Find a Data Champion

Qi received her PhD at the University of California, Los Angeles, where she developed computational methods to study alternative splicing and the evolution of HIV. Later she joined the UCLA Division of Dermatology to help identify the resistance mechanisms of targeted cancer therapies using computational tools. 
    
In 2011 she was awarded the Humboldt Research Fellowship to work in the Division of Theoretical Bioinformatics, and later the Division of Applied Bioinformatics, at the German Cancer Research Centre. There she worked on diverse bioinformatics projects with NGS data, such as studying genome-wide epigenetic regulation of cell differentiation, developing sequencing protocols and detecting somatic mutations from whole-genome sequencing data, as well as data integration and predictive modelling. 
    
Currently, she provides bioinformatics training and support to PhD students and postdoctoral researchers in the lab of Professor Beverley Glover.

Ruoyu Miao is a research associate in the Department of Obstetrics and Gynaecology at the University of Cambridge. She did joint models for mean and variance structures as a postdoc in the Department of Mathematics at the University of Manchester (2022-2023). Her research area is 'Joint modelling approaches for multi-outcome data'. Her current research is related to the joint latent class model. Her research focuses on two concentrations: (1) A joint latent class model of longitudinal and survival data with the time-varying membership probability; (2) A general time-varying joint latent class model with covariance modelling. Now she is the biostatistician at the Department of Obstetrics & Gynaecology, School of Clinical Medicine. Her current research at the University of Cambridge is the proteomic analysis of blood samples from the Pregnancy Outcome Prediction Study (POPS) cohorts. The aim of the study is to determine the association between maternal serum levels of metabolites and the risk of adverse pregnancy outcomes.

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/

 I am a 2nd-year PhD student in computational biology. Currently, I am developing machine learning applications in studying disease mechanisms using omics integration and analysis at the Department of Surgery and the Milner Therapeutic Institute.