News & Activities

2026 the 11th International Conference on Big Data Analytics (ICBDA)April 11-14, 2026 @ Waseda University, Tokyo, JapanLink: https://www.icbda.orgThe realm of Big Data analytics has recently captured significant attention across diverse research communities including computer science, information technology, and social sciences. The term "Big Data" has taken on widespread significance, reshaping realms such as science, engineering, medicine, healthcare, finance, business, and even society at large. In this context, the 11th International Conference on Big Data Analytics (ICBDA2026), serves as a premier platform for disseminating cutting-edge research findings in the field of Big Data Research, Development, and Application.ICBDA 2026 invites contributions from a broad spectrum of domains, encompassing, but not restricted to, Big Data Models and Algorithms, Big Data Architectures, Big Data Search and Mining, healthcare, social networks, and media applications. Authors are encouraged to submit papers covering a diverse array of big data topics, such as service-oriented technologies, machine learning, predictive analytics, data modeling, system architectures, data mining, and simulation. Critical DatesFull-paper Submission Deadline December 20th, 2025 Abstract Submission Deadline December 20th, 2025 Notification Deadline January 20th, 2026 Registration Deadline February 10th, 2026 Camera Ready Date February 20th, 2026 Conference Dates April 11-14, 2026 Paper ReviewAll papers will undergo a thorough peer review process. Accepted full papers that are presented at the conference will be published in proceedings. Publishing through IEEE provides authors with a reputable, global platform on which to share their research with an audience which includes corporate researchers, academia and government.Conference ProceedingsAfter a careful reviewing process, all accepted papers after proper registration and presentation, will be published in the ICBDA 2026 conference Proceedings.Topics of InterestTopics of interest include, but are not limited to:Big Data Models and AlgorithmsFoundational Models for Big DataAlgorithms and Programming Techniques for Big Data ProcessingBig Data Analytics and MetricsRepresentation Formats for Multimedia Big DataBig Data ArchitecturesCloud Computing Techniques for Big DataBig Data as a ServiceBig Data Open PlatformsBig Data in Mobile and Pervasive ComputingBig Data ManagementBig Data Persistence and PreservationBig Data Quality and Provenance ControlManagement Issues of Social Network Big DataBig Data Protection, Integrity and PrivacyModels and Languages for Big Data ProtectionPrivacy Preserving Big Data AnalyticsBig Data EncryptionSecurity Applications of Big DataAnomaly Detection in Very Large Scale SystemsCollaborative Threat Detection using Big Data AnalyticsBig Data Search and MiningAlgorithms and Systems for Big Data SearchDistributed, and Peer-to-peer SearchMachine learning based on Big DataVisualization Analytics for Big DataBig Data for Enterprise, Government and SocietyBig Data EconomicsReal-life Case Studies of Value Creation through Big Data AnalyticsBig Data for Business Model InnovationBig Data ToolkitsBig Data in Business Performance ManagementSME-centric Big Data AnalyticsBig Data for Vertical Industries (including Government, Healthcare, and Environment)Scientific Applications of Big DataLarge-scale Social Media and Recommendation SystemsExperiences with Big Data Project DeploymentsBig Data in Enterprise Management Models and PracticesBig Data in Government Management Models and PracticesBig Data in Smart Planet SolutionsBig Data for Enterprise TransformationBlockchainBlockchain based lightweight data structures for IoT dataBlockchain based IoT security solutionsBlockchain in cyber physical systemsBlockchain in social networkingBolckchain in crowdsourcing and crowdsensingBlockchain in 5GBlockchain in edge and cloud computingBlockchain and trust management

The 10th International Conference on Deep Learning TechnologiesJuly 17-19, 2026, Kunming, Chinahttps://www.icdlt.orgSubmission Deadline: March 1, 2026Notification of Acceptance: April 1, 2026Camera Ready Deadline: April 15, 2026Registration Deadline: April 15, 2026Conference Dates: July 17-19, 2026You are invited to attend 2026 10th International Conference on Deep Learning Technologies will be held during July 17-19, 2026 in Kunming, China. It's sponsored by Kunming University of Science and Technology, China, organized by the Faculty of Information Engineering and Automation of Kunming University of Science and Technology. It's to provide a valuable opportunity for researchers, scholars and scientists to exchange their ideas face to face in Deep Learning Technologies.We invite researchers from deep learning technologies to participate and submit their work to the program. Likewise, any work on deep learning that has a relation to any of these fields or potential for the usage in any of them is welcome. Please refer to the different submission categories under "Call for Papers" above for further details.Track 1: Deep Learning Model and AlgorithmRecurrent Neural Network (RNN) Sparse Coding Neuro-Fuzzy Algorithms Evolutionary Methods Convolutional Neural Networks (CNN) Deep Hierarchical Networks (DHN) Dimensionality Reduction Unsupervised Feature Learning Deep Boltzmann Machines Generative Adversarial Networks (GAN) Autoencoders Deep Belief Networks Meta-Learning and Deep Networks Deep Metric Learning Methods MAP Inference in Deep Networks Deep Reinforcement Learning Learning Deep Generative Models Deep Kernel Learning Graph Representation Learning Gaussian Processes for Machine Learning Clustering, Classification and Regression Classification ExplainabilityTrack 2: Machine Learning Theory and TechnologyNovel machine and deep learningActive learningIncremental learning and online learningAgent-based learningManifold learningMulti-task learningBayesian networks and applicationsCase-based reasoning methodsStatistical models and learningComputational learningEvolutionary algorithms and learningFuzzy logic-based learningGenetic optimizationClustering, classification and regressionNeural network models and learningParallel and distributed learningReinforcement learningSupervised, semi-supervised and unsupervised learningTensor Learning Deep and Machine Learning for Big Data Analytics:Deep/Machine learning based theoretical and computational modelsMachine learning (e.g., deep, reinforcement, statistical relational, transfer)Model-based reasoningTrack 3: Deep and Machine Learning ApplicationsDeep Learning for Computing and Network PlatformsRecommender systemsDeep Learning for Social media and networksDeep Learning in Computer VisionDeep learning in speech recognitionDeep Learning in Nature Language Processing,Deep Learning in Machine TranslationDeep learning in bioinformaticsDeep Learning in Medical Image AnalysisDeep Learning in Climate ScienceDeep Learning in Board Game ProgramsDeep and Machine Learning for Data Mining and KnowledgeTrack 4: Responsible AI, Security, and GovernanceAI safetyPrivacy preservationAlgorithmic FairnessFairness and ethicsClassification Explainability and Explainable AI (XAI)AI EthicsAI governanceGreen Deep LearningSynthetic Data GenerationSocial and Economic Impact of AISustainable AIAI Risk AssessmentThe accepted paper will be included into ICDLT 2026 Conference ProceedingsPUBLICATION HISTORYICDLT 2025 Proceedings - 979-8-4007-1852-6 | ACM Digital Library | Ei & SCOPUS IndexICDLT 2024 Proceedings - 979-8-4007-1686-7 | ACM Digital Library | Ei & SCOPUS IndexICDLT 2023 Proceedings - 979-8-4007-0752-0 | ACM Digital Library | Ei & SCOPUS IndexICDLT 2022 Proceedings - 978-1-4503-9693-6 | ACM Digital Library | Ei & SCOPUS IndexICDLT 2021 Proceedings - 978-1-4503-9016-3 | ACM Digital Library | Ei & SCOPUS IndexICDLT 2020 Proceedings - 978-1-4503-7548-1 | ACM Digital Library | Ei & SCOPUS IndexICDLT 2019 Proceedings - 978-1-4503-7160-5 | ACM Digital Library | Ei & SCOPUS IndexICDLT 2018 Proceedings - 978-1-4503-6473-7 | ACM Digital Library | Ei & SCOPUS IndexICDLT 2017 Proceedings - 978-1-4503-5232-1 | ACM Digital Library | Ei & SCOPUS Index

National Platform Development & Linkages ObjectivesTo build the capacity of country administrators and IT managers to install, configure, and manage the National Talent Platforms.To customize the platform with national branding, content, and user roles while ensuring interoperability with the ASEAN-level ATM system.To train participants in coding, file management, and data analytics for talent informatics.To strengthen regional cooperation by harmonizing technical standards and data-sharing practices across ASEAN.Date & VenueDate: 19–20 October 2025Venue: Swissotel Bangkok Ratchada, Bangkok, Thailand

AI-NEXTHub of Talents in AI and Innovation Nexthttps://www.ai-next.or.thadmin@ai-next.or.thCore Mission of the Center In a world where skills must keep pace with the changes of the digital labor market, we bring together a network of AI and technology experts to create a continuous, comprehensive, and accessible learning ecosystem.Because we believe that AI is not here to replace people, but to serve as a tool that enhances human potential and opens up opportunities for everyone to grow alongside the new era.Thailand's AI expert network showcases a wide range of specialized fields across sectors. Key areas of expertise include:Artificial Intelligence in Natural Language Processing (NLP)AI in Healthcare and Medical TechnologyGeographic Information Systems (GIS) & MobilityBig Data AnalyticsImage Processing & Computer VisionUrban Computing, UAV (Drones), and Remote SensingAI in Agriculture and Agricultural RoboticsAI for Automotive and Intelligent Transportation SystemsDigital Engineering & Computing SciencesMathematical Modeling & Applied Computing Our Strategic Goals1. Develop AI Curriculum and Human CapitalWe design and deliver AI-related training in collaboration with schools and universities to equip people with digital skills tailored to the Thai context and global demands.2. Drive AI Research and InnovationWe support strategic alignment and integration of national priorities to advance urgent and impactful research across diverse regions and sectors.3. Apply AI in IndustryWe initiate projects that enhance operational efficiency, reduce costs, and foster the adoption of AI-powered automation in industrial applications.4. Advance AI Policy and EthicsWe promote AI regulations aligned with international frameworks such as the EU AI Act, and advocate for the ethical use of AI with sustainability, safety, and transparency in mind.5. Foster International CollaborationWe establish global partnerships and formal agreements (MOUs) to position Thailand as a regional hub for international AI cooperation.6. Support AI Startups and Innovation EcosystemWe expand platforms such as AI-as-a-Service to catalyze startup growth, support AI infrastructure, and strengthen the innovation ecosystem.

Chief Data and AI Officer Programmehttps://cmu.to/OG1O6 Join usProgramme OverviewNUS School of Computing has launched the Chief Data and AI Officer Programme to help you lead the data strategy and analytics agenda of your organisation. This programme will teach you the technologies, tools and methodologies to manage data as a strategic asset, generate actionable analytical insights, foster innovation and reach closer to tangible business benefits. Unlock enterprise data value, align strategy with business needs, and become a trusted business partner.Learning OutcomesEvaluate strategic implications and innovative value generation through proficient data managementAnalyse strategies for capitalising on data monetisationExplore innovation and emerging technologies in the domainExamine cyber risk through proficient application of data analytics and ethical considerations, resulting in effective management of risks and complianceEmpower the team through the acquisition of advanced leadership proficiencies in team management, negotiation, influence, and change managementWho is this programme for?Become a data strategist to drive tangible business valueSeasoned executives eager to champion AI strategies and leverage data as a key asset to spearhead sustainable and profitable growth.Senior professionals with a working knowledge of data management who want to foster innovation through the power of data, and AI to propel business expansion and assume CXO positions.Business leaders looking to explore latest opportunities to drive digital innovation and long-term strategic growth. Programme ModulesPillar 1: Data Foundations for Enterprise Value CreationPillar 2: Strategic Levers for Business GrowthPillar 3: Intelligent Technologies and Scalable AIPillar 4: Risk, Resilience and Responsible GovernancePillar 5: Transformational Leadership in the Age of AI

Energy Informaticshttps://www.scimagojr.com/journalsearch.php?q=21101048366&tip=sid&clean=0Indexing servicesThe full text of all articles is deposited in digital archives around the world to guarantee long-term digital preservation. You can also access all articles published by SpringerOpen on SpringerLink. We are working closely with Web of Science (Clarivate Analytics) to ensure that articles published in Energy Informatics will be available in their databases when appropriate. The journal is currently indexed in the following databases:SCOPUSEi CompendexDOAJInstitute of Scientific and Technical Information of ChinaNaverProQuestOCLC WorldCat Discovery ServiceEBSCOGoogle Scholar Article processing charges (APC)Authors who publish open access in Energy Informatics are required to pay an article processing charge (APC). The APC price will be determined from the date on which the article is accepted for publication.The current APC, subject to VAT or local taxes where applicable, is: £940.00/$1490.00/€1190.00Visit our open access support portal and our Journal Pricing FAQs for further information. Aims and scopeThis journal aims to provide a unique platform for researchers and practitioners of energy informatics in various scientific, technological, engineering and social fields to disseminate original research on the application of digital technology and information management theory and practice to facilitate the global transition towards sustainable and resilient energy systems.The journal publishes essential, peer-reviewed, interdisciplinary research addressing scientific, engineering and business topics related to energy, including but not limited to:Innovation in data-driven energy services and business modelsBig Data-driven smart Energy Management SystemsManagement of data and information flow in the Digital Energy System 4.0Data analytics for energy-cost efficient system operationPrivacy issues in energy data managementDigitalization of energy production, delivery and consumption, including policy and strategyDigital technologies for enabling energy-aware user behaviorContextual computing for supporting human and energy system interactionsEnergy-efficient Cloud Computing and data centersCyber security issues for safe and reliable Smart grid operationSocial media’s role in the transition towards sustainable energyApplication of Artificial Intelligence and Agent-based technology in smart energy systemsModel-based and data-driven energy forecasting, including energy production and useManagement of public and private procurement of digital energy technologySocioeconomic opportunities and barriers for adoption of digital energy technologiesSociotechnical aspects related to design and implementation of smart energy systemsInnovation management in smart energy business eco-systemsDigital entrepreneurship in emerging energy marketsSmart grid communication architectures and protocols for improving grid resiliency, including graceful degradation and self-healingDemand-side management, including demand response, dynamic pricing and incentive designMicrogrid and distributed generation management and controlMonitoring and control of Smart Buildings, including Smart Grid interoperability

Programme OverviewPROGRAMME FEE: US$5,000STARTS ON: 31 March 2025DURATION: 20 weeks (4 - 6 Hours/ week)This new year, invest in a learning journey to upskill and gain a competitive edge. Emeritus is collaborating with NUS School of Computing to help you unlock transformative career growth. Enrol before 24 January 2025 using this code: APAC200ALL8364 and get USD200 programme fee benefit. Limited seats to success available. Claim yours now.Application fees: USD60Read more at https://cmu.to/A8Tt0With organisations and consumers being driven by technological innovations, such as Artificial Intelligence, Automation and Data Analytics, organisations are looking for professionals with a deep understanding of these innovations who can foster significant organisational impact. This is the time to boost your career. The NUS School of Computing Technology Leadership and Innovation Programme caters to individuals aspiring to drive strategic transformation in AI, digitalisation and innovation domains. Through the carefully curated modules and capstone project, you develop the requisite mindset and skills to implement impactful strategies using global technological innovations .Programme Modules (Module 1-18)Module 1: Overview of Digital TransformationModule 2: Digital Business ModelsModule 3: Strategy Formulation and Process of Digital TransformationModule 4: Digital Transformation Action PlanModule 5: Role of Data and AnalyticsModule 6: ML-Deep Learning and Neural NetworkModule 7: Robotics and Reinforcement Learning Module 8: Generative AIModule 9: AI and Business ApplicationModule 10: CybersecurityModule 11: Other Technologies: IoT, AR/VRModule 12: Innovation Strategy to Address Digital DisruptionModule 13: System Thinking and Agile MethodologiesModule 14: Implementing Digital InnovationModule 15: Building Team and Digital Culture for TransformationModule 16: Managing Digital Complexities and Change ManagementModule 17: Leading through ESG ChallengeModule 18: Technology and Innovation: Security and Governance

Number of UnicornsDESI - Compare two indicatorshttps://digital-decade-desi.digital-strategy.ec.europa.eu/datasets/desi/charts/compare-two-indicators?indicator=desi_dsk_ab&breakdown=ind_total&unit=pc_ind&country=NL,LU,PL,PT,SK,ES,SE&periodY=desi_2024&periodX=desi_2024&indicatorX=desi_unicorns&indicatorY=desi_da&breakdownY=ent_all_xfin&breakdownX=total&unitY=pc_ent&unitX=nb_unicornsDefinition and scopes:Compare two indicators using a scatter-plot chart. Each country appears as a point whose coordinates are its values on the two selected indicators. It allows clustering countries according to their similarities along the two selected indicators and illustrates how much they are correlated. You can also zoom inside the chart.Key insights from the graph:Sweden (SE): Exhibits a high number of unicorns and a significant percentage of enterprises utilizing data analytics.Netherlands (NL): Shows a moderate number of unicorns with a relatively high adoption of data analytics among enterprises.Spain (ES): Has a lower number of unicorns but demonstrates a considerable percentage of enterprises engaged in data analytics.Poland (PL), Portugal (PT), Slovakia (SK), and Luxembourg (LU): These countries display varying levels of data analytics adoption among enterprises, with a relatively lower presence of unicorns compared to Sweden and the Netherlands.This comparison highlights the relationship between the emergence of unicorns and the adoption of data analytics across different European nations.

Application deadline: July 4th, 2024Location: Nova Information Management School Universidade Nova de Lisboa Campus de Campolide Lisbon, PortugalLink: https://academicpositions.com/ad/nova-ims/2024/faculty-positions-for-assistant-professor-businesss-intelligence-and-analytics/218536The PositionNOVA Information Management School (NOVA IMS) at Universidade Nova de Lisboa invites for an expression of interest for a position as a Professor (at Assistant/ Associate or Full Professor level) in Business Intelligence and Data Warehouses.The position is to be filled in NOVA IMS’ facilities in Lisbon, Portugal.Salary and conditions are competitive and will be commensurate with qualifications and experience.QualificationsNOVA IMS seeks a candidate with a strong commitment to excellence in scholarly research and teaching. He/ she will be expected to teach in undergraduate as well as graduate programme classes related to Business Intelligence and Databases and one or more of the following areas of expertise:Business IntelligenceDatabase Management SystemData WarehousingData AnalyticsThe successful applicants should have a strong academic background and an international profile in the aforementioned expertise areas with a record of research publications in top-ranked field journals. Moreover, relevant experience in obtaining funding and managing national and international research projects will be considered an advantage.The successful candidate should be able to provide strong and committed leadership in education, including curriculum development, course coordination and educational innovation, the preparation and delivery of lectures, seminars and tutorials, consultation with students, and marking and assessment.The main requirements for applicants are listed below:PhD or doctoral degree from a recognized university;Excellent scientific achievements, documented by internationally recognized publications;Excellent didactic skills;Demonstrate an excellent teaching ability at both the undergraduate and graduate levels;High capacity to develop pedagogical innovations and to manage a programme;Excellent communication skills (oral, written, presentation);Proficient in English (oral and written);Ability to teach in English;Collaborative skills, initiative, result oriented, organization, and capacity to work in an interdisciplinary environment.The SchoolNOVA Information Management School (NOVA IMS, www.novaims.unl.pt) is the School of Information Management and Data Science of Universidade Nova de Lisboa. It is one of the 9 academic units of Universidade NOVA de Lisboa, a university institution with internationally recognized research and quality teaching.It is dedicated to converting data into value, under the motto “Data with Purpose”, through a wide range of activities, namely teaching, and research and development activities that are largely supported by international partnerships, offering a unique research environment to address its main research challenges.The quality of its training is recognized globally, through different national and international accreditations, and outstanding positions achieved in various fields, namely in the Eduniversal ranking (seven of its masters and postgraduate degrees are recognized among the best in the world in their respective areas).NOVA IMS has more than 30 years of accumulated experience in data processing and analysis, which it now puts at the service of digital transformation and the “Big Data” environments in which we live in, namely through a wide range of laboratories (NOVA ANALYTICS LABS powered by NOVA IMS). Today, it has more than 3.000 students from 81 different countries, and high levels of internationalization in all the activities it carries out from Lisbon to the world.How to applyInterested candidates are invited to send the application material listed below by email to rh@novaims.unl.pt (mentioning the reference of the application):A cover letter including motivation to join NOVA IMS;A complete curriculum vitae with a list of publications and a list of classes taught;A copy of the three most significant recent publications, communications or working papers.Reference: NOVAIMS_ BusinessIntelligenceandAnalytics