Yuansan Liu

Deep learning has been a critical part of designing inverse design methods that are computationally efficient and accurate. An example of this is the design of photonic metasurfaces by using their photoluminescent spectrum as the input data to predict their topology. One fundamental challenge of these systems is their ability to represent nonlinear relationships between sets of data that have different dimensionalities. Existing design methods often implement a conditional generative adversarial network in order to solve this problem, but in many cases, the solution is unable to generate structures that provide multiple peaks when validated. It is demonstrated that in response to the target spectrum, the bidirectional adversarial autoencoder is able to generate structures that provide multiple peaks on several occasions. As a result, the proposed model represents an important advance toward the generation of nonlinear photonic metasurfaces that can be used in advanced metasurface design.

Hearing loss is an impairment that affects a large population worldwide. Cochlear implant (CI) surgery is an effective solution, especially in patients with residual, natural hearing. However, the risk of intracochlear damage during CI surgery, which impairs residual hearing, remains as a concern. It has been shown that intra-operative monitoring (using electrocochleography, specifically the cochlear microphonic) during CI surgery can detect drops that are associated with such trauma, resulting in its potential prevention. Researchers have attempted to automate the process of drop detection by implementing supervised models. However, these have so far failed to achieve good reliability and generalizability. To address these issues, we develop an unsupervised model based on the Bayesian method with a scoring function derived from the logistic function. The proposed model, as a data-driven method, directly learns from data without the need for any supervision which enhances its adaptability to diverse patient populations. Empirical results demonstrate the proposed model’s high accuracy with a low false alarm rate (18 extra detection against 146 in previous work), as well as its robustness when processing new data. This indicates its potential for real-world application to improve safety during CI surgery.

Finding effective representations for time series data is a useful but challenging task. Several works utilize self-supervised or unsupervised learning methods to address this. However, there still remains the open question of how to leverage available label information for better representations. To answer this question, we exploit pre-existing techniques in time series and representation learning domains and develop a simple, yet novel fusion model, called: \textbf{S}upervised \textbf{CO}ntrastive \textbf{T}emporal \textbf{T}ransformer (SCOTT). We first investigate suitable augmentation methods for various types of time series data to assist with learning change-invariant representations. Secondly, we combine Transformer and Temporal Convolutional Networks in a simple way to efficiently learn both global and local features. Finally, we simplify Supervised Contrastive Loss for representation learning of labelled time series data. We preliminarily evaluate SCOTT on a downstream task, Time Series Classification, using 45 datasets from the UCR archive. The results show that with the representations learnt by SCOTT, even a weak classifier can perform similar to or better than existing state-of-the-art models (best performance on 23/45 datasets and highest rank against 9 baseline models). Afterwards, we investigate SCOTT's ability to address a real-world task, online Change Point Detection (CPD), on two datasets: a human activity dataset and a surgical patient dataset. We show that the model performs with high reliability and efficiency on the online CPD problem ($\sim$98\% and $\sim$97\% area under precision-recall curve respectively). Furthermore, we demonstrate the model's potential in tackling early detection and show it performs best compared to other candidates.

The recent COVID-19 pandemic has promoted vigorous scientific activity in an effort to understand, advice and control the pandemic. Data is now freely available at a staggering rate worldwide. Unfortunately, this unprecedented level of information contains a variety of data sources and formats, and the models do not always conform to the description of the data. Health officials have recognised the need for more accurate models that can adjust to sudden changes, such as produced by changes in behaviour or social restrictions. In this work we formulate a model that fits a “SIR”-type model concurrently with a statistical change detection test on the data. The result is a piece wise autonomous ordinary differential equation, whose parameters change at various points in time (automatically learned from the data). The main contributions of our model are: (a) providing interpretation of the parameters, (b) determining which parameters of the model are more important to produce changes in the spread of the disease, and (c) using data-driven discovery of sudden changes in the evolution of the pandemic. Together, these characteristics provide a new model that better describes the situation and thus, provides better quality of information for decision making.