Research Projects

This page highlights my past and current projects by areas. For detailed descriptions and related publications, click the thumbnail or the "more info" link in each entry. For a complete list of my publications, see my Google Scholar page.

Transferability estimation

Knowledge-guided source task selection

Finding the most transferable task for MRI brain segmentation. (2022)

A knowledge and transferability-based source task selection framework for the transfer learning among MRI brain segmentation tasks. Empirical studies reveals that modality information and segmentation mask structural similarity can significantly improve the transferability estimation between different segmentation tasks. (more info)

Cross-domain cross-task transferability

OTCE: A transferability metric for cross-domain cross-task representations. (2021)

A new transferability metric is presented for transfer learning across heterogeneous data distributions (a.k.a. domains) and distinct tasks. It characterizes transferability as a combination of domain difference and task difference and explicitly evaluates them from data in a unified framework. (more info)

task transfer learning

An information-theoretic metric to transferability for task transfer learning (2019)

Given a common input domain, a transferability metric estimates to what extent knowledge from a source task can help in learning a target task. We present a novel metric, rooted in information theory and statistics, to efficiently compute task transferability between classification problems. (more info)

Domain adaptation & generalization

Distributional robust domain generalization

Class-conditioned domain generalization via wasserstein distributional robust optimization (2021)

Domain generalization aims at learning a domain-agnostic model from multiple source domains for any unseen target domain in the future. In this work, we solve the domain generalization problem using the concept of Wasserstein distributional robust optimization. (more info)

Few-Shot Cross Domain Battery Capacity Estimation

Few-Shot Cross Domain Battery Capacity Estimation (2021)

In data-driven battery capacity estimation, different battery sizes, testing environments and historical load patterns cause large domain shifts between training and testing data. We propose an optimal transport based domain adaptation method that is both efficient and accurate for few-shot cross-domain capacity estimation. (more info)

Multi-task and multi-modal learning

Joint PVL segmenation and hand function classification

Joint PVL segmenation and hand function classification A (2021)

A semi-supervised multitask learning framework to jointly learn PVL lesion segmentation and manual ability classification for MRI scans. Two clinically related auxiliary tasks are incorporated to improve the classification accuracy while requiring only a small amount of manual annotations. (more info)

Multi-label classification with missing labels

Maximal correlation embedding network for multilabel learning with missing labels (2019)

Missing label is a common problem in real world multi-label learning datasets, where some of the labels associated with a sample may be missing due to sensor or human annotation error. We propose a robust maximal correlation embedding network for multi-label classification with missing labels. It has been successfully applied to multimedia and human context recognition data. (more info)

multimodal emotion recognition

Multimodal emotion recognition: extracting public and private information (2019)

An end-to-end emotion recognition framework using both visual and audio input. It learns a multi-modal representation that captures both dependences between different input modalities, and modal-dependent information in each modality. (more info)

Mobility data analysis


Urban mobility pattern mining based on regional dependencies (2018, 2019)

A novel mobility pattern mining algorithm that learns the dynamics between different spatial regions from taxi trip data. It proposes kernelized ACE, which efficiently finds latent embeddings for trip origins and destinations while maintaining the correlation between them. The resulting embedding can be used to extract strong mobility patterns while partitioning the city into non-overlapping regions at the same time. (more info)

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Travel time prediction from limited GPS floating cars (2017, 2019)

A trajectory-based travel time prediction algorithm when only a small number of GPS floating cars are available. It learns the travel time patterns of a compact set of frequently shared paths from historical data. Given a travel time prediction query, we identify the current travel time pattern from recent trajectories, then infer its travel time in the near future. (more info)

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Knowledge-based trajectory completion (2016)

A framework to "densify" sparse GPS trajectories without relying on the roadmap data. We are able to recover geometric details of complex junctions, and improve the accuracy of real life traffic trajectories. (more info)


Data-driven map matching (2013)

A robust multitrack map matching algorithm for sparse and noisy GPS trajectories. It simultaneously learns the regular structures in taxi trajectories and map match all trajectories simultaneously. (more info)


Mining leader-follower realtionships from GPS trajectories (2012)

A case study on understanding the leader-follower phenomenum in the group movement of cows from their trajectories. We use geometric and correlation based constraint to extract leader-follower relationships. (more info)

Social data mining

self-organized scheme

Optimising Self-organized Volunteer Behaviors during COVID-19 Pandemic (2022)

A quantitive study on how volunteers collaborate to achieve rapid mobilisation during the COVID-19 outbreak, using the concept of self-organisation. It proposes a data-driven framework to investigate when and how self-organisation emerged during the pandemic response and how it relates to effectiveness of volunteer organisations in general. (more info)