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Department of Applied Mathematics and Theoretical Physics

Mihaela van der Schaar is the John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge, where she leads the van der Schaar Lab.


Mihaela was elected IEEE Fellow in 2009. She 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.

Mihaela’s work has also led to 35 USA patents (many widely cited and adopted in standards) and 45+ contributions to international standards for which she received 3 International ISO (International Organization for Standardization) Awards.

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 spans 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.



Mihaela has published more than 250 journal articles, several books and book chapters, and more than 275 conference papers. Her research is exceptionally diverse, spanning a wide range of fields including multimedia compression, processing and transmission; multi-user wireless networking; applications of game-theoretic ideas in engineering contexts; multi-agent learning in engineering systems; and machine learning for healthcare (her current focus).

In her early career, while working at Philips (and simultaneously completing her Ph.D.), Mihaela developed both the theoretical foundations and the first practical algorithm for streaming video. Her contributions remain highly visible, thanks to their inclusion in commercial products (including an award-winning Philips webcam) and the 35 U.S. patents that she has been granted. Between 1999 and 2003, she was Philips’ representative to the International Standards Organization (ISO), leading several of the working groups that developed and wrote the MPEG-4 standards for streaming video, to which she contributed more than 40 papers.

Mihaela has developed new methods for detecting, characterising, and forecasting complex events (including road traffic collisions, popularity of videos on social networks, and energy supply and demand in smart-grids, among others) based on a novel machine learning and real-time stream mining paradigm. These methods have been implemented as part of the IBM InfoSpheres Platform for a Smarter Planet.

Her ground-breaking work on machine learning for healthcare includes the development of improved methods for forecasting individual risks and for identifying covariates that are most important for forecasting risk. Her work has identified better treatment options for patients with heart failure, cystic fibrosis, breast cancer, and Alzheimer’s disease. She has also developed a state-of-the-art predictive model (already implemented in a number of hospitals) to manage hospitalised patients at risk of sudden deterioration, in addition to a framework (currently undergoing trials in the UK, in collaboration with NHS Digital and Public Health England) for more efficient allocation of limited resources across hospitals during the current COVID-19 pandemic.   In terms of recent academic output in top machine learning venues, van der Schaar ranked among the 10 researchers with the most accepted papers at ICML 2020 (and was the only female researcher on the list); she was only one of two women among the 40 researchers with the most accepted papers at NeurIPS 2019.

Professional contributions

Mihaela served as Editor-in-Chief of IEEE Transactions on Multimedia between 2011 and 2013, and has held Guest Editor, Associate Editor, and Senior Editorial Board Member roles for numerous other journals (including IEEE Journal on Selected Topics in Signal Processing). As part of the 2018 NHS Topol Review, she co-chaired the Expert Advisory Panel on Artificial Intelligence and Robotics.

She has given plenary and keynote talks at more than thirty international conferences (including, most recently, ICLR 2020 and ICME 2020), tutorials at more than thirty venues (including, most recently, ICML 2020), and summer schools (including at MLSS 2020). She has also delivered invited lectures all over the world (including the Oon International Award Lecture at the University of Cambridge in 2018, the Very Reverend Derek Hole Lecture in 2019 at the University of Leicester and the Alan Tyler Lecture in 2019 at the University of Oxford, as well as Distinguished Seminars at MIT in 2019 and 2020).

Leadership & mentorship

Mihaela was Founder and Director of the UCLA Center for Engineering Economics, Learning, and Networks (2011-2016). She is the Founder and Director of the Cambridge Centre for AI in Medicine (2020) and Co-Director of the European Laboratory for Learning and Intelligent Systems (2019).

Mihaela is a natural leader and mentor in both science and in science communication. Her Ph.D. students and postdocs have gone on to excellent academic positions internationally (in 4 continents!) and are becoming recognised as leaders in their own right.

She has also organised numerous outreach activities, several dedicated to empowering women in engineering and computer science, and has recently launched Inspiration Exchange, an online series of engagement sessions aiming to share ideas with young researchers in machine learning for healthcare.

Honors & awards

Mihaela has received numerous awards and honours for her work. While at Philips, she was awarded three ISO awards for her work leading several MPEG Working Groups, and also received the Philips “Make a Difference” Award. In the course of a single academic year, she received an NSF CAREER award (2004), an Okawa Foundation Award, and an IBM Watson Exploratory Stream Analytics Innovation Award as well as three IBM Faculty Awards.

Mihaela was elected an IEEE Fellow in 2009 and a Distinguished Lecturer for IEEE for 2011-2012, despite being substantially younger than others to have received those honours. She won an IEEE Circuits and Systems Society Darlington Award in 2011, and a Royal Society Wolfson Research Merit Award in 2016 (which she declined, in favour of the endowed Man Chair at Oxford).

Mihaela was awarded the Oon Prize on Preventative Medicine from the University of Cambridge in 2018. In 2019 she was named a Star in Computer Networking and Communications by N2Women (a community of researchers in the fields of networking and communications), and was identified by the National Endowment for Science Technology and the Arts (NESTA) as the most-cited female AI researcher in the UK.


Dynamic network formation with foresighted agents
Y Song, M van der Schaar
– International Journal of Game Theory
Bayesian nonparametric causal inference: Information rates and learning algorithms
AM Alaa, M Van Der Schaar
– IEEE Journal of Selected Topics in Signal Processing
A Hidden Absorbing Semi-Markov Model for Informatively Censored Temporal Data: Learning and Inference
AM Alaa, M van der Schaar
– Journal of Machine Learning Research
Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes
AM Alaa, M Van Der Schaar
– NIPS'17: Proceedings of the 31st International Conference on Neural Information Processing Systems
Predicting grades
Y Meier, J Xu, O Atan, M Van Der Schaar
– IEEE Transactions on Signal Processing
Distributed online learning via cooperative contextual bandits
C Tekin, M Van Der Schaar
– IEEE Transactions on Signal Processing
Demand Side Management in Smart Grids Using a Repeated Game Framework
L Song, Y Xiao, M Van Der Schaar
– IEEE Journal on Selected Areas in Communications

Research Groups

Centre for Mathematical Imaging in Healthcare
Machine Learning and Artificial Intelligence




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