Minhao Li

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About

Minhao Li is a rising researcher at the forefront of generative modeling for sequential data, with a primary focus on time series generation and its applications in decision-making systems. His most notable contribution is the development of **TimeLDM**, a latent diffusion model designed for unconditional time series generation, published in 2024. This work addresses a critical gap in domains such as autonomous driving, healthcare, and robotics, where generating realistic, high-fidelity temporal data is essential for training and simulation. By operating in a compressed latent space rather than directly in the data space, TimeLDM achieves efficient and high-quality generation, offering a scalable alternative to traditional autoregressive or GAN-based methods. Although early in his career, Li’s work has already garnered attention, with his most-cited paper accumulating 3 citations shortly after release—a promising sign of its growing influence. His research sits at the intersection of generative AI and practical decision-making, aiming to bridge the gap between theoretical advances and real-world deployment. As the field of time series generation expands, Minhao Li’s contributions are poised to become foundational for future autonomous systems and intelligent agents.