My focus is in applying causal inference and causal discovery methods on multiple modalities of biological data including RNAseq (single and bulk), single nucleotides variants (germline and somatic), structural variants, copy number variants as well as clinically relevant variables for the purpose of discovering biomarkers and drug targets of various diseases. More recently I have worked with multiple myeloma clinical studies as well as the TCGA cancer datasets. While I mainly work with our proprietary causal Bayesian network learning and predictive tools as part of my job I also investigate other open source machine learning tools that have potential usage for the type of datasets that we deal with.

Responsibilities:

• Work on various research projects involving causal inference, data integration, and biological data.
• Provide scientific support to other scientists in the company as well as external customers
• Present our work to clients

I designed, implemented and tested novel algorithms for the analysis of Nuclear Magnetic Resonance measurements of rocks and fluids that are of interest in oil exploration. I use Matlab extensively for the research and Python in order to interface Matlab code with other company software.

Responsibilities included:

• Designing mathematical models
• Implementing Matlab and Python algorithms
• Testing and validating algorithms

Worked at the Center for NASA Simulation Research Group where I was involved in several projects including:

• Simulating the probability of failure during a space shuttle liftoff
• Investigating the integration of genetic algorithms and support vector machine for a classification problem.