Data Champions Search

Florian Markowetz is a Senior Group Leader at the Cancer Research UK Cambridge Institute. He is a Royal Society Wolfson Research Merit Award holder and received a CRUK Future Leader in Cancer Research prize. He holds degrees in Mathematics (Dipl. math.) and Philosophy (M.A.) from the University of Heidelberg and a Dr. rer. nat. (PhD equivalent) in Computational Biology from Free University Berlin, for which he was awarded an Otto-Hahn Medal by the Max Planck Society. His group at the CRUK Cambridge Institute combines computational work on cancer evolution and image analysis of the tumour tissue with experimental work on understanding key cancer mechanisms like the estrogen receptor.

I provide Bioinformatic support for a few research groups in the Cambridge Stem Cell Institute. For the most part I deal with the analysis of single cell sequencing data, storage management of such data and the deployment of data processing pipelines into HPC environments.

I am a neuroscientist studying human speech perception with the aim of advancing assistive speech (neuro)technologies and AI speech recognition systems. My background is in cognitive neuroscience and medicine.

I am an expert in human neurophysiology methods (electro- and magnetoencephalography, EEG/MEG) combined with machine learning and artificial neural network modelling (deep learning).

Since the beginning of my PhD, I’ve been passionate about reproducible and open science practices such as data and code sharing, and good coding practices including version control. In my department I am leading the adoption of the standardised Brain Imaging Data Structure (BIDS) for storing and sharing human neurophysiology (MEG/EEG) data.

I am a Research Associate at the University of Cambridge, UK. I am also a Postdoctoral Affiliate of Trinity College, Cambridge. I am currently working on several exciting research problems concerning codon usage, optimality and, in general, knowledge discovery from large-scale genomic data. I am focusing on developing novel ML approaches, such as Neuro-Symbolic Models and Deep Neural Networks, to learn from genomic sequences. For more details, please visit my homepage, which is usually updated often: https://tirtharajdash.github.io/