Join us to hear about the latest advances in AI at Northeastern University!
Date, Time and Location
March 26, 2026
9 – 11 AM Pacific
Online. Register for the Zoom!
Scalable and Efficient Deep Learning: From Understanding to Generation
In an era where model complexity and deployment constraints increasingly collide, achieving both scalability and efficiency in deep learning has become essential. Scalable and efficient deep learning ensures that powerful models can be trained, deployed, and adapted under limited computational and data resources, enabling broader accessibility and practical application. From understanding to generation, this talk unifies methods that cut costs while preserving capability.
About the Speaker
Yitian Zhang is a fifth-year PhD student at Northeastern University, advised by Prof. Yun Raymond Fu. His research interests center around Efficient and Scalable AI, spanning Generative Models, Multimodal Large Language Models, and Foundation Models.
Grounding Visual AI Models in Real-World Physics
Generative video models have made rapid progress in visual realism, yet they frequently violate basic physical laws, producing implausible motion and incorrect cause-effect relationships. This talk presents MoReGen, a physics-grounded, agentic text-to-video generation framework that integrates Newtonian physics directly into the generation process via executable physics-engine code.
By coupling vision–language models with trajectory-based physical evaluation and iterative feedback, MoReGen produces videos that are both visually coherent and physically consistent. We further introduce MoRe Metrics and MoReSet, a benchmark and dataset designed to evaluate physics fidelity beyond appearance-based metrics such as FID and FVD. Together, this work demonstrates a path toward visual AI systems that reason about motion, interaction, and causality in the real world rather than hallucinating them.
