Foundergiant: Hello! Tell us a bit about yourself.

Jeffrey Ma: Hi, my name is Jeffrey Ma. I am the founder, CEO, and CTO of dcyphr. I am currently a rising junior at Yale University studying computer science and mathematics. I have previously conducted research in molecular biology, computational neuroscience, and clinical diagnostics.

Jeffrey Ma

FG: Introduce your company and the creators of dcyphr.

JM: dcyphr is a crowd-sourced platform informed by AI that makes academic research more accessible. We empower users to request, create, and engage with distillations of research articles. Our distillation network extends globally and consists of students and researchers from various university campuses.

FG: Tell us about your product.

JM: There are two core problems that contribute to the inaccessibility of research:

The consequences of the inaccessibility of the status quo is 3-pronged: dcyphr establishes a portal between the public and research by creating a platform and community for sharing distillations of academic research. The process is: We believe that a research paper is not a static entity, so once it is published, it can be freely browsed, shared and engaged with by our users. Everything is moderated by a team of experienced researchers.

FG: How did you come up with the product idea? What inspired you?

JM: I took some biology classes at the beginning of sophomore year that required us to read scientific articles. Even though I had done biological research for 2 years prior, I found this to be an extremely frustrating and tedious activity since the articles failed to communicate the story and concepts of the experiment in a jargon-free manner. None of the students understood the articles, and we ended up spending our discussion sections distilling and summarizing the article. Wouldn’t it be great if someone distilled the article, and we could actually understand it ourselves so that in section, we could instead spend time discussing real experimental concepts and limitations? I did some preliminary research and found that this is a pretty big gap in the status quo that is not being filled (i.e. the problem has been widely studied and reported on and is not just limited to students but extends to the general public, researchers, etc.). Motivated by that, I built the first prototype of the web application, seeded it with a few distillations for the articles assigned in biology classes, and distributed it to those students.

FG: How is your product different from your competitors? Why should clients choose your platform?

JM: We don’t have many competitors. Our few competitors (PaperDigest and Scholarcy) do lack a few things:

FG: Was dcyphr bootstrapped or did it receive outside funding?

JM: dcyphr was mainly bootstrapped with a few small non-equity grants.

FG: What is the pricing model of dcyphr?

JM: Right now, we are focusing on developing the product to its full potential. Eventually, we hope to make it a freemium model where the free plan is enough for the casual citizen interested in research, and the premium plan is geared towards institutions to provide a resource for their students and/or researchers.

FG: What advice would you give to budding entrepreneurs?


FG: Any plans for the future?

JM: We plan to build out the full NLP feature by September. We are also launching a joint research fellowship program with Coronavirus Visualization Team for undergraduates taking gap years due to the pandemic. With humans working alongside the NLP model, we hope to reach 10k distillations by January.

FG: Where can people learn more about dcyphr?

Check out our linktree: https://linktr.ee/dcyphr
Visit our website: https://www.dcyphr.org
Join our Slack workspace: https://bit.ly/2Bv473O
Sign up for our newsletter for weekly research distillations: https://bit.ly/3ikRclT