Houssam Hajj Hassan

Researcher @ Orange | Autonomous Cyber-Physical Systems

I’m an applied researcher specializing in autonomous systems, designing and deploying hybrid AI architectures that combine LLM-based reasoning, causal inference, reinforcement learning, and AI planning to enable intelligent, self-adaptive operation in distributed IoT environments.

At Orange Innovation, I’m currently working on LLM-driven perception and network optimization for autonomous robotic swarms in 6G industrial settings. Experienced technical lead on international R&D projects (ANR, EU Horizon), with a track record of guiding junior researchers from design to deployed systems.

Professional Experience
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Postdoc Researcher
Nov. 2025–Present
Orange Innovation
Châtillon, France
- Developing adaptive data fusion techniques combining LLMs and ML models to maximize perception precision across heterogeneous robotic swarm sensors.
- Designing LLM-assisted agentic optimization workflows for 6G industrial IoT communication, translating perception outputs into real-time network decisions via tool-integrated AI agents.
- Leading research for the CANCUN ANR project, enabling efficient and sustainable IIoT communication.
Postdoc Researcher
Jan. 2025–Oct. 2025
Télécom SudParis
Évry, France
- Technical lead and main contributor for the PANDORA EU project for developing scalable, trustworthy, and autonomous AIoT operation.
- Built a causal-GAN synthetic data generation pipeline producing trustworthy, realistic IoT datasets to improve ML model robustness in data-scarce industrial environments.
- Developed an autonomous MLOps framework for adaptive ML model distribution and lifecycle management across heterogeneous Edge-Cloud infrastructures.
- Mentored PhD and MSc researchers in autonomous system design and experimental methodology.
PhD Researcher
Nov. 2021–Dec. 2024
Télécom SudParis
Évry, France
- Developed CRAFTER: a Python/PyTorch causal reinforcement learning system for self-adaptive IoT, reducing response latency by 25% - deployed across a 5-node containerized testbed simulating 100 IoT devices.
- Developed PlanEMQX: an AI planning-enabled MQTT broker for adaptive, priority-aware data flow management, cutting latency by 20% for time-critical flows - Distinguished Artifact Award, IEEE ICSA 2024.
- Designed hybrid AI architecture integrating causal inference, RL, and symbolic planning into a unified self-adaptation middleware - published across 3 A-ranked CORE venues.
- Mentored MSc researchers in autonomous systems and experimental methodology.
R&D Intern
Apr. 2021–Oct. 2021
Télécom SudParis
Évry, France
- Developed a Java-based simulation tool for performance evaluation of IoT data exchange.
- Optimized IoT data flow performance using AI planning methodologies.
Telecom Engineering Intern
May. 2019–Jul. 2019
Dar Al-Handasah
Beirut, Lebanon
- Conducted network analysis and optimization for telecom infrastructures.

Education
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PhD in Computer Science
2024
Institut Polytechnique de Paris (IP Paris), France
Thesis: Enabling autonomous IoT systems : A middleware-based hybrid AI approach to self-adaptation
Master's in Computer Science
2021
Lebanese University, Lebanon
Thesis: Designing an Edge-based Data Exchange Infrastructure for Smart Buildings
Bachelor of Engineering - Computer Engineering
2020
American University of Beirut, Lebanon
Final Year Project: Instructor’s Problem Set Recycling and Evolving

Featured prototypes

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CoAdapt: An LLM-based Framework for Adaptive Collaborative Perception in IIoT Robotic Swarms

CoAdapt is an LLM-based adaptive collaborative perception framework for IIoT robotic swarms.

CRAFTER: Causal Reinforcement Learning for Self-adaptive IoT

CRAFTER uses Causal Reinforcement Learning for autonomous IoT systems, reducing latency by 25%.

SPARQ: QoS-aware Self-protection for IoT

SPARQ is a novel framework for designing self-protecting IoT systems that considers both the security exposure to cyber attacks and the QoS performance.