This page lists my past and current projects in categories. 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.

Transfer learning & transferability metrics

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)

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)

Multi-label and multi-modal learning

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)

Spatial and mobility 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)