Education

Stanford University
Sep. 2020 - Now, Computer Science PhD
Stanford University
Sep. 2016 - Jun. 2020, Mathematics B.S.
• With distinction

Publications

• SDEdit: Image Synthesis and Editing with Stochastic Differential Equations (Preprint)
  Chenlin Meng, Yang Song, Jiaming Song, Jiajun Wu, Jun-Yan Zhu, and Stefano Ermon
  [Paper | Project Page | Code]

• D2C: Diffusion-Denoising Models for Few-shot Conditional Generation (Preprint)
  Abhishek Sinha, Jiaming Song, Chenlin Meng, and Stefano Ermon
  [Paper | Project Page]

• Spatial-Temporal Super-Resolution of Satellite Imagery via Conditional Pixel Synthesis (Preprint)
  Yutong He, Dingjie Wang, Nicholas Lai, William Zhang, Chenlin Meng, Marshall Burke, David B. Lobell, and Stefano Ermon
  [Paper | Project Page]

• Geography-Aware Self-Supervised Learning (ICCV 2021)
  Kumar Ayush, Burak Uzkent, Chenlin Meng, Kumar Tanmay, Marshall Burke, David Lobell, and Stefano Ermon
  [Paper]

• Accelerating Feedforward Computation via Parallel Nonlinear Equation Solving (ICML 2021)
  Yang Song, Chenlin Meng, Renjie Liao, and Stefano Ermon
  [Paper]

• Improved Autoregressive Modeling with Distribution Smoothing (ICLR 2021) Oral Presentation [top: 1.8%]
  Chenlin Meng, Jiaming Song, Yang Song, Shengjia Zhao, and Stefano Ermon
  [Paper | Project Page | Code]

• Denoising Diffusion Implicit Models (ICLR 2021)
  Jiaming Song, Chenlin Meng and Stefano Ermon
  [Paper | Code]

• Autoregressive Score Matching (NeurIPS 2020)
  Chenlin Meng, Lantao Yu, Yang Song, Jiaming Song and Stefano Ermon
  [ Paper | Code ]

• Gaussianization Flows (AISTATS 2020)
  Chenlin Meng, Yang Song, Jiaming Song and Stefano Ermon
  [ Paper | Code ]

• MintNet: Building Invertible Neural Networks with Masked Convolutions (NeurIPS 2019)
  Yang Song*, Chenlin Meng*, and Stefano Ermon
  [ Paper | Code ]

• Learning to Interpret Satellite Images in Global Scale Using Wikipedia (IJCAI 2019)
  Burak Uzkent, Evan Sheehan, Chenlin Meng, Zhongyi Tang, Marshall Burke, David Lobell, and Stefano Ermon
  [ Paper | Code]

• Predicting Economic Development using Geolocated Wikipedia Articles (KDD 2019)
  Evan Sheehan*, Chenlin Meng*, Matthew Tan, Burak Uzkent, Neal Jean, Marshall Burke, David Lobell, and Stefano Ermon
  [ Paper | Code]

Selected Computer Graphics Projects

• Rendering Cotton Candy on the Cup
  

  Grand prize winning project for the 2018 Rendering Competition. Modeled and built the scene Cotton Candy on the Cup in PBRT, a computer graphics rendering system. All the models in the scene are built from scratch. Composed the final scene and tuned the final image.

• Rendering and Building the Beauty and the Beast scene
  

  
  
  
  
  
  

  Modeled and rendered the 3D scene Beauty and the Beast in the given ray tracer. Built and textured all the 3D models (e.g. violin, rose, cloth, books, water, etc) from scratch. Composed the final scene and tuned the final image.

• Hair Simulation in Blender
  
  

  
  
  
  
  

  Modeled and simulated hair motion in Blender.

Selected Coursework

Honors and Awards

• Stanford CS348B Rendering Competition: Grand Prize

Personal