جست‌وجوی استاد و پژوهشگر

Marwan Hassaniمشاهده پروفایل

استادیار

• Process Mining
• Data Mining
• Unsupervised Learning
• +۷ مورد دیگر

Marwan Hassani is an Assistant Professor at Eindhoven University of Technology's Department of Mathematics and Computer Science, affiliated with the Process Analytics and EAISI Foundational groups. He leads the streaming process mining research group and the Customer Journey track at the Data Science Center Eindhoven (DSC/e). His academic background includes a PhD from RWTH Aachen University (2015) and postdoctoral research there until 2016. Research focuses on unsupervised learning methods for streaming event data, including clustering, outlier detection, and sequential pattern mining, with applications in customer journey optimization and real-time process analytics. Over 65 publications span data mining, process mining, and related areas. He serves on program committees for ECML/PKDD, SDM, and journals like KAIS and DAMI, co-chairing multiple events. Key technical contributions include PrefixCDD for concept drift detection, BFSPMiner for sequential pattern mining, and autoencoder-based anomaly detection systems. His work addresses GDPR compliance in business processes and traffic flow prediction using contrastive learning. Awards include a 2025 Best Paper Award at ACM SIGAPP. Education: PhD in Computer Science (RWTH Aachen, 2015) Projects: Led GDPR compliance project (2018-2021), developed BPR4GDPR framework Teaching: Advanced Process Mining, Foundations of Data Analytics Editorial roles: Guest editor for Data & Knowledge Engineering , editorial board member for Process Science Research aligns with UN SDGs through contributions to efficient resource management and privacy-preserving analytics.

• Computational Geometry
• Algorithms
• Visualization
• +۳ مورد دیگر

Bettina Speckmann is a Professor at Eindhoven University of Technology in the Netherlands, specializing in computational geometry and algorithms. Her work spans theoretical computer science, visualization, and geographic information systems. She is actively involved in academic conferences such as SoCG, SODA, and GIScience, and serves as a co-author and editor for multiple journals including Computational Geometry and IEEE Transactions on Visualization and Computer Graphics . Key roles: Conference program committee member, journal reviewer. Research interests: Algorithm design, geometric optimization, topological data analysis, and visual abstraction techniques. Her research focuses on advancing computational methods for spatial data analysis, including Fréchet distance algorithms, modular robotics reconfiguration, and visualizing categorical patterns. Recent work includes optimizing symbol placement in maps and analyzing trajectories in mobility data. She collaborates extensively with researchers in computer science and geography, producing impactful contributions to both theoretical and applied domains.

• Algorithm Engineering
• Health IT
• Information Systems
• +۸ مورد دیگر

Umberto Nanni is a full Professor of Information Management Systems at the University of Rome "La Sapienza" since November 2005. He has held significant leadership roles including Director of the Research Center for Distance Learning and Technologies for Learning at Telematic University Unitelma Sapienza (2015-2022) and President of the Area Council in Information Engineering at the Latina location of University of Rome "La Sapienza" (2014-2020). Professor Nanni's research spans multiple domains with primary focus on Algorithm Engineering (particularly path algorithms and combinatorial problems), Health IT, Information Systems (with applications in Transport and Logistics, Cultural Heritage), and Technology Enhanced Learning. His work integrates Big Data, Graph and Text mining, Incremental Algorithms, Dynamic Data Structures, and Internet of Things technologies to address complex challenges in these areas. His recent publications demonstrate a strong trend toward biomedical applications, particularly in biospecimen management and digital twins for precision medicine, while maintaining his foundational work in algorithm design and educational technology. The research shows increasing interdisciplinary collaboration, especially between computer science and healthcare domains, with notable publications in Cancer Genomics & Proteomics and other high-impact journals. He has advised over 400 undergraduate and master's theses along with 2 PhD students He has led numerous significant research projects including "Artificial Intelligence algorithms to track and detect Covid-19 vaccine-related infodemic on social media" (2023-2025) He coordinated the "ADCATER - Advanced Digital Solutions for Professional Food and Nutrition Catering Service" project (2021-2023) He served as Principal Investigator for the eLF-eLearning Fitness project (2011-2014) with 19 partners and 42 associated partners Professor Nanni co-founded and was instrumental in developing SPRECware, software tools for Standard PREanalytical Code labeling to improve biospecimen management. His work has resulted in over 100 scientific papers with an h-index of 27, demonstrating substantial impact across computer science, healthcare informatics, and educational technology domains.

Harry Kai-Ho Chanمشاهده پروفایل

مدرس

• Data Mining
• Data Science
• Big Data
• +۷ مورد دیگر

Dr. Harry Kai-Ho Chan is a Lecturer in Data Science at the University of Sheffield's School of Information, Journalism and Communication. He holds a BEng, MPhil, and PhD from The Hong Kong University of Science and Technology (HKUST). His research focuses on data mining, big data analytics, and spatial data management, with particular emphasis on spatio-textual data, indoor location-based services, and applying machine learning in databases. Before joining Sheffield in 2022, he was a Postdoc researcher on the "Data Management for Indoor Location-based Services" project at Roskilde University (2020-2022). He currently serves as the Interim Deputy Programme Coordinator for the BSc Data Science program and the Department Employability Lead for Data Science. His work addresses efficient query processing for spatial data, co-location pattern mining, and uncertainty-aware systems in indoor environments. He has contributed to over 15 peer-reviewed publications in top venues like IEEE TKDE and VLDB. Teaching responsibilities include courses like INF114 (Communicating Data), INF217 (Databases and beyond), and INF4000 (Data Visualization). He actively reviews for leading conferences (e.g., SIGKDD, ICDE) and journals (e.g., IEEE TKDE, GeoInformatica).

Amy Dengمشاهده پروفایل

استادیار

• Machine Learning
• Data Mining
• Artificial Intelligence
• +۶ مورد دیگر

Amy Deng is an Assistant Professor at the Department of Mathematics and Computer Science, Eindhoven University of Technology (TU/e). Her research focuses on machine learning and data mining applications in social sciences and e-commerce, emphasizing explainable and robust AI techniques for temporal data analysis. She leads the Data Mining research group, contributing to advancements in graph neural networks, domain generalization, and societal event forecasting. Her work integrates causal reasoning, spatiotemporal modeling, and graph-based approaches to address challenges in event prediction and dynamic knowledge representation. Recent projects include developing adaptive normalization techniques for non-stationary time series and contrastive learning methods for graph homophily analysis. Research Interests: Machine Learning, Data Mining, Explainable AI, Temporal Data Analysis, Social Event Forecasting Labs/Teams: Data Mining Group at TU/e Education: Not explicitly listed in available texts, but affiliated with TU/e's academic programs in computer science and data science. Publications highlight contributions to graph neural networks (e.g., SimGCL), causal event modeling, and robust forecasting frameworks. She currently teaches courses on Generative AI Models, aligning with her research in advanced AI techniques.

• Industrial Engineering
• Operations Research
• Data Analysis
• +۵ مورد دیگر

Dr. Anne LOUIS is a Research Director at CESI, leading the application domain of 'Industry of the Future' and heading the Research & Innovation Department for CESI Nord-Ouest. She holds an HDR in Electronics from the University of Rouen (2006) and a PhD in Microwaves from the University of Limoges (1998). Her work focuses on decision support systems for industrial tasks, predictive maintenance analytics, and Industry 5.0 demonstrators. She leads a research platform on industrial performance (2015–2020) and manages a team in Engineering and Numerical Tools. Research Interests: Decision support tools for task scheduling, maintenance operations analysis, and multi-agent systems for industrial systems. Current Programs: Industry 5.0 demonstrator development and industrial performance platforms. Her research spans robotics optimization in horticulture, bio-waste management systems, and SME open innovation strategies. She supervises four ongoing PhD projects on AI-driven maintenance, multimodal transport supervision, and open innovation sustainability. Completed theses include robotic task optimization (2022), bio-waste treatment (2021), and SME innovation practices (2021). Scientific Leadership: Member of LINEACT Scientific Council, COMUE Normandie's Numerical Sciences pole, and doctoral school committees. Over 25+ publications since 2018, focusing on optimization algorithms, multi-agent simulations, and innovation frameworks.

Simonas Saltenisمشاهده پروفایل

دانشیار

• Database Systems
• Spatial Data Management
• Data Engineering
• +۳ مورد دیگر

Simonas Saltenis is an Associate Professor in the Department of Computer Science at Aalborg University, affiliated with The Technical Faculty of IT and Design. He is part of the Data Engineering, Science and Systems group and the Daisy Center for Data-intensive Systems. His research focuses on database systems, spatial data management, and data engineering with a strong emphasis on temporal and spatial query optimization. He has been involved in key projects such as TimeCenter (active since 2000) and "Effective Shedding and Efficient Processing of Massive Sensor Update Loads" (2008–2011), both funded by Danish research councils. These projects explore database management systems, temporal data handling, and real-time sensor data processing. His research interests include indexing techniques for spatial and temporal data, points-of-interest retrieval, and probabilistic models for energy use prediction. He has also contributed to conference organization, serving as a general co-chair for MDM 2018. Saltenis has supervised at least one PhD student and has actively engaged in peer review for top journals including the VLDB Journal, IEEE Transactions on Knowledge and Data Engineering, and ACM Transactions on Information Systems.

• Biological and Artificial Vision
• Human Sensory Augmentation
• Secure Data Analytics
• +۳ مورد دیگر

Dr. Noel Murphy is an Associate Professor and Head of the School of Electronic Engineering at Dublin City University (DCU). He has held various leadership roles, including Associate Dean for Education in the Faculty of Engineering and Computing (2005-2007), Head of School (2007-2012, 2015-2018, and returning in 2021). He has been teaching at DCU since 1986 and has contributed extensively to academic governance, including roles on Academic Council and Governing Authority committees. Dr. Murphy holds a BA (Mod) in Theoretical Physics from Trinity College Dublin (TCD), a PhD from DCU (2003) on Information Theory models for Biological and Artificial Vision, and a part-time MA in Pure Mathematics from Maynooth University (2015). His research focuses on: Biological and Artificial Vision: Including Information Theoretic models and the work of Stephen Grossberg. Secure Data Analytics: Particularly leveraging SGX-enabled applications for data ownership control. Algebraic Number Theory: Exploring elliptic curves and Apéry's Constant. IoT and Edge Computing: Focusing on secure processing for IoT applications. Human Sensory Augmentation and Multimedia Information Retrieval. In addition to his teaching and leadership roles, Dr. Murphy has supervised numerous PhD and Master's students to completion. He has secured funding as Principal Investigator or co-Investigator on national and EU-funded research projects. He is affiliated with DCU's Entwine Research Centre. Outside academia, Dr. Murphy is Honorary Secretary of the Irish Light Aviation Society, holds a Private Pilot’s License, and is involved in aircraft restoration projects, including a 1940 Piper J5 and a Vans RV-8.

Cong Gaoمشاهده پروفایل

استاد

• Data Science
• Geospatial data management
• Spatio-temporal data mining
• +۷ مورد دیگر

Cong Gao is a Professor and Head of the Division of Data Science at Nanyang Technological University's College of Computing & Data Science. He also holds a courtesy appointment with the School of Physical & Mathematical Sciences. Previously, he served as an Assistant Professor at Aalborg University, Denmark, and worked as a researcher at Microsoft Research Asia. He co-directs the Singtel Cognitive and Artificial Intelligence Lab for Enterprises@NTU (SCALE@NTU). His educational background includes: Ph.D. in Computer Science from National University of Singapore (2004) Master of Engineering from Tianjin University, China (1999) Bachelor of Engineering from Tianjin University, China (1996) Professor Gao's research focuses on Data Science, with particular expertise in geospatial data management, spatio-temporal data mining, recommendation systems, and social media data analysis. His work has significantly impacted areas like spatial-textual indexing, point of interest recommendation, and mining social networks. He has published extensively in top venues including VLDB, SIGMOD, ICDE, KDD, and WSDM, with over 14,000 citations and an H-index of 61. His recent publications demonstrate strong trends in applying machine learning to database systems, with particular focus on spatial and trajectory data management. Key research directions include learned indexing techniques, trajectory data analysis, and integrating large language models with database systems for improved query optimization. Professor Gao has received notable scientific recognition including: Best paper runner-up award at WSDM'22 Best paper award runner-up at WSDM 2020 He has advised numerous students who have become significant contributors in their own right, including Xin Cao, Lisi Chen, Kaiyu Feng, and Kaiqi Zhao. His research has been supported by substantial grants from Ministry of Education, NRF, IAF, Singtel/NCS, Roll-Royce, Alibaba, and Microsoft, including a S$42.4 million funding over 5 years for the SCALE@NTU lab. Professor Gao leads the Data Management Research Group (DANTE) and co-directs the Singtel Cognitive and Artificial Intelligence Lab for Enterprises@NTU (SCALE@NTU), which develops market-leading AI and data science technologies.

• Smart Wireless Systems (IoT)
• Information Security (Biometric Systems)
• Digital Electronics
• +۲ مورد دیگر

About Mohammad Derawi Mohammad Derawi is a Professor at NTNU's Department of Electronic Systems, part of the Faculty of Information Technology and Electrical Engineering. He leads the Smart Wireless Systems (SWS) Research Group and directs the IoT-Lab at NTNU Gjøvik. His academic journey includes a PhD from NISLab (Norway) and CASED (Germany), alongside BSc/MSc degrees in Informatics from DTU (Denmark). Research Interests Derawi specializes in Smart Wireless Systems (IoT), Information Security (Biometric Systems), Digital Electronics, and Machine Learning applications in activity recognition and e-learning. His work bridges theoretical advancements with practical implementations, emphasizing secure and efficient IoT solutions. Research Projects Notable projects include IoT-Safetraffic (RFF Innlandet), Ambulance Drone (NTNU), Wireless ECG (Innovation Norway), and the EU-funded TURBINE project. These projects focus on IoT security, healthcare applications, and smart transportation systems. Awards & Recognition Recipient of NTNU's Quality in Education Award (2017) Honored as Norway's Youngest Professor (2016) Received multiple best paper awards from IEEE/ACM/Springer Invited to the Crown Prince Couple's 50th Birthday Celebration (2023) Grants & Leadership Led multi-institutional initiatives funded by RFF Innlandet, Innovation Norway, and EU grants. His research group collaborates with industry partners to develop cutting-edge IoT and wireless communication solutions. Labs & Teams Scientific Director of NTNU's IoT-Lab, fostering innovation in IoT ecosystems and smart city technologies. Active in mentoring students and industry professionals through workshops and conferences.

• Data Management and Analysis
• Machine Learning
• Knowledge Representation
• +۵ مورد دیگر

Assoc Prof John Wang is an Associate Professor at the School of Information and Communication Technology , Griffith University. His work spans Data Management and Analysis (graph, text, spatial-temporal data), Machine Learning , Knowledge Representation , and Algorithm Design . He has published extensively in top venues like ACM Transactions on Database Systems , IEEE Transactions on Knowledge and Data Engineering , and conferences including SIGMOD , VLDB , and ICDE . Appointments : Member of the Institute for Integrated and Intelligent Systems (2003–2024). Education : PhD in Computer Science (Griffith University, 2003). His research has led to real-world implementations in Graph Data Processing , including techniques for shortest distance queries and temporal reachability. He actively contributes to program committees of conferences like VLDB and ICDE , and his funded projects include "Developing Soil Knowledge..." (ACIAR, 2024–2029) and "Harnessing Social Media..." (QLD Inspector-General, 2020). Recent publications focus on Graph Indexing , Causality Detection , and Spatial-temporal Querying . Examples include optimizing road network queries (2023), 2-hop labeling for shortest paths (2021), and geospatial skyline algorithms (2020). Co-authored works appear in venues such as IEEE Transactions on Knowledge and Data Engineering and Neurocomputing . Consultancy : Delivered commercial research on pandemic-related data analytics (2020). Supervision : Mentored 15+ PhD/Master’s students, including projects on deep learning, data leakage prevention, and XML querying. Teaching : Directed programs like Graduate Certificate in Information Technology and Diploma of Information Technology .

Nicolás C. Ciarloمشاهده پروفایل

پژوهشگر

• maritime archaeology
• underwater archaeology
• naval architecture
• +۳ مورد دیگر

Nicolás C. Ciarlo is a tenured Researcher at the Institute of Archaeology, School of Philosophy & Letters, University of Buenos Aires , and a Ramón y Cajal Fellow at the University of Cádiz. With a PhD in Archaeology (UBA, 2016), his career spans maritime, underwater, and historical archaeology, focusing on technological innovations in naval architecture from the 18th to 19th centuries. Key affiliations: CONICET (2017–present), UCA (2018–2019), School of Philosophy & Letters, UBA Research areas: Shipwreck analysis, archaeometallurgy, metallurgical provenance, cultural heritage management Research interests center on technological change dynamics in maritime contexts. His work integrates historical, archaeological, and archaeometric approaches to study ship construction, ordnance, and ballast materials. Notable projects include the Deltebre I (1813) shipwreck, Roman anchor stock provenance, and 19th-century Chilean naval ballasting practices. Recent articles show a focus on metallurgical analysis of ship fastenings, bronze gunfounding techniques, and conflict archaeology. Collaborations span institutions in Mexico, Spain, Chile, and the U.S. , with methodological emphasis on neutron texture analysis, lead isotope tracing, and geometric morphometrics. Scientific awards include: SAS Early Career Research Support Award (2021) Ramón y Cajal Fellowship (2024) ACUA award (2013) SAS award (2013) As an educator, he has mentored 2 PhD, 9 MA, and 4 BA students , co-chaired symposiums, and developed the Nautical Archaeology Digital Library framework for open-access data sharing. His grants include projects from CONICET, UBA, AEI, and the Institute of Nautical Archaeology.

Luca Iandoliمشاهده پروفایل

استاد

• Collective Intelligence
• Online Collaboration
• Social Networks
• +۴ مورد دیگر

Luca Iandoli serves as the Dean of the Collins College of Professional Studies and Professor in the Division of Computer Science, Mathematics, and Science at St. John's University. Previously, he held roles as Associate Professor at the University of Naples Federico II (Italy), Visiting Research Professor at Stevens Institute of Technology, and Fulbright Visiting Scholar at MIT's Center for Collective Intelligence. He has published over 100 scholarly papers and served as President of the International Council for Small Business (2016–2017). Dr. Iandoli's research examines how digital platforms enhance collective problem-solving through: Design of online collaborative systems Information diffusion mechanisms in social networks Innovation processes in entrepreneurial contexts Computational analysis of group intelligence His recent publications focus on social media epistemology, complexity reduction in system design, entrepreneurial learning patterns, and computational discourse analysis. Research consistently integrates computer science, information systems, and organizational behavior. Awards & Honors: Fulbright Visiting Scholar Fellowship at MIT He teaches interdisciplinary courses including Creativity & Innovation, Entrepreneurship, and Communication Design. No student advisees or laboratory affiliations are detailed in available materials.

Yong Geمشاهده پروفایل

دانشیار

• Business analytics
• Data mining
• Machine learning
• +۱ مورد دیگر

Yong Ge serves as Associate Professor of Management Information Systems at the Eller College of Management, University of Arizona, holding the Eller Fellow designation since joining in 2016 after prior faculty positions at the University of North Carolina at Charlotte. His academic foundation includes a PhD in Management Science and Information Systems from Rutgers University (2013). His research program centers on business analytics, data mining, machine learning, and recommender systems, with emphasis on developing scalable algorithms for real-world business applications. Ge's work bridges theoretical advances in data science with practical implementations across transportation, tourism, mobile ecosystems, and social networks, demonstrating consistent innovation from 2011 through 2021. Publication trends reveal evolving focus from foundational customer analytics (2011) to spatial data mining (2015-2019) and cutting-edge quantization techniques (2021), consistently targeting high-impact journals like IEEE TKDE and MIS Quarterly. His work demonstrates strong interdisciplinary connections between information systems, computer science, and business applications. Key honors include: NSF CAREER Award (2019) for foundational research in data mining Dean's Research Award (Eller College, 2020) Ralph E. Powe Junior Faculty Enhancement Award (2015) IEEE ICDM Best Research Paper (2011) Excellence in Academic Research (Rutgers, 2013) Ge secures significant research funding through competitive grants including the NSF CAREER award, supporting his work on algorithmic foundations for business intelligence. His teaching portfolio includes graduate courses in business intelligence (MIS 587) and design science research methodologies (MIS 611A), reflecting his commitment to developing next-generation analytics professionals. No dedicated research laboratory or named research team is specified in available materials, though his collaborative publications indicate active engagement with cross-institutional research groups in data mining and recommender systems.

Talel Abdessalemمشاهده پروفایل

استاد

• Database Systems
• Data Mining
• Machine Learning
• +۴ مورد دیگر

Talel Abdessalem is a Professor at Télécom Paris , where he has held leadership roles including Director of LTCI Research Laboratory (since 2017) and Dean of Research (since 2018). He currently serves as Deputy Vice-President for Research at Institut Polytechnique de Paris . PhD in Computer Science from Paris-Dauphine University Habilitation (HDR) from UPMC-Sorbonne University His research spans large-scale data management , recommender systems , social network analysis , and uncertain data modeling . He has participated in numerous national (ANR, FEDER) and European (FP7) research projects, with recent work focusing on stream-based learning , graph analytics , and privacy-preserving systems . His publications include diverse contributions to directed graph centrality algorithms (2021), stream recommender frameworks (River, Scikit-Multiflow), and geospatial recommendation models (ALGeoSPF). He has supervised 14 PhD students and collaborates with researchers in France, Brazil, and Indonesia. Co-leads DIG (Data, Intelligence and Graphs) research team Director of LTCI (Information Processing and Communication) laboratory