Bang Liu’s research interest is primarily in the areas of natural language processing (NLP), multimodal and embodied learning, graph machine learning, deep learning, data mining, and AI + X (e.g., science, health, and finance).
Data have always been a fundamental ingredient for scientific discovery. In materials science, large amounts and heterogeneous data are being produced every day, such as scientific publications, lab reports, manuals, tables, and so on. The different materials classes and materials properties that are of interest or explored lead to data that range many orders of magnitude.
To accelerate material discovery (and also scientific discovery in other domains), we need to transform large and heterogeneous data into a form readily consumed and mined. By coupling such data with domain expertise, we can build upon previous ﬁndings, rapidly enter a new ﬁeld, connect individual research efforts, link across disciplines, and accelerate discovery. Natural language processing (NLP) is therefore playing a key role in understanding and unlocking the rich datasets in material science, especially for understanding scientific literature and extracting useful information from them. Capturing unstructured information from the vast and evergrowing number of scientiﬁc publications has substantial promise to enable the creation of experimental-based databases currently lacking and meet the various needs in the materials domain. Developing data mining techniques for material science literature may also prevent information loss. Without structuring information, scientists cannot make the necessary connections among ﬁndings.
However, directly applying NLP techniques developed in the general domain to the material science domain cannot give us satisfactory performance in different tasks. The reasons include but are not limited to the following: i) the content and style of material science literature are different from general domain texts such as news articles, which leads to degraded performance for NLP tasks; ii) understanding the literature requires significant in-domain expert knowledge; and iii) we lack high-quality and large-scale labeled training datasets for NLP tasks in the material science domain. It's also challenging and expensive to create new datasets.
In this research project, we aim to develop effective NLP techniques for materials discovery by mapping current NLP techniques to the domain of material discovery or developing a new algorithmic pipeline.
Reconstructing fine-grained spatial densities from coarse-grained measurements, namely the aggregate observations recorded for each subregion in the spatial field of interest, is a critical problem in many real world applications. In this project, we propose a novel Constrained Spatial Smoothing (CSS) approach for the problem of spatial data reconstruction. We observe that local continuity exists in many types of spatial data. Based on this observation, our approach performs sparse recovery via a finite element method, while in the meantime enforcing the aggregated observation constraints through an innovative use of the Alternating Direction Method of Multipliers (ADMM) algorithm framework. Furthermore, our approach is able to incorporate external information as a regression add-on to further enhance recovery performance.
Online real-estate information systems such as Zillow and Trulia have gained increasing popularity in recent years. One important feature offered by these systems is the online home price estimate through automated data-intensive computation based on housing information and comparative market value analysis. State-of-the-art approaches model house prices as a combination of a latent land desirability surface and a regression from house features. However, by using uniformly damping kernels, they are unable to handle irregularly shaped regions or capture land value discontinuities within the same region due to the existence of implicit sub-communities, which are common in real-world scenarios. In this paper, we explore the novel application of recent advances in spatial functional analysis to house price modeling and propose the Hierarchical Spatial Functional Model (HSFM), which decomposes house values into land desirability at both the global scale and hidden local scales as well as the feature regression component. We propose statistical learning algorithms based on finite-element spatial functional analysis and spatial constrained clustering to train our model.