• HKUST President and professors share Insights with top global leaders at the World Economic Forum in Davos

  • Pro. Pascale Fung as the Associate Chair of HKUST One Million Dollar Entrepreneurship Competition

  • Prof. Pascale Fung on "Talking to Machines" at World Economic Forum 2015

  • Prof. Pascale Fung at the Goldman Sachs Technet Conference Asia Pacific 2016

  • Prof Pascale Fung at the Milken Institute Asia Summit

Spoken Language Understanding

Our research in this area focuses on recognizing and understanding user speech in languages with little data, such as code-mixing speech in Hindi-English or non-standard Chinese. We are also working on spoken language systems where the system answers user queries and learns over time from these interactions. A current focus is to render such systems "empathetic".

Natural Language Processing

We currently focus on using deep learning methods to solve NLP problems such as bilingual lexicon extraction from large and small datasets, semantic parsing, cross-lingual information retrieval, structured summarization. One of our research highlights is the joint optimization of speech and language models for different applications such as spoken language summarization, speech translation, and dialog systems.

Emotion, Mood and Sentiment Analysis

Our research interest in this area includes the recognition of emotion, mood and sentiment from mutli-channel inputs such as speech, language, facial experession and gesture. We also analyze music audio signals as well as music lyrics to enable efficient retrieval of millions and tens of millions of songs by genres, styles, mood, and artist. We are interested in applying emotion and sentiment analysis to social media, the financial domain and creative arts.

Big Data Analytics

We use machine learning approaches such as SVM, LDA, CNNs, RNNs, and LSTMs to discover patterns from large amounts of online data to predict user tastes and behavior, from financial data to predict market sentiment and behavior and from medical data to provide assistance to doctors.