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Carla FerreiraView profile

Professor

• Models of concurrency and distribution
• Foundations of cloud computing
• Language-based techniques for low-code software development
• +5 more

Carla Ferreira is a Full Professor in the Computer Science Department at NOVA University of Lisbon and a researcher at NOVA Laboratory for Computer Science and Informatics (NOVA LINCS) in the Software Systems research group. Her academic career began as an Assistant at Universidade do Minho, followed by positions at the University of Southampton's Dependable Systems & Software Engineering Group, and later as an Assistant Professor at IST before joining her current position at NOVA University Lisbon. Her primary research interests focus on Models of concurrency and distribution , Foundations of cloud computing , and Language-based techniques for low-code software development . She has made significant contributions to distributed systems, particularly in the areas of data replication, consistency models, and programming languages for distributed environments. Her work bridges theoretical foundations with practical applications in cloud and edge computing systems. Dr. Ferreira's recent publications demonstrate a strong focus on low-code development platforms, particularly the OSTRICH language framework she has developed, as well as replicated data types, spatio-temporal monitoring systems, and ontology-based approaches to software development. Her research consistently addresses the challenges of building reliable distributed systems while improving developer productivity through language-based techniques. She has served on the program committees of numerous prestigious conferences including ECOOP, FORTE, POPL, and TACAS, demonstrating her standing in the programming languages and distributed systems communities. As an academic advisor, Dr. Ferreira has supervised numerous PhD and MSc students, both ongoing and completed, working on topics ranging from distributed databases to low-code development and formal verification. She has also led significant research projects including PRECISE (as Principal Investigator), GOLEM, LightKone, and currently TaRDIS (Trustworthy and Resilient Decentralised Intelligence for Edge Systems).

• Artificial Intelligence
• Neuro-Symbolic Integration
• Knowledge Graphs
• +4 more

Luciano Serafini is a researcher at Fondazione Bruno Kessler in Trento, Italy, specializing in Artificial Intelligence with a focus on Neuro-Symbolic Integration and Knowledge Graphs . His work bridges Machine Learning and Symbolic Reasoning , emphasizing Planning , Relational Learning , and Visual-Textual Grounding . Key Research Areas : Neuro-symbolic systems, logic-based knowledge representation, planning under uncertainty, and computer vision. Recent Publications highlight trends in Embodied AI for open-world tasks, Weighted Model Counting , and Graph Generative Models . His contributions include Logic Tensor Networks for integrating deep learning with formal logic and methods to mitigate Data Sparsity through knowledge transfer. Collaborations span institutions like the University of Trento and research teams in Computer Vision and Reasoning , with applications in Social Navigation and Event Recognition .

• Artificial Intelligence
• Machine Learning
• Data Mining
• +5 more

Henry A. Kautz is a Professor at the University of Rochester, New York, USA, with a distinguished career in artificial intelligence, machine learning, and social media analysis. His research spans interdisciplinary applications in healthcare informatics, urban mobility, and computational modeling of human behavior. Research Focus : Integrates AI with public health surveillance (e.g., mental health monitoring via search engine logs), social media analysis for disease transmission modeling, and multimodal data interpretation for activity recognition. Key Contributions : Pioneered nEmesis for foodborne illness detection via social media, developed Markov logic frameworks for complex event recognition, and advanced privacy-preserving systems for cognitive disabilities support. His recent work explores hardware-accelerated SAT solving, explainable AI for deep learning, and longitudinal behavioral analysis during the COVID-19 pandemic. Publications emphasize practical AI applications in health, urban planning, and computational epidemiology.

• Machine Learning
• Deep Learning
• Zero/Few Shot Learning
• +7 more

Fadi Al Machot is an Associate Professor at the Department of Data Science, Norwegian University of Life Sciences (NMBU), specializing in machine learning and human-centered AI systems. His work bridges computational intelligence with practical applications in active and assisted living, emotion/activity recognition, and multimodal sensor analytics.

• Artificial Intelligence
• Business Process Modeling
• Knowledge Engineering
• +9 more

Antoni Ligęza is a Professor at the Department of Applied Informatics, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Science and Technology in Kraków. His research focuses on artificial intelligence, business process modeling, constraint programming, and knowledge engineering. He actively explores explainable AI methodologies, including grammatical evolution and temporal logic-based approaches.

Michal WeiszerView profile

Lecturer

• Optimisation
• Metaheuristics
• Operational Research
• +5 more

Dr. Michal Weiszer is a Teaching Fellow at the School of Engineering and Materials Science, Queen Mary University of London, specializing in optimization, metaheuristics, and operational research with applications in airport operations. His research has significantly contributed to the development of the Active Routing concept for airport ground movement, integrating aircraft engine performance and airframe dynamics with routing and scheduling to achieve greener operations. Dr. Weiszer's research interests focus on routing and scheduling, multi-objective optimisation, simulation, and intelligent transportation systems . His work bridges theoretical optimization techniques with practical aviation applications, particularly in airport ground movement. He has pioneered approaches that integrate aircraft performance models with routing algorithms to reduce emissions and improve efficiency in airport operations. His research spans both algorithmic development and practical implementation, with strong industry connections. Analysis of Dr. Weiszer's publication record shows a consistent focus on airport ground movement optimization using evolutionary algorithms and multi-objective approaches. His research has evolved from basic routing algorithms to more sophisticated integrated systems incorporating aircraft dynamics, emissions modeling, and uncertainty handling. Recent work has expanded into multimodal transportation and open-source simulation platforms, demonstrating the broadening impact of his research beyond traditional airport operations. Dr. Weiszer has received several notable awards and funding: QMUL Flexible Innovation Fund as Principal Investigator EPSRC sponsored TRANSIT project (EP/N029496/1) as Research Co-Investigator Impact Acceleration projects with Avisu as Co-Investigator Impact Acceleration projects with NATS (UK air traffic control) as Co-Investigator His grant portfolio demonstrates strong industry-academia collaboration, with projects translating theoretical research into practical applications. The funding from Royal Society, EPSRC, and industry partners highlights the recognition of his work's potential impact on sustainable aviation. Dr. Weiszer has successfully secured both early-stage innovation funding and larger collaborative projects, showing progression in his research career.

• Proof Complexity
• Computational Complexity
• Algorithmics
• +5 more

Ilario Bonacina is a Lecturer at the Department of Computer Science within the College of Computer Science of Barcelona (FIB) at the Polytechnic University of Catalonia . His research focuses on Proof Complexity , Computational Complexity , and Algorithmics , particularly in Polynomial Calculus and MaxSAT Optimization . Education: Laurea in Mathematics, Laurea Specialistica in Mathematics, and Doctorate from UPC. Research Interests include proof systems (Sherali-Adams, Nullstellensatz), algebraic methods, and theoretical computer science. Recent publications analyze complexity bounds and optimization algorithms. Scientific Awards include recognition for Best Italian PhD Theses in Theoretical Computer Science. He participates in competitive R+D+i projects like the State Plan for Scientific Research and Innovation.

Sophie TourretView profile

Researcher

• Automated Reasoning
• Formal Logic
• Artificial Intelligence
• +8 more

Sophie Tourret is a Researcher at Inria (Nancy & LORIA, France) and a guest researcher in the Automation of Logic group at the Max Planck Institute for Informatics (MPII, Germany). Her research focuses on extending automated reasoning techniques to higher-order logic, improving SMT solvers, and generating prime implicates in equational logic. Education: PhD in Computer Sciences, Grenoble Alpes University (2016) Master in Web and Artificial Intelligence, Joseph Fourier University (2012) Engineering Degree, Grenoble INP - Ensimag (2012) Research Interests include automated reasoning, formal logic, artificial intelligence, and logic programming. She leads projects like Matryoshka (extending superposition to higher-order logic) and CPEC (explanation generation in description logics). Recent Publications span topics such as superposition calculus, prime implicate generation, and neural-to-symbolic learning. Her work bridges symbolic reasoning and machine learning in formal logic contexts. Scientific Awards: ILP Best Paper Award (2019) Lise Meitner Award (2018) 2 Minutes Madness Best Poster and Presentation (2014) Teaching Experience includes formal languages theory, database management, and communication networks at Grenoble INP - Ensimag. She also served on program committees for conferences like CADE, IJCAI, and IJCAR.

Ashutosh GuptaView profile

Associate Professor

• Formal Verification
• Software Verification
• Model Checking
• +7 more

Ashutosh Gupta is an Associate Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Bombay, where he has been a faculty member since 2018. His research focuses on formal methods for software verification, particularly in the areas of model checking, constraint solving, and automated reasoning for both sequential and concurrent programs. He teaches advanced courses including Automated Reasoning (CS433), Analysis of Concurrent Programs (CS766), and Logic for Computer Science (CS228). Dr. Gupta received his Ph.D. in Computer Science from Technical University of Munich (TUM) in 2011, with affiliations during his doctoral studies at TUM, Max Planck Institute for Software Systems (MPI-SWS), and École Polytechnique Fédérale de Lausanne (EPFL). Prior to joining IIT Bombay, he served as a faculty member at Tata Institute of Fundamental Research (TIFR) in Mumbai and completed post-doctoral research in the Henzinger group at IST Austria. His research interests span formal verification of sequential and concurrent software, modeling of biological systems, and constraint solving including constraint logic programming, decision procedures, and automated theorem proving. He has developed several verification tools including VAJRA, HSF, and InvGen. His work bridges theoretical foundations with practical applications, particularly in verifying safety-critical systems and biological processes. Dr. Gupta's publication record shows a consistent trajectory of high-impact research in top venues like POPL, CAV, TACAS, and AAAI. His recent work has expanded into neural network verification, reinforcement learning verification for medical devices, and applying formal methods to biological systems, demonstrating both depth in core verification techniques and breadth in application domains. Best Paper Award at TACAS 2015 Best Paper Award at TACAS 2009 Dr. Gupta actively mentors numerous students through course projects, research presentations, and specialized workshops like SATfest. He has supervised students working on SAT/SMT solvers, verification of concurrent programs, and applications of formal methods to biological systems. His teaching philosophy emphasizes hands-on experience with verification tools and encourages students to engage with cutting-edge research through reading and presenting recent conference papers. He maintains active research collaborations with institutions including TUM, MPI-SWS, EPFL, IST Austria, and TIFR, reflecting his continued integration into the international formal methods research community.

Robert JackView profile

Senior Lecturer

• International Business
• Internationalization of Firms
• Born Global Firms
• +4 more

Robert Jack is a Senior Lecturer in International Business at Macquarie University's Department of Management, serving as Course Director for the Master of Management program. His academic career spans institutions including Monash University, Swinburne University of Technology, City University of Hong Kong, with visiting positions at Copenhagen Business School and Chinese Academy of Social Sciences. His research focuses on international business dynamics with specialization in: Internationalization processes of firms (particularly Chinese multinationals and born global enterprises) Indigenous entrepreneurship and adversity quotient in marginalized communities Service sector internationalization and value co-creation mechanisms Human resource management implications in cross-border expansions Analysis of his 36 research outputs reveals consistent emphasis on emerging market multinationals, institutional constraints in internationalization, and Indigenous entrepreneurship frameworks, with recent work pivoting toward digital empowerment of Indigenous female entrepreneurs through WIL programs. He holds editorial roles at the Journal of Business Research (ABDC-A) and European Journal of International Management, and has served in executive capacities at the Australia and New Zealand International Business Academy since 2011. His current research portfolio includes: Acen Grant: Digitally empowering Indigenous female students through WIL (2024-2025) Indigenous Women Entrepreneurs project exploring identity formation (2021-2023) Chinese service firm internationalization studies (2016-2017)

Lucas WaddellView profile

Associate Professor

• Discrete Optimization
• Operations Research
• Nonlinear Combinatorial Optimization
• +4 more

Lucas Waddell is an Associate Professor of Mathematics at Bucknell University, joining the faculty in 2018. Prior to Bucknell, he worked as an Operations Research Analyst at Sandia National Laboratories in Albuquerque, NM, addressing large-scale decision support problems for national security applications. His academic background includes a Ph.D. and M.S. in Mathematical Sciences from Clemson University and a B.S. in Mathematics from Grove City College. Ph.D. in Mathematical Sciences, Clemson University M.S. in Mathematical Sciences, Clemson University B.S. in Mathematics, Grove City College Waddell's research focuses on discrete optimization, particularly nonlinear combinatorial optimization problems like the Quadratic Assignment Problem and Cubic Knapsack Problem. He develops theoretical frameworks and practical tools to solve computationally challenging problems, including applications in autonomous systems, fleet modernization, and educational scheduling. His work with Hopeful Journeys Educational Center demonstrates his commitment to applying optimization techniques to real-world challenges. His publications highlight advancements in linearization techniques, reformulation-linearization theory, and stochastic programming for security systems. Keywords across his work include Operations Research, Combinatorial Optimization, Integer Programming, and Computational Complexity. He has also contributed to applied projects involving national security and educational resource allocation. Contact: law038@bucknell.edu | Office: 477 Olin Science Building | Phone: 570-577-1902

• Constrained Clustering
• Explainable AI
• Constraint Programming
• +4 more

Thi-Bich-Hanh DIEP-DAO serves as a Lecturer with Habilitation à Diriger des Recherches (HDR) at the University of Orleans, where she is affiliated with the LIFO laboratory (Laboratory of Informatics of Orléans). She holds significant administrative roles including THAT Lab correspondent at the Doctoral School and Member of the Board, demonstrating leadership in academic governance and doctoral education. Her research centers on constrained clustering methodologies, with evolving focus areas including explainable AI, constraint programming applications, and knowledge integration in machine learning systems. Early work established foundational approaches for constraint-based clustering using declarative frameworks, while recent publications demonstrate sophisticated extensions to mineral prospectivity mapping, social media analysis, and knowledge graph-enhanced image classification. Her research consistently bridges theoretical constraint programming with practical applications across diverse domains. Analysis of her publication trends reveals increasing emphasis on explainability and knowledge integration since 2020, with notable contributions to incremental clustering systems and graph-based knowledge representation. Her work maintains strong methodological consistency in constraint programming while expanding into new application areas, particularly evident in her 2023-2025 publications on mineral prospectivity mapping and tweet-based event characterization. As an HDR-qualified researcher, Dr. DIEP-DAO actively supervises doctoral students and contributes to academic training through her roles in the Doctoral School. Her extensive collaboration network, particularly with Christel VRAIN, has produced over 60 publications since 2010, reflecting consistent research productivity. She is integral to the LIFO laboratory's data mining and constraint programming research group, where her work on declarative clustering approaches forms a core research pillar. Current projects focus on knowledge graph integration for explainable image classification systems and constraint-based social media analysis frameworks.

Denys DuchierView profile

Professor

• Constraint programming
• Computational linguistics
• Programming languages
• +3 more

Denys Duchier is a Professor of Computer Science at the University of Orléans, affiliated with the LIFO (Laboratoire d'Informatique Fondamentale d'Orléans) and leading the "Constraints and Learning" team. His research bridges constraint programming, computational linguistics, and programming language design. University: University of Orléans Academic Role: Professor Research Team: Constraints and Learning (LIFO) Research Interests include constraint programming, Tree Adjoining Grammars (TAG), syntax-semantics interfaces, and formal grammar modeling. His work focuses on applying constraint-based methods to natural language processing, particularly for under-resourced languages like Arabic. Publications emphasize grammar engineering, constraint-driven language modeling, and applications of formal grammars to African languages. He collaborates on meta-grammar frameworks (e.g., XMG) and constraint optimization for linguistic analysis. Contact : denys.duchier@univ-orleans.fr, Building IIIA, Rue Léonard de Vinci, BP 6759, F-45067 Orléans Cedex 2.

Yufei DingView profile

Associate Professor

• Quantum Computing
• Machine Learning
• Compiler Optimization
• +3 more

Yufei Ding is an Associate Professor in the Computer Science & Engineering Department at the University of California, San Diego (UCSD), and founder of the PICASSO Lab. She earned a Ph.D. in Computer Science from North Carolina State University and a B.S. in Physics from the University of Science and Technology of China. Research Interests: Quantum Computing Machine Learning Domain-Specific Languages Compiler Optimization Hardware Acceleration High-Performance Computing Her recent publications focus on quantum compilation, error correction, and machine learning systems, with a particular emphasis on hardware-aware optimizations. She has received multiple prestigious awards, including the NSF CAREER Award (2020) and the IEEE TCHPC Early Career Researchers Award (2019). Scientific Awards: NSF CAREER Award (2020) IEEE Computer Society TCHPC Early Career Researchers Award (2019) Yufei actively advises Ph.D. students and postdoctoral researchers in quantum computing and machine learning systems. Her lab offers openings for both quantum computing and machine learning research.

Babak EsfandiariView profile

Professor

• Artificial Intelligence
• Agent-Based Systems
• Network Computing
• +6 more

Babak Esfandiari is a Professor at the Faculty of Engineering and Design , Department of Systems and Computer Engineering , Carleton University. His research focuses on Agent-based systems , Network computing , Artificial intelligence applications , Object-oriented design , and Network management . He leads the NM-AI Research Lab and contributes to Sustainable Energy initiatives. Ph.D. from Université Montpellier II (France) Email: babak@sce.carleton.ca Office: MacKenzie Building, Room 4478 His research bridges Artificial Intelligence , Network Engineering , and Software Engineering , with applications in Mobile Robotics , Digital Twin Emulation , and Network Optimization . He has developed frameworks for BDI Agents , Trust Management , and Network Simulation , as evidenced by his publications. He teaches advanced courses such as SYSC 5103 Software Agents , SYSC 3110 Software Development Lab , and SYSC 4806 Software Engineering Lab . His work has influenced interdisciplinary domains including Energy Policy and Human-Agent Interaction .