Xuanwu Lake, Nanjing, 2019
Lab profile picture: at Gold Falls, Taipei.

Siqi Wang
Courant Institute of Mathematical Sciences, New York University

60 Fifth Avenue
5th Floor, Office 506
New York University
New York, NY 10011, USA

Email: siqi.wang[at]nyu.edu


I'm a second-year Computer Science Ph.D. student at Courant Institute of Mathematical Sciences of New York University where I joined Geometric Computing Lab and started to work with professor Denis Zorin and professor Daniele Panozzo. Prior to NYU, I got my Bachelor's degree at Shanghai Jiao Tong University and worked at DALab (Digital ART Laboratory) of SJTU.

My research interests are Computer Graphics, Geometry Processing and Physics-based Simulation. Here is my resume! You can also view my Google Scholar profile.


What's New

 08/01/2020

I have two papers accepted for SIGGRAPH Asia 2020!

 09/27/2019

My homepage is built today!


Publications

Conference Paper

C1

Appearance-Preserving Tactile Optimization SIGGRAPH Asia '20

Chelsea Tymms, Siqi Wang, Denis Zorin.
ACM Transactions on Graphics (SIGGRAPH Asia 2020)

Textures are encountered often on various common objects and surfaces. Many textures combine visual and tactile aspects, each serving important purposes; most obviously, a texture alters the object’s appearance or tactile feeling as well as serving for visual or tactile identification and improving usability. The tactile feel and visual appearance of objects are often linked, but they may interact in unpredictable ways. Advances in high-resolution 3D printing enable highly flexible control of geometry to permit manipulation of both visual appearance and tactile properties. In this paper, we propose an optimization method to independently control the tactile properties and visual appearance of a texture. Our optimization is enabled by neural network-based models, and allows the creation of textures with a desired tactile feeling while preserving a desired visual appearance at a relatively low computational cost, for use in a variety of applications.
C2

An Adaptive Staggered-Tilted Grid for Incompressible Flow Simulation SIGGRAPH Asia '20

Yuwei Xiao, Szeyu Chan, Siqi Wang, Bo Zhu, Xubo Yang.
ACM Transactions on Graphics (SIGGRAPH Asia 2020)

Enabling adaptivity on a uniform Cartesian grid is challenging due to its highly structured grid cells and axis-aligned grid lines. In this paper, we propose a new grid structure – the adaptive staggered-tilted (AST) grid – to conduct adaptive fluid simulations on a regular discretization. The key mechanics underpinning our new grid structure is to allow the emergence of a new set of tilted grid cells from the nodal positions on a background uniform grid. The original axis-aligned cells, in conjunction with the populated axis-tilted cells, jointly function as the geometric primitives to enable adaptivity on a regular spatial discretization. By controlling the states of the tilted cells both temporally and spatially, we can dynamically evolve the adaptive discretizations on an Eulerian domain. Our grid structure preserves almost all the computational merits of a uniform Cartesian grid, including the cache-coherent data layout, the easiness for parallelization, and the existence of high-performance numerical solvers. Further, our grid structure can be integrated into other adaptive grid structures, such as an Octree or a sparsely populated grid, to accommodate the T-junction-free hierarchy. We demonstrate the efficacy of our AST grid by showing examples of large-scale incompressible flow simulation in domains with irregular boundaries.
C3

Reconstructing Human Joint Motion with Computational Fabrics UbiComp '19

Ruibo Liu, Qijia Shao, Siqi Wang, Christina Ru, Devin Balkcom, and Xia Zhou.
Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies.
Presented at ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp), London, September 2019.

Accurate and continuous monitoring of joint rotational motion is crucial for a wide range of applications such as physical rehabilitation and motion training. Existing motion capture systems, however, either need instrumentation of the environment, or fail to track arbitrary joint motion, or impose wearing discomfort by requiring rigid electrical sensors right around the joint area. This work studies the use of everyday fabrics as a flexible and soft sensing medium to monitor joint angular motion accurately and reliably. Specifically we focus on the primary use of conductive stretchable fabrics to sense the skin deformation during joint motion and infer the joint rotational angle. We tackle challenges of fabric sensing originated by the inherent properties of elastic materials by leveraging two types of sensing fabric and characterizing their properties based on models in material science. We apply models from bio-mechanics to infer joint angles and propose the use of dual strain sensing to enhance sensing robustness against user diversity and fabric position offsets. We fabricate prototypes using off-the-shelf fabrics and micro-controller. Experiments with ten participants show 9.69° median angular error in tracking joint angle and its sensing robustness across various users and activities.
@article{liu2019reconstructing, title={Reconstructing Human Joint Motion with Computational Fabrics}, author={Liu, Ruibo and Shao, Qijia and Wang, Siqi and Ru, Christina and Balkcom, Devin and Zhou, Xia}, journal={Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies}, volume={3}, number={1}, pages={19}, year={2019}, publisher={ACM} }

Thesis

T1

Energy Blockchain Technology for the Sale of Electricity Market

Siqi Wang
Bachelor's thesis, Shanghai Jiao Tong University, 2019.

With the proposal of "three types, two networks" in the report of State Grid on NPC & CPPCC 2019, the concept of "ubiquitous power Internet of things" has brought new opportunities for distributed power trading. Block chain technology, with its characteristics of information symmetry, security, transparency and trustworthiness, is of great significance to the construction of platform-based and sharing-based enterprises in the aspects of multi-party balance and information sharing. It overcomes the shortcomings of expandability, long transaction time and information symmetry in the traditional centralized power trading mode. To this end, this paper designs a transaction-driven, decentralized and autonomous power management mechanism in the sales park, implements a blockchain-based power trading model on Hyperledger Fabric, deploys corresponding smart contracts, and eventually establishes the blockchain-based decentralization technology for distribution network. We discuss the double auction and P2P trading mechanisms between users in the block, the power package agreement and access mechanism between power sales companies and users. Smart contract test on Hyperledger Fabric shows that the model built in this paper can realize the distributed energy transaction in the sales park and visualize the management and transaction process on webpage. The code of smart contract is interpreted in detail. The digitalized and decentralized mechanism for alliance chain assets is introduced into the park power management system to create an open and transparent power transaction market, which comprehensively improves trade efficiency and the overall benefit of traders.


Research

Appearance-Preserving Tactile Optimization
New York University, advised by Denis Zorin

Textures are encountered often on various common objects and surfaces. Many textures combine visual and tactile aspects, each serving important purposes; most obviously, a texture alters the object's appearance or tactile feeling as well as serving for visual or tactile identification and improving usability. The tactile feel and visual appearance of objects are often linked, but they may interact in unpredictable ways. Advances in high-resolution 3D printing enable highly flexible control of geometry to permit manipulation of both visual appearance and tactile properties. In this paper, we propose an optimization method to independently control the tactile properties and visual appearance of a texture. Our optimization is enabled by neural network-based models, and allows the creation of textures with a desired tactile feeling while preserving a desired visual appearance at a relatively low computational cost, for use in a variety of applications.

An Adaptive Staggered-Tilted Grid for Incompressible Flow Simulation
Dartmouth College & SJTU, advised by Bo Zhu and Xubo Yang

Enabling adaptivity on a uniform Cartesian grid is challenging due to its highly structured grid cells and axis-aligned grid lines. In this paper, we propose a new grid structure -- the adaptive staggered-tilted (AST) grid -- to conduct adaptive fluid simulations on a regular discretization. The key mechanics underpinning our new grid structure is to allow the emergence of a new set of tilted grid cells from the nodal positions on a background uniform grid. The original axis-aligned cells, in conjunction with the populated axis-tilted cells, jointly function as the geometric primitives to enable adaptivity on a regular spatial discretization. By controlling the states of the tilted cells both temporally and spatially, we can dynamically evolve the adaptive discretizations on an Eulerian domain. Our grid structure preserves almost all the computational merits of a uniform Cartesian grid, including the cache-coherent data layout, the easiness for parallelization, and the existence of high-performance numerical solvers. Further, our grid structure can be integrated into other adaptive grid structures, such as an Octree or a sparsely populated grid, to accommodate the T-junction-free hierarchy. We demonstrate the efficacy of our AST grid by showing examples of large-scale incompressible flow simulation in domains with irregular boundaries.

Reconstructing Human Joint Motion with Computational Fabrics
Dartmouth College, advised by Xia Zhou

This work studies the use of everyday fabrics as a flexible and soft sensing medium to monitor joint angular motion accurately and reliably. Specifically we focus on the primary use of conductive stretchable fabrics to sense the skin deformation during joint motion and infer the joint rotational angle. We tackle challenges of fabric sensing originated by the inherent properties of elastic materials by leveraging two types of sensing fabric and characterizing their properties based on models in material science. We apply models from bio-mechanics to infer joint angles and propose the use of dual strain sensing to enhance sensing robustness against user diversity and fabric position offsets. We fabricate prototypes using off-the-shelf fabrics and micro-controller. Experiments with ten participants show 9.69° mean angular error in tracking joint angle and its sensing robustness across users and activities.


Awards

08/2019
05/2019
12/2018
04/2018
10/2017

10/2016
09/2014

New York University MacCracken Fellowship
Outstanding Graduates of Shanghai
Hongyi Scholarship (for excellent overseas researchers)
First-class Scholarship of Lee Fushou Fund
Scholarship of the Temasek Foundation International Leadership Enrichment and Regional Networking Programme (TFI LEaRN)
Award for Outstanding Student Cadres
First-Prize in High School Students Mathematics Contest in China (provincial level)


Misc

I am a fan of classical music and play the piano in my leisure time. I'm also well-versed in Cucurbit Flute, a Chinese musical instrument. I learned Chinese folk dance for many years but it's been quite a long time since I last danced.

My first name is pronouced as "Si--Chi" and the last Chinese character is the one with same meaning as "Chess".

It's been 1470 days since I started Ph.D.!
You are the No. HTML Hit Counters th vistor of my homepage.