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Mihaela van der Schaar
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Revolutionizing Healthcare
Revolutionizing Healthcare: Session Archive
Exploring human and machine intelligence
2022 open house
2021 open house
WeCREATE
Tutorials
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
Organ transplantation
Pharmacology
Revolutionising Pharmacological Predictions with Synthetic Model Combination
Hub for Healthcare
Connect
Ph.D. applications
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October 2023
10
Oct
Revolutionizing Healthcare #28
Tuesday
EVENT DETAIL
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The lab
Mihaela van der Schaar
Research team
Funding
Clinical partners
Publications
Big ideas
News
Videos
Events
Software
Engagement sessions
Inspiration Exchange
Revolutionizing Healthcare
Revolutionizing Healthcare: Session Archive
Exploring human and machine intelligence
2022 open house
2021 open house
WeCREATE
Tutorials
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
Organ transplantation
Pharmacology
Revolutionising Pharmacological Predictions with Synthetic Model Combination
Hub for Healthcare
Connect
Ph.D. applications
