Schedule

The October 8 lecture is a review of the October 6 lecture. Please refer to the materials provided for that date.

The October 14 class is a review for the Midterm Exam, a review of Coding Assignment 1, a review of Written Assignment 1, and Class Project Discussions.

Covering Multi-Agent AI, Parameter Servers, Ring All-Reduce, and Practical Distributed Training with JaxMARL.

Covering Consistency Models in Distributed Systems.

Covering Replication Strategies in Distributed Systems and Energy Efficiency in Distributed AI.

Covering Replication Strategies in Distributed Systems and Energy Efficiency in Distributed AI.

Deep Q Learning, Policy Gradients, Actor-Critic, and Distributed RL Techniques.

Agent Ontologies, Distributed Agent Communication, and Communication vs. Cooperation.

Scalability vs. Reliability for Distributed Computing, Quantifying Reliability for Distributed Computing, and Reliability of Distributed Architectures

Distributed Multimodal AI Agents, Addressing Distributed Challenges in Multimodality

Embodied Learning from a Distributed Perspective, Distributed Imitation Learning, Model Predictive Control, Self-Supervised Learning, Evolutionary Learning, Computational Game Theory

Networking in Distributed Systems, Multi-Agent AI for NextGen Networks

Data distribution and coding techniques in Distributed Systems.

Real-world distributed systems, case studies, and applications.

Bottlenecks in traditional computing and how neuromorphic approaches can address them.

Overview of distributed quantum computing architectures.

Techniques and challenges in making distributed AI systems interpretable.

Selected NeurIPS 2025 papers relevant to distributed computing and AI agents.

In-class debugging assignment and final class research project presentations.

Final class research project presentations.