Research Interests

• Speech and Signal Processing
• Data Mining
• Generative Models
• Bayesian Framework

Education

• University of Illinois, Urbana Champaign   USA
  Department of Electrical and Computer Engineering
  Master of Science; Ph.D. Candidate
  


• Tsinghua University   Beijing, China
  Department of Electronic Engineering
  Bachelor of Engineering
  


• Hong Kong University   Hong Kong, China
  Department of Electronic and Communication Engineering
  Exchange Student

Publications

• Generative Models for Speech
  • Ruobai Wang, Yang Zhang, Zhijian Ou, Mark Hasegawa-Johnson, “Use of particle filtering and MCMC for inference in probabilistic acoustic tube model”, IEEE Statistical Signal Processing (SSP). 2016
  
  
  • Yang Zhang, Gautham J. Mysore, Floraine Berthouzoz, Mark Hasegawa-Johnson, “Analysis of Prosody Increment Induced by Pitch Accents for Automatic Emphasis Correction”, Speech Prosody. 2016
  
  
  • Yang Zhang, Zhijian Ou, Mark Hasegawa-Johnson, “Incorporating AM-FM effect in voiced speech for probabilistic acoustic tube model” IEEE Workshop on Applications of Signal Processing to Audio and Acoustics. 2015
  
  
  • Yang Zhang, Zhijian Ou, Mark Hasegawa-Johnson, “Improvement of PAT model for speech decomposition.” International Conference on Acoustic, Speech and Signal Processing (ICASSP). 2014
  
  
  • Zhijian Ou, and Yang Zhang. "Probabilistic acoustic tube: a probabilistic generative model of speech for speech analysis/synthesis." International Conference on Artificial Intelligence and Statistics (AISTATS). 2012
• Streaming Data Mining with Generative Models
  • Shiyu Chang, Yang Zhang, Jiliang Tang, Dawei Yin, Yi Chang, Thomas S. Huang, “Streaming Recommender Systems”, submitted to ACM International Workshop on Data Mining (WSDM). 2017
  
  
  • Shiyu Chang*, Yang Zhang*, Jiliang Tang, Dawei Yin, Yi Chang, Mark Hasegawa-Johnson, Thomas S. Huang, “Positive Unlabeled Learning in Streaming Networks”, ACM SIGKDD. 2016
• Signal Processing with Generative Models
  • Yang Zhang, Dinei Florencio, Mark Hasegawa-Johnson, “Speech Beamforming without Position and Interference Calibration: a Model-based Approach with Ad-hoc Microphone Array, submitted to International Conference on Acoustic, Speech and Signal Processing (ICASSP). 2017
  
  
  • Yang Zhang, Nasser M. Nasrabadi, Mark Hasegawa-Johnson, “Multichannel transient acoustic signal classification using task-driven dictionary with joint sparsity and beamforming” International Conference on Acoustic, Speech and Signal Processing (ICASSP). 2015
• Other Speech Related Research
  • Mark Hasegawa-Johnson; Thomas Huang; William Schuh; Renato Azevedo; Kuangxiao Gu; Yang Zhang; Bidisha Roy; Rocio Garcia-Retamero, “A Multidisciplinary Approach to Designing and Evaluating Electronic Medical Record Portal Messages that Support Patient Self-Care", submitted to Journal of Biological Informatics.
  
  
  • Kaizhi Qian, Yang Zhang, Mark Hasegawa-Johnson, “Acoustic Feature Extraction by Statistics Based Local Binary Pattern for Landmark Detection”, Speech Prosody. 2016
  
  
  • Xiayu Chen, Yang Zhang, Mark Hasegawa-Johnson, “An iterative approach to decision tree construction for context-dependent speech synthesis” Interspeech. 2014.

Patent

• Yang Zhang, Mysore J. Gautham, Floraine Berthouzoz, “Automatic Emphasis of Spoken Words”, US Patent No. P5688US01/ADBS.241185. 2015

Industry Experience

• Microsoft Research   Redmond, USA
  Research Intern
  Project: Beamforming with Heterogeneous Ad-Hoc Microphone Array


• Adobe Creative Technology Lab   San Francisco, USA
  Research Intern
  Project: Automatic Emphasis Correction for Better Voiceover Quality
  


• Logo's Care, Inc.
  Technology Consultant
  Project: Heartrate Tracking under Adverse SNR
  

Teaching Experience

• Digital Communication (ECE 361), Teaching Assistant, Spring 2016
• Pattern Recognition (ECE 544), Teaching Assistant, Fall 2014

Honors

• 2013 Qualcomm Innovative Fellowship Finalist
• 2012 Tsinghua Outstanding Thesis
• 2011 Tsinghua Outstanding Academic Schlarship
• 2008 Tsinghua Freshman Schlarship