Avinash Dhar

Multidisciplinary computational biologist with 8+ years of experience in genomics data analysis and a strong foundation in microbial ecology, bioinformatics, and machine learning. Currently pursuing a PhD in Ecology at CSU, focusing on decoding the structure-function relationships of the airborne microbiome across environmental gradients. Skilled in managing large-scale multi-omics datasets, developing pipelines, and applying AI/ML to predict microbiome-environment interactions. Adept at working across interdisciplinary teams, delivering high-impact insights in academic and applied research settings.

Python, R (Bioconductor), Bash, Shell, Linux, QIIME2, Mothur, STAR, DESeq2, GATK, Bowtie2, MEGAHIT, Scikit-learn, Graph-based models, Ensemble learning (ongoing), Amplicon, RNA-seq, small RNA, WGS, Exome, Metagenomics, Nextflow, Jupyter, RMarkdown

• Structure & Interactions of the Airborne Bacterial Communities, Front Range Microbiome Symposium, CSU Fort Collins, 2023
• Unraveling the Functional Traits of Aerobiome under the Current Challenge of Global Climate Change, American Phytopathological Society (APS) Annual Meeting, Denver, 2023

• Conducted 20+ training programs on NGS data analysis across India
• Mentored students and junior analysts on Linux, R, and Python for genomics
• Engaged in community-based science communication and data democratization

• First Place – Inter-University Eco-Quiz & Eco-Model Competitions
• Poster Presentation – Bangalore India Bio (Whole Genome Sequencing, Prunus spp.)
• IEEE Oral Presentation – IIIT Allahabad

Dr. Pankaj Trivedi, Associate Professor – Microbiome of Plant Systems, Colorado State University, Pankaj.Trivedi@colostate.edu

• Air microbiome and One Health interactions
• Metagenomics, multi-omics integration
• AI/ML applications in environmental genomics
• Microbial ecology and systems biology
• Graph-based models and predictive ecology

Graduate Researcher, Trivedi Lab, CSU – Department of Agricultural Biology, 08/01/22, Present, Leading metagenomic analysis of airborne microbial communities under an NSF-funded project, Integrating multi-omics datasets (metagenomics, transcriptomics, environmental metadata), Exploring ML models to predict microbiome shifts across ecosystems, Collaborating internationally on global-scale air microbiome datasets, Developing reproducible pipelines for high-throughput data processing