SHAOYEN TSENG, PhD Candidate

Signal Processing for Communication Understanding and Behavior Analysis Laboratory (SCUBA)

Signal Analysis and Interpretation Laboratory (SAIL)

Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering

University of Southern California, Los Angeles, CA, United States

Hi and welcome!

I am a PhD candidate in the Electrical and Computer Engineering Department at the University of Southern California where I perform research in the Signal Analysis and Interpretation Laboratory (SAIL) under the advisement of Professors Shrikanth Narayanan and Panayiotis Georgiou.

I received the B.S. and M.Sc. degrees in electrical engineering from National Sun Yat-sen University (NSYSU), and National Chiao Tung University (NCTU), Taiwan, in 2008 and 2010, respectively. I also received a MS degree in Computer Science from USC in 2018.

This summer I am interning at Apple as a Machine Learning Intern.

My research interests are

Machine Learning / Deep Learning for :

  • Behavioral Signal Processing

  • Natural Language Processing

  • Speech and Audio Processing

  • Affective Computing

Education

University of Southern California, Los Angeles, United States

  • Ph.D. in Electrical Engineering - 2020 (expected)

  • M.S. in Computer Science - 2018

National Chiao Tung University, Taiwan

  • M.S. in Electronics Engineering

  • Sept. 2008 to Dec. 2010

National Sun Yat-sen University, Taiwan

  • B.S. in Electrical Engineering

  • Sept. 2004 to June 2008

Work Experience

Machine Learning Intern, Apple

  • June 2020 to present

Deep Learning Intern in Speech, Samsung Semiconductor, San Diego

  • May 2018 to Aug. 2018

Deep Learning Intern in Audio Analytics, Robert Bosch Research and Technology Center, Pittsburgh

  • May 2017 to Aug. 2017

Advanced Engineer, Faraday Technology Corporation, Hsinchu, Taiwan

  • Feb. 2012 to July 2014

Intern, National Chip Implementation Center, Hsinchu, Taiwan

  • July 2008 to Sept. 2008

Selected coursework at USC

  • Machine Learning from Signals: Foundations and Methods

  • Advanced Natural Language Processing

  • Affective Computing

  • Mathematical Pattern Recognition

  • Foundations of Artificial Intelligence

  • Analysis of Algorithms