Hung Bui

Hung Bui
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Main area of research
Natural language & AI

Hung Bui is a principal research scientist at the Laboratory for Natural Language Understanding at Nuance Communications in the Artificial Intelligence and Reasoning Group. His technical expertise and interests include probabilistic graphical models, Bayesian inference, and machine learning, especially models for sequential and relational data, with applications in human activity/intent recognition, video understanding, and natural language processing. These interests center around the challenging problem of how to make the machine better understand humans – a problem that plays a central part in intelligent assistive technologies, personal assistance, and dialogue management. From 2003-2012, he was a senior computer scientist at SRI International, where he led a multi institution research team in developing probabilistic inference technologies for understanding the user activities during the CALO project (the largest AI project in history and the project that spun off Siri). He was a technical lead in probabilistic inference in the DARPA’s Machine Reading project and a principal investigator in DARPA’s Mind’s Eye program on activity recognition. He was an assistant professor at Curtin University, Australia (2000-2003), Ph.D graduate in Computer Science (Curtin, 1998), graduate from HUS high school for gifted students, and a maths Olympian (silver medal, IMO 1989, Braunschweig).

Selected articles

Voice in the user interface

Interacting with voice-based interfaces on a broad variety of devices spanning smartphones, tablets, TVs, cars, and kiosks is becoming a routine part of daily life.

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Plan, activity, and intent recognition: theory and practice

Plan recognition, activity recognition, and intent recognition together combine and unify techniques from user modeling, machine vision, intelligent user interfaces, human/computer interaction, autonomous and multi-agent

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Automorphism groups of graphical models and lifted variational inference

Using the theory of group action, we first introduce the concept of the automorphism group of an exponential family or a graphical model, thus formalizing

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Bayesian minimax estimation of the normal model with incomplete prior covariance matrix specification

This work addresses the issue of Bayesian robustness in the multivariate normal model when the prior covariance matrix is not completely specified, but rather is

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Efficient online learning and prediction of users’ desktop actions

We investigate prediction of users’ desktop activities in the Unix domain. The learning techniques we explore do not require explicit user teaching. We show that

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