KAABiL

Dr. Kaundal's research: information to inference in OMICS data science

Biological data is accumulating faster than people’s capacity to analyze them. Our research interests and goals revolve around mitigating this issue in the context of “information to inference” scope. At USU, Dr. Kaundal has developed an independent and collaborative research program in bioinformatics, primarily focusing on computational mining of large multi-dimensional -omics datasets, and computational modeling using supervised (Machine Learning) and unsupervised (Bayesian-based) learning. Our group is actively developing novel tools and software to apply the gained knowledge towards organismal improvement. Research in KAABiL laboratory generally falls under the following major program objectives:

Artificial Intelligence: OMICS bigdata to novel biological discoveries

Computational modeling through pattern recognition, develop novel algorithms and...

Precision Agriculture: Robotics for integrated weed management (IWM)

Incorporate sensing technology for precise IWM, minimize damage to crop, environment...

NGS studies: Deciphering and making sense of the complex -omics bigdata

Identification of novel expressed genes from transcriptomics studies, methylation patterns...and more text

Host-pathogen interactions: Understanding mechanisms of infectious diseases

Explore cross-kingdom threats and identify novel therapeutic candidates...

Metagenomics: Bioinformatics solutions for crop improvement

Investigate diversity of enzymes in soil metagenomes and develop advanced tools...

Protein function prediction: Annotation of novel proteomes

Identify novel protein features, train in an AI framework, implement as web servers...

Understand the genomic aspects of plant / animal pathogen evolution and/or virulence either at the organismal or population level,
Develop novel computational algorithms to explore cross-kingdom threats and identify novel therapeutic candidates,
Investigate the diversity of enzymes in soil metagenomes and develop advanced bioinformatics solutions to predict and recover novel enzyme encoding genes involved with the nitrogen (N) cycle from soil metagenomes,
Develop and implement cost effective prediction tools / databases (customized searchable web portals),
Establish high-end biocomputing infrastructure (High-Performance Computing Cluster, HPCC) and novel algorithms with AI while advancing bigdata research,
Collaborate extensively and provide leadership in these research areas.