Quentin Stiévenart is a researcher at Université du Québec à Montréal, focusing on abstract interpretation, concurrency, and static analysis. His work spans programming language design, software verification, and tool development for WebAssembly and functional languages like Racket and Scheme. Active in organizing and reviewing for conferences including SPLASH, ICFP, ECOOP, and SAS Developed tools such as Wassail for WebAssembly static analysis and RacketLogger for educational purposes Contributions include theoretical work on effect-driven flow analysis and practical advancements in concolic execution abstraction His research addresses challenges in concurrency verification, cyclic reinforcement in incremental analysis, and security-focused taint tracking across multiple language paradigms.
Ulman Lindenberger is Managing Director (2006-2009, 2016-2019, 2025-2027) and Director of the Center for Lifespan Psychology at the Max Planck Institute for Human Development in Berlin. He concurrently serves as Co-Director of the Max Planck UCL Centre for Computational Psychiatry and Ageing Research. He holds honorary professorships at Freie Universität Berlin, Universität des Saarlandes, and Humboldt-Universität zu Berlin. Education includes a Dipl.-Psych. from Technische Universität Berlin (1985), Dr. phil. from Freie Universität Berlin (1990 summa cum laude ), and Habilitation in Psychology (1998). His research examines: Behavioral and neural plasticity across lifespan Brain-behavior relationships Lifespan developmental theory Multivariate developmental methodology Formal models of behavioral change His publications focus on cognitive aging patterns, neural plasticity mechanisms, and methodological innovations in lifespan psychology. Research demonstrates consistent themes: neurocognitive dedifferentiation in aging, dopaminergic modulation of cognition, and environmental influences on brain plasticity. Awards include: Gottfried Wilhelm Leibniz Prize (2010) Fellow of Royal Society (2025) Foreign Member of Royal Swedish Academy of Sciences (2023) Mentoring Award of German Psychological Society (2011) He has supervised over 50 doctoral students and secured major grants including DFG collaborative projects, BMBF initiatives (Berlin Aging Study I/II), EU Horizon 2020 (LIFEBRAIN), and Max Planck Society strategic funds. Leads multidisciplinary teams at Center for Lifespan Psychology and Max Planck UCL Centre, coordinating international projects like COBRA (Cognition, Brain, and Aging) and SYNAPSE.
Beniamino Accattoli is a Researcher in Computer Science at Inria , where he has been a member of the PARTOUT team since January 2015. His work centers on higher-order computation, particularly the lambda calculus, connecting theoretical mathematics with practical implementations of functional languages. Current Projects : CANofGAS (2022–2025), COCA HOLA (2016–2021). Recent Events : Program Committee member for LSFA 2025, ICFP 2024, and FSCD 2024; lecturer at MPRI 2023–24. Research Themes : Internal : Developing mathematical theories of sharing in lambda calculus to enhance efficiency in functional language implementations. External : Establishing reasonable cost models for time and space in lambda calculus, resolving long-standing open problems in computational complexity. Scientific Awards : Distinguished Paper Award at ICFP 2022. Best Paper Award at RTA 2013. Academic Contributions : Co-inventor of the Linear Substitution Calculus , a canonical framework for higher-order computation with sharing. Proved polynomial equivalence between lambda calculus and Turing machines for time complexity with Ugo Dal Lago. Refuted token-machine conjectures and introduced environment-based machines for space complexity with Ugo Dal Lago and Gabriele Vanoni.
Samuel Mimram is a Professor at the LIX laboratory of École Polytechnique (part of Institut Polytechnique de Paris ). He serves as President of the Computer Science Department and Head of the Cosynus team , focusing on interdisciplinary research at the intersection of denotational semantics , concurrency , rewriting theory , category theory , linear logic , and algebraic topology . His recent work explores homotopy type theory , higher categories , and coherence in rewriting systems , with publications in venues like FSCD , LIPIcs , and Higher Structures . He mentors interns (e.g., Hugo Salou , William Hasley ) and participates in academic juries for PhD theses (e.g., Thomas Jan Mikhail , Luc Chabassier ). Contact: samuel.mimram@polytechnique.edu .
Jan Peleska is a Professor at the Department of Computer Science, Faculty of Mathematics and Computer Science, University of Bremen. He is a member of the Bremen Institute of Safe Systems (BISS) and co-editor of the BISS Monographs series. His research focuses on the application of formal methods and model-based testing to safety-critical embedded systems in domains such as avionics, railways, and automotive engineering. Affiliation: University of Bremen, TZI (Center for Computing Technology), BISS (Bremen Institute of Safe Systems) Academic Role: Professor of Computer Science Research Interests Peleska’s work emphasizes formal methods for dependable systems, particularly distributed and reactive real-time systems. Key areas include: Formal methods (safety, reliability, availability, security) Development of fault-tolerant systems Test automation for reactive systems Tools development for formal methods Integration with software development standards His research is applied to industrial projects involving safety-critical embedded systems, such as avionic systems and railway control systems. A comprehensive overview is provided in his Habilitation Thesis: Formal Methods and the Development of Dependable Systems . Publication Trends Peleska’s recent publications (2012–2022) focus on model-based testing (e.g., symbolic finite state machines, CSP refinement), standardisation of autonomous train control, and tools for automated testing. His work addresses challenges in railway interlocking systems, avionic software verification, and hybrid system validation, often combining formal methods with practical industrial applications. Scientific Awards Best Paper Award at FORMS/FORMAT 2014 Best Paper Award at QA+Test 2007 Other Responsibilities Peleska co-manages the Post Graduate Programme Embedded Systems GESy and serves as a shareholder/consultant for Verified Systems International GmbH. He has delivered invited lectures on industrial verification, model-based testing, and formal methods at institutions like the University of Tunghai (2016) and workshops including CyPhyAssure Spring School (2019).
Stephen Siegel is an Associate Professor at the University of Delaware with a joint appointment in the Department of Computer and Information Sciences and the Department of Mathematical Sciences . Holding a PhD in Mathematics from the University of Chicago (1993), he transitioned from finite group theory research to formal methods in computer science, focusing on verification of parallel and scientific software. His research centers on the Verified Software Laboratory (VSL) and the CIVL Model Checker for HPC program verification. Recent work includes formal verification of PETSc components at CAV 2025 and collective contract frameworks for message-passing programs. Research Interests Formal methods for software verification Parallel and HPC software reliability Model checking techniques Application of mathematical logic to computing Academic Service Highlights Program Committee & Publication Chair, CAV 2025 Co-organizer, International Workshop on Verification of Scientific Software (VSS 2025) Chair, VerifyThis competition (2023) Editorial service at IEEE Transactions on Software Engineering (2015-2019) Teaching Portfolio CISC 404/604: Logic in Computer Science CISC 414/614: Formal Methods in Software Engineering CISC 372: Parallel Computing (MPI/OpenMP/CUDA instruction) Advanced Topics courses: Model Checking, Abstract Interpretation
Nathanaël Fijalkow is a Researcher at CNRS in LaBRI (Bordeaux) and a Research Fellow at The Alan Turing Institute in London. His primary research fields include games , machine learning , automata theory , and dynamical systems , with a focus on synthesizing programs from logical specifications and probabilistic models. Research Interests span program synthesis (programming by example), controller synthesis (temporal logic specifications), games on graphs (parity/mean payoff games), probabilistic automata (bounded ambiguity), and invariants for linear dynamical systems. He bridges formal methods with machine learning through projects like DeepSynth . Scientific Contributions include: Undecidability results for probabilistic automata Advances in parity game algorithms (quasi-polynomial lower bounds) Foundations of probabilistic modal logics Efficient synthesis techniques using SMT solvers and distributional learning Supervision involves guiding postdocs and PhD students such as Guillaume Lagarde, Antonio Casares, and Pierre Ohlmann. He has secured grants like the Momentum DeepSynth project (2019-2021) , aiming to merge formal methods with ML for program synthesis.
Daniel Jimenez Gonzalez is a faculty member at the Universitat Politècnica de Catalunya (UPC), affiliated with the Department of Computer Architecture within the School of Informatics (FIB). He is an active researcher in programming models and high-performance computing, contributing extensively to the field through publications, projects, and academic supervision. Research Interests: His work focuses on Computer Architecture , Programming Models , and Parallel Computing . He investigates task-based programming, runtime systems, compiler optimizations, and performance modeling for multi-core and heterogeneous architectures. His research addresses challenges in scalability, energy efficiency, and resilience in modern computing environments. His recent publications show a consistent trend in task-based programming models , runtime scheduling , and performance optimization across diverse architectures, including NUMA and GPU-accelerated systems. The work spans from theoretical modeling to practical implementation, often targeting real-world HPC applications. Scientific Awards: No awards explicitly mentioned in the provided text. Advising and Grants: While specific students are not listed, his involvement in doctoral theses and R&D projects suggests an active role in mentoring graduate students. He has participated in both competitive and non-competitive R&D+i projects, indicating grant acquisition and project leadership experience in areas related to programming models and computer architecture. Labs and Teams: He is a member of the UPC PM - Programming Models research group, which focuses on the design, analysis, and optimization of modern programming paradigms for high-performance systems.
David Gazze is a Clinical Assistant Professor in the Department of Pharmaceutical Sciences at the Barry and Judy Silverman College of Pharmacy, Nova Southeastern University. He has served as faculty since the institution's inception (originally Southeastern College of Pharmacy) and taught every pharmacy graduating class at the Fort Lauderdale/Davie campus, with contact details including email dgazze@nova.edu and phone (954) 262-1356. Education Ph.D. in Medicinal Chemistry, University of Pittsburgh, 1987 Research Interests His primary research focuses on psychopharmacology , specifically elucidating mechanisms of action for schizophrenia and bipolar disorder treatments. Concurrently, he applies molecular graphics in drug development to visualize molecular interactions and optimize pharmaceutical agents. These interests bridge medicinal chemistry foundations with clinical mental health applications, emphasizing computational approaches to neuropharmacological challenges. Scientific Awards Teacher of the Year (awarded 10 times by students) Advising and Grants Dr. Gazze has directly educated all pharmacy students throughout the college's history since joining at inception, reflecting deep institutional commitment. While the text confirms his research activities in psychopharmacology and molecular graphics-based drug design, it contains no details regarding graduate student advisees, grant funding, or sponsored research projects beyond his instructional and scholarly contributions.
J. David Riel serves as a Professor at Carnegie Mellon University's Heinz College of Information Systems and Public Policy, appointed Distinguished Service Professor in 2018 after joining CMU in 2006. He concurrently holds a teaching appointment with the Institute of Software Research in the School of Computer Science for the eBusiness Technology Master’s Program. His research spans: Educational Technology: Application of Web 2.0, LMS, synchronous platforms, automation, and AI/ML in education Active Learning Methodologies: Learning by doing, blended learning, flipped classrooms, and team-based learning Digital Transformation: IoT, ePayment, Intelligent Automation, Blockchain, and Quantum Computing in academic/business disruption Recent publications analyze Intelligent Automation's business-academia bridge, online flipped learning efficacy, and educational psychology research productivity. His work consistently examines technology-pedagogy integration to address digital disruption across education and business sectors.
Catia Trubiani is a faculty member at the Gran Sasso Science Institute in L'Aquila, Italy, specializing in software performance engineering, architectural analysis, and cyber-physical systems. Her work bridges theoretical modeling with practical applications, focusing on performance antipatterns, uncertainty quantification, and DevOps practices. In research , she explores performance modeling of microservices, federated learning systems, and cyber-physical systems, with a strong emphasis on architectural decision-making under uncertainty. Her recent articles analyze performance regression testing, aging detection in networks, and anti-pattern correlation in distributed systems, reflecting her interest in robust, scalable software solutions. She collaborates extensively with researchers like Raffaela Mirandola , Alberto Avritzer , and Riccardo Pinciroli , contributing to tools and frameworks such as PLUS (Performance Learning for Uncertainty of Software) and VisArch (Visualisation of Performance-Based Architectural Refactorings). Her work has been published in journals including IEEE Transactions on Software Engineering and Future Generation Computer Systems , as well as conferences like ICSE, ECSA, and ICPE.
Radu Sion is a Professor at the Department of Computer Science, Stony Brook University, specializing in Computer Science , Information Security , Cloud Computing , Data Privacy , and Cryptography . His research focuses on secure data outsourcing, trusted hardware applications, and privacy-preserving systems. Recent work includes Wink: Deniable Secure Messaging (2023) and A Study of China's Censorship Evasion (2023), both exploring plausibly deniable communication. Earlier contributions like PEARL (2021) and ConcurDB (2014) address secure storage and database integrity. His 2024 paper INVISILINE introduces invisible plausibly deniable storage solutions. His research spans Oblivious RAM , History-Independent Data Structures , Trusted Execution Environments , and Flash Memory Security . Key collaboration networks include Bogdan Carbunar, Anrin Chakraborti, and Chen Chen.
Adrian Spătaru serves as a Lecturer at the Faculty of Mathematics and Computer Science, West University of Timisoara, Romania, with his office located in room 058. He maintains active academic engagement through direct contact channels including email (adrian.spataru@e-uvt.ro) and phone ((0256) 592 157), reflecting ongoing institutional affiliation and research activities within computer science. His research centers on the integration of edge, cloud, and high-performance computing resources within the cloud continuum framework. Key focus areas include service orchestration, resource management, blockchain applications for decentralized systems, and AI-driven optimization of cloud infrastructures. His work addresses critical challenges in heterogeneous platform integration, fault tolerance, and predictive maintenance across large-scale distributed environments, with notable contributions to container deployment and accelerator-aware application specification. Analysis of his publication trends reveals sustained emphasis on the edge-cloud-HPC continuum since 2018, evolving toward intent-based AI orchestration and heterogeneous hardware integration in recent works. His research bridges theoretical distributed systems concepts with practical applications in environmental monitoring (solar forecasting, freshwater quality assessment) and industrial cloud reliability, demonstrating both academic rigor and real-world impact. No scientific awards are documented in the provided institutional information. Details regarding student supervision, research grants, or laboratory affiliations are not specified in the available materials. Current research directions appear focused on advancing the edge-cloud-HPC continuum through heterogeneous platform integration, with 2025 publications indicating active development in this domain.
Enes Ayan serves as a Doctor Lecturer in the Department of Computer Engineering at Kirikkale University's Faculty of Engineering and Natural Sciences, where he has maintained continuous academic affiliation since his 2014 appointment as Research Assistant. His career trajectory includes completing both master's (2015) and doctoral degrees (2019) in Computer Engineering at the same institution following his 2013 bachelor's graduation from Süleyman Demirel University. Education Bachelor's: Computer Engineering, Süleyman Demirel University (Isparta), 2013 Master's: Computer Engineering, Kirikkale University, 2015 Doctorate: Computer Engineering, Kirikkale University, 2019 Research Focus Dr. Ayan's work centers on deep learning applications across three primary domains: medical imaging (specializing in dental diagnostics, radiology, and endoscopy), security systems (weapon detection and traffic monitoring), and agricultural technology (crop pest classification). His research bridges theoretical AI advancements with practical healthcare solutions, particularly through explainable AI frameworks for dental caries detection and pneumonia diagnosis. Publication Trends Analysis of his 15 most recent publications (2020-2025) reveals intensifying specialization in dental AI applications (60% of 2024-2025 output), with significant contributions to caries detection under prostheses and tooth numbering systems. Concurrently, his work maintains strong threads in security-focused computer vision (UAV-based traffic analysis, weapon detection) and agricultural AI, demonstrating methodological versatility through genetic algorithm optimizations and ensemble CNN architectures. Professional Context No information regarding student supervision, research grants, laboratory facilities, or scientific awards appears in the source materials. His academic progression from Research Assistant to Doctor Lecturer indicates standard career advancement within Kirikkale University's engineering faculty without notable interruptions.
Tomasz Pełech-Pilichowski serves as a Lecturer at the Institute of Computer Science within the Faculty of Computer Science at AGH University of Science and Technology in Kraków. He concurrently holds dual administrative leadership positions as Director of the AGH Recruitment Center and Rector's Representative for Recruitment, demonstrating significant institutional impact beyond his academic role. His research expertise spans artificial intelligence, natural language processing, and legal informatics with methodological foundations in deep learning and time series analysis. Recent work focuses on AI-driven solutions for legal text processing (text segmentation, hypertext law), educational technology (gamification, recruitment systems), and security applications (facial-age detection, anomaly identification). His interdisciplinary approach consistently bridges computer science with law, education, and environmental science through practical implementations. Analysis of his 2023-2025 publications reveals three dominant research trajectories: legal tech innovation (68% of output featuring text segmentation and regulatory automation), educational transformation (22% including gamified AI skill development), and security/environmental systems (10% covering IoT integration and pollution prediction). This progression shows increasing specialization in AI-NLP fusion techniques applied to domain-specific challenges, particularly within legal informatics where he has developed novel text recovery and visualization frameworks.
