Research Topics in Data Science and Analytics
- [KU Leuven]
1. Data Science and Analytics
- 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
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 ...]
