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Johannes LutzeyerView profile

Assistant Professor

• Graph Neural Networks
• Spectral Graph Theory
• Graph Representation Learning
• +6 more

Johannes Lutzeyer is an Assistant Professor in the Data Science and Mining Team at the Computer Science Department of École Polytechnique in France. His academic journey includes a PhD in Mathematics from Imperial College London (supervised by Prof. Andrew Walden) and a postdoctoral fellowship at École Polytechnique with Prof. Michalis Vazirgiannis. Current role: Assistant Professor in Computer Science at École Polytechnique PhD: Mathematics Department, Imperial College London Postdoc: École Polytechnique His research lies at the intersection of Graph Representation Learning , Statistics , and Computer Science , focusing on Graph Neural Networks (GNNs) and spectral properties of graph shift operator matrices. Recent work explores robustness against adversarial attacks, virtual nodes, and theoretical advancements in GNN expressiveness. The 15 most recent publications span Machine Learning , Deep Learning , and Network Science , with subfields including GNN Robustness , Hyper-Heuristics , Bioinformatics Applications , and Spectral Analysis . Collaborations include leading conferences like ICML, ICLR, and AAAI. Scientific Recognition LOG Top Reviewer Awards (2024) ICML Best Reviewer Awards (2024) NeurIPS Top Reviewer Awards (2023) Johannes actively engages in academic knowledge dissemination through keynote talks, including titles like “Understanding Virtual Nodes in Graph Neural Networks” (ICLR 2025) and “Methodological Advances in Graph Neural Networks” (Ericsson Research 2024). He also invites applications for PhD positions, reflecting his mentorship role.

Laurence Mylène BrandenbergerView profile

Researcher

• Legislative Behavior
• Parliamentary Networks
• Social Influence in Politics
• +5 more

Laurence Mylène Brandenberger is a Postdoctoral Researcher at the Department of Political Science, University of Zurich, specializing in legislative behavior and parliamentary networks. Their research examines the intersection of social relations and political decision-making, with a focus on systemic spill-over effects in policy networks and enhancing democratic transparency through projects like the DemocraSci Knowledge Graph. Current focus on temporal reciprocity in congressional collaborations Developing computational tools (embed2discover, Data2neo) for policy network analysis Recipient of SNF and SDSC grants for political performance and advocacy studies Teaching course: The Politics of Influence: Interest Groups, Lobbying, and Political Advocacy (2025) Their work bridges network science and political decision-making, utilizing large-scale datasets spanning decades and multiple policy sectors. Laurence applies causal network analysis to unravel dependencies in political systems and advocates for citizen-oriented platforms to reverse electoral decline and enhance civic engagement. Prior research includes quantifying triadic closure in political networks, studying cross-continental policy networks, and analyzing conflict event data through relational event models. Their methodological contributions in dictionary-based content analysis and multi-edge network modeling have advanced empirical approaches in political science. SNF Grant (2024): Measuring Political Performance of Swiss MPs SDSC Grant (2025): Studying Political Advocacy As part of the Institute of Political Science at the Faculty of Philosophy, Laurence actively contributes to computational infrastructure development and open science initiatives aimed at improving policy transparency and reducing polarization in Western democracies.

• Bioinformatics
• Computational Biology
• Network Analysis
• +4 more

Simona Ester Rombo is a Full Professor in the Department of Mathematics and Computer Science at Università degli Studi di Palermo (UNIPA), Italy. Her research spans computational biology, bioinformatics, network analysis, and big data approaches applied to biological and social systems. Her primary research interests include: Bioinformatics and computational genomics, particularly focusing on lncRNA-disease associations and RNA editing Network analysis and graph algorithms for biological and social networks Development of software tools for large-scale data analysis in biology Machine learning applications in drug discovery and repurposing Big data approaches for genomic sequence analysis and social network analysis Professor Rombo's publications demonstrate a strong focus on developing computational methods for biological network analysis, with particular emphasis on protein-protein interaction networks, molecular interaction networks, and lncRNA-disease association prediction. Her work bridges computer science and biology, creating practical tools and algorithms that address real biological challenges. Her scientific contributions include: Development of DIAMIN, a software library for distributed analysis of molecular interaction networks Creation of FEDRO, a tool for discovering candidate ORFs in plants with RNA editing Research on topological properties of biological networks and their functional implications Applications of network analysis to drug discovery and repurposing Professor Rombo actively teaches courses related to big data management and software engineering, mentoring students in computer science and artificial intelligence programs. Her office is located at Via Archirafi 34, Floor 2, Room 220 at UNIPA, with office hours on Mondays from 9:30 to 13:30.

Yashwant RammaView profile

Professor

• Physics Education
• Teacher Training
• Educational Technology
• +5 more

Professor Yashwant Ramma is the Head of the School of Science & Mathematics at the Mauritius Institute of Education, where he leads research on physics education, teacher content knowledge, and technology integration in pedagogy. He holds a PhD in Physics (1993) and M.A in Education (2001) from the University of Brighton, and a Post Graduate Certificate in Professional Studies (2007). His work emphasizes addressing misconceptions in physics teaching through professional development workshops and formative assessments. Research Focus Student-teacher-parent collaboration via technology Misconceptions in kinematics among physics teachers Affective domain (motivation, values) in physics learning Curriculum implications of mathematics-science integration Grants & Collaborations Ramma's research is funded by the Mauritius Research Council. He collaborates with institutions like Brunel University and the African Deans of Education Forum, and serves as a consultant for UNESCO projects. Professional Roles Chair, Research at Mauritius Institute of Education Member of Association for Science Education (UK), AAAS (USA), and Mauritius Academy of Science and Technology Lead of large-scale studies on teacher education reform

• Computer Science
• Artificial Intelligence
• Knowledge Representation and Reasoning
• +3 more

Matthew Mantle is a Senior Lecturer in the Department of Computer Science at the University of Huddersfield. He is affiliated with the Centre for Planning, Autonomy and Representation of Knowledge and the Centre for Autonomous and Intelligent Systems. His research focuses on large-scale knowledge representation, reasoning, and graph-based methodologies. Current research involves integrating large language models (LLMs) with model checking for temporal reasoning systems Active in developing distributed spatio-temporal reasoning frameworks Collaborates on projects like MIREL (Mining and Reasoning with Legal texts) Research interests span artificial intelligence , knowledge representation , and graph theory , with applications in real-world knowledge processing. Recent work explores qualitative spatial reasoning and temporal logic verification. As an active PhD supervisor, he contributes to student research in interactive media and web application development. He served as Programme Committee Member for the 11th IEEE International Conference on Knowledge Graph.

Johannes WachsView profile

Associate Professor

• Network Science
• Data Science
• Complexity Science
• +14 more

Johannes Wachs is an Associate Professor at the Institute of Data Analytics and Information Science, Corvinus University of Budapest, and a Research Fellow at the Centre for Economic and Regional Studies. He is affiliated with the Complexity Science Hub Vienna, where he has been a faculty member since April 2020. His interdisciplinary research bridges data science, network science, and complexity to study digital economies, open source software, and societal challenges. PhD in Network Science, Central European University (2019) MS in Applied Mathematics, Central European University (2012) BS in Mathematics and Economics, Tulane University (2009) His research focuses on the application of network and data science to understand social, economic, and technical systems. Key interests include open source software ecosystems, AI’s impact on knowledge sharing, corruption detection, urban inequality, and digital innovation. He uses large-scale digital trace data to model complex behaviors in online communities, software development, and public policy. His recent publications reveal a strong trend in analyzing digital platforms such as GitHub and Stack Overflow, studying brain drain in tech, and assessing climate and health risks in Austria. His work combines network modeling, machine learning, and empirical analysis to uncover patterns in human behavior and systemic risk. IMF Anti-Corruption Challenge Winner (2020) Principal Investigator, CRISP Project (2021–2024), funded by FFG Grants from Hungarian Research Funding Agency (OTKA), City of Vienna, and WU Projects Johannes Wachs actively supervises PhD and Master’s students, including Hannah Schuster and Brigi Németh. He has taught courses in computational social science, social networks, and data mining at institutions including RWTH Aachen, CEU, and WU Vienna. He is launching a new MSc in Social Data Science at Corvinus in 2025. He leads the CRISP project, which builds semantic data pools for real-time crisis response and intervention, integrating heterogeneous data sources for impact forecasting and policy transparency.

Giovanna GuerriniView profile

Associate Professor

• Data Management
• Large Scale Data Management
• Semantic Data Management
• +15 more

Giovanna Guerrini is an Associate Professor in the Department of Informatics, Bioengineering, Robotics, and Systems Engineering (DIBRIS) at the University of Genoa, Italy. She is actively involved in research, teaching, and academic service, with leadership roles in major conferences such as EDBT (Executive Board Member and Treasurer), SOFSEM (Track Chair), and UMAP (Workshop Chair). Her research interests include: Data Management Large Scale and Semantic Data Management Approximate and Adaptive Query Processing Linked Data and Ontology Matching Graph Matching and Geospatial Ontologies Spatio-Temporal Data and Location Inference Computer Science Education and Computational Thinking Her recent publications show a growing focus on computer science education, particularly in using gamified and extended reality environments, Sonic Pi for teaching concurrency, and AI-driven tools for enhancing computational thinking. This reflects a shift toward pedagogical innovation and student-centered learning methodologies. She has been recognized through active participation in top-tier program committees (SIGMOD, ISWC, ICDE) and organizing key workshops and schools (EDBT School 2017, APCSE at UMAP). No formal scientific awards are listed in the provided text. She advises multiple PhD students, both current and past, and contributes to academic grants and collaborative projects, particularly in database education and data science initiatives. She is affiliated with research groups including the DaMA Research Group, Data Science and Engineering Research Program, Big Data Interest Group UniGe, and CINI Big Data Research Lab.

Peng JiangView profile

Assistant Professor

• Machine Learning Systems
• High-Performance Computing
• Graph Processing & Mining
• +5 more

Peng Jiang is an Assistant Professor in the Computer Science Department at the University of Iowa. His research focuses on machine learning systems, high-performance computing, and graph processing, with a particular emphasis on compiler and programming techniques for GPU acceleration. He earned his Ph.D. in Computer Science from The Ohio State University in 2019 under Dr. Gagan Agrawal. Education: Ph.D., The Ohio State University, 2019 His work spans sparse training, knowledge graph embedding, and subgraph matching, often leveraging fine-grained parameter management and GPU optimization. Key trends in his publications include compiler design for high-performance systems, parallel programming models, and performance-aware weight pruning for neural networks. Scientific Awards 2024 NSF CAREER Award Peng Jiang has collaborated extensively with researchers such as Lihan Hu, Yihua Wei, Shihui Song, and Gagan Agrawal. His contributions to sparse matrix multiplication, distributed learning communication optimization, and PIM architecture-aware frameworks highlight his expertise in bridging machine learning and systems research.

Paolo FalcarinView profile

Associate Professor

• Cybersecurity
• Software Engineering
• Artificial Intelligence
• +5 more

Paolo Falcarin is an Associate Professor at Ca' Foscari University of Venice, affiliated with the Department of Environmental Sciences, Computer Science and Statistics. His research focuses on cybersecurity, software engineering, and AI compliance, with notable contributions to software protection mechanisms, reverse engineering defenses, and cyber-physical systems. He actively participates in editorial roles for journals like Computers & Security and serves on program committees for IEEE/ACM conferences. Key Projects: ASPIRE (EU FP7), SERICS (PNRR), and Flexymob (Currant s.r.l.) Research Interests: Cybersecurity Knowledge Graphs, Runtime Security (Falco), AI Act Compliance, and Software Renewability His work bridges theoretical cybersecurity advancements with practical applications in distributed systems and IoT. Recent publications emphasize anomaly detection in large-scale systems and regulatory compliance frameworks for AI.

• Geospatial Digital Twins
• Virtual Geographic Environments
• Remote Sensing
• +7 more

Jun Zhu is a professor at the Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, China. His research is centered on geospatial digital twins, virtual geographic environments, and intelligent visualization for disaster risk management, with strong interdisciplinary work in AI, remote sensing, and VR-based simulation. His research interests include: Geospatial Digital Twins Virtual Geographic Environments AI for Remote Sensing 3D and VR-based Disaster Visualization Knowledge Graphs in GIS Public Risk Communication The recent articles (2023–2025) demonstrate a strong trend in integrating large language models, knowledge graphs, and deep learning with geospatial data to build intelligent, interactive, and immersive systems for infrastructure monitoring, disaster simulation, and public engagement. His work emphasizes data-knowledge fusion, human-centered visualization, and real-world applicability in urban and environmental contexts. Scientific Awards: No awards listed in the provided text. Advising and Grants: While Jun Zhu has extensive collaboration with researchers such as Weilian Li, Qing Zhu, Yakun Xie, and Jianbo Lai, and appears to lead research projects, there is no explicit mention of student advising, grant funding, or project titles in the provided data. Labs and Teams: Jun Zhu is likely part of a research group focused on digital twins and geospatial AI at Southwest Jiaotong University, collaborating closely with colleagues in geoinformatics and remote sensing, though specific lab names are not mentioned.

• Sexology
• Gender Studies
• Behavioral Sciences
• +3 more

Christian Graugaard is a Professor of Sexology at Aalborg University, affiliated with the Faculty of Medicine and the Department of Clinical Medicine. He is a key member of the Center for Sexology Research and leads Project SEXUS, aiming to study Danish sexual behavior comprehensively. His work emphasizes the intersection of biological, psychological, and cultural factors in human sexuality, challenging simplistic gender-based stereotypes. Research interests include gender differences in sexual behavior, societal norms influencing sexual health, and the cultural dimensions of human sexuality. He actively participates in public discourse, as seen in his DR-podcast interview on 'Ramt af kærlighed,' where he discussed the complex interplay between biology and culture in shaping sexual identities. Though no specific awards or grants are detailed here, his publications span advanced technical domains like spatiotemporal data analysis, federated learning, and trajectory modeling, suggesting interdisciplinary research collaborations. His work on systems like OneDB and SWASH highlights contributions to distributed computing and data science, which may underpin his methodologies in large-scale sexual behavior studies. He currently holds no listed students or formal advisees in the provided texts, and his involvement in labs/teams is limited to the Center for Sexology Research and Project SEXUS.

Raquel Garrido AlhamaView profile

Assistant Professor

• Computational modelling of language learning
• Language acquisition
• Language evolution
• +7 more

Dr Raquel Garrido Alhama is an Assistant Professor of Artificial Intelligence for text and human interaction at the Institute for Logic, Language and Computation (ILLC) , University of Amsterdam. She also co-directs the /k/omputation and Language Lab and previously served as Assistant Professor at the Cognitive Science and Artificial Intelligence department of Tilburg University. She earned her PhD from the ILLC under the supervision of Jelle Zuidema, Remko Scha and Carel Ten Cate, with a dissertation entitled Computational Modelling of Artificial Language Learning . Prior post-doctoral positions include the Max Planck Institute for Psycholinguistics and the Basque Center on Cognition, Brain and Language. Research Interests: Computational modelling of language learning and evolution Language acquisition, word learning and sentence processing Neural-symbolic integration and connectionist architectures Visual and auditory word recognition, syntactic parsing and part-of-speech tagging In recent years her work has converged on validating developmental milestones—such as the onset of productive determiner–noun combinations—using large-scale child corpora and neural models. She also investigates how multi-agent referential games can lead to emergent compositional languages, moving from image to richer graph representations. Scientific Awards: Best Article Award (2019) from Psychonomic Bulletin & Review for the review on computational models of rule learning Best Poster Award (2015) at the International Conference on Cognitive Modeling Teaching & Supervision: Dr Alhama has designed and lectured courses on advanced programming for cognitive science, computational linguistics, linear algebra and language acquisition at Tilburg University, University of Amsterdam, Radboud University and the University of the Basque Country. She currently supervises eleven MSc and BSc students in AI, Logic and Cognitive Science. Labs & Teams: She is the founding co-director of the /k/omputation and Language Lab together with Phong Le, where they use Language Emergence frameworks to explore the cognitive prerequisites for displacement and compositionality in human language.

Rob H. BisselingView profile

Professor

• Parallel Algorithms
• Sparse Matrix/Tensor Computations
• Graph and Hypergraph Algorithms
• +5 more

Rob H. Bisseling is a Full Professor in Scientific Computing at Utrecht University's Mathematical Institute and a visiting professor at ENS de Lyon's LIP laboratory (March–May 2024). He holds a BSc/MSc in Mathematics (cum laude) from the Catholic University of Nijmegen and a PhD in Theoretical Chemistry from the Hebrew University of Jerusalem. His research focuses on parallel algorithms, sparse matrix/tensor computations, and hypergraph partitioning, with applications in high-performance computing and numerical methods. He has authored a seminal textbook on parallel scientific computing and contributes to pedagogical resources like video lectures. During his visit to LIP, Bisseling collaborates with the ROMA team under Bora Uçar to advance parallel algorithms for large-scale irregular applications. His work includes developing tools like PMondriaan for sparse matrix partitioning and promoting knowledge transfer through lectures on BSP programming. He engages with researchers, PhD students, and engineers at LIP, extending collaborations to Lyon's Institut Camille Jordan and LabPhys for tomographic reconstruction and statistical physics modeling. His academic career includes roles at Royal Dutch Shell and as Utrecht University's Director of Education (2012–2015). He advocates interdisciplinary approaches, bridging computational methods with applied sciences and engineering challenges.

Mehmet Kerem TurkcanView profile

Researcher

• Computer Vision
• Deep Learning
• Neural Circuits Simulation
• +7 more

Mehmet Kerem Turkcan is an Associate Research Scientist at Columbia University, affiliated with the Center for Smart Streetscapes (CS3) and the Department of Civil Engineering & Engineering Mechanics. He specializes in computer vision, deep learning, and their applications to urban streetscapes and robotic surgeries. Current Position: Associate Research Scientist at Columbia University (since Jul 2024) Previous Role: Postdoctoral Research Scientist in Electrical Engineering at Columbia Research Interests span real-world deployment of object detection/tracking systems, retrieval-augmented generation via large language models, and GPU-driven simulations of neural circuits. His work bridges computational neuroscience with urban informatics through platforms like FlyBrainLab and Fruit Fly Brain Observatory . Publication Trends show interdisciplinary focus: (1) Robotic surgery tracking (2025), (2) Cloud-edge vision-language processing (2025), (3) Urban navigation for accessibility (2024), and (4) Neurogenetic circuit modeling (2024). Earlier work includes Drosophila brain simulations and biomarker discovery for coronary disease.

• Database Systems
• Cloud Computing
• Machine Learning Integration
• +3 more

Carlo Curino is a researcher at Microsoft Research , focusing on database systems, cloud computing, and machine learning integration. He has collaborated extensively with institutions including MIT, Microsoft, and the University of Wisconsin-Madison. His research spans Geo-distributed data analytics Automated configuration tuning Tensor-based database systems Data lake optimization Spark performance engineering Recent publications highlight his work on AI-driven systems like MotherNet and Rockhopper , alongside contributions to query processing over compressed data and log-structured tables. Collaborators include prominent figures such as Raghu Ramakrishnan and Jesús Camacho-Rodríguez . Key projects involve LST-Bench (cloud storage benchmarking), AutoComp (data compaction), and PyFroid (commodity workstation analytics). His work bridges database optimization with modern machine learning demands in enterprise environments.