Data Champions Search

I am a post-doctoral physicist working in the Department of Radiology, with a background in programming and a particular interest in medical image analysis and software design. I am currently involved in setting up and maintenance of processes for data storage, sharing and archiving for the department - with particular emphasis on medical research image data.
 

I am a PhD student at the MRC Biostatistics Unit, interested in applying machine learning techniques to biological data. I have worked on classification of cancer (sub)types using XGBoost models. I am currently working on trying to uncover biological details about the immune system using graph data and graph machine learning.

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/