Maximilian Kerz



I graduated from the life science department at King’s College London in 2014, receiving a First Class Honour BSc in Molecular Genetics. During my time as an undergraduate, I discovered an interest in computer science and became sufficient in several programming languages. I then began to apply my programming skills on biological problems within my degree, and received the Layton Research Science Award for my bachelor thesis in bioinformatics.

After my BSc, I shifted my focus to computational analyses in order to quantify biological phenotypes and behaviours. Mental illnesses are hard to monitor and even more difficult to consistently treat. To investigate remote detection of symptomatic behaviour of Schizophrenia patients, I received a traditionally funded PhD Studentship from the Biomedical Research Centre (BRC) in 2014.

Parts of my work have been published in Wired.


BSc in Molecular Genetics, First Class Honours, King’s College London
Exchange year at the University of Melbourne
Awarded the Layton Science Research Award for the best promise of aptitude and genius for original scientific work

Bioinformatic demonstrator at the MSc Genes Environment and Development (GED) run by the MRC SGDP at the IoPPN
Summer intern at the IP and patent attorney Grünecker, Kinkeldey, Stockmair & Schwanhäusser in the department of genetics and biochemistry
Summer intern at City Trading and Investment in optimising portfolios

The use of descriptive, inferential and machine learning-based analyses to address the challenge of large biomedical time series datasets

Everyday pentabytes of information are collected from each and everyone of us, ranging from online behaviour to medical data. The wellness and medical sector in particular is currently undergoing a drastic shift towards digitalisation of personalised data. One of today's challenges related to analysing such data is data-point time dependency. Time adds an unidirectional dimension to data sets. This results in a data-point being dependent on its neighbouring data-points within the time dimension, forming individual-specific patterns.

In order to explore different methods for analysing time series-based bioinformatic data sets, I worked on three projects which span the bioinformatic scale from the cellular, over individual patient to a large population level.

  1. HipSci Live-Imaging: Quantifying cell population dynamics as part of the Human Induced Pluripotent Stem Cell Initiative, using 24 hours live-imaging.
    The objective for this project is to develop an image analysis pipeline (IAP) that reliably identifies iPS cells with a highly variable morphology, to enable the quantification of cell population dynamics over 24 hours period. Both processes will then be integrated into the cellular phenotyping pipeline of the Human Induced Pluripotent Stem Cell Initiative.
  2. SleepSight: Identifying associations between sleep-wake pattern and clinical Schizophrenia ratings, using SleepSight, a wearables-based intervention system.
    This project is primarily focused on system development and data analysis. The primary objective is to test the feasibility of the developed system for data acquisition on patients, and to improve the system such that it can be used in larger studies. The secondary objective is to investigate how sleep¬-wake patterns are associated with clinical variables, such as severity of symptoms.
  3. SocialHealth: Identifying clinical symptoms from behavioural biomarkers, using a time-series based physical activity and social media driven data set.
    SocialHealth extends on SleepSight in platform development and types of data being collected. The development of a data collection system that captures a population’s objective and subjective experiences of physical and social behaviour is the primary objective of the project. The platform will enable the identification of statistically significant subgroups, defined by specific social and/or physical behaviour.


Dr Richard Dobson, Dr Amos Folarin and Dr Stephen Newhouse



Bioinformatics & statistics

Our bioinformatics and statistics research integrates complex rich clinical data from patient records with large datasets from other areas including genomics & brain imaging to better understand psychiatric disorders.

Training & development

We aim to attract outstanding candidates with a range of experience and offer a variety of training schemes and secondment opportunities, spanning all academic career pathways.
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