Currently I am an independent scientist. I left academia because the politics and power structure results in inefficient science that is prone to false positives. I believe science would be much more effective with a silicon valley type of structure and mindset. I've expressed many of these views in various blog posts. My scientific/academic career is known for various accomplishments: Academics I was valedictorian of my high school and graduated at the top of my class at UC Berkeley with 22 A+'s. I then went on to score at the top of my medical school and graduate school classes, receiving a five year F31 grant in my first year of grad school. Despite my success in traditional schooling, I believe self-learning is far more efficient and have taken numerous online courses, teaching myself programming, web development, and advanced mathematics. Small RNA Biology I am the first person to show that a non-canonical class of small RNAs can target thousands of transcripts. I wrote up these results my first year of graduate school and the paper has 66 citations on Google Scholar. Cancer Biology I've performed several pan-cancer survival analyses with TCGA data, and developed a web application for easy exploration of survival correlations. OncoLnc received 116K hits in the month of May, 2017 alone. I also developed a pipeline for finding novel lncRNAs in TCGA brain cancer RNA-SEQ samples, which was recently published in PLOS Medicine. Open Science Once I left academia my eyes were opened to the world of open science. To support preprints I developed the first index of preprints, PrePubMed, with a search engine that mimics PubMed. I also interact with various members and organizations of the open science community, such as ASAPbio. Granularity Testing When I read the preprint for this technique I knew it was a game changer, and immediately developed a web tool for the authors. I then extended granularity testing to standard deviations, which is needed to further extend granularity testing to test statistics and will be beneficial for attempts at automation. Methodological Terrorism Anyone with expertise in a field knows bad science when they see it. With a diverse academic background I'm able to recognize a lot of bad science and have recently used my platforms to do something about it. When this story made it to Retraction Watch due to plagiarism concerns I took a look at the paper and despite knowing nothing about bitcoins saw that the authors had no idea what they were doing. I wrote two scathing blog posts about its quality, and nearly a year later the journal found a competent reviewer and the paper was retracted. When I came across the pizza publications by Brian Wansink alarm bells immediately went off and I wrote a preprint detailing the errors. Unsatisfied with the lab's response, I then went on to call Brian Wansink the "Donald Trump of food research" in a blog post. My post was highly criticized, but the preprint has been downloaded over 5,000 times, received a large amount of media attention, and my colleagues and I have found problems with 45 papers from Wansink's lab and thus far one paper has been retracted.