Mihaela van der Schaar
John Humphrey Plummer Professor of Machine Learning, AI and Medicine
Develops machine learning and AI methods for medicine, including synthetic data, time-series analysis and models of clinical decision-making.
Mihaela van der Schaar's lab at Cambridge works on medical imaging & healthcare AI, physics, climate & scientific computing and reinforcement learning. 120 papers since 2023, 100 at top venues, mostly ICML and NeurIPS. Applications close .
At a glance
- papers, 2023–2026
- 31/ year
- at T1 venues
- 83%
- PhD students
- —
- MS students
- —
- alumni listed
- —
Papers by yearfrom DBLP, 2023–2026
- 10 papers2026 · T1 7 · collecting
- 38 papers2025 · T1 34
- 38 papers2024 · T1 29
- 34 papers2023 · T1 30
T1 venuesOtherVenues: ICML, NeurIPS, ICLR, AISTATS, ACL
Research focuspaper titles that use each topic's words
- Reinforcement learning30 papers
- Medical imaging & healthcare AI6 papers
- synthetic data4 papers
- time-series2 papers
- Physics, climate & scientific computing1 paper
Paper list
From DBLP.
202610 papers
- ICLR (T1)A Study of Posterior Stability in Time-Series Latent Diffusion (opens in a new tab)
- ICLR (T1)Deep Hierarchical Learning with Nested Subspace Networks for Large Language Models (opens in a new tab)
- ICLR (T1)Eliciting Numerical Predictive Distributions of LLMs Without Auto-Regression (opens in a new tab)
- ICLR (T1)Hyperparameter Trajectory Inference with Conditional Lagrangian Optimal Transport (opens in a new tab)
- ICLR (T1)MATHMO: Automated Mathematical Modeling Through Adaptive Search (opens in a new tab)
- Trans. Mach. Learn. Res.Modelling Complex Tabular Datasets with a Mixture of Diverse Generative Models (opens in a new tab)
- ICLR (T1)Operator Theory-Driven Autoformulation of MDPs for Control of Queueing Systems (opens in a new tab)
- ICLR (T1)Overlap-weighted orthogonal meta-learner for treatment effect estimation over time (opens in a new tab)
- IEEE J. Biomed. Health InformaticsRetrieval Augmented Thought Process for Private Data Handling in Healthcare (opens in a new tab)
- J. Am. Medical Informatics Assoc.Translation readiness of model-based synthetic tabular data in healthcare: a systematic review and governance audit (opens in a new tab)
202538 papers
- AISTATS (T1)Active Feature Acquisition for Personalised Treatment Assignment (opens in a new tab)
- ICLR (T1)Active Task Disambiguation with LLMs (opens in a new tab)
- Nat. Comput. Sci.Algorithms for reliable decision-making need causal reasoning (opens in a new tab)
- ICML (T1)AutoCATE: End-to-End, Automated Treatment Effect Estimation (opens in a new tab)
- ICML (T1)Autoformulation of Mathematical Optimization Models Using LLMs (opens in a new tab)
- IEEE J. Biomed. Health InformaticsAutomated Ensemble Multimodal Machine Learning for Healthcare (opens in a new tab)
- AISTATS (T1)Beyond Size-Based Metrics: Measuring Task-Specific Complexity in Symbolic Regression (opens in a new tab)
- ICML (T1)Bootstrapping Self-Improvement of Language Model Programs for Zero-Shot Schema Matching (opens in a new tab)
- NeurIPS (T1)Cascaded Language Models for Cost-Effective Human-AI Decision-Making (opens in a new tab)
- ICML (T1)Continuously Updating Digital Twins using Large Language Models (opens in a new tab)
- ICLR (T1)Decision Tree Induction Through LLMs via Semantically-Aware Evolution (opens in a new tab)
- AISTATS (T1)Differentiable Causal Structure Learning with Identifiability by NOTIME (opens in a new tab)
202438 papers
- ICLR (T1)A Neural Framework for Generalized Causal Sensitivity Analysis (opens in a new tab)
- NeurIPS (T1)A theoretical design of concept sets: improving the predictability of concept bottleneck models (opens in a new tab)
- NeurIPS (T1)Active Learning with LLMs for Partially Observed and Cost-Aware Scenarios (opens in a new tab)
- AISTATS (T1)Adaptive Experiment Design with Synthetic Controls (opens in a new tab)
- NeurIPS (T1)Automatically Learning Hybrid Digital Twins of Dynamical Systems (opens in a new tab)
- Found. Trends Signal Process.Causal Deep Learning: Encouraging Impact on Real-world Problems Through Causality (opens in a new tab)
- NeurIPS (T1)Context-Aware Testing: A New Paradigm for Model Testing with Large Language Models (opens in a new tab)
- ICML (T1)Curated LLM: Synergy of LLMs and Data Curation for tabular augmentation in low-data regimes (opens in a new tab)
- AISTATS (T1)DAGnosis: Localized Identification of Data Inconsistencies using Structures (opens in a new tab)
- NeurIPS (T1)Data-Driven Discovery of Dynamical Systems in Pharmacology using Large Language Models (opens in a new tab)
- NeurIPS (T1)Deep Learning Through A Telescoping Lens: A Simple Model Provides Empirical Insights On Grokking, Gradient Boosting & Beyond (opens in a new tab)
- ICLR (T1)Defining Expertise: Applications to Treatment Effect Estimation (opens in a new tab)
202334 papers
- NeurIPS (T1)A U-turn on Double Descent: Rethinking Parameter Counting in Statistical Learning (opens in a new tab)
- NeurIPS (T1)Accountability in Offline Reinforcement Learning: Explaining Decisions with a Corpus of Examples (opens in a new tab)
- ICML (T1)Accounting For Informative Sampling When Learning to Forecast Treatment Outcomes Over Time (opens in a new tab)
- NeurIPS (T1)Active Observing in Continuous-time Control (opens in a new tab)
- ICML (T1)Adaptive Identification of Populations with Treatment Benefit in Clinical Trials: Machine Learning Challenges and Solutions (opens in a new tab)
- NeurIPS (T1)AllSim: Simulating and Benchmarking Resource Allocation Policies in Multi-User Systems (opens in a new tab)
- NeurIPS (T1)Can You Rely on Your Model Evaluation? Improving Model Evaluation with Synthetic Test Data (opens in a new tab)
- IEEE Trans. Neural Networks Learn. Syst.Conservative Policy Construction Using Variational Autoencoders for Logged Data With Missing Values (opens in a new tab)
- NeurIPS (T1)D-CIPHER: Discovery of Closed-form Partial Differential Equations (opens in a new tab)
- IEEE Trans. Serv. Comput.Data-Driven Online Recommender Systems With Costly Information Acquisition (opens in a new tab)
- ICLR (T1)Deep Generative Symbolic Regression (opens in a new tab)
- ICML (T1)Differentiable and Transportable Structure Learning (opens in a new tab)
Where alumni went
Alumni are not listed on the lab homepage.
Apply to CambridgeMichaelmas 2027
PhD in Computer Science
- Round
- Funding round (8 Dec 2026)
- Opens
- GRE
- See program page
- English
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- Fee
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Confirmed on the program page.
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Data
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