Thind - Research interests

I am passionate about multi-omics and integrative computational biology, combining genomics, transcriptomics, and other omics layers to uncover meaningful biological insights. My work focuses on developing robust, reproducible workflows and applying statistical/ML methods to high-dimensional datasets, enabling predictive modeling and biomarker discovery.

I am particularly interested in mix-omics approaches that integrate diverse data types, bridging molecular measurements with clinical and phenotypic information to inform translational research. Alongside methodology development, I enjoy designing omics data analysis workshops and training materials, helping researchers apply advanced computational techniques effectively.

Through this combination of multi-omics analysis, and knowledge translation, I aim to contribute to innovative, collaborative research in computational biology, translational genomics, and data-driven biomedical science.

Omics Analytics for Research Insight

Building on statistical foundation, my research in omics analytics focuses on extracting biological insight from complex and heterogeneous datasets. This includes applied multi-omics integration, differential and compositional analysis, and the use of spatial omics techniques to study cellular organisation and context. I have a strong research interest in immuno-informatics & cancer, particularly in leveraging bulk, single-cell, and spatial omics data to investigate immune repertoires, cell–cell interactions, and immune system dynamics. Through integrative analytical frameworks and reproducible computational pipelines, my work aims to connect quantitative modelling with biologically meaningful interpretation, enabling data-driven discovery across diverse areas of life science research.

Statistics for Omics

My research traning focuses on the development and application of statistical methodologies for high-dimensional omics data. I work on statistical design planing for multi-omics data integration for reseach experiemnts, with the aim of coherently modelling information across genomic, transcriptomic, proteomic, metabolomic, single-cell, and spatial data modalities. I place strong emphasis on principled statistical inference, uncertainty quantification, and reproducibility, seeking to distinguish true biological signal from technical and analytical variation in modern omics studies..