Hernán Carrillo

Hernán Carrillo

PhD student in computer science

Université de Bordeaux



Hernán Carrillo is a PhD student in computer science at Université de Bordeaux. He is part of the image and sound research team at LaBRI, and currently working in the PostProdLEAP project funded by the ANR (French National Agency for Research). His research interests include imaging inverse problems, deep learning, computational imaging and image and video processing.

His thesis focuses on developing new tools for video archive post-production by leveraging both, recent deep learning approaches and patch-based and variational approaches. More precisely, he designs deep learning models which include spatial and temporal regularization, and constraints on texture features as a way to automate the process of colorization on post-production videos and images.


  • Inverse problems
  • Deep learning
  • Image and video processing
  • Image colorization
  • Computer vision
  • Embedded systems


  • PhD in Computer science, 2020 - Now

    Université de Bordeaux - France

  • MSc in Signal and Image Processing, 2018 - 2020

    Ecole Centrale de Nantes - France

  • B.Eng in Electronics, 2012 - 2017

    Universidad del Norte - Colombia

Skills & Hobbies









Deep learning


video games



Doctoral researcher

Laboratoire Bordelais de Recherche en Informatique (LaBRI)

Oct 2020 – Present Bordeaux - France
Description: PhD thesis - Deep learning for archive video colorization

Graduate research interm (M2)

Laboratoire des Sciences du Numérique de Nantes (LS2N)

Feb 2020 – Aug 2020 Nantes - France
Description: Master thesis internship - Deep learning for image reconstruction

Graduate research interm (M1)

Laboratoire des Sciences du Numérique de Nantes (LS2N)

Feb 2019 – Jul 2019 Nantes - France
Description: M1 Final project - Auto-calibration using LIDAR and Visual SLAM


IBM AI Engineering

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Scalable Machine Learning on Big Data using Apache Spark

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Machine Learning with Python

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Semantic segmentation with Deep Learning

For quantitative analysis, cells in microscopic images need to be segmented from the image. In this project it model the problem in terms of semantic segmentation and approach it by means of deep learning, and more specifically with the U-Net architecture.

Auto-calibration using LIDAR and Visual SLAM

In this project it is proposed a method for the calibration of a vehicle equipped with a 3DLIDAR and cameras. Combining known vehicle trajectory with the poses from camera and LIDAR, the idea is to propose a non-linear minimization framework to solve the relative transformation between the LIDAR sensor and the camera.

Automatic music transcription

This project aims to automatically transcripts music notes based on a scoring metric. The signal is divided into successive time slots of small duration, on which spectral analysis is performed by Fourier transform.

Coin Counter

This project presents a coin counter system based on Convolutional Neural Networks (CNNs) and Digital Image Processing techniques. The framework’s uses images taken by a cellphone camera as input and output the quantity of coins and the amount of money. Note: it should be noted that the system has been tests ONLY on Colombian coins.

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  • 351 cours de la Libération, Talance, Nouvelle-Aquitaine 33405