Bioinformatics, NGS, data analysis, and high-level programming languages
Hi there! I'm Daniel, a bioinformatics scientist/computational biologist
with both industry and academic training. I obtained my B.S. in Computer Science
and am striving for my Ph.D. in Computational Science—Bioinformatics.
I am currently performing research as a member of Professor Rob Edwards bioinformatics lab
at San Diego State University where I am involved in modeling bacteria metabolism, bioinformatics tool development, genomics and metagenomics research,
and all-around data analysis. At SDSU, I work and collaborate closely with many students and faculty across the microbiology and ecology departments.
My passion for learning, creatively solving problems, and helping others arises from my curiosity of natural and complex systems found in the world. My origins in
computer science began with two parts: my interest in how computer software operates and my admiration in how technology moves the mountains of society. The
field of microbiology research exploits this computational power to acquire data and, in turn, refine that data into information and knowledge. By working with
others well-versed in computer science, statistics, mathematics, and biology, we can advance our knowledgebase of the natural and biomedical sciences.
My professional interests involve keeping up with new and interesting genomics and microbiome advancements,
exploring new data analysis and visualization techniques, and learning more about machine learning and statistical learning methods.
On my free time I love to laugh and spend time with my wife, often taking trips to Disneyland and embarking on hikes around the San Diego scenery.
AREAS OF EXPERTISE
- Proficient languages: Python, Perl, C, R, PHP, SQL, bash
- Intermediate languages: Java, MATLAB
- Git and SVN software versioning and development
- Bioinformatics and NGS tools, algorithms, and databases
- Custom algorithm development
- Genomics and metagenomics assembly, binning, and annotation
- Multivariate statistical analysis, regression, and classification
- Large-scale big data visualizations
- Random forest, neural networks, and other supervised/unsupervised machine learning techniques
- Sun Grid Engine (SGE) parallel programming and Amazon Web Services (AWS)