Carl Henrik Ek
University Senior Lecturer
Develops probabilistic machine learning models such as Gaussian processes and latent variable models, linking knowledge representation with inference, with medical applications in intensive care and cardiology.
Carl Henrik Ek's lab at Cambridge works on probabilistic & causal inference, reasoning & neuro-symbolic AI and efficient training & inference. 17 papers since 2023, 7 at top venues, mostly NeurIPS and ICLR. 5 PhD students. 60 alumni. Applications close .
At a glance
- papers, 2023–2026
- 4/ year
- at T1 venues
- 41%
- PhD students
- 5
- MS students
- 0
- alumni listed
- 60
Papers by yearfrom the lab homepage, 2023–2026
- 2 papers2026 · T1 1 · collecting
- 9 papers2025 · T1 4
- 3 papers2024 · T1 1
- 3 papers2023 · T1 1
T1 venuesOtherVenues: NeurIPS, ICLR, AISTATS, Bioinformatics
Research focuspaper titles that use each topic's words
- Efficient training & inference3 papers
- Probabilistic & causal inference2 papers
- latent1 paper
- Reasoning & neuro-symbolic AI0 papers
- medical applications0 papers
Paper list
From the lab homepage.
20262 papers
- BioinformaticsDeepSynBa: actionable drug combination prediction with complete dose-response profiles
- NeurIPS (T1)Forgetting to Improve: Principled Data Removal in Active Learning
20259 papers
- Royal Statistical Society: Data Science and Artificial IntelligenceBeyond Quantification: Navigating Uncertainty in Professional AI Systems
- AMS Contemporary MathematicsCalabi–Yau metrics through Grassmannian learning and Donaldson’s algorithm
- NeurIPS (T1)Deep variational inference with stochastic projections
- Cancer ResearchDeepSynBa: A deep learning method to improve drug combination predictions using full dose-response matrices and contextual features of cell lines and cancer indications
- ICLR (T1)Efficient Model-Based Reinforcement Learning Through Optimistic Thompson Sampling
- ICLR (T1)Linear combinations of Gaussian latents in generative models: interpolation and beyond
- European Heart Journal-Digital HealthMachine-learning electrocardiography model to differentiate Takotsubo syndrome from myocardial infarction
- NeurIPS (T1)No-Regret Thompson Sampling for Finite-Horizon Markov Decision Processes with Gaussian Processes
- European Journal of Trauma and Emergency SurgeryPrognostic value of admission ROTEM in trauma: enhancing 30-day all-cause mortality prediction using machine learning
20243 papers
- —Automatic Knowledge Extraction for Decision Support in the Structural Design Process
- NeurIPS (T1)Reparameterization invariance in approximate Bayesian inference
- —Representing engineering design changes in finite element models using directed point cloud autoencoders
20233 papers
- AISTATS (T1)Mode Constrained Model-Based Reinforcement Learning via Gaussian Processes
- MathematicsSurface Approximation by Means of Gaussian Process Latent Variable Models and Line Element Geometry
- Intelligent Systems for Molecular BiologySynBa: Improved estimation of drug combination synergies with uncertainty quantification
Where alumni went60 listed
Apply to CambridgeMichaelmas 2027
PhD in Computer Science
- Round
- Funding round (8 Dec 2026)
- Opens
- GRE
- See program page
- English
- See program page
- Fee
- See program page
Confirmed on the program page.
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