Siyi Chen

I am an ECE PhD student in University of Michigan-Ann Arbor (2022 - Present), luckily advised by Professor Qing Qu.

My research interests encompass generative models, representation learning, and their connections. I am keen on understanding their learning mechanisms and acquired information structures, and aim to utilize the theoretical understandings for unified and interpretable applications with a combined inspirations from optimization techniques, principles of physics, and human cognition. Specifically, I am exploring these questions in diffusion models, self-supervised learning, multimodality, and video learning.

Before that, I obtained my B.S.E in Computer Science from University of Michigan-Ann Arbor, and my B.S.E in Electrical and Computer Engineering from Shanghai Jiao Tong University. I was luckily advised by professor David Fouhey and mentored by Shengyi Qian on 3D computer vision.

Email / CV / Google Scholar / Twitter / Github

News

1. [10/2024] One paper on self-superised video representation learning is accepted by NeurIPS 2024 SSL Workshop!

2. [10/2024] Two papers (paper1, paper2) on diffusion models are accepted by NeurIPS 2024 M3L Workshop!

3. [09/2024] Our work LOCO Edit is accepted by NeurIPS 2024!

4. [05/2024] I have passed my PhD qualification exam!

Publicaions

	Exploring Low-Dimensional Subspaces in Diffusion Models for Controllable Image Editing Siyi Chen, Huijie Zhang, Minzhe Guo, Yifu Lu, Peng Wang, Qing Qu NeurIPS, 2024 website / code / paper We enable localized, transferable, linear, and composable image editing on diffsion models by exploring their low-rank and locally linear semantic spaces.
	Unfolding Videos Dynamics via Taylor Expansion Siyi Chen, Minkyu Choi, Zesen Zhao, Kuan Han, Qing Qu, Zhongming Liu NeurIPS SSL Workshop, 2024 paper Inspired by physical motion, we unfold a video clip via Taylor expansion and design an alternative algorithm for self-supervised video representation learning. Our proposed method can steer the model to dynamic parts in the video.
	Diffusion Models Learn Low-Dimensional Distributions via Subspace Clustering Peng Wang, Huijie Zhang, Zekai Zhang, Siyi Chen, Yi Ma, Qing Qu NeurIPS M3L Workshop, 2024 code / paper We provide theoretical insights into the connection between diffusion model and subspace clustering, which sheds light into the transition of diffusion model from memorization to generalization.
	Understanding 3D Object Articulation in Internet Videos Shengyi Qian, Linyi Jin, Chris Rockwell, Siyi Chen, David Fouhey CVPR, 2022 website / code / paper We propose to investigate detecting and characterizing the 3D planar articulation of objects from ordinary videos.

Teaching

Graduate Student Instructor, EECS 559 Optimization, 2024

Undergraduate Instructional Assistant, EECS 442 Computer Vision, 2022

Teaching Assistant, VE 401 Probabilitic Methods, 2020

Teaching Assistant, VV 286 Honorable Mathematics, 2020

Selected Projects

	Design A Roller Coaster Siyi Chen, Yigao Fang, Qi Shen Gold Medal Winner (Top 2%) , The University Physics Competition 2019 paper We devise a rule to evaluate the safety and difficulty level of roller coasters, propose a novel roller coster model, and give a through analysis based on Euler's method and natural axes.
	Convex Presentations of Translation Surfaces Siyi Chen, Andrew Keisling, Kaiwen Lu, Brendan Nell Computational Geometry Research, University of Michigan, 2021 Advisor: Chaya Norton, Paul Apisa code / poster / paper We designed and implemented beta versions of enumerating origamis in H(2) and utilized SageMath to implement the convexity test presented by Lelievre and Weiss.
	Combined Understanding of 3D Plane Articulation and Partial Human Pose Estimation Siyi Chen 3D Computer Vision Research, University of Michigan, 2021 Advisor: Shengyi Qian, David Fouhey code / poster We predict 3D partial human poses as SMPL meshes, predict 2D plane masks as well as 3D articulation information, and use a differential render to optimize the position and pose of the person considering 3d space interactions.
	Generate 3D Indoor Synthetic Dataset Siyi Chen 3D Computer Vision Research, University of Michigan, 2022 Advisor: Shengyi Qian, David Fouhey code We generate 3D synthetic video dataset containing a moving object and a scene. The pose and position of the object is optimized via a differential render.

This webpage is based on Jon Barron.