Physical

Enabling the collaborative research in Precision Health that is instrumental in the transition to preventative medicine



Ulrich Keyser

ufk20@cam.ac.uk

Physical  Biological Physics  

Prof Keyser's research focuses on understanding all transport processes through membranes of both biological and technological origin. Specifically, his team is interested in the physics of ions, macromolecules and particles in confined geometries at the single molecule or particle level.  They aim to exert maximum control over all parameters in the experiment using techniques such as: DNA (origami) self-assembly, optical trapping, particle tracking, fluorescence microscopy, electrophysiology, or micro-/nanofluidics.  

membrane transportation process  optical trapping  particle tracking  microfluidics  nanofluidics  

Sarah Bohndiek

seb53@cam.ac.uk

Physical  Imaging  

Prof Bohndiek's research focuses on advancing the understanding of tumour evolution using next-generation imaging sciences. She is particularly interested in the study of blood vessel formation in early cancer. Her team is also active in translating the findings into first-in-human clinical trials.

biomedical optics  endoscopy  photoacoustic  cancer  clinical trials  

Carola Schönlieb

cbs31@cam.ac.uk

Applied Mathematics  Physical  

Prof Schönlieb specialises in the mathematics of digital image and video processing using partial differential equations and variational methods. The research from her group ranges from the modelling and analysis of such methods to the computational realisation and application.

image processing  machine learning  

John Aston

j.aston@statslab.cam.ac.uk

Physical  Mathematical Statistics  

Prof Aston's research interests include all areas of Applied Statistics but particularly Official and Public Policy Statistics, Statistical Neuroimaging and Statistical Linguistics; and the links between statistics and other areas of pure and applied mathematics. He is also involved in some of Cambridge's AI and COVID-19 work through AIX-COVNET.

statistical neuroimaging  statistical linguistics  public policy statistics  

Sergio Bacallado

sb2116@cam.ac.uk

Physical  Mathematical Statistics  

Dr Bacallado is a statistician specialising in Bayesian methods and Bayesian nonparametrics. In particular, he has a background in Structural Biology. He develops methods for the analysis of human microbiome studies, and previously worked on applications to molecular dynamics simulations and single-molecule biophysics.

statistics  microbiome studies  molecular dynamics simulations  

Martin Bennett

mrb24@medschl.cam.ac.uk

Imaging  Cardiovascular Research  

Prof Bennett studies the regulation of cell accumulation and interactions in vascular disease, using both human and mouse cells in vitro and models of atherosclerosis with genetic manipulation. He also has a major interest in vulnerable plaque imaging in patients using both invasive and non-invasive modalities.

plaque imaging  regulation of cell accumulation and interactions in vascular disease  

Ruth Cameron

rec11@cam.ac.uk

Physical  Material Science & Metallurgy  

Prof Cameron's research considers materials which interact therapeutically with the body. Her research interests lie in bioactive biodegradable composites, biodegradable polymers, tissue engineered scaffold, surface patterning, drug delivery, and pharmaceutics.

drug delivery  tissue engineering  biodegradable polymers  

Colm-cille Caulfield

c.p.caulfield@damtp.cam.ac.uk

Physical  Applied Mathematics  

Prof Caulfield is interested in various fluid flows in the environment, particularly in cases where density differences play a dynamical role. Understanding the fundamental properties of the associated fluid dynamics is key to ensuring sustainable human activity.

building ventilation  fluid dynamics  stratified flows  turbulent mixing  

Pietro Cicuta

pc245@cam.ac.uk

Biological Physics  Physical  

Prof Cicuta's research focuses on living systems, with main efforts on hydrodynamic synchronisation of motile cilia, physics of bacteria, malaria blood-stage infection, phospholipid membranes and single cell imaging systems.

soft matter  hydrodynamics  phospholipid membranes  

David Klenerman

dk10012@cam.ac.uk

Chemistry  

Prof Sir Klenerman's research interests lie in developing and applying a range of new quantitative biophysical methods, based on single molecule fluorescence and scanning probe microscopy, to important yet unaddressed problems in biology.

biophysical methods  single molecule fluorescence  scanning probe microscopy  

Joan Lasenby

jl221@cam.ac.uk

Physical  Imaging  

Prof Lasenby's research interests include: 3D reconstruction from multiple cameras; human motion analysis; optical motion capture; applications of Geometric Algebra in engineering; non-invasive monitoring techniques in medical applications; sports engineering.

medical applications  3D reconstruction  human motion analysis  sport engineering  

Andy Parker

map1001@cam.ac.uk

Physical  Partical Physics  

Prof Parker’s current research interests involve experiments to reveal new physics such as extra space dimensions, quantum-sized black holes, and supersymmetry. He has worked with the Oncology Department on computational methods for radiotherapy, including the Voxtox and Radnet projects.

computational methods for radiotherapy  oncology  ATLAS  supersymmetry  

Richard Samworth

r.samworth@statslab.cam.ac.uk

Physical  Mathematical Statistics  

Prof Samworth's main research interests are in nonparametric and high-dimensional statistics. Particular topics include shape-constrained density estimation and other nonparametric function estimation problems, nonparametric classification, clustering and regression, the bootstrap and high-dimensional variable selection problem. Further applciations include public health, genetics, archaeology and oceanography.

public health  genetics  nonparametric  high-dimensional statistics  

Mihaela van der Schaar

mv472@cam.ac.uk

Physical  Applied Mathematics  

Prof van der Shaar's research develops cutting-edge machine learning & AI theory and methods, with the goal of improving healthcare and medical knowledge. Her team is one of the most impactful and diverse ones in the field, employing a wide range of ML approaches including deep learning, causal inference, AutoML, time series analysis, ensemble learning, and many more.

distributed systems  image processing  machine learning  medical applications  game theory