About me

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

  • 2019 - present: Postdoctoral Fellow in the Departament of Computer Science.
    • Leader: Professor Eyal de Lara.
    • Hosting team: Computer Systems and Networks Group in Department of Computer Science at the University of Toronto.
    • Description: Distributed Stream Processing (DSP) applications are increasingly used in new pervasive services that process enormous amounts of data in a seamless and near real-time fashion. Edge computing has emerged as a means to minimise the time to handle events by enabling processing (i.e., operators) to be offloaded from the Cloud to the edges of the Internet, where the data is often generated. Deciding where to execute such operations (i.e., edge or cloud) during application deployment or at runtime is not a trivial problem. One of my goals is to improve performance metrics by introducing mechanisms for deploying DSP applications across Cloud and edge resources. I also participate in the research projects of Professor Eyal de Lara.
  • 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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