Andi is a computer scientist with 8+ years of interdisciplinary experience in bioinformatics of complex disease, computational genomics, biomedical data analytics, and database optimizations. Additionally, he has experience in machine learning, and statistical genetics. In particular, he has also developed computational methods, hardware-optimized algorithms to understand the evolution of animal and plant genomes on a large scale. In machine learning, Andi has worked on scalable inference methods for Support Vector Machine models to discriminate cancer from non-cancerous cell-lines irrespective of the tissue of origin. More recently, he created a framework to detect protein effectors in gram-negative bacteria across all six secretion systems without prior knowledge of bacterial secretion system(s) or cryptic secretion signals.

Specialties: Comparative & Computational Genomics, Machine Learning, Biomedical Data Mining, Information visualization, High-Performance Computing, Theory of Computation, Programming/Script Languages, Unix, Mobile/Web Applications.