Hi! I’m Phi. I am an PhD student in Computer Science in Nanyang Technological University, Singapore (NTU). I work in Neural Natural Language Processing under Prof. Shafiq Joty. I focus in various aspects of Neural Machine Translation methods, especially unsupervised and semi-supervised machine translation.

Work and Research

My work involves NLP…..

I use qualitative, quantitative, and computational methods to holistically investigate socio-technical systems of technology and knowledge production. I have a particular focus on decentralized communities and institutions, such as open source software, scientific research, peer production platforms (like Wikipedia), and social media sites. Most of my previous work has focused on Wikipedia, where I’ve studied the people and algorithms that produce and maintain an open encyclopedia. I’ve also studied scientific research networks and projects, including the Long-Term Ecological Research Network, I also often focus on how these issues all intersect with and are embedded in the design of software and automated systems.

Working Experience

  1. Salesforce AI Research Asia - NLP Research Intern (May 2019 – Aug 2019)

    • Research on different aspects of linguistic structures languages on the performances of neural architectures on various natural language processing tasks. Propose new state-of-the-art methods and write papers submitted to machine learning and NLP conferences.
  2. Natural Language Processing Group, NTU - Research Assistant (Mar 2018 – May 2019)

    • Research on different limitations and improvements on Neural Machine Translation, such as document-level machine translation, discourse phenomena, phrase-based, parsing-tree-based and unsupervised neural machine translation. Write papers submitted to various machine learning and NLP conferences, e.g: ICLR, ACL, EMNLP.
  3. Visa Inc. - Software Engineer Intern (May 2017 – Jul 2017 & Jan 2018 – Jul 2018)

    • Developed a novel lightweight character-level convolutional neural network to perform scripted text classification tasks at up to 98 accuracy while consuming 1000 times less resources and achieving 10 times faster training time than standard deep models. Developed production-level code to deploy the models.
  4. Panasonic R&D Center Singapore - Software Engineer Intern (May 2016 – Jul 2016)

    • Researched and cooperated to develop a new machine learning algorithm based on Support Vector Machine to classify electrical signals, achieving 94% of experimental accuracy. Assisted to design a Raspberry Pi robot for collecting sensor signals and communicating with server to manipulate a real car’s system. Real-time accuracy reached 83.8


  1. Nanyang Technological University - Doctor of Philosophy in Computer Science & Artificial Intelligence (2019-present)

  2. Nanyang Technological University - Bachelor in Electrical & Electronics Engineering (2015-2019)