Xueyuan Michael Han-Vanbastelaer is an Assistant Professor in the Department of Computer Science at Wake Forest University , focusing on systems-level security and privacy research. His work integrates data provenance, machine learning, and distributed systems to develop robust security mechanisms. Ph.D. in Computer Science (2022) from Harvard University Master of Science in Computer Science (2022) from Harvard University B.Sc. in Computer Science (2015) from University of California, Los Angeles His research interests span systems security , privacy , data provenance , and graph analysis , where he develops operating system infrastructures, language-level frameworks, and algorithms to enhance system transparency and detect sophisticated attacks. His publications reveal a trend of advancing provenance-based security solutions, including intrusion detection systems like KAIROS and Unicorn, eBPF security frameworks like SafeBPF, and data deletion techniques like Splice. He has contributed to 15 major publications since 2016, with recent work (2024–2025) focusing on hardware-assisted kernel security and real-time provenance auditing. His teaching includes courses like CSC 111: Introduction to Computer Science and CSC 250: Computer Systems I , covering programming fundamentals, system architecture, and memory management.
Emilio Jesús Gallego Arias is a non-tenured Research Fellow at the French National Center for Scientific Research (CNRS), hosted at the Institute of Fundamental Computer Research (IRIF) of CNRS and University of Paris Cité. He is also a member of the PiCube Inria team. Previously, he held postdoctoral positions at the University of Pennsylvania (2012–2014) and MINES ParisTech (2014–2019). His research spans mechanically-verified functional and logic programming , with a focus on the Coq proof assistant and the Mathematical Components Library . He develops tools like coq-lsp (language-server for Coq IDEs) and jsCoq (web interface), replacing earlier projects like SerAPI . His work bridges programming language theory , digital signal processing , and formal verification , particularly in the ANR FEEVER project for verifying Faust programs. His 15 most recent works (2014–2024) address type systems , differential privacy , and formal verification in domains like audio processing and mechanism design . Publications span journals (e.g., Journal of Privacy and Confidentiality), conferences (ICML, POPL, FARM), and workshops (CoqPL, UITP). He contributes to open-source projects (GitHub), including DFuzz (linear dependent types), DualQuery (privacy algorithms), and RAM (relational machine). He uses formal methods in collaborative development platforms (Gitter, GitLab) and advocates for free software and accessible audio technology .
Kathleen Fisher is an Adjunct Professor in the Computer Science Department at Tufts University and currently serves as the Director of the Information Innovation Office at DARPA. She previously held roles as Professor and Department Chair at Tufts (2016-2021), Program Manager at DARPA, and Principal Member of Technical Staff at AT&T Labs Research. Her academic journey began with a PhD in Computer Science from Stanford University. Kathleen’s research focuses on advancing programming languages through domain-specific languages (DSLs), program synthesis, and formal methods. Her work addresses challenges in ad hoc data management, secure systems, and integrating machine learning with programming language design. Notable projects include the Hancock and PADS systems for data processing, Forest for filestore management, and verified parser generators. She has received prestigious accolades, including ACM Fellow, Hertz Foundation Fellow, and SIGPLAN Distinguished Service Award. Her service includes leadership roles in ACM SIGPLAN, CRA-W, and as General Chair for ICFP 2015. Kathleen has advised PhD student Matt Ahrens and led impactful DARPA programs like HACMS and PPAML. As co-founder of the Programming Language Mentoring Workshop (PLMW), she actively contributes to diversity initiatives in computer science. Her research group, TuPL, explores DSLs, program synthesis, and language-based security, maintaining projects such as Autobahn and PADS.
Coen De Roover is a Professor at the Software Languages Lab (SOFT) of Vrije Universiteit Brussel (VUB) since October 2015, where he leads the Code Analysis and ManiPulation (CAMP) subgroup. His research focuses on program analysis design and its applications to software quality, including soft verification of contracts, incremental abstract interpretation, vulnerability detection in infrastructure code, and mining change patterns in commits. Key Research Areas: static analysis, dynamic analysis, mining software repositories, software engineering tools, security, and empirical studies. Conference Roles: Organizing Committee Chair for ECOOP Academy (2026), Steering Committee Member for GPCE, Program Committee Member for ICFP, SANER, and VMCAI, and Session Chair for multiple research tracks. Notable Contributions: Publications on WebAssembly analysis, Ansible security, concolic testing, and abstract interpretation frameworks. He also serves as Programme Director for the Bachelor in Computer Science at VUB since 2019-2020.
Bernardino D'Amico is an Associate Professor at Edinburgh Napier University's School of Computing Engineering and the Built Environment. With a background in architecture and computational structural design, he focuses on sustainable building practices with particular expertise in embodied carbon analysis, timber engineering, and innovative construction systems. His research bridges the gap between computational methods and practical sustainable construction solutions. Laurea Magistrale (Master's) in Architecture, University of Naples Federico II (2010) PhD in Computer-Aided Methods for Free-Form Grid-Shell Structural Systems, Edinburgh Napier University D'Amico's research centers on computational structural design and optimization for sustainable buildings. His work explores embodied carbon quantification, material efficiency in structural systems, and innovative use of sustainable materials like timber and bamboo. He investigates how computational approaches can reduce environmental impacts while maintaining structural integrity and functionality. His research has significant implications for decarbonizing the construction sector through data-driven design methodologies. His publication record shows a strong focus on embodied carbon assessment methodologies, sustainable material applications, and whole-life carbon analysis of buildings. Recent work includes probabilistic approaches to carbon prediction, bamboo-based construction systems, and critical analyses of net zero building frameworks. His research increasingly incorporates machine learning techniques to improve accuracy in early-stage design carbon estimation. D'Amico has received significant research funding from diverse sources including the Royal Academy of Engineering, Construction Industry Council of Hong Kong, Innovate UK, and the Scottish Government for projects addressing sustainable construction challenges. His work spans from material-level innovations to system-wide analyses of building stock carbon emissions. He actively supervises numerous PhD students working on cutting-edge sustainability topics including carbon neutrality verification, material substitution with hemp insulation, bamboo-timber composites, and embodied carbon responsibility frameworks. His supervision portfolio demonstrates his commitment to developing the next generation of sustainability-focused researchers in the built environment sector. D'Amico leads or contributes to several research groups focused on sustainable construction, embodied carbon quantification, and innovative structural systems. His work connects computational design, environmental assessment, and practical construction applications, creating a unique interdisciplinary approach to reducing the environmental impact of the built environment.
Professor Serhiy Timofeevich Yarimbash is a faculty member at Zaporizhzhia Polytechnic National University, where he serves in the Department of Electrical Machines within the Faculty of Electrical Engineering. Holding the academic title of Professor with a Candidate of Technical Sciences degree, he graduated from Zaporizhia Machine-Building Institute named after V.Ya. Chubar in 1975 as an electromechanical engineer. His research expertise spans electrical engineering, electromechanics, and computer-aided design systems, with a particular focus on the technological preparation of electrical insulation production. Professor Yarimbash teaches specialized courses including Fundamentals of computer-aided design of electrical devices and electromechanical systems, Engineering design of electrical machines and transformers, and Mathematical models and computational research methods in electromechanics. His scholarly contributions have been published in prominent electrical engineering periodicals such as Power Engineering, Electromachinery and Equipment, Electrical Magazine, and Electrical Engineering and Energy. His work demonstrates a consistent focus on integrating computational methods with traditional electrical engineering practices to improve design and manufacturing processes. Professor Yarimbash has published extensively on CAD support systems for electrical equipment production technology and related topics in electrical machine design and manufacturing. His research trajectory shows a clear progression from theoretical foundations to practical applications of computer-aided systems in electrical engineering production environments. He is proficient in Ukrainian, Russian, and English, facilitating international academic collaboration. His office is located in room 260 at the university's main campus at 64 Zhukovsky Street in Zaporizhia, Ukraine.
Søren Wengel Mogensen is an Associate Professor at the Department of Finance, Copenhagen Business School, Denmark. His research focuses on developing advanced statistical and machine learning methodologies for complex systems analysis. Research Interests: Dr. Mogensen's work spans causal inference, stochastic processes, survival analysis, and time-series modeling. Key themes include: Causal discovery algorithms for industrial and biological systems Graphical representations of dependencies in high-dimensional data Time-varying mediation in survival contexts Bayesian networks for cascade modeling Publication Trends: His recent articles (2021-2025) demonstrate a strong emphasis on theoretical-statistical innovation with applications in healthcare, industrial monitoring, and computational finance. Dominant methodologies include kernel-based independence tests, continuous-time Bayesian networks, and constrained stochastic process modeling.
Sakari Penttilä serves as a Postdoctoral Researcher within the Department of Mechanical Engineering at the School of Energy Systems, LUT University (Lappeenranta-Lahti University of Technology) in Lappeenranta, Finland. His professional role centers on cutting-edge research in advanced manufacturing, with a specific focus on welding automation, robotics, and the integration of digital technologies to improve manufacturing processes and product quality. Penttilä's research spans several key areas in modern manufacturing engineering. Primary interests include welding technology and its automation, particularly through multi-robot systems and digital twin implementations. He actively explores the application of artificial intelligence for real-time process control and quality assurance in welding. Additionally, his work addresses Industry 4.0 readiness in regional manufacturing sectors, additive manufacturing quality assessment, and the use of extended reality for manufacturing complex components. His interdisciplinary approach combines mechanical engineering principles with information technology to solve persistent challenges in manufacturing reliability and efficiency. A review of Penttilä's recent publications indicates a consistent trajectory toward intelligent manufacturing systems. His work increasingly integrates artificial intelligence, digital twins, and extended reality to tackle welding distortion, quality control, and process optimization. Notably, he has contributed to advancing multi-robot welding systems with pre-setting and feedback mechanisms, and has investigated fatigue strength in additively manufactured parts. His research also extends to regional Industry 4.0 adoption, particularly in the Baltic Sea Region, highlighting both technological and organizational aspects of digital transformation in manufacturing. Scientific Awards: No scientific awards, fellowships, or medals were mentioned in the provided information. Advising and Grants: The available information does not specify any students advised by Dr. Penttilä or any research grants he has secured. His role as a postdoctoral researcher suggests he may be involved in collaborative projects, but specific details are not provided.
Manling Li is an Assistant Professor at the Department of Computer Science, Northwestern University. She previously served as a postdoc at Stanford University's Vision and Learning Lab under Prof. Jiajun Wu and received her Ph.D. from the University of Illinois at Urbana-Champaign (advisor: Prof. Heng Ji). Her research spans Language + Vision + Robotics with applications in Embodied AI and AI for Science . Key Research Areas : Knowledgeable Foundation Models, Reasoning & Planning, Compositionality, Multimodal Knowledge Extraction, Factuality & Trustworthiness in AI Leadership Roles : Organizing Committee for ACL 2025, NAACL 2025, EMNLP 2024 Research Trends in her 15 most recent publications show: Advancing Embodied AI through structured reasoning and planning frameworks Developing Vision-Language Models for 3D layout optimization and video understanding Addressing LLM Hallucinations via knowledge shadowing and mechanistic interpretability Creating collaborative agent systems with out-of-sync recovery mechanisms Scientific Recognition : ACL 2024 Outstanding Paper SoCal NLP 2024 Best Paper Microsoft Research Fellowships (PhD & Postdoc) DARPA Riser & EE CS Rising Star Advising Impact : Mentored 19 students in developing the UIUC information extraction system. Currently advising 12 students across PhD, Master's, and undergraduate levels, with particular emphasis on supporting underrepresented groups. Led teams to rank 1st in DARPA AIDA evaluations.
Peter Kieseberg is a Lecturer and Senior Researcher at the St. Pölten University of Applied Sciences , affiliated with the Institute for IT Security Research and Department of Computer Science and Security . He has held his lecturer position since November 2017 and contributes to research in Cybersecurity , Artificial Intelligence , and Blockchain Technologies . His research spans AI Security , Cyber Resilience , Data Privacy , and Explainable AI . He has co-authored numerous publications on topics such as Secure Drone Integration , AI Procurement Guidelines , and Blockchain-based Auditing . His work often bridges technical and legal domains, emphasizing Ethics and Transparency . Key Trends: Recent articles focus on Cyber Resilience Fundamentals , Controllable AI , and Risk Factors in AI Applications . Projects: Active in initiatives like A3 (AI Act for Austria) , Dataskop , and Josef Ressel Center for Blockchain Technologies . Education: Studied Technical Mathematics in Computer Science at TU Wien (2001–2007).
Dr Anthony J H Simons is a Senior Lecturer in the Department of Computer Science at the University of Sheffield, where he serves as Deputy Director of UG Admissions. He is a member of the Testing research group and has been affiliated with the university since completing his PhD there. His academic journey spans several decades, moving from speech recognition systems to object-oriented programming languages and currently focusing on model-based testing and cloud computing applications. Dr Simons holds an MA in Modern Languages from the University of Cambridge and a PhD in Computer Science from the University of Sheffield. His educational background in both humanities and technical fields has informed his interdisciplinary approach to software engineering research. His primary research interests center around turning formal verification results into practical software engineering benefits. Currently, he investigates Model-Based Testing and Model-Driven Engineering with applications to Cloud Computing. Earlier in his career, he made significant contributions to object-oriented software engineering, including type theory and software development methods. He is the inventor of the JWalk automatic software testing tool for Java and the JAST library for processing XML in Java, and co-author of the OPEN Toolbox of Techniques. His work bridges theoretical computer science with practical software development needs. Analysis of his recent publications reveals a clear trajectory from foundational work in object-oriented type theory to applied research in cloud computing and model-based testing. His scholarship demonstrates consistent focus on formal methods applied to practical software engineering challenges, with increasing emphasis on cloud infrastructure testing and verification in recent years. Dr Simons has secured significant research funding as Principal Investigator, including the Broker@Cloud project (EC-FP7, £323,688, 2012-2015), Future Engineering System (InnovateUK, £199,874, 2016-2019), and Ferromone Trails Concept (Department for Transport, £24,635, 2017). He has supervised numerous undergraduate and masters' projects throughout his career and continues to mentor students despite being semi-retired. He leads the Testing research group at Sheffield and has developed several research projects including CatWalk (a software testing tool for Java), ReMoDeL (a conceptual modeling language), and tools for verifying specifications and generating tests for software services in the cloud. His research has practical applications in cloud service brokerage and quality assurance.
Marcelo D'Amorim is an Associate Professor in the Department of Computer Science at North Carolina State University's College of Engineering. His research focuses on improving software reliability through advanced program analysis and systematic testing methodologies. With a Ph.D. from the University of Illinois Urbana-Champaign (2007), he has established himself as a leading researcher in software engineering and programming languages. Dr. D'Amorim's educational background includes a Master's and Bachelor's degree from Universidade Federal de Pernambuco (2001 and 1996 respectively), providing him with a strong foundation in computer science before his doctoral studies in the United States. His research interests center on Software Engineering and Programming Languages , with specific focus on improving software reliability through program analysis and systematic testing. He investigates practical methods to prevent, detect, and fix bugs in code, developing tools to automate software testing and debugging activities. His recent work has increasingly incorporated machine learning approaches, particularly large language models, to address longstanding challenges in software quality assurance. His research spans areas including software testing, bug detection, program analysis, and security analysis, with applications in various domains including deep learning libraries and cryptographic APIs. Analysis of his recent publications reveals a clear trend toward leveraging artificial intelligence to solve traditional software engineering problems. His work increasingly focuses on LLM applications for test oracle generation, vulnerability repair, and code quality improvement, while maintaining strong foundations in traditional program analysis techniques. The research spans both theoretical foundations and practical tool development, with many of his publications including publicly available implementations. Dr. D'Amorim has secured significant research funding, including an NSF grant for 'eSLIC: Enhanced Security Static Analysis for Detecting Insecure Configuration Scripts' (2020-2025, $199,978). This project aims to develop automated techniques to identify security weaknesses in configuration scripts to prevent large-scale security attacks and data breaches. His academic service includes roles on program committees for major conferences including ASE'25 and ICSE'26. He teaches courses such as 'Software Testing and Reliability' at NC State University, connecting his research directly to classroom instruction. His research group appears to focus on practical software engineering tools with real-world applications, particularly in the areas of software testing, debugging, and security analysis.
Michael Reiter is the James B. Duke Distinguished Professor in the Departments of Computer Science and Electrical & Computer Engineering at Duke University's Pratt School of Engineering. With a career spanning over three decades, he has established himself as a leading authority in computer security, distributed systems, and cryptography. His academic journey includes significant positions at Carnegie Mellon University, where he served as founding Technical Director of CyLab, and the University of North Carolina at Chapel Hill. Ph.D. from Cornell University, 1993 James B. Duke Distinguished Professor, Duke University Former Professor at Carnegie Mellon University Former Distinguished Professor at UNC Chapel Hill Former Director of Secure Systems Research at Bell Labs Professor Reiter's research spans the critical intersection of security, cryptography, and distributed computing. His work addresses fundamental challenges in computer and network security, with particular focus on Byzantine fault-tolerant systems, privacy-preserving protocols, and applied cryptography. His recent research has expanded into machine learning security, blockchain technologies, and the security implications of emerging network infrastructures like 5G. Reiter's approach combines theoretical rigor with practical implementation, resulting in systems that have influenced both academic research and industry practice. Analysis of Reiter's recent publications reveals a strong trend toward addressing security challenges in modern computing environments. His work bridges traditional security domains with emerging technologies, particularly focusing on the security implications of machine learning systems, blockchain applications, and next-generation network architectures. The breadth of his research demonstrates how foundational security principles can be adapted to address novel threats in increasingly complex computing ecosystems. Test of Time Award, ACM Conference on Data and Application Security and Privacy (2024) Lasting Research Award, ACM Conference on Data and Application Security and Privacy (2024) Test of Time Award, ACM Conference on Computer and Communications Security (2022, 2019) Outstanding Contributions Award, ACM SIGSAC (2016) Fellow, IEEE (2014) Fellow, ACM (2008) Throughout his career, Reiter has mentored numerous students and collaborated extensively with researchers across academia and industry. His work has been supported by significant research grants from NSF, DARPA, and other funding agencies, focusing on foundational security mechanisms and their application to real-world systems. He has taught courses ranging from introductory security to advanced cryptography and distributed systems. Reiter maintains an active research group at Duke that explores cutting-edge security challenges. His team works at the intersection of theory and practice, developing both novel security mechanisms and practical implementations that address real-world vulnerabilities. Current projects focus on securing machine learning systems, enhancing blockchain security through trusted execution environments, and developing privacy-preserving protocols for distributed applications.
Milton Aguirre serves as Assistant Professor at Purdue Polytechnic Institute, Purdue University, leveraging over ten years of market-oriented product design expertise to bridge academic research and entrepreneurship. His work in creative mechanism design has driven international technology valorization initiatives, including concept-to-market projects during his post-doctoral tenure at Delft University of Technology. His academic foundation includes a Post-Doctorate from Delft University of Technology's Department of Mechanical, Maritime, and Materials Engineering, Ph.D. and M.S. degrees in Mechanical Engineering from Pennsylvania State University, and a B.S. in Mechanical Engineering from Virginia Military Institute. Dr. Aguirre's research centers on compliant mechanisms, wearables and exoskeletons, and human-centered product design, with strong emphasis on medical device innovation. He investigates mechanical systems that enhance user interaction, particularly in surgical tools and mobility assistance, focusing on translating academic concepts into commercial products through rigorous market-oriented development. His 2021-2025 publications reveal dominant trends in medical device engineering, especially compliant mechanisms for surgical safety and mobility aids. Key themes include haptic feedback integration in walking canes, force-sensitive surgical graspers, and stress management wearables, demonstrating consistent focus on user-centered solutions for healthcare challenges. Major funded projects include: 2024 NIH STTR/SBIR Phase I grant for stress management intervention development 2023 NIST grant on standards in product innovation Multiple NIH MedTech Think Tank projects for mental health and mobility devices Industry-sponsored mobility aid device research As Seed Lab director, he leads interdisciplinary teams developing stress management technologies and mobility aids through NIH collaborations and industry partnerships. His work emphasizes rapid prototyping and commercialization pathways, with recent projects including an NIH-funded stress management device and mobility assistance systems.
Michele Loreti is Full Professor in Computer Science at the University of Camerino within the School of Science and Technology and serves as Director of the School of Advanced Studies. His career includes roles as Research Associate at University of Firenze (2002-2017) and Visiting Professor at IMT Institute for Advanced Studies (2012-2016). He holds a PhD in Mathematical Logic and Theoretical Computer Science from University of Siena (2001) and a Computer Science degree from University of Rome 'La Sapienza' (1997). Research interests: Formal tools for concurrent/distributed systems Quantitative analysis of collective adaptive systems Spatio-temporal model checking Process calculi and modal logics Programming languages for network-aware applications Runtime verification and dynamic system adaptation Article trends: His work focuses on formal verification of cyber-physical systems, spatio-temporal properties, stochastic process calculi, attribute-based communication, and tools like CARMA and muG for collective system analysis. Editorial roles: Assistant editor for Elsevier Journal on Logical and Algebraic Methods in Programming and member of the Reproducibility Board for ACM Transactions on Modelling and Computer Simulation.
