

• Computer Science
• Electronic Engineering
• Integrated Circuit Testing
• +6 more

Marcello Dalpasso is an Associate Professor of Computer Science at the School of Engineering, University of Padova, Italy, and a member of the Department of Information Engineering. He has held this position since 2004 after serving as a researcher and teaching assistant at the same university from 1998. Born in Ferrara, Italy (1965) Graduated with highest honors in Electronic Engineering (1990), University of Bologna PhD in Electronic Engineering and Computer Science (1994), Rome His research focuses on integrated circuit testing , fault simulation , and algorithm design . He has developed techniques for IDDQ testing , bridging fault modeling , and Boolean satisfiability applications in digital systems. Other contributions include optimization algorithms for Traveling Salesman Problem (TSP) and efficient data structures. Recent publications highlight his work on Answer Set Programming for timing analysis, Python programming education , and TSP neighborhood exploration . His research spans both theoretical and applied domains, from hardware testing to software development and computational biology. He has co-authored textbooks on Computer Networks , Software Design , and Programming in Java/Python/C++ , serving as a key contributor to educational materials in computer science.

Ziyang LiView profile

Assistant Professor

• Neurosymbolic Programming
• Machine Learning
• Programming Languages
• +5 more

Ziyang Li is an Assistant Professor of Computer Science at Johns Hopkins University and a member of the Data Science and AI Institute. He holds a Ph.D. in Computer Science from the University of Pennsylvania (2025) and dual bachelor's degrees in Computer Science and Mathematics from UCSD (2019). Research Areas: Neurosymbolic Programming, AI4Code His research bridges programming languages and machine learning, focusing on neurosymbolic methods that combine symbolic reasoning with learning-based techniques. Applications span software security, computer vision, natural language processing, bioinformatics, and clinical decision-making. He developed Scallop , a neurosymbolic programming language, and Lobster , a GPU-accelerated framework for neurosymbolic applications, with impacts in cybersecurity and biomedical domains. Recent publications highlight neurosymbolic approaches for RNA structure prediction, Long COVID modeling, and safety-critical systems. His work emphasizes data-efficient learning, weak supervision, and hybrid AI for scalable reasoning. Scientific Awards : AWS Fellowship (2023) KPCB Fellows, Engineering (2018) NIH L3C Honorable Mention Award Li has mentored students including Jason Liu, Felix Zhu, and Eric Zhao, and served as Teaching Assistant for courses at UPenn and UCSD. He co-organized the TACPS Workshop and reviewed for NeurIPS, ICLR, and ICML.

Ofer ShayevitzView profile

Professor

• Information Theory
• Statistical Inference
• Data Science
• +3 more

Prof. Ofer Shayevitz is a faculty member at the School of Electrical Engineering , Tel Aviv University , holding the academic rank of Professor . He is affiliated with the Department of Systems and leads interdisciplinary research at the intersection of information theory , statistical inference , and data science . His research explores theoretical challenges in interactive communication , machine learning , and quantum information , with applications to communication complexity , graph analysis , and non-stationary environments . Notable work includes advances in high-dimensional regression , entropy estimation , and memory-constrained algorithms . The trends in his recent publications highlight information-theoretic bounds , statistical inference under constraints , and interactive protocols . His group has made significant contributions to quantum key distribution , planted graph detection , and guesswork analysis . Scientific awards include the Best Student Paper Award at ISIT 2020 . His research is supported by major grants from the Israel Science Foundation (ISF) , ERC Starting Grant , and Israel Innovation Authority . Prof. Shayevitz advises current PhD students Assaf Ben-Yishai , Uri Hadar , and Shahar Stein Ioushua , as well as M.Sc. students Inbar Pinsly and Oz Ben Hamo . Former advisees include faculty members at institutions like Kyushu University and University of British Columbia .

• 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.

• Machine Learning
• Software Testing
• Safety Critical Systems
• +7 more

Karl Meinke is a Professor at KTH Royal Institute of Technology, where he serves as Head of the Computer Science Department and Head of the Division of Theoretical Computer Science within the School of Electrical Engineering and Computer Science. His research focuses on applying machine learning techniques to software testing, particularly for safety-critical systems like autonomous vehicles and embedded systems. His research interests span machine learning, software testing, safety critical systems, embedded systems, autonomous driving, digital pathology, and graph neural networks. Meinke has developed innovative approaches like Learning-Based Testing that combine machine learning with formal methods for system validation. His work bridges theoretical computer science with practical applications in automotive systems and medical diagnostics. His recent publications show a strong trend toward applying graph neural networks to diverse domains including program analysis, digital pathology, and autonomous vehicle testing. His research demonstrates a consistent focus on solving the test oracle problem and generating meaningful test cases for complex systems where traditional testing approaches fall short. Meinke actively collaborates with Karolinska Institutet (KI), indicating interdisciplinary work between computer science and medical research. He is responsible for Masters level education in software testing at KTH and serves as examiner for several advanced courses including Degree Projects in Computer Science and Software Reliability. His research group has developed tools like LBTest for learning-based testing of reactive systems, and he has secured funding for projects such as the ITEA3 Testomat Project focused on next-level test automation. His work has significant implications for validating autonomous systems where safety is paramount. Meinke leads research in using machine learning to address fundamental challenges in software testing, particularly for systems where traditional test oracles are unavailable or impractical. His approach of combining active learning with formal specifications has created new pathways for validating complex cyber-physical systems.

Demba BaView profile

Associate Professor

• Computational Neuroscience
• Model-Based Deep Learning
• Signal Processing
• +3 more

Demba Ba is an Associate Professor of Electrical Engineering and Bioengineering at Harvard University's School of Engineering and Applied Sciences (SEAS). He serves as the Dean of Undergraduate Studies for Bioengineering since 2020 and joined SEAS in 2015 after a postdoctoral fellowship at MIT (2007-2014). His research bridges computational neuroscience and artificial intelligence, focusing on sparse signal representations, interpretable AI, and neural network theory. Fluent in Wolof, Fulani, French, Spanish, English, and Arabic, he also contributes to signal processing, statistical learning, and dynamic systems. PhD in EECS from MIT (2011) MS in EECS from MIT (2006) BS in Electrical Engineering from University of Maryland (2004) His work explores connections between sparse coding and neural networks through publications in top venues like NeurIPS, ICML, and IEEE Transactions. Articles emphasize convolutional dictionary learning, Bayesian frameworks, and applications to neural data analysis. He has received the 2016 Sloan Fellowship in Neuroscience and 2021 Roslyn Abramson Award for undergraduate teaching excellence. 2021: Gaussian process convolutional dictionary learning (Submitted) 2020: Deep residual auto-encoders for dictionary learning 2018: Multitaper time-frequency analysis for neuroscience Demba Ba leads the CRISP research group and holds advisory roles at Harvard. His awards include: 2021 Roslyn Abramson Award 2016 Alfred P. Sloan Foundation Fellow 2010 ICME Best Student Paper Award He teaches courses like ES 201 (Decision Theory) and ES 157 (Biomedical Signal Processing), while maintaining interdisciplinary collaborations in neuroscience, machine learning, and signal processing.

• Embedded Systems
• Compartmentalization
• Firmware
• +5 more

Arslan Khan is an Assistant Professor specializing in Computer Science and Engineering. His research focuses on enhancing security for embedded systems and industrial control systems through innovative techniques like symbolic differentiation and privilege separation. Research Interests: Embedded Systems, Compartmentalization, Firmware Security, Software Guard Extensions, Fuzzing, and Industrial Control Systems Security. Recent publications (2023-2024) highlight his work on kernel isolation, enclave fuzzing, and state-aware attack detection. These studies emphasize reducing attack surfaces and improving security for resource-constrained devices. While no specific awards or students are mentioned, his contributions to security frameworks and binary analysis are evident through multiple conference proceedings in high-impact venues like USENIX and IEEE Security & Privacy.

• Computer Science
• Cybersecurity
• Internet of Things
• +5 more

Gang Tan is an Associate Professor at the Pennsylvania State University's College of Engineering, Department of Computer Science and Engineering. He also holds the James F. Will Career Development Professorship and is affiliated with the Institute for Computational and Data Sciences (ICDS). His research focuses on binary reverse engineering , cybersecurity , Internet of Things (IoT) security , machine learning fairness , and information flow security . He has led numerous NSF-funded projects, including work on precise binary analysis, IoT policy enforcement, and automated fairness repair in AI systems. Recent work trends include memory safety validation , pseudocode extraction , and control-flow integrity mechanisms. His 127+ research outputs reflect deep engagement with static program analysis , cache side-channel detection , and secure kernel-driver interfaces . Scientific Awards: James F. Will Career Development Professorship Gang Tan has secured multiple grants from the National Science Foundation (NSF) and U.S. Navy for projects like Sliver (information flow verification) and Semantics-Directed Binary Reverse Engineering . His work involves advising teams on IoT safety, and he has 19 active or completed grants since 2008.

Rolf DrechslerView profile

Professor

• Formal Verification
• Quantum Computing
• RISC-V Architecture
• +4 more

Rolf Drechsler is a Full Professor and Head of the Group of Computer Architecture at the University of Bremen's Institute of Computer Science since 2001, and Director of the Cyber-Physical Systems Group at DFKI Bremen since 2011. He holds an adjunct professorship at the Indian Statistical Institute and has been affiliated with Duke University. Education: Diploma (1992) and Dr. phil. nat. (1995) in Computer Science from Goethe University Frankfurt Academic Leadership: Dean of Mathematics and Computer Science Faculty (2018-2025), Vice Rector for Research (2008-2013) His research focuses on formal verification , RISC-V architectures , and quantum/in-memory computing . Recent work explores LLM integration in hardware testing and polynomial-based verification techniques. Publications from 2024-2025 span IEEE Transactions , DATE , and DAC , emphasizing automated verification , quantum circuit mapping , and LLM-driven testbench generation . Scientific Awards IEEE/ACM Best Paper Awards (2013, 2018) Berninghausen-Preis for Innovative Teaching (2018) IEEE Fellow (2015) Founder Award for Solvertec (2013) He has served on program committees for DAC, ICCAD, DATE, and founded graduate schools in Embedded Systems and System Design under Germany's Excellence Initiative.

• Software Engineering
• Computer Engineering
• Formal Methods
• +4 more

Kamkin Alexander Sergeevich is an Associate Professor at the National Research University Higher School of Economics (HSE) and the Institute for System Programming named after V.P. Ivannikov of the Russian Academy of Sciences (ISP RAS). Affiliated with the Faculty of Computer Science and the Moscow Institute of Electronics and Mathematics, he specializes in software and computer engineering with a focus on formal methods for program and microprocessor verification. Education: Candidate of Physical and Mathematical Sciences (2009), Moscow State University (2003) in Applied Mathematics and Computer Science Research Areas: Formal methods, program verification, microprocessor verification, model-based testing, static analysis His recent publications highlight the application of formal specifications in test program generation for architectures like RISC-V and ARMv8, emphasizing simulation modeling and ISA specification maintenance. Key trends include the integration of constraint-based testing, formal modeling, and automated verification tools. Kamkin supervises students in software engineering and collaborates with colleagues such as Tatarnikov A.D., Protsenko A.S., and Chupilko M.M. At HSE, he has taught courses including Software Verification (Master's, 09.04.04 Software Engineering) and High-Level and Simulation Modeling of Digital Systems (Bachelor's, 09.03.01 Computer Science and Engineering). His work is associated with the MicroTESK framework, which automates test generation for microprocessors.

• computer security
• compiler technologies
• parallel programming
• +2 more

Padaryan Vartan Andronikovich serves as an Associate Professor at the Faculty of Computer Science of the National Research University Higher School of Economics (HSE University) since 2022. He concurrently holds positions at the Basic Department "System Programming" of the Institute for System Programming named after V.P. Ivannikov of the Russian Academy of Sciences (ISP RAS), as Associate Professor at Moscow State University's Faculty of Computational Mathematics and Cybernetics since 2010, and as Head of the Laboratory of Reverse Engineering of Binary Code at ISP RAS since 1999. Since 2018, he has chaired Subcommittee 4 "Secure Software Development" of Technical Committee for Standardization 362 "Information Security". Education: 2005: Candidate of Physical and Mathematical Sciences 2000: Specialty in "Applied Mathematics" from Moscow State University, qualification "Mathematician. Systems Programmer" Dr. Padaryan's research centers on binary code analysis and security applications. His expertise spans computer security, compiler technologies, parallel programming, binary code analysis, and emulation and virtualization. His work addresses critical challenges in understanding and securing software systems when source code is unavailable, with particular focus on reverse engineering techniques, vulnerability detection, and secure software development methodologies. His research bridges theoretical computer science with practical security applications, contributing to innovative tools for binary code analysis. His publication record reveals a strong trajectory in binary code analysis, with consistent contributions to algorithm extraction, intermediate representation recovery, and security vulnerability detection. The interdisciplinary nature of his work combines programming languages, software engineering, and cybersecurity to develop practical solutions for analyzing and securing software systems. His research has evolved to address increasingly complex security challenges, particularly in network interface testing and data leakage detection in binary environments. Dr. Padaryan actively contributes to academic and professional communities through teaching and leadership roles. He teaches "Computer Architecture and Assembly Language" for Software Engineering bachelor's students at HSE University across multiple academic years. His laboratory leadership at ISP RAS has fostered significant research output, while his standardization committee role demonstrates influence in shaping secure software development practices in Russia. His laboratory work focuses on developing advanced security tools including the "LAKMUS" system for labeled data analysis and the "PROTOSPHERE" system for network traffic analysis. These tools represent practical applications of his research in real-world security scenarios, enabling security professionals to detect vulnerabilities and analyze software behavior without access to source code.

• Distributed Computing
• Distributed Systems
• Computer Security
• +4 more

Giuseppe Antonio Di Luna is an Associate Professor at the Dipartimento di Ingegneria Informatica, Automatica e Gestionale (DIAG), Sapienza University of Rome . His research spans critical areas of Distributed Computing, Distributed Systems , and Computer Security , with a focus on Dynamic Networks, Mobile Agents, Anonymous Communication , and NLP techniques applied to binary analysis . Current research themes include anonymity in distributed environments Security challenges in confidential computing Algorithm design for mobile robots and dynamic networks Applying NLP to enhance binary analysis security His recent publications across top venues like DSN, EuroS&P, and JPDC reflect these interdisciplinary interests, with notable work on: Black hole detection in dynamic rings Robustness of binary similarity systems Confidential virtual machine evaluation tools Self-stabilizing computation in anonymous networks He has received prestigious awards including the Axa Fellowship (2020-2022) , ASPLOS 2019 Distinguished Paper Award , and DIMVA 2019 Best Paper Runner-Up . His collaborative efforts include organizing the EuroSys 2023 conference in Rome.

• Embedded Systems
• Real-Time Systems
• System-on-Chip Design
• +7 more

Adriano José Conceição Tavares is an Associate Professor at the School of Engineering, University of Minho, Portugal. He serves as a Senior Researcher at Centro ALGORITMI, where he is affiliated with both the IE R&D Group and the ESRG R&D Lab. His academic credentials include a PhD in Industrial Electronics from the University of Minho, a Master of Science in Information Technology from the University of Coimbra, and an undergraduate degree in Informatics from the University of Coimbra. Professor Tavares specializes in embedded systems with particular expertise in: Embedded systems modeling and design System software design System-on-chip design Real-time operating systems Hardware acceleration and FPGA design Virtualization for embedded systems IoT frameworks and protocols His publication record demonstrates a strong research trajectory in hardware-software co-design, with recent work showing an increasing integration of machine learning techniques into embedded systems. His publications span theoretical frameworks to practical implementations addressing real-time performance, resource constraints, and security challenges. Among his scholarly metrics: h-index of 18 126 publications with 1148 citations 20 publications in Q1/Q2 journals Author of a book on microcontroller programming Professor Tavares has supervised students including Miguel Ângelo Fernandes Silva and has established international collaborations through the Erasmus Program with institutions in China, Iran, Thailand, Jordan, and Cambodia. He teaches advanced courses on embedded and real-time systems modeling, compiler design, system-on-chip design, real-time operating system design, and advanced computer architectures at University of Minho.