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Senwei

PhD Student

CV (update on 02/01/2022)

Hello friends, welcome to my page! I am Senwei Liang from China. I joined Lawrence Berkeley National Laboratory working as postdoc in Aug 2022 under supervision by Prof. Chao Yang and Prof. Lin Lin. Before that, I obtained PhD from Purdue University supervised by Prof. Haizhao Yang, and got my MSc degree in National University of Singapore and BSc degree in Sun Yat-sen University.

Research Interest: AI for science.

Google scholar; Semantic scholar; Github.


News

  • [09/10/2022] One paper is accepted by SIAM Journal on Numerical Analysis! Thanks for efforts of collaborators. &#x1F34E
  • [08/08/2022] I am starting my new career at Lawrence Berkeley Lab!

Awards

  • CVPR Outstanding Reviewer (USD 100) Link
  • Ross-Lynn fellowship, Purdue University, 2021-2022.
  • Top Graduate Tutors for AY2019/20 (SGD 100), Department of Mathematics, NUS.
  • 2020 Thirty-fourth AAAI Conference Scholarship (USD 100).

Work Experience

  • Wallace Givens Associate at Argonne National Laboratory mentored by Dr. Hong Zhang, from May 2021 to Jul 2021.
  • Research Assistant at Computational Medical Imaging Laboratory mentored by Prof. Yao Lu, from Jun 2016 to Jan 2017.

Academic Service

  • Conference reviewer: AAAI, CVPR, ECCV, ICANN
  • Journal reviewer: Journal of Scientific Computing
  • Organizer: AMS Sectional meeting at Purdue, the SIAM Texas-Louisiana Section
  • Assistant: IMA Workshop at Purdue

Publications or Manuscripts


(2022) Accelerating numerical solvers for large-scale simulation of dynamical system via NeurVec

We propose a data-driven corrector method that allows using large step sizes while compensating for the integration error for high accuracy.

Z. Huang, S. Liang, H. Zhang, H. Yang, L. Lin, submitted (Joint first) [PDF], [Code].


(2022) Finite Expression Method for Solving High-Dimensional Partial Differential Equations

We introduces a new methodology that seeks an approximate PDE solution in the space of functions with finitely many analytic expressions and, hence, this methodology is named the finite expression method (FEX).

S. Liang, H. Yang, submitted (2022) [PDF], [Code].


(2022) Quantifying spatial homogeneity of urban road networks via graph neural networks

We borrow the power of graph neural networks to model the road network system and use its predictability to quantify the spatial homogeneity. The proposed measurement is shown to be a non-linear integration of multiple geometric properties. We demonstrate its connection with the road irregularity and the socioeconomic status indicators.

J. Xue, N. Jiang, S. Liang, Q. Pang, T. Yabe, S. Ukkusuri, J. Ma, Nature Machine Intelligence, 4, 246–257 (2022) [Code, PDF].


(2021) Stiffness-aware neural network for learning Hamiltonian systems

We propose stiffness-aware neural network, a new method for learning stiff Hamiltonian dynamical systems from data. SANN identifies and splits the training data into stiff and nonstiff portions based on a stiffness-aware index, a metric to quantify the stiffness of the dynamical system.

S. Liang, Z. Huang, H. Zhang, Submitted, ICLR 2022 (Poster) (First author) [PDF].


(2021) Stationary Density Estimation of Itô Diffusions Using Deep Learning

We propose a deep learning scheme to estimate the density from a discrete-time series that approximate the solutions of the stochastic differential equations. We establish the convergence of the proposed scheme.

Y. Gu, J. Harlim, S. Liang, H. Yang, To appear in SIAM Journal on Numerical Analysis [PDF].


(2021) Solving PDEs on Unknown Manifolds with Machine Learning

We propose a mesh-free computational framework and machine learning theory for solving elliptic PDEs on unknown manifolds, identified with point clouds, based on diffusion maps (DM) and deep learning.

S. Liang, S. Jiang, J. Harlim, H. Yang, Submitted, (First author) [PDF].


(2021) Reproducing Activation Function for Deep Learning

We propose reproducing activation functions which employs several basic functions and their learnable linear combination to construct neuron-wise data-driven activation functions for each neuron.

S. Liang, L. Lyu, C. Wang, H. Yang, Submitted, (Joint first author) [PDF].


(2021) Efficient Attention Network: Accelerate Attention by Searching Where to Plug

To improve the efficiency for the existing attention modules, we leverage the sharing mechanism to share the attention module within the backbone and search where to connect the shared attention module via reinforcement learning.

Z. Huang, S. Liang, M. Liang, W. He, H. Yang, Submitted (Joint first author) [PDF, Code].


(2021) Blending Pruning Criteria for Convolutional Neural Networks

We propose a novel framework to integrate the existing filter pruning criteria by exploring the criteria diversity. The proposed framework contains two stages: Criteria Clustering and Filters Importance Calibration.

W. He, Z. Huang, M. Liang, S. Liang, H. Yang, ICANN 2021 [PDF].


(2019) Machine learning for prediction with missing dynamics

We propose a framework that reformulates the prediction problem as a supervised learning problem to approximate a map that takes the memories of the resolved and identifiable unresolved variables to the missing components in the resolved dynamics.

J. Harlim, S. Jiang, S. Liang, H. Yang, J. Comput. Phys., (Alphabetical order) [PDF].


(2019) Instance Enhancement Batch Normalization: An Adaptive Regulator of Batch Noise

We point out that self-attention mechanism can help to regulate the noise by enhancing instance-specific information and propose a normalization that recalibrates the information of each channel by a simple linear transformation.

S. Liang, Z. Huang, M. Liang, H. Yang, AAAI-2020, (Joint first author) [PDF, Code].


(2019) DIANet: Dense-and-Implicit Attention Network

We propose a framework that shares an attention module throughout different network layers to encourage the integration of layer-wise information.

Z. Huang, S. Liang, M. Liang, H. Yang, AAAI-2020, (Joint first author) [PDF, Code].


(2018) Drop-activation: Implicit parameter reduction and harmonic regularization

We propose a regularization method that drops nonlinear activation functions by setting them to be identity functions randomly during training time.

S. Liang, Y. Khoo, H. Yang, Communications on Applied Mathematics and Computation, (First author) [PDF, Code].


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