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Research Topics in Data Science and Analytics

KU Leuven_031422A
[KU Leuven]

1. Data Science and Analytics

  • Data augmentation and feature engineering
  • The core of data mining process
  • Data visualization
  • Data modeling
  • Data modelling for AI/ML, analytics and visualization
  • Dimension reduction methods and techniques
  • Classification
  • Simple and multiple linear regression
  • K-nearest neighbor (k-NN) 
  • Naive Bayes
  • Classification and regression trees (CART)
  • Logistic regression
  • Neural Networks
  • Discriminant analysis
  • Association rules
  • Cluster analysis
  • Time series
  • Regression-based forecasting
  • Smoothing methods
  • Time stamps and financial modeling
  • Fraud detection
  • Data engineering – Hadoop, MapReduce, Pregel.
  • GIS and spatial data
  • Data governance
  • Data masking
  • Visualization analytics for big data 
  • Predictive analytics
  • Causal structure discovery
  • Causal discovery algorithms

2. Data Mining

  • Data mining and data analytics - knowledge discovery and representation
  • Text and data mining for life science
  • Data mining, data analytics, and machine learning approaches in materials science 
  • Multimedia data mining
  • Reality mining
  • Mining dark data
  • Mining and learning with graphs
  • Educational data mining and human-computer interaction
  • Statistical learning and data mining
  • Applied machine learning and data mining
  • Foundations, algorithms, models and theory of data mining, including big data mining 
  • Deep learning and statistical methods for data mining 
  • Mining from heterogeneous data sources, including text, semi-structured, spatio-temporal, streaming, graph, web, and multimedia data 
  • Data mining systems and platforms, and their efficiency, scalability, security and privacy 
  • Data mining for modelling, visualization, personalization, and recommendation 
  • Data mining for cyber-physical systems and complex, time-evolving networks 
  • Applications of data mining in social sciences, physical sciences, engineering, life sciences, web, marketing, finance, precision medicine, health informatics, and other domains

3. Sensor Fusion, Data Fusion, Information Fusion

  • Theory and Representation:  Probability theory, Bayesian inference, argumentation, Dempster-Shafer theory, possibility and fuzzy set theory, rough sets, logic fusion, preference aggregation, decision theory, random sets, finite point processes and others.
  • Algorithms: Cognitive methods, signal processing and localisation, recognition, classification, identification, nonlinear filtering, data association, tracking, prediction, situation/impact assessment, alignment and registration, pattern/behavioural analysis, image fusion, fusion architectures, resource management, machine learning and artificial intelligence, topic modelling, natural language processing, contextual adaptation, anomaly/change detection.
  • Applications: Soft-hard fusion, autonomous systems, defence/security, robotics, intelligent transportation, mining/manufacturing, wireless sensor networks, economics, finance, fintech, environmental monitoring, medical care/e-health, bioinformatics, radio astronomy, critical infrastructure protection, condition monitoring, precision agriculture, video streaming, streaming and sketching and other emerging applications.
  • Methods/tools: Sequential inference, data mining, graph analysis, ontologies/semantics, modelling/realisation/evaluation, target/sensor modelling, benchmarks/testbeds, trust in fusion systems, computational methods, cloud/edge computing/fusion, fusion performance. 
[Antalya, Turkey - Civil Engineering Discoveries]

4. Big Data Ecosystem

  • Big data science and foundations
  • Big data infrastructure and management
  • Big data and data cyberinfrastructure
  • Big data models and algorithms
  • Big data and causality
  • Big data algorithms and systems
  • Big data searching and mining
  • Big data mining and learning
  • Big data privacy and security
  • Big data applications
  • Big Data and 'omics
  • Scalable big data management and analytics
  • Big data analytic algorithms, knowledge discovery & data engineering 
  • Cloud-based big data management and analytics
  • Big data and efficient learning algorithms
  • Big data visualization and representation 
  • Large-scale data management
  • Media data cloud
  • Data sonification

5. Programming Models and Algorithms for Big Data

  • Energy-efficient programming and computing models for IoT related big data applications
  • Advances in programming models and algorithms for big data open platforms
  • Innovative programming models and algorithms for big data beyond Hadoop/Map Reduce
  • Efficient big data programming models and algorithms for big data search
  • Programming models and algorithms for big data visual analytics and applications
  • Programming models for big data assisted link and graph mining
  • Semantic-based big data-mining programming models and algorithms
  • Privacy-preserving secure big data analytics and algorithmic models for data-intensive applications
  • Algorithms and efficient programming models for multimedia big data analytics and management processes
  • New and innovative computational models for big data
  • High performance/ parallel computing assisted programming models for big data

6. Big Data in Energy Systems

  • Big data in power system operation and control
  • Data classification and clustering in energy systems
  • Big data analytics in clean energy application
  • Big Data and the UN Sustainability Goals in energy
  • Data analytics in electric traction systems
  • Data analytics in load forecasting
  • Data analytics and electric vehicles
  • Artificial intelligence in energy systems
  • Advance statistics for energy
  • Internet of Energy
  • Collection and visualization of data
  • Data infrastructure for utilities
  • Data and system awareness
  • Digital transformation of energy systems
  • Real-time data management
  • Data based energy system optimization
  • Big data and energy system reliability
  • Operations data on supervisory control and data acquisition
  • Data in energy management
  • Distributed energy resource management
  • Energy data in sustainable energy scenarios

 [More to come ...] 




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