Group: ML department

Team: MLOps

Description: Monitoring and ensuring the performance, reliability, and observability of ML models and their infrastructure. Designing and implementing ML-specific metrics, alerts, and monitoring systems to proactively detect performance degradation and infrastructure issues.

Group: Workflow and Distributed Computing

Team: Provenance, Metadata and Reproducibility

Description: Working on enhancing reproducibility and traceability in HPC environments by developing metadata capture, storage, and workflow integration within the COMPSs framework

Specialization: Computer Network and Distributed Systems

Thesis: Metadata Capture, Knowledge Extraction, and Predictive Analysis in HPC environment

Description: Optimize workflow execution in HPC environments by developing a profiling tool implemented in COMPSs framework, contributing to Provenance Storage architecture improvement, and studying different ML models for predicting execution time

Coursework: Advanced Software Engineering, Real-Time Programming, Applied Cryptography and Network Security

Thesis: Algorithms and Consensus Protocols in Distributed Systems

Description: This thesis is the study concerning the principles of distributed consensus and the operation of the most used algorithms such as Practical Byzantine Fault Tolerance, Paxos and Raft