Rabéa Ameur-Boulifa is an Associate Professor at Télécom Paris, affiliated with the Communications and Electronics (Comelec) Department. She is a member of the LabSoc research team within the Information Processing and Communication Laboratory (LTCI). Her work focuses on integrated and embedded systems, emphasizing design, modeling, verification, and security. LabSoc (System on Chip research team) LTCI (Information Processing and Communication Laboratory) Her research spans formal methods for system verification, security guidelines specification, and compositional analysis of distributed systems. Publications highlight applications in automotive software safety, asynchronous component modeling, and detection of vulnerabilities like integer overflow in embedded systems. The majority of her recent work (2022–2025) centers on open automata refinements, formal requirement validation, and compositional verification techniques. Keywords include formal methods, embedded systems, security guidelines, and distributed component modeling.
Robert Woroch is a research assistant at the Chair of Business Information Systems and Software Engineering at the University of Duisburg-Essen's Faculty of Computer Science. His work focuses on digital business models, platform ecosystems, and value creation analysis in fintech and e-commerce domains. Current affiliation: Universitaet Duisburg-Essen Academic rank: Researcher Research specializations: Ecosystems, Value Networks, Fintech, E-commerce, IoT-enabled business models. His studies explore digital transformation mechanisms and value flow analysis in platform economies. Publication trends: Recent work examines value creation frameworks in e-commerce ecosystems, sustainability-driven initiatives in retail, data monetization strategies, and fintech customer dynamics. Key methodologies include multi-case studies and systematic literature reviews. Academic engagement: Active in conference reviewing (Electronic Markets, WI, ECIS) and has contributed to several peer-reviewed publications on digital platforms.
Rodrigo Nunes Laigner is a Postdoc researcher at the Department of Computer Science (DIKU), Faculty of Science, University of Copenhagen. He is a member of the Software, Data, People & Society (SDPS) section, which focuses on research in software, process and data management systems, and methods for developing software systems suited for people while creating value for society. His research interests span database systems, microservices architecture, event-driven systems, distributed systems, software engineering, and data management. Laigner's work often involves interdisciplinary collaboration with industry partners. Analysis of his publication record reveals a strong focus on data management challenges in modern distributed architectures, particularly microservices. His research addresses critical issues including consistency models, scalability, benchmarking methodologies, and state management in cloud-native applications. A significant portion of his work explores event-driven approaches to solve data management problems in distributed environments. Laigner has been actively publishing in top-tier venues including Proceedings of the ACM on Management of Data, IEEE International Conference on Data Engineering (ICDE), ACM Symposium on Cloud Computing (SoCC), and the Proceedings of the VLDB Endowment. His publications demonstrate both theoretical contributions and practical implementations addressing real-world challenges in data management.
Thomas Troels Hildebrandt is a Professor in the Department of Computer Science at the University of Copenhagen, where he heads the Software, Data, People & Society research section. His work focuses on developing reliable and flexible software systems that adapt to user needs and legislative changes, with applications in digital law, workflows, and business processes. His educational background includes: PhD in Computer Science from Aarhus University (awarded February 23, 2000) Professor Hildebrandt's research spans Software Engineering , Process Modeling , and Business Process Management , with a focus on declarative approaches like Dynamic Condition Response (DCR) graphs. His work integrates formal methods to ensure system reliability in contexts ranging from smart contracts to public governance. He actively explores societal implications of AI, advocating for transparency and user-centered design in digital systems. Recent publications demonstrate a strong trend toward declarative process modeling applied to smart contracts and public governance . Key developments include DCR graphs for dynamic behavior modeling, cross-chain business logic monitoring, and digital compliance frameworks. His interdisciplinary approach bridges computer science with real-world societal challenges, particularly in adapting systems to evolving legislation and user requirements. No specific scientific awards are mentioned in the provided information. Professor Hildebrandt leads interdisciplinary research projects and serves on advisory boards for digitalization and AI. His work has fostered industry collaboration, including founding DCR Solutions based on his research. He acts as an independent consultant and speaker in digital transformation, with recent projects focusing on blockchain integration and public sector AI ethics. He directs the Software, Data, People & Society research section, which develops human-centered methods for adaptable digital systems. Current initiatives include DCR graph applications for GDPR compliance, smart contract security, and transparent AI in public services, with strong industry and government partnerships.
Jarno Alanko is a Postdoctoral Researcher at the University of Helsinki within the Department of Computer Science . Specializing in algorithmic bioinformatics and computational genomics, he is affiliated with the Genome-scale Algorithmics research group led by Professor Veli Mäkinen. Research Interests: Bioinformatics algorithms String processing Genomic data structures Graph-based sequence representation Metagenomic analysis Space-efficient computing Notable Research Contributions: His work focuses on optimizing k-mer-based analyses through novel data structures like Finimizers and Eulertigs, improving sequence alignment efficiency, and developing graph indexing methods beyond Wheeler graphs. Recent publications in IEEE/ACM Transactions on Computational Biology and Bioinformatics and Algorithmica highlight his contributions. External Collaborations: Alanko has collaborated with institutions such as the Max Planck Institute for Molecular Cell Biology and Genetics during his 2017 academic visit. Contact: jarno.alanko@helsinki.fi | ORCID: 0000-0002-8003-9225
Seyoung Kim is an Associate Professor in the Department of Epidemiology at the University of Pittsburgh School of Public Health. She holds a PhD in Computer Science from University of California, Irvine (2007), preceded by a BS in Computer Engineering from Seoul National University (2001), and completed postdoctoral training at Carnegie Mellon University (2010). Her methodological research focuses on statistical machine learning for systems genomics, with applications to gene network reconstruction, eQTL mapping, and longitudinal data analysis. Education : BS in Computer Engineering, Seoul National University (2001) PhD in Computer Science, University of California, Irvine (2007) Postdoctoral Fellow in Computer Science and Machine Learning, Carnegie Mellon University (2010) Her lab develops computational tools for analyzing complex genomic datasets, including methods for: Learning gene networks under SNP perturbations Allele-specific expression quantification via kallisto extensions Integrating multi-omics data with scalable algorithms Doubly mixed-effects Gaussian process regression for spatio-temporal modeling Joint covariance estimation in high-dimensional biological datasets Recent work demonstrates methodological advancements in handling dependencies among samples and features in genomic studies. She teaches EPIDEM 2186 Introduction to R Programming within the epidemiology curriculum.
Aaron G. Cass is an Associate Professor of Computer Science at Union College in Schenectady, NY. His research focuses on human aspects of computing, particularly software design and user interface design. Ph.D. in Computer Science (2005) from University of Massachusetts Amherst Master’s (1996) and Bachelor’s (1993) degrees from University of Virginia Three-year industry experience at Motorola Research interests center on improving software design practices through experimental studies and tool development, with collaborations in human-computer interaction (HCI) focusing on undo mechanisms and human-robot interaction (HRI) challenges. His work spans process modeling patterns, constraint-based design guidance, and empirical evaluations of HCI/UX mechanisms. Recent publications (2000-2018) demonstrate sustained contributions to software engineering (process modeling, constraint management), human-computer interaction (undo systems, task modeling), and human-robot interaction. The research consistently bridges theoretical process models with practical implementation tools for novice designers.
Michael S. Hsiao is a Professor in the Bradley Department of Electrical and Computer Engineering at Virginia Tech. His research focuses on design, test, verification, and diagnosis of complex hardware and software systems. He earned his Ph.D., M.S., and B.S. in Electrical Engineering from the University of Illinois. Notably, he was elected an IEEE Fellow in 2013 for contributions to automatic test pattern generation. His work spans natural language processing in hardware verification, hybrid AI systems, and formal methods. He has authored over 80 peer-reviewed publications and led significant research projects. Education: Ph.D., University of Illinois, 1997 M.S., University of Illinois, 1993 B.S., University of Illinois, 1992 Research Interests: Testing and Verification of Hardware Systems Computer Architecture and Digital Design Algorithm Development for Hardware Diagnostics Natural Language Processing in Design Automation Recent Article Trends: Recent work emphasizes integrating NLP with formal verification (e.g., translating English specifications to SystemVerilog assertions), hybrid AI systems for intent clarification, and robotic path-finding using natural language. These contributions bridge abstract language-based specifications with rigorous engineering validation. Awards: IEEE Fellow (2013) – For contributions to automatic test pattern generation of integrated circuits Advising & Grants: While specific student names are not listed here, his research has been supported by over 160 projects. He has also contributed to industry collaborations, including work on anti-counterfeit ICs and hardware security.
Rodolphe Lepigre is a researcher in computer science affiliated with the Max Planck Institute for Software Systems (MPI-SWS) in Saarbrücken, Germany, within Derek Dreyer's group. He holds a Researcher academic rank. His work focuses on formal methods, programming languages, and type systems. Previously, he was a postdoctoral researcher at Inria (Deducteam project) and completed his PhD at Université Savoie Mont Blanc in the LAMA laboratory. Research Interests: His research spans formal verification of concurrent systems, separation logic, proof assistants, and the integration of program certification within ML-style languages. He has contributed to projects like PML₂ and RefinedC, aiming to bridge practical programming with formal guarantees. Key Contributions: His work includes the VIP framework for verifying C idioms, extensions to separation logic with prophecy variables, and foundational type systems for Curry-style languages. He has also developed tools like the Dedukti logical framework and the Bindlib library for OCaml. Awards: Recipient of the PLDI 2021 Distinguished Paper and Artifact Awards for contributions to C code verification. His work on RefinedC highlights advancements in automated verification tools. Grants & Collaborations: Collaborations include projects with Inria, MPI-SWS, and contributions to open-source tools like the PML₂ language and the Lambdapi proof assistant. His research is supported by grants from European and German research institutions.
Erika Olimpiew is a Collegiate Associate Professor in the Department of Computer Science at Virginia Tech's College of Engineering. She holds a Ph.D. in Information Technology from George Mason University (2008). Her research focuses on requirements analysis, software design methodologies, and software product line engineering, with additional expertise in cloud computing, cybersecurity, and mobile computing systems. She has pioneered work on model-based testing strategies for large-scale software ecosystems. Her research portfolio includes over 13 publications spanning software engineering fundamentals and contemporary challenges. Notable themes include variability management in software product lines, reusable testing frameworks, and the intersection of emotional factors in educational technology applications. Her work bridges theoretical modeling approaches with practical system implementation challenges in distributed environments. No academic awards or grant details were explicitly listed in the provided materials. While no current lab affiliations are noted, her publications suggest active involvement in collaborative software engineering research initiatives. Advising activities and past grants remain unreported in this data source.
Wayne Enright is a Professor of Computer Science at the University of Toronto, specializing in numerical analysis and scientific computing. His research focuses on numerical methods for ordinary differential equations (ODEs), integro-differential equations (IDEs), and delay differential equations (DDEs), with an emphasis on reliability, error analysis, and software development. He has held leadership roles, including Chair of the Department of Computer Science (1993–1998) and President of the Canadian Applied and Industrial Mathematics Society (CAIMS). Enright’s contributions include advancements in numerical software like MUSN and pioneering work on defect control and sensitivity analysis. He has received the IFIP Silver Core Award and contributed to international conferences and editorial boards. His work bridges theoretical foundations with practical applications in computational biology, engineering, and stochastic modeling. Education: BSc in Mathematics (1968, University of British Columbia) MSc in Mathematics (1969, University of Toronto) PhD in Computer Science (1972, University of Toronto) Research Interests: Enright’s work centers on developing robust numerical methods for solving ODEs, IDEs, and DDEs. He emphasizes reliable error estimation, algorithm efficiency, and software implementation. Key areas include: - Superconvergent interpolants for collocation methods - Sensitivity analysis for delay differential equations - Contouring of PDE solutions on unstructured meshes - Stochastic models in biochemical kinetics Publications: Over 150 peer-reviewed articles, including seminal works on numerical methods for differential equations and computational tools. Recent trends focus on enhancing algorithm reliability, adaptive time-stepping, and exploiting problem structure for efficiency. Awards & Recognition: IFIP Silver Core Award Past President, CAIMS Executive Member, IFIP WG2.5 on Numerical Software Editorial Board Member, ACM Transactions on Mathematical Software Grants & Collaborations: Extensive funding from NSERC and international collaborations. Leads projects integrating numerical methods with real-world applications in computational science and engineering. Labs & Teams: Head of the Numerical Analysis and Scientific Computing Group at the University of Toronto, fostering interdisciplinary research in computational mathematics and software development.
Antoine Levitt is an Assistant Professor (CPJ) at the Laboratoire de Mathématiques d'Orsay, Université Paris-Saclay. Previously, he was a researcher at Inria Paris in the MATHERIALS project-team, affiliated with CERMICS at École des Ponts. His research focuses on numerical methods for quantum chemistry and materials science, including density functional theory (DFT), Wannier functions, and electronic structure calculations. He has contributed to theoretical advances in numerical analysis, such as error bounds for DFT and preconditioning techniques for eigenvalue problems. Levitt’s work spans computational physics and applied mathematics, with applications to quantum dynamics, response properties, and defect systems. He has developed open-source software tools like DFTK.jl and Wannierization algorithms . His teaching includes courses on scientific computing, functional analysis, and quantum mechanics at both undergraduate and graduate levels. Key research themes include numerical stability in DFT, analysis of linear response theory, and efficient implementations of conjugate gradient methods. His recent work explores machine learning applications in canonical sampling and mathematical foundations of Density Matrix Embedding Theory.
Shakoor Pooseh is a Postdoctoral Research Fellow at the Freiburg Center for Data Analysis and Modeling (FDM) , affiliated with the Institute of Physics at the University of Freiburg. His research focuses on computational approaches to decision-making, resilience dynamics, and neuropsychopharmacology. He leads the Value-Based Decision-Making Battery project, a Bayesian adaptive assessment tool for impulsive and risky behavior, and contributes to the EU Horizon 2020 DynaMORE initiative modeling psychological resilience. Education: Completed a PhD in Computational Methods in the Fractional Calculus of Variations at the University of Aveiro, Portugal (2013), with prior work in fractional optimal control problems. His interdisciplinary background bridges mathematics, neuroscience, and clinical psychology. Research Interests: Explores mathematical psychology, computational neuroscience, and statistical modeling of decision processes. Key topics include serotonin/dopamine modulation of impulsivity, resilience dynamics under stress, and translational research in addictive disorders. He develops software tools for behavioral experiments and machine learning models. Grants/Projects: Principal investigator for the DynaM-OBS longitudinal study on resilience, and collaborates on interventions like DynaM-INT . His work integrates clinical data with computational models to understand mental health outcomes. Labs/Teams: Active in the FDM interdisciplinary group and the Institute of Physics’ neuroscience unit. Collaborates with psychiatric research teams at the University Medical Center Freiburg.
Aleksandar Nanevski is a Research Professor at the IMDEA Software Institute since 2009. He holds a Ph.D. in Computer Science from Carnegie Mellon University (2004), with postdoctoral positions at Harvard University and Microsoft Research, Cambridge. His research focuses on programming languages for formal verification, integrating programming with theorem proving via type theory. Key interests include verifying imperative programs with modern language features, concurrency, and pointer arithmetic. He leads projects like FCSL (Functional Concurrent Separation Logic) and RHTT (Relational Hoare Type Theory). Education: Ph.D., Computer Science, Carnegie Mellon University, 2004 M.S., Computer Science, Carnegie Mellon University B.S., Computer Science, University of Skopje, Macedonia, 1995 Research Interests: Design and implementation of programming languages for formal verification, dependent type systems, concurrency, and security. His work bridges programming methodology with interactive and automated theorem proving, emphasizing scalable verification of imperative programs. Professional Activities: Program chair for HOPE (2016-2017), PC member for OOPSLA, POPL, and ESOP. Invited keynotes at MFPS, ICTI, and VS-THEORY. Active in foundational research on separation logic and concurrent systems. Labs/Projects: FCFS (Functional Concurrent Separation Logic) and RHTT (Relational Hoare Type Theory), focusing on security-aware and concurrent program verification.
Anne M. Andrews is a Professor-in-Residence in the Department of Psychiatry and Biobehavioral Sciences at the David Geffen School of Medicine, University of California, Los Angeles (UCLA). With over two decades of continuous NIH funding, she leads an innovative research program at the intersection of neuroscience, chemistry, and nanotechnology, developing cutting-edge tools for neurochemical monitoring and psychiatric research. Her primary research focuses on developing micro- and nanoscale sensors for real-time monitoring of neurotransmitters, particularly serotonin. Dr. Andrews pioneers aptamer-field-effect transistor (FET) biosensors and advanced voltammetry techniques to overcome traditional limitations in neurotransmitter detection. Her work bridges neurochemistry, nanotechnology, biosensor development, and psychiatric disorders, with significant contributions to understanding serotonin systems in depression, anxiety, and pain mechanisms. She has developed implantable and wearable biosensors that enable unprecedented spatial and temporal resolution in neurochemical monitoring. Her recent publications demonstrate a clear trajectory toward practical clinical applications of neurochemical sensing technology. She has made significant advances in aptamer-based FET technology, particularly for serotonin detection, with applications in understanding depression, anxiety, and pain mechanisms. Her interdisciplinary approach combines chemistry, neuroscience, and engineering to create tools that bridge molecular neuroscience with clinical applications, potentially transforming how we understand and treat psychiatric disorders. Dr. Andrews has maintained continuous NIH funding for over 20 years, with multiple R01 and R03 grants as Principal Investigator. Her research program, Micro- to Nanoscale Neurochemical Sensors (NIH R01DA045550), represents the culmination of her pioneering work in developing tools to monitor neurotransmitters with unprecedented precision. She has mentored numerous graduate students and postdoctoral researchers who have gone on to successful careers in academia and industry. Her laboratory has developed innovative chemical lift-off lithography techniques and aptamer-based sensing platforms that have been widely adopted by other researchers. The lab maintains strong collaborations with materials scientists, electrical engineers, and clinical researchers to translate basic discoveries into practical applications for neuroscience and psychiatry.
