|
Size: 2392
Comment:
|
Size: 3161
Comment:
|
| Deletions are marked like this. | Additions are marked like this. |
| Line 4: | Line 4: |
| === Parallel Computing === * Parallel algorithms in numerical analysis, optimization, evolutionary computing, data mining, computational geometry, and computer graphics |
=== Cloud Computing, High Performance Computing, and Internet of Things === * Exascale Data Processing * Monitoring data-intensive applications * Portability in Clouds and open source Platform as a Service * Cloud resources management and self-organization in heterogeneous cloud * Data-intensive applications * Ontologies for Cloud services * Cloud governance * HPC service exposure in the Cloud * Empirical software engineering for cloud-based applications === Big Data Applications and Data Analysis === |
| Line 7: | Line 17: |
| * Applications of parallel computing to computational fluid dynamics in crystal growing or airfoil design, Web or medical data mining * Scheduling techniques and scalability for HPC === Distributed Computing === |
|
| Line 14: | Line 19: |
| * Building Web and Grid services platforms | * Accelerating applications on clouds and HPC * Graph processing * Astronomy image processing and object detection * Satellite-based image processing * Smart grids |
| Line 16: | Line 25: |
| * Applications of distributed computing to image processing in Earth observation, and to symbolic computing | * Hybrid processing on clouds, IoT, and HPC * Mixing GPUCPU, edge devices, and clouds for optimizing execution of large scale distributed applications |
| Line 18: | Line 28: |
| * Cloud computing and sensor networks | * Scheduling algorithms for distributed systems * Optimal and suboptimal heuristics with energy, cost and execution constraints |
| Line 20: | Line 31: |
| * Workflow design and execution | |
| Line 22: | Line 32: |
| * Task scheduling | === Artificial Intelligence and Machine Learning === |
| Line 24: | Line 34: |
| === Artificial Intelligence === | * Multi-agent approaches and recommender systems: * health monitoring and healthcare systems * customer-relationship management * Machine learning techniques for: * knowledge extraction from data (medical, biological, financial) * intrusion and anomaly detection * prediction for auto-scaling of resources in distributed systems * Metaheuristic algorithms for: * scalable cloud resource allocation * model inference and parameter estimation in biological systems * Explainable Artificial Intelligence * Signal and image processing using deep neural networks |
| Line 26: | Line 51: |
| * Uncertainty in deep neural networks | |
| Line 27: | Line 53: |
| * Intelligent systems | === Applications of Machine Learning in Earth Observation === ([[http://www.sage.ieat.ro/|EO+ML Research Group]]) * Application of advanced computational techniques for Earth Observation problems * Big Data Processing platforms (processing of massive Earth observation data) * Machine Learning algorithms in Remote Sensing |
| Line 29: | Line 59: |
| * Multi-agent systems | |
| Line 31: | Line 60: |
| * Knowledge discovery * Recommender systems * Automated reasoning * Intelligent ambient * Applications of artificial intelligence to e-commerce, scientific computing and medicine * Self-adapting and self-healing systems === Computational Intelligence and Nature Inspired Metaheuristics === * Evolutionary algorithms in optimization and data mining * Other nature inspired meta-heuristics: ant systems, particle swarm optimization etc. * Neural Networks === Theoretical Computer Science === |
=== Theory of Computing === |
| Line 54: | Line 64: |
| * Probabilistic methods and algorithms * Applications of game theory to theoretical computer science and multiagent systems. * Interconnections of physics (mostly complex systems) and theory of computing * Logic and its interconnections with symbolic computing and computational complexity * Automated theorem proving * Formal languages and its interrelation with XML processing |
* Agent-based models, game theory and complex networks * Symbolic computation * Unification and matching * Constraint logic programming * Proof-based algorithm synthesis |
| Line 61: | Line 70: |
| === Computational Mathematics === * Numerical methods for nonlinear equations |
|
| Line 64: | Line 71: |
| * Estimation of stability domains * Mathematical models in crystals growth and nano-technology * Computational methods in flight control * Mathematics of neural networks * Mathematical models in biology |
=== Computational and Applied Mathematics === * Qualitative and quantitative aspects of Dynamical Systems (time-delayed systems, impulsive systems, fractional-order systems) * Simulation of fractional-order systems using HPC * Analysis of nonlinear and chaotic phenomena in: fractional-order neural networks models, complex-valued neural networks * Applications of the theory of dynamical systems to: medicine, neuroscience, aerodynamics, economy |
Research Center in Computer Science
The center groups researchers and PhD students working in one of the following directions:
Cloud Computing, High Performance Computing, and Internet of Things
- Exascale Data Processing
- Monitoring data-intensive applications
- Portability in Clouds and open source Platform as a Service
- Cloud resources management and self-organization in heterogeneous cloud
- Data-intensive applications
- Ontologies for Cloud services
- Cloud governance
- HPC service exposure in the Cloud
- Empirical software engineering for cloud-based applications
Big Data Applications and Data Analysis
(Group - Cloud Enhancing Research on Big Data and Applications)
- Accelerating applications on clouds and HPC
- Graph processing
- Astronomy image processing and object detection
- Satellite-based image processing
- Smart grids
- Hybrid processing on clouds, IoT, and HPC
- Mixing GPUCPU, edge devices, and clouds for optimizing execution of large scale distributed applications
- Scheduling algorithms for distributed systems
- Optimal and suboptimal heuristics with energy, cost and execution constraints
Artificial Intelligence and Machine Learning
- Multi-agent approaches and recommender systems:
- health monitoring and healthcare systems
- customer-relationship management
- Machine learning techniques for:
- knowledge extraction from data (medical, biological, financial)
- intrusion and anomaly detection
- prediction for auto-scaling of resources in distributed systems
- Metaheuristic algorithms for:
- scalable cloud resource allocation
- model inference and parameter estimation in biological systems
- Explainable Artificial Intelligence
- Signal and image processing using deep neural networks
(Group - Signal, Image and Machine Learning)
- Uncertainty in deep neural networks
Applications of Machine Learning in Earth Observation
- Application of advanced computational techniques for Earth Observation problems
- Big Data Processing platforms (processing of massive Earth observation data)
- Machine Learning algorithms in Remote Sensing
Theory of Computing
- Algorithms and Computational Complexity
- Agent-based models, game theory and complex networks
- Symbolic computation
- Unification and matching
- Constraint logic programming
- Proof-based algorithm synthesis
Computational and Applied Mathematics
- Qualitative and quantitative aspects of Dynamical Systems (time-delayed systems, impulsive systems, fractional-order systems)
- Simulation of fractional-order systems using HPC
- Analysis of nonlinear and chaotic phenomena in: fractional-order neural networks models, complex-valued neural networks
- Applications of the theory of dynamical systems to: medicine, neuroscience, aerodynamics, economy