Data Champions Search

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.

I studied biomedical engineering for my bachelor's degree at Southeast University in Nanjing, China. During my senior year, I interned at BGI-Shenzhen, where I began to extend my knowledge of bioinformatics by working on the genomics of plants, such as the ginkgo tree, and animals. During my Master's at the same university, I had the opportunity to start exploring the formation of algae blooms by characterizing the compositions of the bloom-forming microbial communities in Lake Taihu. Using metagenomics, we found that complementation in bioprocesses, such as nitrogen fixation, between cyanobacteria and their physically attached microbes contributes to the formation of aggregates. In 2017, I moved to Germany for my PhD at the Max Planck Institute for Plant Breeding Research. There, I applied and developed computational tools to help us understand host-microbe interactions. Specifically, I focused on unravelling the complex relationships between plants and the microbes living inside and in the immediate vicinity of their roots.
 

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/