Yunsheng Tian

Yunsheng Tian

PhD Student

MIT CSAIL

Yunsheng Tian (田韵声) is a fourth-year PhD student in the Computational Design & Fabrication Group at MIT CSAIL advised by Prof. Wojciech Matusik. His research lies in the intersection of computer graphics, robotics and machine learning.

Before coming to MIT, he obtained the bachelor’s degree from Nankai University, China, advised by Prof. Bo Ren and Prof. Ming-Ming Cheng. He had also worked at Microsoft Research Asia and The University of Hong Kong as a research intern.

Feel free to drop me an email if you are interested in my research or related ideas. We are also hiring interns for research projects occasionally.

Interests

  • Computer Graphics
  • Robotics
  • Machine Learning

Education

  • PhD in Computer Science, 2021 - Present

    Massachusetts Institute of Technology, MA, U.S.

  • MS in Computer Science, 2019 - 2021

    Massachusetts Institute of Technology, MA, U.S.

  • BEng in Software Engineering, 2015 - 2019

    Nankai University, Tianjin, China

Publications

Note: * indicates equal contribution.

Assemble Them All: Physics-Based Planning for Generalizable Assembly by Disassembly

ACM Transactions on Graphics (SIGGRAPH Asia) 2022
Physics makes motion planning for complex assemblies simple, fast, and generalizable.
PDF Code Dataset Project Video Talk MIT News
Assemble Them All: Physics-Based Planning for Generalizable Assembly by Disassembly

JoinABLe: Learning Bottom-up Assembly of Parametric CAD Joints

Computer Vision and Pattern Recognition (CVPR) 2022
Learning to assemble on a big CAD assembly dataset approaching human-level accuracy.
PDF Code Dataset
JoinABLe: Learning Bottom-up Assembly of Parametric CAD Joints

Evolution Gym: A Large-Scale Benchmark for Evolving Soft Robots

Neural Information Processing Systems (NeurIPS) 2021
The first and largest benchmark for evolving soft robot morphology.
PDF Code Project Poster Video MIT News Wired IEEE Spectrum
Evolution Gym: A Large-Scale Benchmark for Evolving Soft Robots

AutoOED: Automated Optimal Experiment Design Platform

arXiv Preprint 2021
Open-source experimental design platform powered by ML (no need to code).
PDF Code Project Docs MIT CSAIL Alliances News
AutoOED: Automated Optimal Experiment Design Platform

Diversity-Guided Multi-Objective Bayesian Optimization With Batch Evaluations

Neural Information Processing Systems (NeurIPS) 2020
Efficient multi-objective optimizer for expensive black-box functions.
PDF Code Poster Video Supplementary
Diversity-Guided Multi-Objective Bayesian Optimization With Batch Evaluations

Prediction-Guided Multi-Objective Reinforcement Learning for Continuous Robot Control

International Conference on Machine Learning (ICML) 2020
Efficient multi-objective RL for discovering all Pareto-optimal policies.
PDF Code Project Video Talk Supplementary
Prediction-Guided Multi-Objective Reinforcement Learning for Continuous Robot Control

Fluid Directed Rigid Body Control using Deep Reinforcement Learning

ACM Transactions on Graphics (SIGGRAPH) 2018
Deep RL can control high-dimensional fluid-rigid coupling dynamics.
PDF Project Video DOI Talk
Fluid Directed Rigid Body Control using Deep Reinforcement Learning

Experience

 
 
 
 
 

Research Intern (Incoming)

AI Lab, Autodesk Research

May 2023 – Aug 2023 San Francisco, CA, U.S.
Advised by Dr. Jieliang Luo
 
 
 
 
 

Research Assistant

Computational Design & Fabrication Group, MIT CSAIL

Aug 2019 – Present Cambridge, MA, U.S.
Advised by Prof. Wojciech Matusik
 
 
 
 
 

Research Assistant

Department of Computer Science, The University of Hong Kong

Feb 2019 – May 2019 Hong Kong, China
Advised by Prof. Jia Pan
 
 
 
 
 

Research Intern

Visual Computing Group, Microsoft Research Asia

Jul 2018 – Sep 2018 Beijing, China
Advised by Mr. Zheng Zhang and Dr. Jifeng Dai
 
 
 
 
 

Research Assistant

Media Computing Lab, Nankai University

Apr 2016 – Jun 2019 Tianjin, China
Advised by Prof. Bo Ren

Services

Conference Reviewer: SIGGRAPH, IROS, ICLR, ICML, NeurIPS

Journal Reviewer: TEVC, Soft Computing

Invited Talks:

  • [2023/03] MIT Graphics Seminar: “Physics-Based Planning for Automating Contact-Rich Assembly”
  • [2022/11] Autodesk AI Lab Sharing Session: “Assemble Them All: Physics-Based Planning for Generalizable Assembly by Disassembly”
  • [2021/10] MIT EI Submissions Seminar: “AutoOED: Automated Optimal Experimental Design Platform With Data- and Time-Efficient Multi-Objective Optimization”
  • [2020/11] Lam Research Seminar: “Multi-Objective Bayesian Optimization for Adaptive Experimental Design”

Contact

  • yunsheng [at] csail [dot] mit [dot] edu
  • 32-311, 32 Vassar Street, Cambridge, MA 02139