About me

I am a permanent researcher in computer science at Inria (the French Institute for Research in Computer Science and Control) since Feb 2023. I am a member of the STACK team hosted at IMT Atlantique (Nantes, France) , and also affiliated with the Scientific Computing Institute (SCI) at the University of Utah (Salt Lake City, USA).

Open positions

Postdoc positions available on edge computing topics at IMT Atlantique in Nantes (Brittany, France). Please contact me for further information.

Research Interests

My research interests are broadly in the areas of next-generation Utility Computing Infrastructures (i.e., Cloud, Fog, Edge, and beyond) [1].
My current research is focused on leveraging the Computing Continuum for Urgent Science [2], i.e. realizing a fluid ecosystem where distributed computing resources and services (Computing Continuum) are aggregated on-demand to support delay-sensitive and data-driven workflows (Urgent Computing). Distributed observatories , AI/ML models [3], and data-centric/middleware systems [4] are some of the applications of this research.

Recent Activity

[Scientific Animation]

• Consider submitting to QuickPar 2023 - The 1st Workshop on Urgent Analytics for Distributed Computing co-conducted with Euro-Par2023 (Aug 28-29, Limassol, Cyprus)
• and ICPP23 (Aug 7-10, Salt Lake City, Utah).

[Programme Committees] PC member for Euro-Par 2023, IEEE/ACM SuperComputing 2023 Reproducibility challenge, ICCS 2023.
[Service] 2022: Panelist for NSF CSSI Elements.
[Tutorial] Oct 2022: Tutorial on streaming application development on the Edge-to-Cloud Continuum in collaboration with WiFire (UC San Diego) @eScience2022.
[Funding] Sept 2022: I received an NSF grant for managing urgent analytics on advanced cyberinfrastructure.
[Programme Committees] 2022: I am serving as a PC member for IEEE/ACM CCGrid 2022 (Artificial intelligence, Machine Learning and Deep Learning Track), IEEE/ACM SuperComputing 2022 Reproducibility challenge and ICCS 2022.
[PhD Defense] 2021: My first PhD student, Zeina Houmani, has successfully defending her thesis: Data-driven microservices architectures.
[Impact] 2021: Our joint work with Inria on Earthquake Early Warning was cited by Le Monde.
[Award] 2020: Outstanding paper award in Artificial Intelligence for Social Impact, 34th AAAI Conference on Artificial Intelligence (AAAI-20, A* rank)

Bio

Before joining Inria, I was a staff scientist in the SCI Institute at the University of Utah. I also worked as a Research Associate at the Rutgers Discovery Informatics Institute led by Manish Parashar, where I investigated programming support and resource management for data-driven analytics and edge computing applications (2017-2021). I received a Ph.D. degree from the Ecole Normale Superieure de Lyon in France (2016) under the supervision of Eddy Caron and Laurent Lefevre as part of the Inria Avalon Team. My PhD research focused on implementing tradeoff mechanisms between performance and energy consumption for large scale cloud applications. I've extented this work as a visiting fellow at Mahindra Ecole Centrale in Hyderabad, India (2015). Before that, I worked as a Junior Engineer at Inria, the French Institute for Research in Computer Science (2011-2013) and interned at the National Institute of Informatics in Tokyo, Japan (2009-2010).

Selected Publications

[1] Balouek-Thomert, D., Renart, E. G., Zamani, A. R., Simonet, A., Parashar, M. (2019). Towards a computing continuum: Enabling edge-to-cloud integration for data-driven workflows. The International Journal of High Performance Computing Applications
[2] Balouek-Thomert, D., Rodero, I., Parashar, M. (2020). Harnessing the computing continuum for urgent science. ACM SIGMETRICS Performance Evaluation Review, 48(2), 41-46.
[3] Fauvel, K., Balouek-Thomert, D., Melgar, D., Silva, P., Simonet, A., Antoniu, G., ... , Termier, A. (2020, April). A distributed multi-sensor machine learning approach to earthquake early warning. In Proceedings of the AAAI Conference on Artificial Intelligence.
[4] Renart, E. G., Balouek-Thomert, D., Parashar, M. (2019, May). An edge-based framework for enabling data-driven pipelines for iot systems. In 2019 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) (pp. 885-894). IEEE.

Current Projects

     

Past projects on my former page.

Supervision

[2023 - ] Sangshin Park. PhD candidate.
Subject: Intelligent services across the Computing Continuum.

[2018 - 2021] Zeina Houmani. PhD candidate in joint supervision with ENS Lyon, France.
Subject: A Data-driven microservices architecture for Deep Learning applications.

Teaching

Programming for Engineers | Graduate course (School of Computing, Univ. of Utah, 2022)
Principles of Data and Information Management (Rutgers University, 2019)
Introduction to Computer Science (Rutgers University, 2018|2019)
Parallel Systems | Graduate course (Lyon 1, France, 2016)
Operating Systems | Undergraduate course (Lyon 1, France, 2014, 2015)
Software Engineering | Graduate course (Lyon 1, France, 2014, 2015)
Databases | Undegraduate course (Lyon 1, France, 2014, 2015)
Project Management and Software Engineering | Graduate course (Lyon 1, France, 2014|2015|2016)
Network and OS Programming | Applied graduate course (IUT Lyon1, France, 2016)
Initiation to Windows | Applied graduate course (IUT Lyon1, France, 2015)