Stefania Costantini is a Professor in Computer Science with a focus on logic programming, multi-agent systems, and healthcare applications. She has contributed to the development of intelligent ecosystems for patient monitoring, ontology frameworks for medical wearables, and formal verification methods for agent systems. Research Interests: Logic-based agent modeling Complex event processing Healthcare technology integration Temporal and metalevel logic applications Wearable device classification Recent Article Trends: Stefania's work combines artificial intelligence with biomedical engineering, emphasizing real-time data analysis from wearables, noise pollution mitigation, and agent-based healthcare systems. Her publications show a consistent focus on computational logic foundations applied to practical healthcare scenarios. Collaboration Network: Key co-authors include Lorenzo De Lauretis, Fabio Persia, and C. Bertoncelli across interdisciplinary projects blending computer science with medical research.
Carmine Dodaro is an active researcher in Answer Set Programming (ASP) at the University of Calabria's Department of Mathematics and Computer Science. With over 130 publications from 2011-2025, his work bridges theoretical advances in logic programming with practical healthcare applications. His primary research interests include: Answer Set Programming theory and implementation Compiler techniques for ASP solvers Healthcare scheduling optimization (operating rooms, nurse staffing, chemotherapy) Integration of ASP with other AI paradigms Real-world constraint satisfaction problems Dodaro's recent work demonstrates exceptional focus on healthcare applications of ASP, with multiple 2023-2024 publications addressing nurse scheduling, operating room management, rehabilitation planning, and nuclear medicine scheduling. His approach typically involves developing specialized ASP encodings that handle complex real-world constraints while maintaining computational efficiency. He maintains a highly productive collaboration network, particularly with Marco Maratea (52 co-authored papers), Mario Alviano (40), and Giuseppe Galatà (23), forming one of Italy's leading ASP research groups. His publications appear consistently in top venues including Theory and Practice of Logic Programming, IJCAI, AAAI, and specialized logic programming conferences. Dodaro has contributed significantly to both theoretical foundations (unsatisfiable core analysis, paracoherent reasoning) and practical implementations (WASP solver extensions, CNL2ASP translation tools). His 2024-2025 publications indicate ongoing research momentum with no signs of reduced activity.
Prof. Dr. Matthias Tichy is a Full Professor and head of the Institute of Software Engineering and Programming Languages at Ulm University, Germany, since 2015. His research focuses on domain-specific languages (DSLs), model-driven engineering (MDE), self-adaptive software , and cyber-physical systems , with an emphasis on safety-critical applications and graph transformation formalisms. He employs empirical research methods to evaluate technical contributions and human factors in software engineering. University: Ulm University Role: Professor & Institute Head Research Interests span domain-specific languages for mechatronic systems, collaborative modeling , performance prediction in model transformations, and software evolution in industrial contexts. His work often bridges graph transformations and safety assurance for self-adaptive systems. Recent Publications highlight trends in model versioning (e.g., operation-based caching), DSL design (e.g., flowR for R code analysis), and automotive software testing (e.g., clustering test case specifications). He frequently collaborates with international institutions on topics like cyber-physical systems and IoT resilience . Key Collaborations include projects with Chalmers University, University of Gothenburg, and industrial partners like dSPACE GmbH. His grants and industry partnerships focus on automotive software , robotics , and self-healing systems .
Stephen Chong is a Gordon McKay Professor of Computer Science in the Harvard John A. Paulson School of Engineering and Applied Sciences, where he serves as Co-Director of Undergraduate Studies for Computer Science. His academic career spans over a decade of teaching and research at Harvard, where he has made significant contributions to programming languages and information security. Chong received his PhD from Cornell University under the guidance of Andrew Myers, and a bachelor's degree from Victoria University of Wellington, New Zealand. Prior to graduate school, he worked as a consultant and contractor in the software industry, bringing practical experience to his academic research. Professor Chong's research focuses on language-based information security, using programming language techniques to provide information security assurance. His work bridges the gap between theoretical foundations and practical applications, developing tools and frameworks that help programmers write trustworthy programs. His research has evolved to address increasingly complex security challenges in modern computing environments, from web applications to cyber-physical systems. His recent publications reveal a strong trend toward integrating advanced programming language techniques with security analysis, particularly through the use of Datalog, SMT solvers, and program synthesis. His work on Formulog has been particularly influential, extending Datalog with mechanisms to construct and reason about SMT formulas for static analysis. His research has expanded to address security challenges in cyber-physical systems, where sensor attacks pose unique threats to safety-critical infrastructure. Chong has received numerous prestigious awards including an NSF CAREER award, an AFOSR Young Investigator award, and a Sloan Research Fellowship. He has also served in leadership roles for major conferences including CSF 2012-2013, PLMW @ PLDI 2021, and as SIGPLAN-M Chair for 2025-2026. As an educator, Chong has mentored numerous students through Harvard's undergraduate research programs and has served as a thesis advisor. His teaching portfolio includes foundational courses like CS51, systems courses like CS61, and advanced topics in programming languages (CS152) and compilers (CS1530). He has been instrumental in shaping Harvard's computer science curriculum, particularly in security and programming languages. Chong leads a research group focused on language-based security, with projects including Formulog (for SMT-based static analysis), PRINCESS (for autonomous adaptation of software), and work on secure shell scripting (Shill). His group collaborates with researchers across Harvard and other institutions to tackle challenging problems at the intersection of programming languages and security.
Konstantin Schekotihin is an Associate Professor at the Department of Artificial Intelligence and Cybersecurity, Alpen-Adria University of Klagenfurt. His research focuses on artificial intelligence, machine learning, and semantic technologies with applications in industrial systems and semiconductor manufacturing. Reinforcement learning for industrial scheduling Answer Set Programming (ASP) and stream reasoning Failure analysis automation and ontology engineering Neuro-symbolic AI integration Knowledge-based systems in manufacturing Recent publications emphasize AI-driven optimization in semiconductor production, decomposition strategies for scheduling problems, and multi-agent systems for workflow management. His work combines symbolic reasoning with machine learning to address complex industrial challenges. Contact: Konstantin.Schekotihin@aau.at
Walker M. White serves as Stephen H. Weiss Provost's Teaching Fellow and Director of the Game Design Initiative at Cornell (GDIAC) within Cornell University's Department of Computer Science. He teaches core game design courses (CS/INFO 3152 and CS/INFO 4152), CS 1110 (Introduction to Computing in Python), and mentors independent study projects through CS/INFO 4999. His academic leadership spans curriculum development, career advising for game design students, and cross-departmental coordination for Cornell's game design minor. White's research centers on two interconnected domains: data-driven game development and data stream processing. In data-driven games, he pioneers declarative specification methods for non-player character behavior, addressing performance bottlenecks in massively multiplayer environments through innovations like the SGL language. His concurrent Cayuga project develops scalable data stream processing systems that balance expressive query capabilities with publish/subscribe system efficiency, yielding theoretical advances in temporal query semantics and practical implementations for event monitoring. His educational scholarship focuses on inquiry-based learning techniques adapted from mathematical logic to computer science pedagogy. White's publication record from 2006-2011 reveals a cohesive trajectory where database theory informs gaming innovation. Early work established foundational challenges in virtual world scalability, evolving into specialized techniques for checkpoint recovery, MapReduce-based behavioral simulation, and declarative game languages. Simultaneously, his Cayuga research advanced publish/subscribe systems through multi-query optimization and formal stream semantics, demonstrating consistent methodological rigor across both domains. Stephen H. Weiss Provost's Teaching Fellow As GDIAC Director, White advises undergraduate game design students through competitive independent study projects, with select student games featured at independent game festivals. He facilitates industry recruitment by major studios including Electronic Arts, Valve, and Bungie, while supporting student startups in the mobile gaming space. His career advising leverages strong industry connections cultivated through Cornell's game design program. White leads the Game Design Initiative at Cornell (GDIAC), coordinating game design education across multiple academic departments. He collaborates extensively with Cornell's database research group, particularly with Johannes Gehrke and Alan Demers on data stream processing and gaming projects. His educational initiatives include developing inquiry-based learning materials for computer science and mathematics bridge courses.
Thomas Ströder is a Lecturer in Business Informatics at FHDW University of Applied Sciences since 2022, specializing in software engineering and formal methods. Previously, he served as Head of Full Stack Development at METRO (2016-2019) and Site Manager at IT-P GmbH (2020-2022), combining academic research with industrial leadership in software development organizations. His educational background includes: Diploma in Computer Science with Business Administration minor from RWTH Aachen University (2004-2010), featuring an exchange semester at UNSW Sydney (2008) funded by the Studienstiftung des Deutschen Volkes PhD in Computer Science from RWTH Aachen University (2019) focused on automatic quality assurance and software synthesis Ströder's research bridges theoretical computer science and industrial software engineering through core interests in program verification, formal methods, and software architecture. His work emphasizes practical applications of automated reasoning for termination analysis, memory safety, and complexity bounds in real-world systems, while extending to organizational development and data-driven decision processes in agile environments. This dual focus enables translation of formal verification techniques into enterprise software solutions. His 15 publications (2009-2018) reveal a consistent trajectory in program termination analysis and verification, primarily through the AProVE framework. Early work established foundations in term rewriting and logic program analysis, evolving toward memory-safe C program verification and bitvector arithmetic handling. Publications in top venues like Journal of Automated Reasoning and TACAS demonstrate specialization in bridging theoretical formal methods with practical software engineering challenges, particularly in pointer arithmetic and memory manipulation contexts. Professional activities include research collaborations with Microsoft Research Cambridge (2013) and leadership in building in-house software development organizations at METRO, reflecting strong industry-academia integration in his career trajectory.
Viorel Grigorcea is a Senior Lecturer at the Department of Computer Science within the Faculty of Mathematics and Informatics at the University of State of Moldova (USM). He has been with USM since 1997, becoming a senior lecturer by competition in 2002, and has served as Vice-Dean of the Faculty of Mathematics and Informatics since that same year. His academic journey began with a Bachelor's degree in Applied Mathematics from USM (1991-1996), followed by a Doctorate in Computer Programming from the Institute of Mathematics and Informatics of the Romanian Academy of Sciences (1996-1999). His teaching portfolio includes a comprehensive range of computer science courses: Artificial Intelligence Logic Programming Functional Programming Object-Oriented Programming Programming Fundamentals Programming Techniques Computer Architecture Operating Systems Expert Systems Grigorcea's research has evolved over time, with early work focusing on analogical reasoning, case-based reasoning, and attribute modeling in artificial intelligence systems. In recent years, his research has expanded to include educational assessment, particularly evaluating school results and basic skills of primary and secondary school graduates in Mathematics, Romanian, and Russian. His scientific activity includes continuous collaboration since 2001 on the institutional project 'Research and development of theoretical and practical aspects of modern programming technologies,' building on his earlier work in artificial intelligence systems. Grigorcea is associated with the Web Programming Section at USM, reflecting his engagement with practical programming technologies and applications.
Maria-Cristina Marinescu serves as a Lecturer in the Department of Mathematics and Data Analytics at IQS School of Management. With a strong foundation in computational methods and data analysis, she contributes significantly to both teaching and research within the institution. Her academic profile demonstrates consistent scholarly activity with 44 documented scientific production items spanning over two decades. Dr. Marinescu's research interests center on Machine Learning, Data Analytics, and Artificial Intelligence, with notable applications in epidemic modeling, wireless sensor networks, and programming models. Her work bridges theoretical computer science with practical applications addressing real-world challenges in public health, digital well-being, and medical diagnostics. The fingerprint analysis of her work shows strong emphasis on Machine Learning (100%), Learning Systems (100%), and Transportation Models (100%), with substantial contributions to Programming Models (84%) and Wireless Sensor Networks (77%). Her recent publications reveal a trend toward interdisciplinary research that combines computational methods with societal challenges. She has made significant contributions to epidemic modeling during the COVID-19 pandemic, developing methods for accurate incidence rate estimation in Spain and analyzing information gains from multiple epidemic model outputs. Additionally, her work extends to medical applications including research on ocular ischemia and glaucoma, as well as innovative approaches to automated metadata annotation using machine learning techniques. Dr. Marinescu actively leads and participates in multiple research projects including Uncovering patterns of unconscious reactions to fake content (as Principal Investigator), MobilePressure (focused on reducing children's smartphone exposure), and ADAMIQS: Applied Data Analytics and Modelling IQS . These projects demonstrate her commitment to addressing contemporary issues through data-driven approaches while mentoring students and collaborating with interdisciplinary research teams.
Morten Sørensen serves as a Clinical Instructor in the Department of Odontology at the Faculty of Health and Medical Sciences, University of Copenhagen. Based at Nørre Allé 20, 2200 Copenhagen N, he is contactable via phone (+4535325178) or email ( morten.sorensen@sund.ku.dk ). His research centers on logic programming with specialized expertise in partial deduction and program transformation. He develops foundational theories and algorithms for conjunctive partial deduction, advancing applications in automated reasoning and declarative programming systems. This work bridges theoretical computer science with practical implementation challenges. His seminal 1999 publication in the Journal of Logic Programming demonstrates significant scholarly impact, accumulating 82 Scopus citations. The research establishes critical frameworks for program specialization within artificial intelligence, reflecting sustained relevance in programming language theory despite its age.
