Alan Smith

Bihan Wen

PhD Candidate

Personal Profile

Bihan Wen received the B.Eng. degree in electrical and electronic engineering from Nanyang Technological University, Singapore, in 2012 and the M.S. degree in electrical and computer engineering from the University of Illinois at Urbana-Champaign, in 2015.

He is currently pursuing the Ph.D. degree at the University of Illinois at Urbana-Champaign, Urbana, IL, USA. His current research interests include signal and image processing, machine learning, sparse representation, and big data applications.

Education

University of Illinois at Urbana-Champaign, USA

Aug 2012 - Present

MS / PhD, Electrical and Computer Engineering.

Nanyang Technological University, Singapore

Aug 2008 - May 2012

B.Eng., Electrical and Electronic Engineering.

Industry Experience

Advanced Digital Science Center (ADSC)

Research Intern, May – Aug 2015

Worked with Stefan Winkler and Rama Ratnam on Multi-model Signal Processing (MMSP) and Human-Computer Interaction (HCI) projects. Proposed multi-model sparse learning scheme for HCI system, processing multimedia, eye tracking, and EEG data simultaneously with low latency and high performance.

Plunify

Software Engineer Intern, May – Aug 2011

Worked with start-up company for big data analytics and SaaS cloud computing. Participated in the EDAxtend platform development and optimization, reducing the system power consumption, and run time. -

Research Interests

  • Machine Learning
  • Multimedia
  • Sparse Representation
  • Big Data
  • Computer Vision
  • Imaging

Publications / Patents

Journal

  1. D. Liu, Z. Wang, B. Wen, J. Yang, W. Han and T. Huang, “Robust Image Super-Resolution via Deep Networks with Sparse Prior,” submitted to IEEE Trans. Image Processing (TIP).

  2. S. Dev, B. Wen, Y-H Lee, and S. Winkler, “Machine Learning Techniques and Applications for Ground-based Image Analysis,” to appear in IEEE Geo. and Remote Sensing Magazine (GRSM).

  3. B. Wen, S. Ravishankar and Y. Brelser, “Structured Overcomplete Sparsifying Transform Learning with Convergence Guarantees and Applications,” Int. Journal of Computer Vision (IJCV), 2015.

  4. S. Ravishankar, B. Wen and Y. Brelser, “Online Sparsifying Transform Learning – Part I: Algorithms,” IEEE Journal of Selected Topics in Signal Processing (JSTSP), 2015.

Conference

  1. B. Wen, S. Ravishankar and Y. Bresler, “FRIST – Flipping and Rotational Invariant Sparsifying Transform Learning,” submitted to Int. Conf. Learning Representation (ICLR), 2016.

  2. B. Wen, S. Ravishankar and Y. Brelser, “Video Denoising Using Online 3-D Sparsifying Transform Learning,” in Proc. IEEE Int. Conf. Image Processing (ICIP), 2015.

  3. S. Ravishankar, B. Wen and Y. Brelser, “Online Sparsifying Transform Learning for Big Data Signal Processing,” in Proc. IEEE Global Conf. on Sig. & Info. Processing (GlobalSIP), 2014.

  4. B. Wen, S. Ravishankar and Y. Brelser, “Learning Overcomplete Sparsifying Transforms with Block Cosparsity,” in Proc. IEEE Int. Conf. Image Processing (ICIP), 2014.

  5. B. Wen and Y. Lu, “A study of synthetic aperture radar imaging with compressed sensing,” in Proc. IEEE Asia-Pacific Conf. on Antennas and Propagation (APCAP), 2012.

  6. B. Wen and Y. Lu, “MATLAB tools for EnviSAT ASAR data visualization and image enhancement,” in Proc. SPIE Int. Symp. Lidar and Radar Mapping Tech., 2011.

Patent

  1. B. Wen, S. Ravishankar and Y. Brelser, “Data-Driven Adaptation of a Union of Sparse Models and its Applications,” US Provisional Patent Application, UIUC2015-137-01, filed Sep 10, 2015.

  2. S. Ravishankar, B. Wen and Y. Brelser, “Efficient Online Data-Driven Learning of Sparsifying Transforms for Large-Scale Signal Processing Applications,” US Provisional Patent Application, filed Dec 2, 2015.

Programming Skills

  • Java
  • Matlab
  • C / C++
  • CUDA
  • Python
  • LaTeX