جست‌وجوی استاد و پژوهشگر

Johannes Scholzمشاهده پروفایل

استاد

• Geospatial Artificial Intelligence (GeoAI)
• Geospatial Knowledge Graphs
• Semantics and Ontologies in Geography
• +۱۲ مورد دیگر

Johannes Scholz is a Full Professor for Geoinformatics at the Department of Geoinformatics (Z_GIS) at Paris-Lodron-University Salzburg. He leads the GeoAI, GeoKG, and GeoSemantics research group and holds secondary roles in academic governance, including membership in the Curricularkommission Geographie at Salzburg and the Austrian Standards TC 084 committee. He has held visiting positions at the University of California, Santa Barbara, and prior affiliations include Associate Professor at Graz University of Technology and Senior Researcher at Research Studios Austria - Studio iSPACE. His research interests span geospatial artificial intelligence, knowledge graphs, semantics, and numerical optimization, with applications in energy transition, digital tourism, and cyber-physical systems. Projects include Agent-based simulation for energy systems GeoHumanities integration Indoor geography analysis Selected scientific awards include the ISPRS Outstanding Reviewer Award (2020) and the Forschungspreis der Steiermark (2011). He has secured significant funding for projects such as "Virtual Shepherd" (FFG 2024) and "ABM4EnergyTransition" (FFG 2022).

• Artificial Intelligence
• Strategic Reasoning in Multi-Agent Systems
• Formal Verification
• +۱۰ مورد دیگر

Aniello Murano is a Professor in Computer Science at the Department of Electrical Engineering and Information Technology of the University of Naples Federico II. He serves as Scientific Director of the ASTREA (Automated Strategic REAsoning) Laboratory and has been actively involved in numerous research projects including H2020-MSCA project SEAL as Principal Coordinator and PNRR Research Unit Coordinator on Resilient Strategic Reasoning in AI (2023-2025). His primary research interests focus on Artificial Intelligence, Formal Aspects for Strategic Reasoning in Multi-Agent Systems under Perfect and Imperfect Information, Specification, Verification, and Synthesis of Reactive Systems, Model Checking and Module Checking, Formal Languages, Automata Theory, Real-time Systems, Pushdown and Hierarchical Systems, Temporal and Modal Logics, Logics for Strategic Reasoning in AI, Game Theory, Parity Games, Solution Concepts, and Automated Mechanism Design. His work bridges theoretical computer science with practical applications in AI systems. Murano has published extensively in top-tier venues, with recent publications spanning KR, IJCAI, AAAI, AAMAS, and other premier conferences. His research shows strong trends in strategic reasoning frameworks, verification techniques for multi-agent systems, and applications of game theory to mechanism design. He has made significant contributions to Strategy Logic and its extensions. JPMorgan Faculty Research Award 2022 Best Paper Award PRIMA 2015 Vigevani Research Project Prize 2010 In KR Top 100, best AI-2000 world researchers Italian Scientific Abilitation as Full Professor in Engineering Computer Science (March 2018) Italian Scientific Abilitation as Full Professor in Computer Science (April 2017) Royal Society Award 2016 Murano has supervised numerous PhD students including Silvia Stranieri, Antonio Di Stasio, Vadim Malvone, and Giuseppe Perelli. He has served as Program Chair for major conferences including ECAI 2025 (Program co-chair), KR 2024 (Area chair), and CSL 2024. His research has been supported by significant grants including Horizon 2020 projects, JPMorgan funding, and Italian national research programs. He leads the ASTREA Laboratory focused on automated strategic reasoning and has been instrumental in establishing collaborative research initiatives with institutions worldwide.

François Briatteمشاهده پروفایل

استادیار

• Legislative networks
• Comparative health policies
• Sociology of social movements
• +۷ مورد دیگر

François Briatte is an Assistant Professor in Political Science at the Catholic University of Lille , affiliated with the European School of Political and Social Sciences (ESPOL). He serves as Co-Director of International Mobility and has previously taught at Sciences Po Paris, Grenoble, Reims, the University of Lille 2, and the University of Edinburgh. His research focuses on legislative networks in European parliaments and comparative health policies . He combines methodologies from network analysis, political sociology, and digital politics to study legislative collaboration patterns, electoral behavior, and healthcare system reforms. His work often addresses Political polarization Electoral turnout dynamics Open data governance Health policy analysis Recent publications include empirical studies on Voting indecision in the 2022 French presidential election Covid-19’s impact on 2020 French local elections Network visualization tools for political science Comparative analysis of legislative cosponsorship He has developed open-source software packages like GGally and ggnetwork for network analysis in R, and actively participates in academic conferences across Europe and North America.

Jukka Suomelaمشاهده پروفایل

دانشیار

• Distributed Computing
• Distributed Algorithms
• Local algorithms
• +۷ مورد دیگر

Jukka Suomela is an Associate Professor in the Department of Computer Science at Aalto University, Finland. His research focuses on the theoretical foundations of distributed and parallel computing, with a strong emphasis on locality and algorithmic complexity. Current roles: Associate Professor, Aalto University Research group: Distributed Algorithms Key conferences: PC Chair for DISC 2019, SIROCCO 2016, Local Chair for ALGO 2018 Steering Committee: Vice Chair, DISC Research Interests include distributed algorithms, local algorithms, parallel computing, graph algorithms, and computational complexity. His work explores fundamental limits of distributed systems and the role of locality in algorithm design. Notable Scientific Awards : FOCS 2019 Best Paper Award DISC 2012 & 2017 Best Paper Awards MSc Thesis Award 2006, University of Helsinki Best Junior Researcher 2008, University of Helsinki Nokia Scholarship 2008 Teaching includes Programming Parallel Computers (freely available), CS-E4510 Distributed Algorithms, and other courses in the Finnish Computer Science major/minor programs. His publications span distributed systems theory, graph algorithms, and interdisciplinary projects in linguistics and cultural analytics.

• Data Modeling
• Data Security and Privacy
• Healthcare Data Management
• +۵ مورد دیگر

Faiz Currim serves as Professor of Practice in Management Information Systems and Assistant Director of the INSITE: Center for Business Intelligence and Analytics at the University of Arizona's Eller College of Management. He joined the institution in 2011 after six years at the University of Iowa, having earned his PhD from the University of Arizona in 2004. His educational background includes: PhD in Management Information Systems, University of Arizona (2004) Dr. Currim's research focuses on data modeling, security, privacy, and specialized management of healthcare, temporal, and spatial data systems. His work bridges database theory with practical applications in urban mobility, patient care, and workplace wellbeing through advanced techniques including deep learning, network science, and big data analytics. Current projects address real-world challenges in healthcare analytics and smart city infrastructure. Analysis of his recent publications reveals strong trends in applying artificial intelligence to healthcare cost prediction and urban transportation systems, with consistent use of heterogeneous data sources and machine learning frameworks across diverse domains from patient monitoring to bike-sharing optimization. His scientific recognition includes: Best Paper Award at IEEE 2nd International Smart Cities Conference (2016) Dr. Currim directs the INSITE center's initiatives in business intelligence while teaching core courses including Enterprise Data Management and Business Data Communications. His academic leadership spans curriculum development in data security, XML schema, and spatial database applications, with active collaboration across healthcare and urban planning domains. He maintains leadership roles in professional organizations including the Association for Information Systems (AIS), INFORMS, and Association for Computing Machinery (ACM), driving interdisciplinary research through the INSITE center's industry partnerships and analytics projects.

Paul Boniolمشاهده پروفایل

پژوهشگر

• Time Series Analytics
• Anomaly Detection
• Machine Learning
• +۲ مورد دیگر

Paul Boniol is a researcher at Inria, affiliated with the VALDA project-team—a collaboration between Inria Paris, École Normale Supérieure, and CNRS. His work focuses on time series analytics, anomaly detection, and machine learning applications. Ph.D. in Computer Science and Applied Mathematics (University of Paris, EDF R&D) Visiting Ph.D. at University of Chicago Education: Grenoble INP ENSIMAG Engineering School Research interests span: Unsupervised anomaly detection in large time series Time series management systems Machine learning for predictive maintenance Graph-based time series analysis Explainable AI for temporal data Recent publications emphasize advancements in: Weakly supervised anomaly localization Graph embedding techniques Model selection frameworks Interactive visualization tools Smart meter data analysis Scientific recognition: Paul Caseau Thesis Prize 2022 Lambdamu Congress Research-Industry Prize 2022 BDA & INFORSID Ph.D. Prizes 2022

Mary Lauren Bentonمشاهده پروفایل

استادیار

• Computational Biology
• Gene Regulation
• Biomedical Informatics
• +۳ مورد دیگر

Dr. Mary Lauren Benton is an Assistant Professor in the Department of Computer Science at Baylor University's College of Engineering and Computer Science. She holds a Ph.D. and M.S. in Biomedical Informatics from Vanderbilt University (2020, 2018) and a B.S.I. in Bioinformatics from Baylor University (2015). Education: Ph.D. (Vanderbilt, 2020), M.S. (Vanderbilt, 2018), B.S.I. (Baylor, 2015) Her research focuses on computational biology and gene regulation, particularly how DNA sequences influence genome function and human disease risk through integrated datasets. Recent work explores enhancer-gene interactions, epigenetic mechanisms, and the application of graph neural networks to genomic problems. Selected publications highlight interdisciplinary trends across genomics (e.g., Cis-regulatory landscapes , CTCF motif accessibility ) and computer science (e.g., Dynamic graph attention , Node classification ).

Giovanni Maria Farinellaمشاهده پروفایل

استاد

• First Person (Egocentric) Vision
• Computer Vision
• Pattern Recognition
• +۱ مورد دیگر

Giovanni Maria Farinella is a Full Professor at the Department of Mathematics and Computer Science, University of Catania, Italy. He is the Founder Member of the IPLAB Research Group at University of Catania since 2005, an Associate Member of the Computer Vision and Robotics Research Group at University of Cambridge since 2006, and an Associate Member of the Italian National Research Council since 2018. He serves as Scientific Advisor of the NVIDIA AI Technology Centre and board member of the CINI Laboratory of Artificial Intelligence and Intelligent Systems. Dr. Farinella obtained his degree in Computer Science (summa cum laude) from the University of Catania in 2004 and was awarded a Doctor of Philosophy (Computer Vision) from the same institution in 2008. He has obtained the National Scientific Qualification to Associate Professor in 2013-2014 and to Full Professor in 2018. His primary research interests focus on First Person (Egocentric) Vision, Computer Vision, Pattern Recognition, and Machine Learning. Dr. Farinella has pioneered research in egocentric video analysis, object interaction detection, activity recognition, and scene understanding from a first-person perspective. His work bridges theoretical computer vision with practical applications in industrial settings, healthcare, and cultural heritage sites. His recent publications demonstrate a clear trajectory toward more sophisticated, real-time egocentric vision systems capable of understanding complex human activities, anticipating actions, and detecting procedural mistakes through integration of visual analysis, gaze tracking, and large language models. Dr. Farinella has published extensively in top-tier computer vision venues, with his most recent work focusing on egocentric action anticipation, mistake detection in procedural tasks, and the integration of multimodal large language models with visual understanding. His research shows strong emphasis on practical industrial applications while advancing fundamental computer vision techniques. PAMI Mark Everingham Prize 2017 Intel's 2022 Outstanding Researcher Award Dr. Farinella has served as Associate Editor for IEEE Transactions on Pattern Analysis and Machine Intelligence, Pattern Recognition - Elsevier, and International Journal of Computer Vision. He has held leadership roles as Area Chair for CVPR 2020/21/22/23, ICCV 2017/19/21/23, ECCV 2020, and as Program Chair of ECCV 2022. He founded and directs the International Computer Vision Summer School (since 2006) and the Medical Imaging Summer School (since 2014). As PI of the EGO4D project, he helped create one of the largest egocentric video datasets with over 3,670 hours of footage from 923 participants across 74 locations worldwide. His IPLAB research group leads multiple significant projects including VALUE, ENIGMA, and FIPEVIS, with strong industry collaborations.

• Autonomous Driving
• Computer Vision
• Robotics
• +۵ مورد دیگر

Holger Caesar is a tenured Assistant Professor at Delft University of Technology (TU Delft) in the Intelligent Vehicles Lab . He leads research on scalable approaches for autonomous vehicle perception, prediction, and data annotation, with a focus on sensor fusion, domain adaptation, and minimal supervision. His work has been cited over 15,000 times. Education PhD in Computer Vision, University of Edinburgh Prior studies at KIT Karlsruhe, EPF Lausanne, and ETH Zurich Research Interests span autonomous driving perception, weakly supervised learning, novel view synthesis (NeRFs), diffusion models, and collaborative perception. He emphasizes reducing reliance on manual annotations through active learning and partial labeling techniques. Recent Publications include work on 4D Gaussian Splatting, camera-radar fusion, open-set scene graph generation, and safety benchmarking. These reflect trends toward multi-modal perception, robust sensor fusion, and foundation models for scalable autonomous systems. Scientific Awards ELLIS Europe Scholar (2024) TU Delft Cohesion Grant (100k EUR, 2024) Climate Action Grant (30k EUR, 2024) TKI High Tech Systems Grant (502k EUR, 2022) AiNed XS Grant (80k EUR, 2023) Argoverse Scene Flow Challenge Winner (unsupervised track, 2024) Advising & Grants include EU Horizon funding for the MOSAIC project (1 PhD and 1 Postdoc, 2024), EU KDT Cynergie4MIE grant (450k EUR, 2024), and industrial collaborations with Bosch, Motional, and partners across Europe. Labs & Teams include the Intelligent Vehicles Lab at TU Delft and founding roles in Motional's Data Annotation, Autolabeling, and Data Mining teams. He co-organizes workshops at ICCV, CVPR, and NCCV, and leads the ELLIS Delft Unit seminar series.

• Data Analysis
• Visualization
• Computational Linguistics
• +۲ مورد دیگر

Mennatallah El-Assady is an Assistant Professor at the Department of Computer Science, ETH Zurich, leading the Interactive Visualization and Intelligence Augmentation Lab (IVIA). Prior roles include research fellow at ETH Zurich's AI Center, research associate and doctoral student at the University of Konstanz (Germany) and OntarioTech University (Canada). Her research focuses on integrating data mining, machine learning, and visual analytics for text data, with applications in explainable AI, digital humanities, computational linguistics, and political communication analysis. Key projects include explAIner (interactive explainable ML), LingVis (linguistic visualization), and VisArgue (rhetorical analysis in debates). Notable scientific awards include the Best Paper Award and Honorable Mention at IEEE VIS and ACM TIIS. Her work spans interdisciplinary collaborations, particularly bridging visualization techniques with social sciences and musicology. Labs and teams highlight her leadership at IVIA and partnerships with institutions in Switzerland, Germany, and Canada.

• AI for Healthcare
• Large Vision-Language Models
• 3D Computer Vision
• +۳ مورد دیگر

Zhenyue Qin is a Postdoctoral Associate at the Yale School of Medicine , affiliated with the Biomedical Informatics & Data Science department. His research focuses on AI for healthcare , large vision-language models , and 3D computer vision . Prior to Yale, he worked as a Senior Researcher at Tencent XR Vision Labs , following his PhD and undergraduate studies at the Australian National University . Education: PhD in Computer Vision (2022) BS (Hon) in Computer Science (2017) His research interests span AI applications in healthcare , with specific emphasis on medical imaging , emotion recognition , and skeleton-based action analysis . He also explores graph neural networks , vision-language models , and modularity-inducing systems in evolutionary computation. Zhenyue has published in top-tier conferences and journals including CVPR , AAAI , EMNLP , and TNNLS . His recent work addresses challenges in medical dataset benchmarking , diffusion model applications , and privacy-preserving action recognition . Scientific Honors Awarded 1st Honors in Computer Science at Australian National University

Mark Stevensonمشاهده پروفایل

مدرس ارشد

• Natural Language Processing
• Information Retrieval
• Machine Learning
• +۸ مورد دیگر

Mark Stevenson is a Senior Lecturer in the School of Computer Science at the University of Sheffield, UK. He leads undergraduate programs and serves as a key member of the Natural Language Processing research group , focusing on knowledge extraction from text and user information access solutions. Research Interests : Natural Language Processing Information Retrieval Machine Learning Biomedical Text Disambiguation Lexical Semantics Exploratory Search Systems Scientific Awards : EPSRC Advanced Research Fellowship (2006-2011) Best Paper Award at CLEF 2004 Grants & Projects : He has secured significant funding including the EU FP7 PATHS project (£709,407), EPSRC grants for biomedical disambiguation (£239,920) and Lexical Adaptation (£30,000), and NIHR funding for public health research access systems.

• Data Mining
• Knowledge Graphs
• Pattern Analysis
• +۷ مورد دیگر

Dr. Arnaud Soulet is a researcher at the University of Tours , affiliated with the Polytechnic School of the University of Tours and the Department of Computer Science . He works at the intersection of the LIFAT Laboratory (UR 6300) and contributes to the fields of pattern mining and knowledge graph analysis. Research Focus: Data Mining, Knowledge Graphs, Sequential Pattern Mining, Distributed Databases, Cultural Heritage Informatics Publication Trends His recent publications emphasize knowledge graph analysis (e.g., Rankingdom architecture, structural modeling) and pattern mining (reservoir sampling, frequency constraints, sequential patterns). Applications span employee attrition analysis , cultural heritage data , and data stream processing .

Jie Luمشاهده پروفایل

استاد

• Artificial intelligence
• Machine learning
• Computational intelligence
• +۴ مورد دیگر

Distinguished Professor Jie Lu AO is an internationally renowned scientist in computational intelligence at the University of Technology Sydney, where she serves as Associate Dean (Research Excellence) in the Faculty of Engineering and Information Technology and Director of the Australian Artificial Intelligence Institute (AAII), the largest AI hub in Australia with 35 researchers and 230 PhD students. She has been a Professor at UTS since 2007 after serving as Associate Professor from 2004-2006. Professor Lu earned her PhD from Curtin University, Perth, Australia. Her research focuses on computational intelligence with significant contributions to fuzzy transfer learning, concept drift, data-driven decision support systems, and recommender systems. She has developed machine learning models, intelligent recommender systems, and AI-driven decision support systems through collaborations with industry partners including Optus, Sydney Trains, Domain Holdings Australia Ltd, and Workforce Health Assessors Transport NSW. Her recent publications demonstrate a strong focus on addressing challenges in non-stationary environments, out-of-distribution detection, multi-stream concept drift, and applying AI to healthcare applications such as stroke risk prediction and cancer risk assessment. She has pioneered approaches combining traditional AI techniques with large language models for more robust and explainable systems, particularly in legal case recommendation and women's health applications. Officer of the Order of Australia (AO) IEEE Fellow, IFSA Fellow, Australian Computer Society Fellow Australian Laureate Fellow in AI and Industry Laureate Fellow in AI-for-Health UTS Chancellor's Research Medal for Research Excellence (2019) IEEE Transactions on Fuzzy Systems Outstanding Paper award (2019, 2022) Australian Most Innovative Engineer award (2019) NeurIPS 2022 Paper Award Australasian AI Distinguished Research Contribution Award (2022) Australian NSW Premier Prize on Excellence in Engineering (2023) Professor Lu has supervised 60 PhD students to graduation and serves as Editor-In-Chief for Knowledge-Based Systems journal. She has secured 47 ARC grants and over 110 industry projects since 2017, with funding from ARC Discovery projects, ARC Laureate Fellowships, and industry partners. Her leadership has established UTS as a leading center for AI research in Australia, with significant impact across multiple sectors including transportation, telecommunications, healthcare, and education. As Director of the Australian Artificial Intelligence Institute, Professor Lu has built a thriving research ecosystem that bridges academic research with practical industry applications. Her work on concept drift and transfer learning addresses fundamental challenges in adapting machine learning models to changing environments, with direct applications to real-world problems requiring continuous learning and adaptation.

Louis Duvivierمشاهده پروفایل

مدرس ارشد

• Network modeling
• Graph analysis
• Community detection
• +۹ مورد دیگر

Louis Duvivier is a Senior Lecturer at the University of Grenoble Alpes since October 2024, affiliated with the Science Department Drôme Ardèche. He is associated with the Coordination, Cooperation & Control of Complex Systems (CO4SYS) team within the Systems Design and Integration Laboratory (LCIS). Former ATER at École Centrale de Lyon (2023–2024) Postdoctoral Fellow at ENS Lyon (2022–2023) ATER at Institute of Financial and Insurance Science (2021–2022) His research focuses on network modeling , graph analysis , and statistical inference , particularly in stochastic block models , community detection , and temporal graph analysis . His recent work explores probabilistic validation techniques and geometric interpretations of network structures. Publications demonstrate expertise in Bayesian inference , model selection , and sensor network estimation . Earlier contributions to distributed estimation and link prediction date back to 2019. He teaches courses in complementary computing , algorithms , numerical analysis , and statistics at both bachelor's and master's levels.