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.

• ROTA — Smart Transportation — Built edge AI pipelines for real-time vehicle detection, multi-stream RTSP video analytics, vehicle counting, and smart parking occupancy detection using NVIDIA Jetson, DeepStream, YOLO, and IP cameras, operating continuously (24/7) in real-world production environments.
• IRT Saint-Exupéry / TAS — Contributed to the Reactive & Autonomous Image Chain research project, developing ProActive-orchestrated deep learning workflows for aerial and satellite image analysis, including semantic segmentation pipelines for maritime threat detection and situational awareness.
• SAFRAN Aircraft Engines — Built AutoML-driven predictive maintenance and anomaly detection workflows over aircraft engine sensor data (2,000+ sensors), integrating feature engineering, ML experimentation across multiple algorithms, real-time streaming analysis during test campaigns, and automated engineering report generation.
• Thales Alenia Space (TAS) — Developed industrial AI workflows for satellite manufacturing and telemetry analysis, including deep learning-based wire defect detection on hybrid circuits, sensor anomaly detection using signal processing, TSFresh feature extraction with XGBoost classification, and automated telemetry verification and incident forecasting — reducing per-test analysis time to minutes.
• INRAE / OVHcloud — Led technical workshops and hands-on training for distributed AI and MLOps adoption across cloud and HPC infrastructures, covering deep learning workflows, SLURM-native orchestration, AutoML-driven hyperparameter optimization, and production AI workflow orchestration.
• CHUV / MORPHEMIC Project — Contributed to the design of adaptive AI workflow orchestration and distributed execution architectures for healthcare scientific workloads across hybrid cloud, on-premise, and HPC infrastructures, featuring elastic resource provisioning and utility-driven, accelerator-aware 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.