Selected Research

Self-Supervised Learning of Contextual Embeddings for Link Prediction in Heterogeneous Networks (WWW 2021)

We develop a framework bridging static representation learning methods using global information from the entire graph with localized attention driven mechanisms to learn contextual node representations. We first pre-train our model in a self-supervised manner by introducing higher-order semantic associations and masking nodes, and then fine-tune our model for a specific link prediction task. Instead of training node representations by aggregating information from all semantic neighbors connected via metapaths, we automatically learn the composition of different metapaths that characterize the context for a specific task without the need for any pre-defined metapaths.

Text-to-SQL Generation for Question Answering on EMR (WWW 2020)

We tackle the challenges of text-to-SQL by developing a deep learning based TRanslate-Edit Model for Question-to-SQL (TREQS) generation, which adapts the widely used sequence-to-sequence model to directly generate the SQL query for a given question, and further performs the required edits using an attentive-copying mechanism and task-specific look-up tables. Based on the widely used publicly available electronic medical database, we create a new large-scale Question-SQL pair dataset, named MIMICSQL, in order to perform the Question-to-SQL generation task in healthcare domain

Tensor-based Temporal Multi-Task Survival Analysis (TKDE 2020)

We formulate a temporal(Multiple Time points) Multi-Task learning framework (MTMT) for survival analysis problems using tensor representation. Morespecifically, given a survival dataset and a sequence of time points, which are considered as the monitored time points for the events ofinterest, we reformulate the survival analysis problem to jointly handle each task at each time point and optimize them simultaneously.

Machine Learning for Survival Analysis: A Survey (CSUR 2019)

We provide a comprehensive and structured review of the representative statistical methods along with the machine learning techniques used in survival analysis and provide a detailed taxonomy of the existing methods. We also discuss several topics that are closely related to survival analysis and illustrate several successful applications in various real-world application domains. We hope that this paper will provide a more thorough understanding of the recent advances in survival analysis and offer some guidelines on applying these approaches to solve new problems that arise in applications with censored data.

Consensus Regularized Selection based Prediction (CIKM 2016)

We propose a method to generate a committee of non-convex regularized linear regression models, and use a consensus criterion to determine the optimal model for prediction. Each corresponding non-convex optimization problem in the committee is solved efficiently using the cyclic-coordinate descent algorithm with the generalized thresholding operator.