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
My scientific/academic career is known for various accomplishments:
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.
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.
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.
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.
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.