PhD Position in Graphical Models at NTU in Singapore, 2013

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Nanyang Technological University, School of Electrical & Electronic Engineering is inviting applications for PhD Position in Graphical Models. Applicants must have Bsc degree in applied statistics, applied mathematics, electrical engineering, or computer engineering. Position is available for Singaporean applicants and it covers salary and tuition. Application should be submitted by email. Application closing date is November 01, 2013.

Study Subject (s): The position is awarded in graphical models at Nanyang Technological University in Singapore.
Course Level: This studentship is for pursuing PhD research level at Nanyang Technological University, Singapore.
Scholarship Provider: Nanyang Technological University, Singapore
Scholarship can be taken at: Singapore

Eligibility: Bsc in applied statistics, applied mathematics, electrical engineering, or computer engineering

Scholarship Open for Students of Following Countries: PhD research position is open for Singapore applicants.

Scholarship Description: Graphical models, referred to in various guises as “Markov random fields,” “Bayesian networks,” or “factor graphs,” provides a statistical framework to encapsulate our knowledge of a system and to extract information from incomplete data. With carefully chosen assumptions (e.g., conditional independence of selected random variables), graphical models can be used to derive highly efficient techniques for data analysis; the main idea is to exploit the (existing or imposed) structure in the statistical model. However, graphical models are at present mostly limited to Gaussian or discrete random variables, while many real-life statistical problems involve non-Gaussian random variables. For example, this is the case of all positive quantities (amplitude, energy, magnitude), which are commonplace in physics and earth sciences. Moreover, extreme events such as earthquakes, hurricanes and floods, which are of special interest in natural hazards and risk analysis, cannot be described accurately by means of Gaussian distributions.

Consequently, there is a tremendous need for new types of graphical models for dealing with non-Gaussian data. Such graphical models would enable us to solve large-scale real-life inference problems efficiently, while relaxing many simplifying assumptions about the data, and allowing us to better quantify uncertainties and trade-offs in model parameters. To move toward this goal, we will integrate copula theory in the framework of graphical models. Statistical copulas enable us to tie any kind of marginal distributions (Gaussian, non-Gaussian and even non-parametric) together to form a joint distribution. Through the language of graphical models, we will impose structure on the resulting non-Gaussian joint distributions, so that they can describe the relationship between thousands or even millions of non-Gaussian random variables in an accurate and compact manner. By exploiting the structure in those high-dimensional statistical models, we will derive highly efficient algorithms for i) learning the model parameters; ii) imputing missing data; iii) extrapolation; iv) conditional simulation; v) forecasting; vi) other important statistical tasks. We will derive statistical performance guarantees for those learning and inference algorithms. We will then apply the proposed models to several important problems in earth sciences. We will consider copula graphical models for both “nominal” (non-extremal) data and for extreme events.

Number of awards offered: Not Known

Duration of award(s): Not Known

What does it cover? It covers salary and tuition.

How to Apply: Please send detailed curriculum vitae, statements of research interests, three references and relevant publications (if applicable), electronically, to:
Prof. Justin Dauwels
Nanyang Technological University
School of Electrical & Electronic Engineering

Scholarship Application Deadline: The deadline for applications is November 01, 2013.

Further Official Scholarship Information and Application

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