Romain Thalineau, Developer in Cluj-Napoca, Cluj County, Romania
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Romain Thalineau

Verified Expert  in Engineering

Statistical Analysis Developer

Cluj-Napoca, Cluj County, Romania
Toptal Member Since
January 29, 2018

Romain is a seasoned data scientist and machine learning engineer with a Ph.D. in quantum physics and expertise in Python and the PyData stack. Having worked in three different countries for companies of all sizes, from startups to global organizations, Romain is a versatile and communicative engineer.

Docker, Kubernetes, R, Data Science, Machine Learning, Computer Vision...
Django, TensorFlow, Keras, Seaborn, SciPy, Scikit-learn, Matplotlib, Pandas...
Docker, Celery, RabbitMQ, Redis, Cassandra, Elasticsearch, PostgreSQL, Django...




Preferred Environment

Jupyter, Git, PyCharm, Ubuntu

The most amazing...

...thing I've created is a very sensitive electron detector using a spin qubit.

Work Experience

Software Engineer/Cofounder

2018 - PRESENT
  • Developed a data analysis platform for a research center using Python, Django, and R.
  • Designed and developed the architecture of a highly available content streaming solution.
  • Developed a model for detecting default on a production line. Involved image processing and image recognition/segmentation with deep learning (CNN).
  • Developed a model for recognizing vineyard diseases. Involved image processing and image recognition with Deep Learning.
  • Worked on diverse data science and machine learning projects.
Technologies: Docker, Kubernetes, R, Data Science, Machine Learning, Computer Vision, Elasticsearch, PostgreSQL, Django, Scikit-learn, Pandas, NumPy, Keras, PyTorch, TensorFlow, Python

Data Scientist

2017 - 2017
  • Supported the development of a AI based transportation application.
  • Completed data exploration and visualization of geolocation data.
  • Contributed to the development of a model for destination prediction.
Technologies: Django, TensorFlow, Keras, Seaborn, SciPy, Scikit-learn, Matplotlib, Pandas, NumPy, Python

Software Engineer

2016 - 2017
  • Maintained and improved an application matching candidates with jobs.
  • Developed REST API with Django + Django REST Framework.
  • Designed and implemented data pipelines.
  • Implemented an integration test framework for all the micro services with Docker.
Technologies: Docker, Celery, RabbitMQ, Redis, Cassandra, Elasticsearch, PostgreSQL, Django, Python

Data scientist - Model engineer

2014 - 2016
  • Created electrical models of semiconductor devices.
  • Completed data analysis using the PyData stack.
  • Provided statistical analysis of PCM (process control monitoring) data and statistical modeling.
  • Automated the model report and QA process via Python scripts.
  • Created Spice, Spectre nominal model according to characterization and customer specifications.
Technologies: MATLAB, SciPy, Scikit-learn, Matplotlib, Pandas, NumPy, Python

Research associate

2009 - 2013
Institut Neel - CNRS
  • Researched single electron devices in the context of quantum computing.
  • Automated the data collection process with Labview.
  • Completed data analysis and modeling using tools like Matlab and the PyData stack.
  • Tutored interns.
  • Communicated results in international conferences and in peer-reviewed journals.
Technologies: LabVIEW, MATLAB, SciPy, Scikit-learn, Matplotlib, Pandas, NumPy, Python

Implementation of Neural Style Transfer in PyTorch
PyTorch implementation of "A Neural Algorithm of Artistic Style" from Gatys et al.

Deep Learning With Point Clouds
A point cloud is a set of points and can be generated by 3D scanner such as the one used by self-driving vehicles. Being unordered and irregular, features cannot be learned by simply convolving kernels against points like it is done for data represented in regular domains such as images. In this article, I expose the problem related to learning features from point clouds, review the pioneer architecture PointNet and proposes a PyTorch implementation of it.

Introduction to Backpropagation
In this notebook, I try to explain the backpropagation algorithm, heavily used in neural networks. I also proposed a bare numpy implementation of it.

Monitoring the French Presidential Election in Real Time on Twitter
As a side project, I implemented a real-time monitoring of the French presidential election on Twitter. This included:
- streaming the related tweets using the Twitter streaming API
- parsing and analyzing them
- storing them in a graph DB (Neo4J)
- updating in real time analyses
- serving these analyses via a REST API
- visualizing the analyses via a AngularJS front end

The tweet collection process is available on Github:

Spin Qubits: From the Transport and the Manipulation of a Single Electron Spin to its use as a Highly Sensitive Detector
Ph.D. project:

In this thesis we described a series of experimental works, which have been realized in the context of spin qubits, going from their use as information carriers to their use as very sensitive detectors.

We demonstrate the first experimental realization of a single electron transport along a closed path inside a system composed of four coupled quantum dots. By considering spin-orbit interaction, this experiment opens the way toward coherent topological spin manipulations. In the context of quantum computing and spin qubits, we study the two-qubit gates. By considering two tunnel-coupled quantum dots, we demonstrate by controlling the local Zeeman splitting that the natural two-qubit gate for spin qubits evolves from the SWAP gate to the C-phase gate. This work demonstrates the feasibility of the C-phase gate. Finally, we use spin qubits as very sensitive detectors. A singlet-triplet qubit is a quantum system which can be tuned in order to be very sensitive to the electrostatic environment. Here we report the use of such a qubit to detect a single electron transported next to the detector.
2009 - 2012

Ph.D. in Physics

Grenoble Unviersity - Grenoble, France

2006 - 2009

Master of Science Degree in Physics

PHELMA - INPG - Grenoble, France

2003 - 2006

Undergraduate Intensive Course in Mathematics and Physics

Preparatory classes Lycee Descartes - Tours (France)


Deep Learning Specialization



OpenCV, Tidyverse, Ggplot2, SciPy, TensorFlow, Keras, Scikit-learn, Matplotlib, NumPy, Pandas, PyTorch, PCL, Dask, Twitter API


Scikit-image, Dplyr, Seaborn, PyCharm, Git, Celery, Jupyter, MATLAB, NGINX, LabVIEW, uWSGI, Jenkins, RabbitMQ, CloudCompare, Apache Airflow, GIS


Django REST Framework, Django, Flask, Bootstrap


Markdown, R, Python, SQL, HTML5, JavaScript, CSS, C, Rust, C++


Data Science, Object-oriented Programming (OOP), Test-driven Development (TDD), Continuous Integration (CI), Continuous Deployment, Microservices, REST, Agile, Scrum, Functional Programming


RStudio, Jupyter Notebook, Docker, Linux, Ubuntu, Amazon Web Services (AWS), Azure, Kubernetes


Elasticsearch, Redis, Cassandra, PostgreSQL, Neo4j, GeoServer, PostGIS, Memcached, MongoDB


Mathematics, Computer Vision, Deep Learning, Physics, Statistical Analysis, Statistical Modeling, Predictive Modeling, Predictive Analytics, Statistics, Artificial Intelligence (AI), Data Mining, Machine Learning, Bokeh, Point Clouds, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNNs), Reinforcement Learning, Deep Reinforcement Learning, Neural Networks, Deep Neural Networks, Prefect Cloud, Computer Vision Algorithms, Image Processing, Prefect, Distributed Systems, Numba, Cython, Natural Language Processing (NLP), GPT, Generative Pre-trained Transformers (GPT)

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