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Research Associate
Area of Study/Expertise
Distributed Systems, Edge Computing, Energy Efficiency
Office Location
CoRE Building, Rm 625

Daniel Balouek-Thomert, Ph.D.

Computer Scientist


Daniel Balouek-Thomert is a Research Associate at the Rutgers Discovery Informatics Institute (RDI2) at Rutgers, the State University of New Jersey.

His research interests fall in the broad area of parallel and distributed computing and include Cloud computing, Energy Efficiency, and Artificial Intelligence/Machine Learning applications. His current research addresses the control and advances usages of Edge computing systems, with a particular interest in the orchestration of analytics with regards to the content of the data, cost of computations, and urgency of the results.

Daniel has been teaching Introductory computer science (CS111) and Principles of data management (CS336) at Rutgers University, NJ. He also taught courses in the area of parallel and distributed systems, databases, and project management at the University of Lyon, France.

Daniel has been part of the program committee of IEEE CCGRID, IEEE ICDCS, IEEE Big Data, and HPBench. He also acted as a Program Committee Chair for IEEE InterCloud-HPC 2019.

He received his Master’s degrees from both Université Pierre et Marie Curie (Paris 6) and Télécom ParisTech and a Ph.D. degree from the Ecole Normale Supérieure de Lyon (France). Before joining Rutgers, he was Research & Development Engineer at NewGeneration SR (Paris, France), an Engineer at Inria (Lyon, France), and a research intern at National Institute Informatics (Tokyo, Japan).

Curriculum Vitae

Selected Publications

  • A Decentralized Multi-Sensor Machine Learning Approach for Earthquake Early Warning. With Kevin Fauvel, Diego Melgar, Pedro Silva, Anthony Simonet, Gabriel Antoniu, Alexandru Costan, Véronique Masson, Manish Parashar, Ivan Rodero and Alexandre Termier. In AAAI-20: The 34th AAAI Conference on Artificial Intelligence, 2020. Outstanding paper award in Artificial Intelligence for Social Impact.
  • Towards a computing continuum: Enabling edge-to-cloud integration for data-driven workflows. With Eduard Gibert Renart, Ali Reza Zamani, Anthony Simonet, Manish Parashar. In the Journal of High-Performance Computing and Applications (IJHPCA), 2019
  • Distributed Operator Placement for IoT Data Analytics Across Edge and Cloud Resources. With Eduard Gibert Renart, Alexandre da Silva Veith, Marcos Dias de Assunção, Laurent Lefèvre and Manish Parashar. In the proceedings of the IEEE International Symposium in Cluster, Cloud, and Grid Computing (CCGrid) 2019
  • Edge Enhanced Deep Learning System for Large-scale Video Stream Analytics. With Muhammad Ali, Ashiq Anjum, M.U Yaseen, Ali Reza Zamani, Omer Rana, Manish Parashar. In ICFEC 2018: The 2nd IEEE International Conference on Fog and Edge Computing (ICFEC) 2018
  • Parallel Differential Evolution approach for Cloud workflow placements under simultaneous optimization of multiple objectives. With Arya K. Bhattacharya, Eddy Caron, Gadireddy Karunakar, Laurent Lefèvre. In CEC 2016: The IEEE Congress on Evolutionary Computation (CEC) 2016