| Time | Tuesday 8 Sep | Wednesday 9 Sep | Thursday 10 Sep | Friday 11 Sep |
|---|---|---|---|---|
| 08:30-09:00 | Registration | |||
| 09:00-10:30 | Opening | Keynote: AI in radio access networks: challenges and open problems Speaker: Zwi Altman (Orange Lab) |
Course: Robust Machine Learning: A Quest to Learning in Untrusted Environment (I)
Speaker: Nirupam Gupta (University of Copenhagen) |
Course: Robust Machine Learning: A Quest to Learning in Untrusted Environment (II)
Speaker: Nirupam Gupta (University of Copenhagen) |
|
Keynote: Distributed AI at the Edge
Speaker: Alexandru Dobrila (Hivenet/Antimatter) |
||||
| 10:30-11:00 | Break | |||
| 11:00-12:30 |
Lecture: Distributed inference in the edge-network-cloud continuum
Speaker: Frédéric Giroire (CNRS) |
Lecture: Bayesian optimization: Theory and applications to telecommunications Speaker: Lorenzo Maggi (NVIDIA) |
Course: Reinforcement learning: from bandits to structured MDPs (I)
Speaker: Bruno Gaujal (Inria) |
Course: Reinforcement learning: from bandits to structured MDPs (II)
Speaker: Bruno Gaujal (Inria) |
| 12:30-14:00 | Lunch | |||
| 14:00-15:30 | Lecture: The Road to Autonomous Networks: AI/ML Foundations and Frontiers
Speaker: Farnaz Moradi (Ericsson) |
Panel: Application and Role of AI in Industry Panelists: |
Student Presentations (Session 3) | Student Presentations (Session 5) |
| 15:30-16:00 | Break | END | ||
| 16:00-17:30 | Student Presentations (Session 1) | Student Presentations (Session 2) |
Student Presentations (Session 4) | |
| 19:00-21:30 | Social Dinner at l’Atelier 67 | |||
Session 1 (Tuesday 8 Sep): Efficient Edge Inference
Léo Bernard — Threshold-based Routing for Energy-efficient Experts
Kyrylo Tymchenko — Confidence-Shaped Regression Cascades for Efficient Edge Inference
Session 2 (Wednesday 9 Sep): Resource Allocation and Efficient Training
Isidoor Pinillo Esquivel — Quantized Online Gradient Descent for Constant-Time Caching with Dynamic Regret Guarantees
Mariya Peter — Fairness-Aware Optimal Transport Framework for Flow Allocation in Interconnected Network Systems
Nicolas Helson — Network-Adaptive Gradient Compression for Faster ML Model Training in Datacenters
Session 3 (Thursday 10 Sep): Federated Learning: Fairness, Robustness, and Unlearning
Neeraja Sudhakaran — Towards Fair Federated Learning: Analyzing the Impact of Data Heterogeneity and Data Repair
Jingye Wang — The Praetorian Guard: Unveiling the Vulnerability of Trust-Based Defense in Federated Learning
Florian Zimmer — Enabling Decentralised Federated Unlearning in Industrial Cross-Organisational Environments
Panos Raptis — Dynamic Fairness in Multi-Task Edge Learning
Session 4 (Thursday 10 Sep): Reinforcement Learning and AI-Driven Network Intelligence
Sooraj Skanda — Model-Based Multi-Agent Reinforcement Learning for Integrated Sensing and Communication
Andreas Pattichis — Continual Learning from Streams of Unlabeled Data in LLM Systems
Michele Simeone — Exploring KPI Trade-offs in O-RAN Conflict Resolution with Deep Reinforcement Learning
Luigi Rachiele — From Fixed Pipelines to Agentic Decisions: An LLM Orchestrator for Anomaly Detection in Mobile Network Drive Tests
Session 5 (Friday 11 Sep): Learning and Control under Resource Constraints
Muyun Li — RL-based Edge Access Control for Time-Sensitive Tasks under Resource Contention
Oihan Azkarate Iriarte — Optimal Control and Learning of Tandem Queues with Transfer Costs
Haoming Lin — Structure of Optimal Admission Control under Resource Constraints
Berfin Dinc — Closed-Loop Control with Delayed Information