CV

Visiting Assistant Professor

• Deep learning approaches for numerical methods and partial differential equations.

Research Scientist

• Deep generative models (especially diffusion models), mathematically grounded deep learning, robustness.

Senior Research Engineer

• Robustness of deep learning.

Research Assistant

• Harmonic analysis and microlocal analysis of PDEs.

PhD in Mathematics

• Theory and applications of deep learning, anomaly detection, robustness, generative models
  • Advisor. Gilad Lerman
  • Period. August 2018 – May 2021
• Reaction-diffusion partial differential equations (PDEs), harmonic analysis
  • Advisor. Wei-Ming Ni
  • Period. June 2017 – June 2018

Bachelor in Mathematics

Invited talks at Crunch Seminar of Division of Applied Mathematics at Brown University

• Exploring the Intersection of Diffusion Models and (Partial) Differential Equation Solving
• YouTube Link

Invited talks on Scientific Machine Learning at National Yang Ming Chiao Tung University (Part-II)

• Diffusion Models, Insights into Their Connection with PDE solving
• Webinar Page

Invited talks on Scientific Machine Learning at National Yang Ming Chiao Tung University (Part-I)

• Theoretical Foundation of Diffusion Models
• Webinar Page

Guest lecture at Duke Kushan University

• Evolution of Diffusion Models, From Birth to Enhanced Efficiency and Controllability

Invited talk at Department of Mathematics, National Tsing Hua University, Taiwan

• Theory of Diffusion Models
• YouTube Link (in Mandarin). You can find a list of credits for the materials used in the video here (under construction...).

Invited talk at Appier, Taiwan

• Evolution of Diffusion Models, Its Birth and Applications

Invited talk at Department of Mathematics, National Central University, Taiwan

• Theory of Diffusion Models

Invited talk at Robotic Search Lab, National Central University, Taiwan

• Evolution of Diffusion Models, Its Birth and Applications

Invited talk at Department of EE, National Taiwan University, Taiwan

• Evolution of Diffusion Models, From Birth to Enhanced Efficiency and Controllability

Invited talk at NVIDIA, Taiwan

• Evolution of Diffusion Models, From Birth to Enhanced Efficiency and Controllability

Presented at Learning on Graphs & Geometry (LoGG) reading group

• Consistency Trajectory Models, Learning Probability Flow ODE Trajectory of Diffusion
• YouTube Link. You can find a list of credits for the materials used in the video here (under construction...).

Presented at NSF ATD and AMPS workshop 2019, Washington D.C.

• Robust Subspace Recovery Layer for Unsupervised Anomaly Detection

Tutorial at ISMIR 2024

• From White Noise to Symphony🎼 -- Diffusion Models for Music and Sound
• Our official webpage

Organized social event at ICLR 2024, Vienna

• Recent advances on diffusion and GAN
• ICLR Page

Organized Expo workshop at NeurIPS 2023, New Orleans

• Media Content Restoration and Editing with Deep Generative Models and Beyond
• NeurIPS Page

IEEE TPAMI, ACM TKDD Reviewer

ICLR, ICML, NeurIPS Reviewer

Consistency Trajectory Model (CTM)

• For single-step diffusion model sampling, CTM achieves SOTA on CIFAR-10 and ImageNet 64x64. CTM offers diverse sampling options and balances computational budget with sample fidelity effectively.

Slicing Adversarial Network (SAN)

• A theoretically grounded and simple modification scheme for discriminators of almost any GANs to enhance GAN performance. Applying SAN to StyleGAN-XL results in SOTA performance on ImageNet 256x256.

FP-Diffusion

• Improving density estimation of diffusion models by regularizing with the underlying equation describing the temporal evolution of scores, theoretically supported.

GibbsDDRM

• Solving blind linear inverse problems by utilizing the pre-trained diffusion models in a Gibbs sampling manner.