Posts

Fetal health monitoring and assessment play a crucial role in reducing perinatal mortality and ensuring safe childbirth outcomes. While traditional machine learning approaches to fetal health classification have shown promise, they often struggle with capturing complex temporal dependencies and subtle feature interactions in Cardiotocography (CTG) data.

Environmental research often attracts significant public attention for its relevance to society sustainability. Enormous resources (including research funding) have been allocated to the development of environmental research in the past years.

Our paper, The comprehensive and systematic identification of BLCA-specific SF-regulated, survival-related AS events, is accepted by Gene. In this paper, we comprehensively and systematically identified five SFs and 46 AS events.

I was selected as a candidate for the Research Talent Hub by the Innovation and Technology Commission of the HKSAR in September 2020.

Our paper, Recursive Balanced k-Subset Sum Partition for Rule-constrained Resource Allocation, is accepted by CIKM 2020. This is an algorithm paper. In this paper, we proposed a new approach to rule-constrained resource allocation by regarding it as a partition problem.

Our paper, IRDA: Incremental Reinforcement Learning for Dynamic Resource Allocation, is accepted by IEEE Transactions on Big Data. This is a journal paper. We mine the task patterns from the large volume of historical allocation data and propose a reinforcement learning model termed IRDA to learn the allocation strategy in an incremental way.

Our paper, A Big-data-driven Airport Resource Management Engine and Application Tools, is accepted by DASFAA 2020. This is a Demo paper. In this paper, we showcase our research in big data analytics for resource management.

EY NextWave Data Science competition 2019 focuses on how data can help the next smart city thrive, and boost the mobility of the future. In this competition, systematic data mining and processing ideas are used.

Our paper, A Sequential Attention Based Convolutional Neural Network for Anomaly Detection, is accepted by Journal of Zhengzhou University (Natural Science Edition). This paper focuses on appying a sequential attention based CNN in anomaly detection.