About me

  • I work on placement and optimisation problems.
  • I like to code.
  • I am self-motivated.

Research Interests

  • Elasticity for data stream processing applications.
  • Modelling distributed system behaviours.
  • Mono- and multi-objective optimisation problems.
  • (Near) Real-time solutions for big data analytics.
  • Machine Learning (ML) and Reinforcement Learning (RL).
  • Cloud, Edge and Fog computing.
  • Internet of Things (IoT) issues.
  • Here you can find my research statement.

Research Activities

  • 2016 - 2019: Ph.D. in Computer Science.
    • Title: Algorithms for big data analytics. Thesis here
    • Advisors: Marcos Dias de Assunção and Laurent Lefèvre.
    • Hosting team: AVALON-Team in LIP at ENS-Lyon.
    • Defense: September 2019.
    • Approach: I worked on the subject “Algorithms for Elastic Big-Data Stream Analytics” where I developed QoS-aware mechanisms for (re)configuring data stream processing applications across edge and cloud resources. During my investigation, I introduced models and solutions for placing (near)real-time applications on heterogeneous infrastructures addressing single and multiple performance metrics. The techniques and methods covered by the research include: queueing theory, Markov Decision Process (MDP), Reinforcement Learning (RL), series-parallel graphs, Monte-Carlo Tree Search (MCTS), Temporal Difference Tree Search (TDTS), Q-learning and greedy algorithms.
  • 2012 - 2014: M.Sc. in Computer Science.

Program committees

  • PC member of ICPP 2020.

Other paper reviews

  • Review for CCGrid 2017, IEEE Globecom 2017, and IEEE Globecom 2018.

Volunteer Organiser

  • SBAC-PAD 2018.

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