Keynotes
IJCAI 2024 AI4TS Workshop: Learning Healthcare Foundation Models: From Pre-training to Fine-tuning [Link]
Foundation models have recently garnered significant attention due to their powerful capabilities across various tasks. In the medical domain,
although some medical foundation models have been developed, their ability to handle diverse medical tasks remains limited.
To address this gap, Dr. Ma's lab has developed a series of medical foundation models using pre-training and fine-tuning techniques,
tailored to the unique characteristics of multi-sourced and multi-modal clinical data. In this talk, Dr. Ma will detail the development
and capabilities of these medical foundation models.
Tutorials
SDM 2024: Heterogeneity in Federated Learning [Link]
Federated learning is a distributed machine learning paradigm, which enables multiple participants to cooperate in training
machine learning models without sharing data. Heterogeneity is one of the main challenges in federated learning.
To solve this challenge, in this tutorial, we will cover the state-of-the-art federated learning techniques to
handle the heterogeneity issue. In particular, we focus on the following three aspects: (1) providing a comprehensive
review of heterogeneity challenges in federated learning from three perspectives, including data heterogeneity, model
heterogeneity, and system heterogeneity; (2) introducing cutting-edge techniques to solve the heterogeneity issue in
federated learning from both algorithm and application perspectives; and (3) identifying open challenges and proposing
convincing future research directions in heterogeneous federated learning. We believe this is an emerging and potentially
high-impact topic in distributed machine learning, which will attract both researchers and practitioners from academia and industry.
KDD 2021: Advances in Mining Heterogeneous Healthcare Data [Link]
Thanks to the explosion of heterogeneous healthcare data and advanced machine learning and data mining techniques,
specifically deep learning methods, we now have an opportunity to make difference in healthcare. In this tutorial,
we will present state-of-the-art deep learning methods and their real-world applications, specifically focusing
on exploring the unique characteristics of different types of healthcare data. The first half will be spent on
introducing recent advances in mining structured healthcare data, including computational phenotyping, disease
early detection/risk prediction and treatment recommendation. In the second half, we will focus on challenges
specific to the unstructured healthcare data, and introduce advanced deep learning methods in automated ICD coding,
understandable medical language translation, clinical trial mining, and medical report generation.
This tutorial is intended for students, engineers and researchers who are interested in applying deep learning
methods to healthcare, and prerequisite knowledge will be minimal. The tutorial will be concluded with open problems and a Q&A session.
WSDM 2020: Learning with Small Data [Link]
In the era of big data, it is easy for us collect a huge number of image and text data. However,
we frequently face the real-world problems with only small (labeled) data in some domains,
such as healthcare and urban computing. The challenge is how to make machine learn algorithms
still work well with small data? To solve this challenge, in this tutorial, we will cover
the state-of-the-art machine learning techniques to handle small data issue. In particular,
we focus on the following three aspects: (1) Providing a comprehensive review of recent
advances in exploring the power of knowledge transfer, especially focusing on meta-learning;
(2) introducing the cutting-edge techniques of incorporating human/expert knowledge into
machine learning models; and (3) identifying the open challenges to data augmentation techniques,
such as generative adversarial networks.
KDD 2019: Optimizing the Wisdom of the Crowd: Inference, Learning, and Teaching [Link]
The increasing need for labeled data has brought the booming growth of crowdsourcing in a wide range of
high-impact real-world applications, such as collaborative knowledge (e.g., data annotations,
language translations), collective creativity (e.g., analogy mining, crowdfunding),
and reverse Turing test (e.g., CAPTCHA-like systems), etc. In the context of supervised learning,
crowdsourcing refers to the annotation procedure where the data items are outsourced and processed by
a group of mostly unskilled online workers. Thus, the researchers or the organizations are able to
collect large amount of information via the feedback of the crowd in a short time with a low cost.
Despite the wide adoption of crowdsourcing, several of its fundamental problems remain unsolved especially
at the information and cognitive levels with respect to incentive design, information aggregation,
and heterogeneous learning. This tutorial aims to: (1) provide a comprehensive review of recent advances
in exploring the power of crowdsourcing from the perspective of optimizing the wisdom of the crowd;
and (2) identify the open challenges and provide insights to the future trends in the context of
human-in- the-loop learning. We believe this is an emerging and potentially high-impact topic
in computational data science, which will attract both researchers and practitioners from academia
and industry.
Selected Recent Invited Talks
Selected Conference Presentations