Andrews Cordolino Sobral, Ph.D.

AI Platform Architect | MLOps | AI Orchestration | Ph.D. in Computer Vision & Machine Learning

With 25 years of experience building software systems and over 15 years in AI, machine learning, and computer vision across academia and industry, I specialize in ML platforms, MLOps, and AI orchestration across cloud, HPC, and hybrid environments, helping organizations automate AI pipelines and move AI from research toward production.

AI Team Leadership @ ActiveEon (2017–2025)

At ActiveEon, I led PhD-level AI researchers and architected AI platforms for enterprise and industrial applications, orchestrating and automating research prototypes toward production. My responsibilities included:

  • Led PhD-level AI researchers in AI/ML, GenAI, and Vision Systems
  • Architected ProActive AI Orchestration (PAIO) for distributed AI workflows across hybrid cloud and on-prem environments
  • Prototyped and deployed LLMs, deep learning, and AutoML pipelines across multi-node, multi-GPU, HPC, and hybrid cloud environments
  • Built custom SDKs and tools for dynamic workflow automation (e.g., ProActive Python SDK, ProActive Jupyter Kernel)
  • Led the ExtremeXP (Horizon Europe) runtime implementation for scheduling and executing complex analytics workflows on distributed infrastructure, integrating distributed AutoML, resource monitoring, and dynamic service orchestration.

Selected Enterprise, Industrial & Research AI Use Cases

  • LuxProvide — MeluXina Tier-1 Supercomputer — Designed and deployed AI orchestration workflows for distributed multi-node/multi-GPU training with PyTorch DDP, AutoML, MLflow-integrated experiment tracking with SLURM, and high-performance LLM inference (Llama 3.1 405B FP8) using vLLM on Ray clusters.
  • IDRIS / Jean Zay (HPE partnership) — Developed distributed AI workflows for multi-node/multi-GPU orchestration with MPI, gRPC, Horovod/NCCL, containerized and bare-metal execution, and distributed AutoML-driven hyperparameter optimization with integrated experiment tracking and SLURM-native scheduling.

Academic Background & Research

Ph.D. in Computer Vision & Machine Learning – Université de La Rochelle, France (2013-2017)

Specialized in:

  • Low-rank & sparse matrix/tensor decomposition
  • Subspace learning & optimization
  • Multimodal and real-time video analytics
  • Embedded computer vision (Jetson, Raspberry Pi, PandaBoard)

My academic research has led to 2800+ citations (h-index: 17) and contributions to peer-reviewed journals and top conferences (IEEE, Elsevier, Springer, CVIU, TIP, TNNLS, ICCV). I also actively contribute as a peer reviewer and open-source developer.

Open-Source & Contributions

🔗 GitHub: github.com/andrewssobral

  • BGSLibrary (C++) – Widely used background-subtraction library for moving-object detection.
  • LRSLibrary (MATLAB) – Framework for low-rank/sparse decomposition.
  • MTT (MATLAB) – Tools for tensor manipulation and decomposition.
  • OSTD (MATLAB) – Online stochastic tensor decomposition for multispectral video.
  • VDTC (C++ & Python) – Vehicle detection, tracking & counting pipeline.
  • GoDec (Python) – Low-rank + sparse decomposition.
  • DTT (C++) – Header-only library for seamless data type conversions (Eigen, OpenCV, Armadillo, LibTorch, ArrayFire).

These projects are used in academia, industry, robotics, surveillance systems, and research labs worldwide.