I Got Depth Anything 3 Running on My MacBook
200x faster model loading, 14% faster inference, and full Apple Silicon support. Here's how I optimized ByteDance's depth estimation model for production.
Read on MediumDelanoe Pirard
Reinforcement Learning · Computer Vision · Robotics · Brussels
Building intelligent automation systems and bridging the gap between research and real-world applications.
About Me
Professional Background
I'm an AI engineer and researcher with more than 9 years of experience blending software engineering, data science, and deep learning. Right now, I'm specializing as an AI Automation Engineer, working on smart systems that automate complex tasks, while remaining an active AI researcher exploring new frontiers in the field.
Technical Expertise & Interests
I love building robust, reliable AI models that actually work in real life. Recently, I've been focusing on robotics projects using reinforcement learning, realistic simulations with Mujoco and IsaacSim, and computer vision techniques to make robots smarter and more precise.
Academic Journey
I earned my Master's in Bioinformatics and Modeling from Université Libre de Bruxelles. My master thesis focused on predicting protein melting temperatures using advanced NLP methods, specifically fine-tuning ProteinBERT. I was also involved in developing computer vision models to detect pancreatic cancer from histological images.
Beyond Engineering & Research
When I'm not at work, you might find me replicating AI research papers to stay sharp, composing electronic music (something I've been passionate about for over a decade) or diving into philosophical readings, another deep interest of mine.
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Years in Tech & AI
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Years as AI Automation Engineer
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AI & ML Projects
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Professional Certifications
Skills & Expertise
Skills overview: Machine Learning 80%, Deep Learning 75%, Reinforcement Learning 55%, Computer Vision 55%, NLP 50%, Data Engineering 70%
Frameworks & Tools
Research Skills
Data Analysis
Statistical analysis, hypothesis testing, and experimental design
Literature Review
Comprehensive analysis of research papers and state-of-the-art methods
Technical Writing
Redaction of complete technical documentations and research papers
Collaboration
Cross-functional teamwork with researchers from diverse backgrounds
Research Areas
Deep Learning & Automation
I develop practical deep learning models and AI agents for intelligent automation, enhancing real-time decision-making in robotics and industry.
Bioinformatics
I apply advanced NLP and machine learning techniques to tackle real-world bioinformatics challenges, such as predicting protein stability and analyzing biological sequences.
Computer Vision
My work involves developing accurate and reliable computer vision algorithms, particularly for biomedical applications such as early and precise cancer detection.
Reinforcement Learning & Simulation
I design advanced reinforcement learning methods combined with realistic simulations (like Mujoco and IsaacSim) to enhance robotic performance and autonomy.
Featured Projects
Awesome Depth Anything 3
Production-optimized fork of ByteDance's Depth Anything 3 with 200x faster cached model loading, adaptive batching, Apple Silicon optimization, and PyPI distribution.
View Code
Lang-Efficient-SAM
Text-prompted object segmentation model combining EfficientSAM and GroundingDINO for efficient, language-guided image segmentation with minimal computational overhead.
View Code📄 Paper Replication
ProteinBERT PyTorch Implementation
Full PyTorch replication of ProteinBERT (Brandes et al., 2022) for protein sequence analysis using self-supervised transformers and transfer learning on biological sequences.
View Code
RGBD Depth Estimation
An optimized RGB-D depth refinement pipeline powered by a Vision Transformer, offering high-performance processing with cross-platform support for CUDA, MPS, and CPU.
View Code📄 Paper Replication
Vision Transformer (ViT) Replication
PyTorch implementation of Vision Transformer (Dosovitskiy et al., 2020), demonstrating pure attention-based architecture for image classification without convolutions.
View Code
ProteinMEGA
Novel deep learning model designed to outperform current benchmarks in protein sequence prediction, leveraging advanced transformer architectures and protein-specific embeddings.
In Development📄 Paper Replication
Fair Efficient Network
A PyTorch replication of the Fair and Efficient Network (FEN) for multi-agent reinforcement learning, focusing on dynamically balancing fairness and group efficiency.
View CodeExperience
2023 – Present
Artificial Intelligence Researcher
Alpaflow
Lead AI initiatives on an industrial-scale autonomous sewing robot, blending classical planning, reinforcement learning, computer vision, and large-scale simulation. The goal is to deliver reliable, high-performance robotics for demanding textile environments, from proof-of-concept through integration in production systems.
- Designed and deployed robust AI pipelines for a distributed, multi-agent robotic platform, combining real-time decision-making with dynamic task allocation under uncertainty.
- Built advanced scheduling logic using classical planning and reinforcement learning, allowing the robot to intelligently sequence and execute complex sewing operations with optimal use of resources.
- Developed and tuned computer vision models for fast, accurate textile detection and manipulation, handling a range of lighting and occlusion challenges.
- Created realistic simulation environments (IsaacSim, Mujoco) to validate algorithms, test edge cases, and accelerate reinforcement learning with synthetic data.
2022 – 2023
Bioinformatician / Deep Learning Researcher
3BIO Research Group – Université libre de Bruxelles
Drived machine learning research in computational biology, tackling the prediction of protein melting temperatures using state-of-the-art deep learning and NLP methods.
- Developed, fine-tuned, and benchmarked transformer-based models—including ProteinBERT—for the prediction of protein stability from raw amino acid sequences, consistently outperforming published baselines.
- Applied NLP concepts such as sequence embedding, transfer learning, and tokenization to biological data, achieving both predictive accuracy and scientific interpretability.
- Implemented and validated statistical protocols (cross-validation, significance testing) to ensure robust, reproducible results.
2022
Bioinformatician / Deep Learning Research Intern
IRIBHM – Université libre de Bruxelles
During my research internship, I helped develop machine learning solutions for cancer detection in histopathology images. My contributions ranged from core algorithm design to data engineering and clinical collaboration.
- Created unsupervised clustering pipelines to reveal morphological signatures of pancreatic cancer in high-resolution pathology slides.
- Automated large-scale image preprocessing and augmentation with Python and TensorFlow, streamlining the path from raw data to analysis-ready datasets.
- Ran advanced statistical analyses to connect discovered morphological clusters to patient outcomes and clinical hypotheses.
- Documented methodology and key findings to support ongoing work within the lab.
2020 – 2023
IT Consultant & Full Stack Developer
Freelance
As an independent consultant, I delivered bespoke software and AI solutions to a range of clients—mostly in IoT, data analytics, and digital transformation—guiding projects from requirements to production.
- Designed and built complete web-based IoT platforms, such as parking management systems, leveraging C#, React.js, Redux, .NET, and MySQL to enable real-time data flows and analytics.
- Implemented business intelligence dashboards, custom reporting, and predictive analytics to drive client decision-making and resource optimization.
- Architected scalable, maintainable cloud infrastructure using Microsoft Azure services (CosmosDB, DevOps CI/CD, App Services), ensuring reliability and automated deployments.
- Provided technical guidance on IoT integration and supply chain optimization in industrial settings (notably in food processing and manufacturing).
- Developed and deployed machine learning models and data pipelines (PyTorch, Scikit-learn, SciPy) for operational forecasting and process automation.
2018 – 2020
Co-founder & Full Stack Developer
Eavox
As co-founder, I was responsible for both the technical architecture and business growth of Eavox—a mobile platform designed to energize social interaction through gamified sports challenges. I managed the full product lifecycle, from concept and MVP to launch and user acquisition.
- Designed and delivered the complete technical stack: Xamarin-based iOS and Android apps, .NET Core APIs, SQL databases, and Azure cloud services for real-time features.
- Added advanced features: user profiles, live leaderboards, geo-tagged challenges, push notifications, camera integration, and social media sharing.
- Set up CI/CD pipelines to automate build, testing, and deployment to the App Store and Google Play, enabling fast iterations and reliable releases.
- Oversaw product strategy, including feature prioritization based on analytics and direct user feedback, resulting in sustained engagement and regular press coverage.
- Wrote technical and user documentation, led support and maintenance, and ensured system stability as usage grew.
- Collaborated with designers and marketing teams to increase brand visibility and expand the active user base.
2017 – 2018
Full Stack Developer & Xamarin Expert
LevelApp
Led the development of both mobile and backend solutions for LevelApp, starting as an intern and moving into a lead engineering role. My responsibilities spanned cross-platform app architecture, classic business app development, integration of IoT and chatbot technologies, and end-to-end DevOps.
- Developed Xamarin-based iOS/Android apps using the MVVM pattern to enable clean, maintainable code and rapid feature development.
- Implemented classic business app features: secure authentication, account management, push notifications, intuitive navigation, and custom-designed UI/UX.
- Built robust RESTful APIs with ASP.NET Core and managed both relational (SQL) and NoSQL (Azure CosmosDB) data models.
- Integrated IoT devices for real-time communication and control within the mobile platform.
- Designed and integrated conversational chatbot logic with Azure Bot Framework to deliver interactive guidance and automation.
- Set up secure, cloud-native DevOps pipelines with Azure DevOps for automated build, test, and deployment, ensuring quality and rapid delivery.
- Worked side-by-side with product and UX teams to align software capabilities with user needs and deliver high-quality releases on time.
Professional Certifications
2025
NVIDIA Isaac for Accelerated Robotics
NVIDIA
2025
Hugging Face Agents Course
Hugging Face
2025
Generative AI Engineering with LLMs Specialization
IBM / Coursera
2023
Machine Learning in Production
DeepLearning.AI / Coursera
2023
Generative AI with Large Language Models
DeepLearning.AI / Coursera
2023
PyTorch for Deep Learning: Zero to Mastery
Udemy
2021
Deep Learning Specialization
DeepLearning.AI / Coursera
2020
Machine Learning
Stanford University / Coursera (Andrew Ng)
Blog & Articles
Technical insights on AI, reinforcement learning, and autonomous systems.
RGBD-Depth: Optimizing ByteDance's Depth Refinement for Production
How I transformed ByteDance's research code into a production-ready PyPI package with 2x faster inference, multi-platform support, and a live HuggingFace demo.
Read on Medium
RL-VLM-F: The AI That Learns Without Human Supervision
Revolution or Algorithmic Mirage? Discover how robots can now evaluate their own performance using Vision-Language Models, eliminating the need for human feedback.
Read on Medium
The Dawn of Autonomous AI Agents: From Vision to Reality
Tech giants are racing to build AI that doesn't just follow commands — it predicts your needs and takes actions on your behalf. How close are we to this future?
Read on MediumGet in Touch
I'd love to hear about your project or research collaboration ideas. Let's discuss how we can work together!
Contact Information
Location
Brussels, Belgium
Central European Time (CET/CEST)Availability
Open to new projects and collaborations
AI Automation • ML Development • Research Projects • Technical ConsultingLanguages
French, English
Comfortable working in bothQuick Answers
Research Partnerships:
Interested in collaborations on AI-driven automation, reinforcement learning, NLP, and computer vision.
Speaking Opportunities:
Open to speaking engagements at conferences, workshops, and industry events (remote or in-person).
Technical Consulting:
I provide specialized consulting on AI strategy, automation techniques, and machine learning model development.