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- Policy Impact Predictor for COVID-19
- AutoML: powering the new human-machine learning ecosystem
- Research team
- Engagement sessions
- Hub for Healthcare
- Individualized treatment effect inference
- Revolutionizing Healthcare and Hub for Healthcare: please share your thoughts
- Survival analysis, competing risks, and comorbidities
- Tutorial series: individualized treatment effect inference
- Quantitative epistemology: conceiving a new human-machine partnership
- Interpretable machine learning
- Adjutorium: state-of-the-art automated machine learning for breast cancer
- Publications
- Time series in healthcare: challenges and solutions
- Impact
- Clinical partners
- 2021 open house – showcasing the lab’s vision and leadership
- Next-generation clinical trials
- ML in clinical trials – impact survey
- Self-supervised, semi-supervised, and multi-view learning
- Clustering
- Uncertainty quantification
- Early detection and diagnosis: how machine learning can bring healthcare’s holy grail within reach
- 2022 work placement (6-8 months)
- AAAI-22: Time series in healthcare
- Causal deep learning
- Demonstrators
- Checkout
- Order Confirmation
- Order Failed
- 2022 open house – showcasing the lab’s vision and leadership
- DC-Check: Data-Centric AI Checklist
- Data-Centric AI
- Prof Mihaela van der Schaar’s Speaking Engagements
- reality
Posts by category
- Category: NeurIPS
- Category: AAAI
- Category: AISTATS
- Category: ICLR
- Category: Alumni
- Category: External mention
- Mihaela van der Schaar featured in Guardian article on reporting guidelines for AI clinical trials
- ICML 2020: Mihaela van der Schaar among top 10 authors by number of papers
- Lab mentioned in WIRED story on digitization of healthcare
- Mihaela van der Schaar features in IEEE’s Stars in Computer Networking and Communications
- NESTA names Mihaela van der Schaar the most cited woman in AI
- Mihaela van der Schaar’s 2018 Oon International Award and Lecture in Preventative Medicine
- Mihaela van der Schaar on The Economist’s “Babbage” podcast
- Lab’s work mentioned in Newsweek story
- Mihaela van der Schaar in Nature’s “tips to kick-start your career”
- Lab’s work mentioned in Nature article
- Category: COVID-19
- Zhaozhi Qian receives CSAR Ph.D. student award
- Paper on COVID-19 risk factors in Brazil published in Nature Scientific Reports
- Paper on AI and ML in the response to COVID-19 published in Machine Learning
- Study of admission timing and mortality following COVID-19 infection published in BMJ Open
- Paper on COVID-19 hospital capacity planning published in Machine Learning
- How machine learning is changing the response to COVID-19
- Paper on COVID-19 clinical trials published in Statistics in Biopharmaceutical Research
- ICME 2020 keynote: A Nationally-Implemented AI Solution for COVID-19
- “Pioneering” COVID-19 study published in Lancet Global Health
- Mihaela van der Schaar to introduce key COVID-19 initiatives at ICME 2020
- New tool tackles the “What if?” questions of COVID-19
- Study shows importance of ethnicity as a COVID-19 risk factor in England
- Paper on COVID-19 between-centre mortality accepted for publication in Intensive Care Medicine
- Ethnicity is a leading risk factor in Brazilian COVID-19 mortality
- Tortoise Media interview with Mihaela van der Schaar: “Can AI solve the big issues?”
- Partnering with NHS Digital and Public Health England
- Progress using COVID-19 patient data to train machine learning models for healthcare
- Responding to COVID-19 with AI and machine learning
- Category: Big ideas
- Causal deep learning: a new framework
- The case for Reality-centric AI
- Synthetic data: breaking the data logjam in machine learning for healthcare
- The map of causal deep learning
- DC-Check, a new framework to practically engage with Data-centric AI
- Revolutionizing Clinical Trials using Machine Learning
- Data Imputation: An essential yet overlooked problem in machine learning
- Healthcare visionaries discuss the future of AI and machine learning
- Clinicians share guidance for the machine learning community
- Machine learning for early detection and diagnosis: a dialog with clinicians
- Spotlight on early detection and diagnosis
- Generating and evaluating synthetic data: a two-sided research agenda
- 2021 open house – showcasing the lab’s vision and leadership
- Making machine learning interpretable: a dialog with clinicians
- Quantitative epistemology: conceiving a new human-machine partnership
- Machine learning for healthcare: Towards a unifying framework
- Revolutionizing healthcare: an invitation to clinical professionals everywhere
- AutoML: powering the new human-machine learning ecosystem
- Center for Data Innovation: “5 Q’s for Mihaela van der Schaar”
- Automated machine learning will empower some and replace others
- Why medicine is creating exciting new frontiers for machine learning
- Interpretability: from black boxes to white boxes
- Responding to COVID-19 with AI and machine learning
- A creative approach to tackling the AI gender imbalance
- NESTA names Mihaela van der Schaar the most cited woman in AI
- Turing impact story: Transforming medicine through AI-enabled healthcare
- Mihaela van der Schaar on The Economist’s “Babbage” podcast
- Turing impact story: Augmenting clinical decision-making
- Category: New publication
- Inspiration Exchange: AutoPrognosis 2.0
- Revolutionizing Healthcare: Second session on AutoPrognosis
- The map of causal deep learning
- DC-Check, a new framework to practically engage with Data-centric AI
- van der Schaar Lab at NeurIPS 2022: 7 papers, hosting 2 workshops, and presenting at 3 others
- Revolutionizing Healthcare: AutoPrognosis: Using the next generation of ML tools
- Revolutionizing Clinical Trials using Machine Learning
- Machine learning tackles the problem of dynamic disease progression in prostate cancer patients
- van der Schaar Lab at ICML 2022: 7 papers and 3 workshops
- van der Schaar Lab at ICLR 2022: 5 papers accepted
- Mihaela van der Schaar to give AAAI-22 tutorial and workshop talk
- van der Schaar Lab at ICLR 2022: 5 papers accepted
- Mihaela van der Schaar to give AISTATS 2022 keynote
- van der Schaar Lab at NeurIPS 2021: 14 papers and 3 workshops
- Paper on COVID-19 risk factors in Brazil published in Nature Scientific Reports
- van der Schaar Lab at ICML 2021: tutorial, 4 papers, and 4 workshops
- Million-patient study shows strength of machine learning in recommending breast cancer therapies
- van der Schaar Lab at ICLR 2021: 5 papers and 3 workshops
- van der Schaar Lab at AISTATS 2021: 4 papers accepted
- Personalized education explored in new paper and panel discussion
- Paper on AI and ML in the response to COVID-19 published in Machine Learning
- Study of admission timing and mortality following COVID-19 infection published in BMJ Open
- van der Schaar Lab at NeurIPS 2020: 9 papers accepted
- Paper on COVID-19 hospital capacity planning published in Machine Learning
- Paper on COVID-19 clinical trials published in Statistics in Biopharmaceutical Research
- “Pioneering” COVID-19 study published in Lancet Global Health
- Study shows importance of ethnicity as a COVID-19 risk factor in England
- Paper on COVID-19 between-centre mortality accepted for publication in Intensive Care Medicine
- van der Schaar Lab at ICML 2020: seven papers and a tutorial
- Ethnicity is a leading risk factor in Brazilian COVID-19 mortality
- Paper published in Clinical Pharmacology and Therapeutics
- clairvoyance alpha: the first pipeline toolkit for medical time series
- van der Schaar Lab at ICLR 2020: two papers and a keynote
- Responding to COVID-19 with AI and machine learning
- [PDF] Mihaela van der Schaar’s contribution to the 2019 NHS Topol Review
- Helping cystic fibrosis teams know when to talk about transplants
- New research shows machine learning could significantly augment clinical decision-making in cystic fibrosis care
- Category: Video
- Revolutionizing Healthcare: Concrete steps for clinicians to make machine learning a reality in a clinical setting
- Inspiration Exchange: Revolutionizing Clinical Trials with Machine Learning
- Inspiration Exchange: Causal Deep Learning
- Revolutionizing Healthcare: Concrete steps for clinicians to make machine learning a reality in a clinical setting
- Inspiration Exchange: Synthetic Data
- Inspiration Exchange: Data-centric AI
- Inspiration Exchange: AutoPrognosis 2.0
- Revolutionizing Healthcare: Machine Learning Interpretability – Making ML output useful and actionable for clinicians and researchers.
- Revolutionizing Healthcare: Second session on AutoPrognosis
- Inspiration Exchange: Frontiers in ML Interpretability
- Revolutionizing Healthcare: AutoPrognosis: Using the next generation of ML tools
- Inspiration Exchange: Neural differential equations
- Revolutionizing Healthcare: machine learning and cystic fibrosis
- WeCREATE: Inspiration Session 2
- Inspiration Exchange: ICML 2022 Preview
- WeCREATE: Inspiration Session 1
- Inspiration Exchange: Causal Deep Learning Part 2
- Revolutionizing Healthcare: using machine learning to power clinical trials
- AISTATS 2022 keynote
- Revolutionizing Healthcare: synthetic data in healthcare
- Revolutionizing Healthcare: Leading clinical voices on the future of AI/ML for healthcare
- Inspiration Exchange: Causal Deep Learning
- Revolutionizing Healthcare: AI and ML for early detection and diagnosis (2/2)
- Inspiration Exchange: Discovery Using Machine Learning
- WeCREATE: Launch event International Women’s Day 2022
- AI4MED keynote
- AAAI 2022 tutorial: Time series in healthcare
- Revolutionizing Healthcare: AI and ML for early detection and diagnosis
- Inspiration Exchange: next frontiers in interpretable ML
- Inspiration Exchange: ML to transform organ transplantation
- Inspiration Exchange: how can we make ML models as robust and useful as possible?
- NeurIPS 2021: self-supervised learning for genomics
- NeurIPS 2021: synthetic data generation & assessment
- Invited talk at conference on interpretability, safety, and security in AI
- NeurIPS 2021: human meta-learning
- Revolutionizing Healthcare: how can AI/ML transform organ transplantation?
- Inspiration Exchange: what’s next for individualized treatment effects?
- 2021 open house – showcasing the lab’s vision and leadership
- Revolutionizing Healthcare: getting ML-powered tools in the hands of clinicians (part 2)
- Inspiration Exchange: time series in healthcare
- Revolutionizing Healthcare: getting ML-powered tools in the hands of clinicians (part 1)
- ICML 2021 tutorial: Synthetic healthcare data generation and assessment
- ICML 2021: IMLH keynote on quantitative epistemology
- ICML 2021: Time Series Workshop invited talk
- Inspiration Exchange: quantitative epistemology
- Revolutionizing Healthcare: roundtable on AI/ML decision-support tools
- Inspiration Exchange: ITE inference in the time series setting
- Revolutionizing Healthcare: roundtable on personalized therapeutics and individualized treatment effects
- Inspiration Exchange: individualized treatment effect inference (first session)
- Revolutionizing Healthcare: second roundtable on interpretability in ML/AI for healthcare
- New video tutorial series on individualized treatment effect inference
- Revolutionizing Healthcare: roundtable on interpretability in ML/AI for healthcare
- Inspiration Exchange: application-oriented projects in machine learning for healthcare
- Revolutionizing Healthcare: ML tools for cancer (post-diagnosis care)
- Inspiration Exchange: synthetic data evaluation
- Revolutionizing Healthcare: ML tools for cancer (risks, screening, diagnosis)
- Inspiration Exchange: synthetic data concepts and approaches
- Revolutionizing Healthcare: tools for acute care
- Inspiration Exchange: recent projects in machine learning for healthcare
- Revolutionizing Healthcare: a framework for ML for healthcare
- Inspiration Exchange: software packages for automated machine learning
- Presentation on AutoML and interpretability (Microsoft Research event)
- Inspiration Exchange: automated machine learning pipelines
- Revolutionizing Healthcare: what machine learning can offer healthcare
- How machine learning is changing the response to COVID-19
- Inspiration Exchange: introduction to automated machine learning
- CompAge 2020 keynote
- RIIAA 2020 Keynote
- MiLeTS 2020: Machine Learning for Healthcare in the COVID-19 Era
- ICML 2020: Automated ML and its transformative impact on medicine and healthcare
- ICML 2020: Learning despite the unknown – missing data imputation in healthcare
- ICML 2020: Machine Learning for Healthcare: Challenges, Methods, and Frontiers
- Machine Learning for Healthcare tutorials – MLSS 2020, Tübingen
- ICME 2020 keynote: A Nationally-Implemented AI Solution for COVID-19
- ICLR 2020: Ioana Bica’s presentation
- ICLR 2020: Dan Jarrett’s presentation
- ICLR 2020: Mihaela van der Schaar’s Keynote and Q&A
- Turing Lecture: from black boxes to white boxes
- Turing Lecture: Transforming medicine through AI-enabled healthcare pathways
- 2018 Oon Lecture – Medicine 2.0: Transforming Clinical Practice and Discovery Through Machine Learning and Learning Engines
- AI for social good: Machine learning and data science for medicine
- Turing Lecture: Medicine 2.0 – Using Machine Learning to Transform Medical Practice and Discovery
- Turing short talk: Machine learning, data science and decisions for a better planet
- Category: News
- Data for clinical machine learning
- Data-Centric AI: What is it all about?
- Causal deep learning: a new framework
- Conference Season 2023: 7 papers at AISTATS & 4 papers at ICLR
- The case for Reality-centric AI
- Conference Season 2023: 7 papers at AISTATS & 4 papers at ICLR
- Synthcity and using Synthetic Data
- AAAI-23: Synthetic Data Tutorial
- Revolutionizing Healthcare 2022
- Inspiration Exchange 2022
- The map of causal deep learning
- The fun der Schaar lab
- We discuss the future of clinical trials with clinicians
- DC-Check, a new framework to practically engage with Data-centric AI
- van der Schaar Lab at NeurIPS 2022: 7 papers, hosting 2 workshops, and presenting at 3 others
- van der Schaar Lab welcomes 5 new researchers in 2022
- The van der Schaar Lab congratulates Ioana Bica on her Graduation
- Revolutionizing Clinical Trials using Machine Learning
- Data Imputation: An essential yet overlooked problem in machine learning
- SyntheticData4ML Workshop
- Machine learning tackles the problem of dynamic disease progression in prostate cancer patients
- The van der Schaar Lab congratulates Yao Zhang on his Graduation
- The van der Schaar Lab congratulates Ahmed Alaa on his appointment as Assistant Professor
- van der Schaar Lab at ICML 2022: 7 papers and 3 workshops
- Mihaela van der Schaar to give a talk at conference in West Africa
- van der Schaar Lab at ICLR 2022: 5 papers accepted
- Healthcare visionaries discuss the future of AI and machine learning
- Research pillar: causal deep learning
- Clinicians share guidance for the machine learning community
- Machine learning for early detection and diagnosis: a dialog with clinicians
- WeCREATE March 8 launch event
- Spotlight on early detection and diagnosis
- Mihaela van der Schaar to give AAAI-22 tutorial and workshop talk
- van der Schaar Lab at ICLR 2022: 5 papers accepted
- Mihaela van der Schaar to give AISTATS 2022 keynote
- Building a shared vision for early detection and diagnosis
- van der Schaar Lab congratulates Trent Kyono on graduation
- Research pillar: self-supervised, semi-supervised, & multi-view learning
- Research pillar: uncertainty quantification
- van der Schaar Lab at NeurIPS 2021: 14 papers and 3 workshops
- Zhaozhi Qian receives CSAR Ph.D. student award
- Research pillar: clustering
- 2021 open house – showcasing the lab’s vision and leadership
- Research pillar: adaptive clinical trials
- van der Schaar Lab congratulates 3 researchers on graduation
- van der Schaar Lab welcomes 5 new researchers in 2021
- Clarification on Strictly Batch Imitation Learning by Energy-based Distribution Matching
- Inspiration Exchange: returning on October 7
- Revolutionizing Healthcare: returning on September 29
- Paper on COVID-19 risk factors in Brazil published in Nature Scientific Reports
- Research pillar: time series in healthcare
- van der Schaar Lab at ICML 2021: tutorial, 4 papers, and 4 workshops
- Research pillar: interpretable machine learning
- Million-patient study shows strength of machine learning in recommending breast cancer therapies
- van der Schaar Lab congratulates Alexis Bellot on doctoral defense, graduation
- van der Schaar Lab collaborations recognized with major NHS award
- Machine learning for mammography article named “Best of 2020” by JACR
- Ahmed Alaa receives 2021 Edward K. Rice Outstanding Doctoral Student Award from UCLA
- Quantitative epistemology: conceiving a new human-machine partnership
- Spotlight on organ transplantation research projects
- van der Schaar Lab at ICLR 2021: 5 papers and 3 workshops
- New video tutorial series on individualized treatment effect inference
- van der Schaar Lab at AISTATS 2021: 4 papers accepted
- Research pillar: survival analysis, competing risks, and comorbidities
- Fergus Imrie joins van der Schaar Lab as postdoc
- Spotlight on cystic fibrosis research projects
- Personalized education explored in new paper and panel discussion
- Research pillar: Individualized treatment effect inference
- Spotlight on cancer research projects
- Paper on AI and ML in the response to COVID-19 published in Machine Learning
- Lab’s pioneering projects on show at NeurIPS 2020
- Study of admission timing and mortality following COVID-19 infection published in BMJ Open
- Spotlight on Alzheimer’s research projects
- van der Schaar Lab at NeurIPS 2020: 9 papers accepted
- van der Schaar Lab welcomes 6 new researchers
- Paper on COVID-19 hospital capacity planning published in Machine Learning
- Mihaela van der Schaar featured in Guardian article on reporting guidelines for AI clinical trials
- Paper on COVID-19 clinical trials published in Statistics in Biopharmaceutical Research
- “Pioneering” COVID-19 study published in Lancet Global Health
- ICML 2020: Mihaela van der Schaar among top 10 authors by number of papers
- Mihaela van der Schaar to introduce key COVID-19 initiatives at ICME 2020
- Mihaela van der Schaar to give tutorial at MLSS 2020
- New tool tackles the “What if?” questions of COVID-19
- Study shows importance of ethnicity as a COVID-19 risk factor in England
- Paper on COVID-19 between-centre mortality accepted for publication in Intensive Care Medicine
- van der Schaar Lab at ICML 2020: seven papers and a tutorial
- Announcing the NeurIPS 2020 hide-and-seek privacy challenge
- clairvoyance alpha: the first pipeline toolkit for medical time series
- Partnering with NHS Digital and Public Health England
- Progress using COVID-19 patient data to train machine learning models for healthcare
- Helping cystic fibrosis teams know when to talk about transplants
- New research shows machine learning could significantly augment clinical decision-making in cystic fibrosis care
- Forging Human-Machine Partnerships
- Category: Events
- Data for clinical machine learning
- Revolutionizing Healthcare: Concrete steps for clinicians to make machine learning a reality in a clinical setting
- Data-Centric AI: What is it all about?
- Inspiration Exchange: Revolutionizing Clinical Trials with Machine Learning
- Inspiration Exchange: Causal Deep Learning
- Revolutionizing Healthcare: Concrete steps for clinicians to make machine learning a reality in a clinical setting
- Inspiration Exchange: Synthetic Data
- Inspiration Exchange: Data-centric AI
- Inspiration Exchange: AutoPrognosis 2.0
- Revolutionizing Healthcare: Machine Learning Interpretability – Making ML output useful and actionable for clinicians and researchers.
- Revolutionizing Healthcare: Second session on AutoPrognosis
- Conference Season 2023: 7 papers at AISTATS & 4 papers at ICLR
- Synthcity and using Synthetic Data
- Revolutionizing Healthcare: Session Archive
- AAAI-23: Synthetic Data Tutorial
- Revolutionizing Healthcare 2022
- Inspiration Exchange 2022
- We discuss the future of clinical trials with clinicians
- Inspiration Exchange: Frontiers in ML Interpretability
- van der Schaar Lab at NeurIPS 2022: 7 papers, hosting 2 workshops, and presenting at 3 others
- Revolutionizing Healthcare: AutoPrognosis: Using the next generation of ML tools
- Inspiration Exchange: Neural differential equations
- Revolutionizing Healthcare: machine learning and cystic fibrosis
- WeCREATE: Inspiration Session 2
- SyntheticData4ML Workshop
- van der Schaar Lab at ICML 2022: 7 papers and 3 workshops
- Inspiration Exchange: ICML 2022 Preview
- WeCREATE: Inspiration Session 1
- Inspiration Exchange: Causal Deep Learning Part 2
- Revolutionizing Healthcare: using machine learning to power clinical trials
- AISTATS 2022 keynote
- Mihaela van der Schaar to give a talk at conference in West Africa
- Revolutionizing Healthcare: synthetic data in healthcare
- Revolutionizing Healthcare: Leading clinical voices on the future of AI/ML for healthcare
- Inspiration Exchange: Causal Deep Learning
- van der Schaar Lab at ICLR 2022: 5 papers accepted
- Revolutionizing Healthcare: AI and ML for early detection and diagnosis (2/2)
- Inspiration Exchange: Discovery Using Machine Learning
- WeCREATE: Launch event International Women’s Day 2022
- AI4MED keynote
- WeCREATE March 8 launch event
- AAAI 2022 tutorial: Time series in healthcare
- Revolutionizing Healthcare: AI and ML for early detection and diagnosis
- Inspiration Exchange: next frontiers in interpretable ML
- Mihaela van der Schaar to give AAAI-22 tutorial and workshop talk
- van der Schaar Lab at ICLR 2022: 5 papers accepted
- Mihaela van der Schaar to give AISTATS 2022 keynote
- Inspiration Exchange: ML to transform organ transplantation
- Inspiration Exchange: how can we make ML models as robust and useful as possible?
- NeurIPS 2021: self-supervised learning for genomics
- NeurIPS 2021: synthetic data generation & assessment
- Generating and evaluating synthetic data: a two-sided research agenda
- Invited talk at conference on interpretability, safety, and security in AI
- NeurIPS 2021: human meta-learning
- Revolutionizing Healthcare: how can AI/ML transform organ transplantation?
- van der Schaar Lab at NeurIPS 2021: 14 papers and 3 workshops
- Inspiration Exchange: what’s next for individualized treatment effects?
- 2021 open house – showcasing the lab’s vision and leadership
- Revolutionizing Healthcare: getting ML-powered tools in the hands of clinicians (part 2)
- Inspiration Exchange: time series in healthcare
- Revolutionizing Healthcare: getting ML-powered tools in the hands of clinicians (part 1)
- Inspiration Exchange: returning on October 7
- Revolutionizing Healthcare: returning on September 29
- ICML 2021 tutorial: Synthetic healthcare data generation and assessment
- ICML 2021: IMLH keynote on quantitative epistemology
- ICML 2021: Time Series Workshop invited talk
- Inspiration Exchange: quantitative epistemology
- Revolutionizing Healthcare: roundtable on AI/ML decision-support tools
- van der Schaar Lab at ICML 2021: tutorial, 4 papers, and 4 workshops
- Making machine learning interpretable: a dialog with clinicians
- Inspiration Exchange: ITE inference in the time series setting
- Revolutionizing Healthcare: roundtable on personalized therapeutics and individualized treatment effects
- Inspiration Exchange: individualized treatment effect inference (first session)
- Revolutionizing Healthcare: second roundtable on interpretability in ML/AI for healthcare
- van der Schaar Lab at ICLR 2021: 5 papers and 3 workshops
- van der Schaar Lab at AISTATS 2021: 4 papers accepted
- Revolutionizing Healthcare: roundtable on interpretability in ML/AI for healthcare
- Inspiration Exchange: application-oriented projects in machine learning for healthcare
- Revolutionizing Healthcare: ML tools for cancer (post-diagnosis care)
- Inspiration Exchange: synthetic data evaluation
- Personalized education explored in new paper and panel discussion
- Revolutionizing Healthcare: ML tools for cancer (risks, screening, diagnosis)
- Inspiration Exchange: synthetic data concepts and approaches
- Revolutionizing Healthcare: tools for acute care
- Inspiration Exchange: recent projects in machine learning for healthcare
- Lab’s pioneering projects on show at NeurIPS 2020
- Revolutionizing Healthcare: a framework for ML for healthcare
- Inspiration Exchange: software packages for automated machine learning
- van der Schaar Lab at NeurIPS 2020: 9 papers accepted
- Presentation on AutoML and interpretability (Microsoft Research event)
- Inspiration Exchange: automated machine learning pipelines
- Revolutionizing Healthcare: what machine learning can offer healthcare
- Revolutionizing healthcare: an invitation to clinical professionals everywhere
- Inspiration Exchange: introduction to automated machine learning
- CompAge 2020 keynote
- RIIAA 2020 Keynote
- MiLeTS 2020: Machine Learning for Healthcare in the COVID-19 Era
- ICML 2020: Automated ML and its transformative impact on medicine and healthcare
- ICML 2020: Learning despite the unknown – missing data imputation in healthcare
- ICML 2020: Machine Learning for Healthcare: Challenges, Methods, and Frontiers
- Machine Learning for Healthcare tutorials – MLSS 2020, Tübingen
- ICME 2020 keynote: A Nationally-Implemented AI Solution for COVID-19
- ICML 2020: Mihaela van der Schaar among top 10 authors by number of papers
- Mihaela van der Schaar to introduce key COVID-19 initiatives at ICME 2020
- Mihaela van der Schaar to give tutorial at MLSS 2020
- van der Schaar Lab at ICML 2020: seven papers and a tutorial
- Announcing the NeurIPS 2020 hide-and-seek privacy challenge
- Automated machine learning will empower some and replace others
- ICLR 2020: Ioana Bica’s presentation
- ICLR 2020: Dan Jarrett’s presentation
- ICLR 2020: Mihaela van der Schaar’s Keynote and Q&A
- Why medicine is creating exciting new frontiers for machine learning
- van der Schaar Lab at ICLR 2020: two papers and a keynote
- Turing Lecture: from black boxes to white boxes
- Turing Lecture: Transforming medicine through AI-enabled healthcare pathways
- MIT LIDS presentation by Mihaela van der Schaar
- Mihaela van der Schaar’s 2018 Oon International Award and Lecture in Preventative Medicine
- 2018 Oon Lecture – Medicine 2.0: Transforming Clinical Practice and Discovery Through Machine Learning and Learning Engines
- AI for social good: Machine learning and data science for medicine
- Turing Lecture: Medicine 2.0 – Using Machine Learning to Transform Medical Practice and Discovery
- Turing short talk: Machine learning, data science and decisions for a better planet
- Category: WeCREATE
- Category: Inspiration Exchange
- Data-Centric AI: What is it all about?
- Inspiration Exchange: Revolutionizing Clinical Trials with Machine Learning
- Inspiration Exchange: Causal Deep Learning
- Inspiration Exchange: Synthetic Data
- Inspiration Exchange: Data-centric AI
- Inspiration Exchange: AutoPrognosis 2.0
- Synthcity and using Synthetic Data
- Category: Revolutionizing Healthcare
- Data for clinical machine learning
- Revolutionizing Healthcare: Concrete steps for clinicians to make machine learning a reality in a clinical setting
- Revolutionizing Healthcare: Concrete steps for clinicians to make machine learning a reality in a clinical setting
- Revolutionizing Healthcare: Machine Learning Interpretability – Making ML output useful and actionable for clinicians and researchers.
- Revolutionizing Healthcare: Second session on AutoPrognosis
- Revolutionizing Healthcare: Session Archive
- Revolutionizing Healthcare 2022
- We discuss the future of clinical trials with clinicians
