van der Schaar Lab

Machine Learning meets Pharmacology

Our belief at the van der Schaar lab is that the integration of machine learning with pharmacology is unlocking new frontiers in personalised medicine and clinical trials. Below we highlight how, by working closely with pharmacologists from all over the world over the past 5 years, we have developed cutting-edge machine learning which can transform predictive modelling for pharmacokinetics and pharmacodynamics, enable precision dosing that is personalised to the patient, and change the way information is extracted from clinical trials to enable treatment and dose personalisation to better treat patients. The publications listed on this page showcase our latest research in applying advanced ML techniques to pharmacological challenges, highlighting how these computational tools are not only accelerating the pace of discovery but also enhancing the precision of therapeutic interventions.

We are excited about numerous other opportunities to join forces to enable this transformation. If you are a pharmacologist interested in doing joint research and collaborating with us or if you would like to provide us feedback, please reach out here.

From Real-World Patient Data to Individualized Treatment Effects Using Machine Learning: Current and Future Methods to Address Underlying Challenges

Ioana Bica, Ahmed M Alaa, Craig Lambert, Mihaela van der Schaar


Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks

Ioana Bica, James Jordon, Mihaela van der Schaar


Integrating Expert ODEs into Neural ODEs: Pharmacology and Disease Progression

Zhaozhi Qian, William R Zame, Lucas M Fleuren, Paul Elbers, Mihaela van der Schaar


Neural Laplace: Learning diverse classes of differential equations in the Laplace domain

Samuel HoltZhaozhi QianMihaela van der Schaar


Continuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations

Nabeel Seedat
Fergus ImrieAlexis BellotZhaozhi Qian,
Mihaela van der Schaar


D-CIPHER: Discovery of Closed-form Partial Differential Equations

Krzysztof KacprzykZhaozhi Qian, Mihaela van der Schaar


D-CODE: Discovering Closed-form ODEs from Observed Trajectories

Zhaozhi QianKrzysztof KacprzykMihaela van der Schaar


Deep Generative Symbolic Regression

Sam Holt,
Zhaozhi Qian, Mihaela van der Schaar


The potential and pitfalls of artificial intelligence in clinical pharmacology

Martin JohnsonMishal PatelAlex Phipps,
Mihaela van der Schaar, Dave BoultonMegan Gibbs


Synthetic Model Combination: A new machine-learning method for pharmacometric model ensembling

Alex Chan, Richard Peck, Megan Gibbs, Mihaela van der Schaar


Bridging the Worlds of Pharmacometrics and Machine Learning

Kamile Stankeviciute, Jean-Baptiste Woillard, Richard Peck, Pierre Marquet, Mihaela van der Schaar


Synthetic Model Combination: An Instance-wise Approach to Unsupervised Ensemble Learning

Alex Chan, Mihaela van der Schaar


Also, see this conversation between Prof Richard Peck and Alex Chan about Synthetic Model Combination, a novel machine learning method for constructing new model ensembles that changes the game for drug development:

Mihaela van der Schaar

Mihaela van der Schaar is the John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge and a Fellow at The Alan Turing Institute in London.

Mihaela has received numerous awards, including the Oon Prize on Preventative Medicine from the University of Cambridge (2018), a National Science Foundation CAREER Award (2004), 3 IBM Faculty Awards, the IBM Exploratory Stream Analytics Innovation Award, the Philips Make a Difference Award and several best paper awards, including the IEEE Darlington Award.

In 2019, she was identified by National Endowment for Science, Technology and the Arts as the most-cited female AI researcher in the UK. She was also elected as a 2019 “Star in Computer Networking and Communications” by N²Women. Her research expertise span signal and image processing, communication networks, network science, multimedia, game theory, distributed systems, machine learning and AI.

Mihaela’s research focus is on machine learning, AI and operations research for healthcare and medicine.