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Last page update: 11/08/2022

Ph.D. on Computer Vision and Machine Learning.

Head of AI at ActiveEon, Paris - France.

Computer Vision Specialist (10+ years of experience).
Experienced C++ and Python Developer.

Proactive, problem-solver and self-learning skills.

Contact: andrewssobral at gmail dot com

20+ years of professional experience with software engineering, software architecture, and software design. I have been working since Nov 2000 with low-level and high-level software development at several IT companies.

For more info, please see my CV.

Currently, I'm working as a Head of the Artificial Intelligence (AI) team at ActiveEon by focusing on the intersection of scientific research and software engineering, bringing cutting-edge AI technologies to the core platform of ActiveEon. 


My main focus is on AI at Scale, HPC+IA, and MLOps


From 2017, my team and I worked on the development of the Proactive AI Orchestration platform, helping customers to automate and orchestrate AI-based workflows, scaling-up with parallel and distributed execution. 


My responsibilities include defining the AI roadmap, supporting AI team members, and driving AI key projects.

In short, my activities are:
Lead a team of 4 (four) PhDs on AI & Machine Learning;
Drive AI roadmap creation and execution;
Drive end-to-end AI product design, architecture, and operation;
Develop end-to-end AI workflows;
Develop large-scale AI workflows with parallel & distributed execution;
Develop multi-node and multi-GPU AI workflows;
Develop a distributed AutoML tool for large-scale hyperparameter optimization and neural architecture search;
Develop the Proactive Python SDK to submit runtime dynamic AI workflows;
Develop the Proactive Jupyter Kernel to submit runtime dynamic AI workflows from Jupyter Lab;


Summary of my AI research experience:


Since 2010, my AI research activities include intelligent video analysis for object detection, segmentation, and tracking, to perform event/action recognition and behavior classification/prediction.

I also developed and deployed computer vision and machine learning models at the edge on system-on-a-chip (SoC) devices such as Raspberry PI, PandaBoard, and NVIDIA Jetson boards (Nano, Xavier NX, TX2, and AGX Xavier).

My doctoral research was mainly focused on the R&D of advanced matrix and tensor methods for robust low-rank/sparse representation and subspace learning of multidimensional and streaming data, such as moving object detection and background-foreground separation in multi-spectral and multi-featured video sequences.

In the past, between my B.Sc. and my M.Sc, I worked on several embedded systems and robotic projects using microcontrollers and SoC boards to read sensors, I/O devices, performing data acquisition, signal processing, image/video processing, wireless communication, and motor control.


For more information, please see the Projects page at the top of this page.


Main research interests: Computer Vision, Machine / Deep Learning, Pattern Recognition, Applied Mathematics, Matrix / Tensor Decomposition, Optimization, Big Data.

Journal reviewer: I peer-reviewed for more than 10 high-quality journals, such as Elsevier (CVIU, IVC, PRL, NC, IF), Springer (NCA, CC, FITEE, JIVP), IEEE (TIP, TNNLS, TCSVT), MDPI (Sensors), JEI, JOSA-A, PLOS ONE. For more information, please see my Publons profile for an updated version.


Int. Conferences/Workshop reviewer: ICCV 2015 (RSL-CV), ICIAP 2015 (SBMI), ISCV 2017.


My open source libraries and source code:

BGSLibrary - A Background Subtraction Library.

VDTC - Vehicle Detection, Tracking, and Counting.

LRSLibrary - Low-Rank and Sparse tools for Background Modeling and Subtraction in Videos.

MTT - Matlab Tensor Tools for Computer Vision.

IMTSL - Incremental and Multi-feature Tensor Subspace Learning.

OSTD - Online Stochastic Tensor Decomposition.

For more details, please visit my GitHub profile:

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