Heading into the new academic year, Alexander Norcliffe, Amir Gavrieli, Charles Harris, and Tennison Liu will join the CCAIM research team as PhD students. These students will work with members of the Centre’s Faculty and University of Cambridge networks to develop AI and ML solutions to the most complex challenges faced by healthcare professionals. Each student will bring a new perspective, fresh ideas, and an exceptional academic record to the Centre’s ongoing development of world-leading techniques in the field of machine learning for healthcare.
Alex moved into the domain of AI in Medicine after previously studying Physics at the Univeristy of Cambridge and Machine Learning at University College London (UCL); where he published to both NeurIPS and ICLR.
He is jointly supervised by Prof. Mihaela van der Schaar and Prof. Pietro Lio, working on time-series models for prognosis and using data to further our understanding of underlying biological processes.
Alex joined CCAIM to work with the world’s best researchers and industrial partners in both machine learning and medicine, to take full advantage of co-supervision, and to apply his research in an impactful way. In the coming year, Alex looks forward to collaborating with the CCAIM team to help solve some of the most difficult problems we face in medicine today and will face in the future.
Outside of his research, Alex is an enthusiastic water polo player – playing for the University and local Cambridge teams.
Charlie has a background in Biochemistry and Bioinformatics from Imperial College London. His research focuses primarily on developing bespoke AI methods to solve problems in structural biology, helping us better understand disease and improve drug discovery. He has a particular focus on Geometric Deep Learning and generative models.
Charlie is also interested in moving beyond supervised learning on biological data to see what new fundamental principles of biology can be learned by using self-supervised and reinforcement learning techniques.
At CCAIM, he is supervised by Prof. Andres Floto and Prof. Pietro Lio as well as Prof. Sir Tom Blundell. Notably, Charlie is a contributor to the Graphein package (which aims to democratise access to Geometric Deep Learning on biological data) and Founder of the Imperial College Computational Biology Society.
Outside of his research, Charlie is a passionate about science communication and is an avid glider pilot, with hopes of soon participating in cross country flying competitions.
Amir is a first year PhD student at CCAIM under the supervision of Prof. Mihaela
van der Schaar, Prof. Andres Floto and Dr. Angela Wood. Amir completed his BSc. in Mathematics (magna cum laude) and Economics at Tel Aviv University. His MSc. is from Weizmann Institute of Science (Applied Mathematics and Computer Science), where – under the supervision of Prof. Eran Segal – he focused on machine learning and statistical applications in healthcare. Amir also worked at K Health (a ML healthcare company funded by Google venture capital) and as a private DS consultant.
Amir’s interests lie in developing AI solutions and applications to medical and healthcare problems, with a focus on Value of Information and active sensing. In particular, he is interested in quantifying the benefit of collecting additional information to reduce uncertainty and cost in decision-making contexts such as clinical trials and patient treatment.
Amir joined CCAIM as he believes that the Centre’s unique combination of researchers from diverse backgrounds and the collaboration opportunities that it provides are tailored for such challenging tasks.
Starting January 2022.
In May, CCAIM hosted an ‘Ideas Incubator’ to hear research collaboration ideas from quantitative researchers, biomedical researchers, and clinicians. Candidates pitched their ML and AI methods for biomedicine and healthcare to the Centre’s Faculty, sharing impactful datasets and challenging problems that might be solved using ML. Following this event, the team successfully recruited three exceptional University of Cambridge researchers to serve as the think tank’s Associate Faculty: Dr. George Mells of the Department of Medical Genetics, Dr. Namshik Han of the Milner Therapeutics Institute, and Prof. Qingyuan Zhao of the Department of Pure Mathematics and Mathematical Statistics (DPMMS).
Dr. Namshik Han
Dr. Han is a computational drug discovery scientist, coming from a background in machine learning, computational biology, cancer genomics and cancer epigenomics. His lab develop and apply dedicated and bespoke AI technologies that deal with the uncertainty and complexity of biological datasets to reveal novel disease pathways and mechanisms.
In addition to his academic research, Dr. Han has a passion for working at the interface between academia and industry to apply his research to real-world problems. In his case this is in therapeutics and patient care where AI promises to revolutionize the ability to identify disease mechanisms and potential drug targets, informing patient care from disease diagnosis through to treatment.
Towards this aim, Dr. Han facilitates access of the Milner Therapeutics Consortium partner organizations to cutting-edge AI technology and to develop new computational methods fulfilling the global mission of identifying new or better therapies from the analysis of biological data.
Dr. George Mells
UK-PBC has established the world’s largest patient cohort with deep phenotype, genotype data and disease outcome data. Dr. Mells is using this unique cohort for statistical modelling of disease to derive clinical prediction models and inform the design and interpretation of omic experiments aimed at re-purposing of drugs and development of predictive biomarkers.
Dr. Qingyuan Zhao
Qingyuan Zhao is a University Assistant Professor in the Statistical Laboratory, Department of Pure Mathematics and Mathematical Statistics (DPMMS) at University of Cambridge and a Turing Fellow at the Alan Turing Institute.
Dr. Zhao is interested in improving the quality and appraisal of statistical research, including new methodology and a better understanding of causal inference, novel study designs, sensitivity analysis, multiple testing, and selective inference. His substantive research focuses on causal inference problems arising in genetics and epidemiology.
Click here for Dr. Zhao’s name in Chinese and how to pronounce it.