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Mihaela van der Schaar
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Revolutionizing Healthcare
Revolutionizing Healthcare: Session Archive
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Exploring human and machine intelligence
WeCREATE
2023 open house
2022 open house
2021 open house
Tutorials
NeurIPS 2023: Data-Centric AI Tutorial
IJCAI-23: Data-Centric AI Tutorial
AAAI-23: Synthetic Data Tutorial
AAAI-22: Time series in healthcare
Individualized treatment effect inference
ICML 2021: Synthetic Healthcare Data Generation and Assessment
ICML 2020: Machine Learning for Healthcare: Challenges, Methods, and Frontiers
Research pillars
Automated machine learning
Causal deep learning
Clustering
Data-Centric AI
Individualized treatment effect inference
Interpretable ML
Next-gen clinical trials
Quantitative epistemology
Self-supervised, semi-supervised, and multi-view learning
Survival analysis, competing risks, and comorbidities
Synthetic data
Overview
Privacy challenge
Time series
Uncertainty quantification
Impact
AutoPrognosis 2.0
SynthCity
TemporAI
DC-Check
What is Data-Centric AI?
DC-Check: about the Tool
Resources
Our lab’s impact
Demonstrators
Overview
Demonstrator: Synthetic Data
Demonstrator: Survival Analysis
Alzheimer’s
Cancer
Our projects
Prostate Cancer
Breast cancer
The future of clinical trials
COVID-19
Our projects
Announcements
Policy Impact Predictor
Cystic fibrosis
Early detection and diagnosis
A Foundational Framework for Personalized Early and Timely Diagnosis
Organ transplantation
Pharmacology
Revolutionising Pharmacological Predictions with Synthetic Model Combination
Hub for Healthcare
Connect
Ph.D. applications
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Tag - Safety Summit
The van der Schaar Lab’s crucial role in AI safety and trustworthiness
Andreas Bedorf
October 26, 2018
The lab
Mihaela van der Schaar
Research team
Funding
Clinical partners
Publications
Big ideas
News
Videos
Events
Software
Engagement sessions
Revolutionizing Healthcare
Revolutionizing Healthcare: Session Archive
Inspiration Exchange
Exploring human and machine intelligence
WeCREATE
2023 open house
2022 open house
2021 open house
Tutorials
NeurIPS 2023: Data-Centric AI Tutorial
IJCAI-23: Data-Centric AI Tutorial
AAAI-23: Synthetic Data Tutorial
AAAI-22: Time series in healthcare
Individualized treatment effect inference
ICML 2021: Synthetic Healthcare Data Generation and Assessment
ICML 2020: Machine Learning for Healthcare: Challenges, Methods, and Frontiers
Research pillars
Automated machine learning
Causal deep learning
Clustering
Data-Centric AI
Individualized treatment effect inference
Interpretable ML
Next-gen clinical trials
Quantitative epistemology
Self-supervised, semi-supervised, and multi-view learning
Survival analysis, competing risks, and comorbidities
Synthetic data
Overview
Privacy challenge
Time series
Uncertainty quantification
Impact
AutoPrognosis 2.0
SynthCity
TemporAI
DC-Check
What is Data-Centric AI?
DC-Check: about the Tool
Resources
Our lab’s impact
Demonstrators
Overview
Demonstrator: Synthetic Data
Demonstrator: Survival Analysis
Alzheimer’s
Cancer
Our projects
Prostate Cancer
Breast cancer
The future of clinical trials
COVID-19
Our projects
Announcements
Policy Impact Predictor
Cystic fibrosis
Early detection and diagnosis
A Foundational Framework for Personalized Early and Timely Diagnosis
Organ transplantation
Pharmacology
Revolutionising Pharmacological Predictions with Synthetic Model Combination
Hub for Healthcare
Connect
Ph.D. applications
Andreas Bedorf