I am currently affiliated as an Assistant Professor in the Department of Data Science, Centre of Biomedical Research, Lucknow, India.

Academic Profile

PostDoc (University Hospital Erlangen, Germany; University of Würzburg, Germany; University of Düsseldorf, Germany)
PhD in Bioinformatics, University of Würzburg, Germany
Scientific Staff, University Hospital Heidelberg, Germany
MSc (Bioinformatics), UIET, CSJM University, Kanpur, India

Teaching & Research Experience

Postdoctoral Research Experience: 8 years
Research Experience (including PhD): 16 years
Teaching Experience : 5 years

Research Guidance (MSc, PhD)

PhD co-supervision: 1
MSc Dissertation: 5, guiding 1
Internships: 15+

• Our research primarily focuses on developing solutions for complex diseases by understanding their underlying mechanisms and identifying diagnostic and prognostic biomarkers. Our goal is to translate these findings into clinical practice to improve patient outcomes through personalized medicine, enabling more accurate diagnoses, targeted therapies, and better disease management. 

  Vision
  By combining OMICS, systems biology, and artificial intelligence, we will enhance our understanding of disease pathology, improve diagnostic accuracy, and develop patient-specific treatments. Computational modeling will be pivotal in advancing our comprehension and treatment of complex diseases.

  Aim
  To achieve this vision, we integrate prior scientific knowledge with diverse multi-omics, clinical, and healthcare data to elucidate disease mechanisms. We then create dynamic network models and validate our predictions through in-silico and in-vivo studies, in collaboration with experimental partners and clinicians.

  We primarily work along three lines of research-

  Integrating multi-omics data (genomics, transcriptomics, and metabolomics) with AI/ML: we focus on reconstructing dynamic pathway models, performing GWAS, metabolic modeling, and variant analysis to identify key mechanisms driving disease progression. By incorporating medical image classification techniques, we enhance our ability to analyze imaging data alongside multi-omics data. These approaches enable us to classify diseases more accurately and develop targeted diagnostic and therapeutic strategies. 

  Evolutionary approaches for understanding disease: exploring why humans might have evolved to be susceptible to certain diseases, studying the evolution of disease-associated genes, and applying evolutionary theories to fill gaps in disease maps.

  Investigation of infection mechanisms: examining pathogen-host interactions, co-evolution, cell-cell communication, cell signaling and regulation, and the non-linear dynamics of disease progression.

please click here for full list of publications