Academic researchers have their work cut out for them.
Not only do they have to complete their own experiments, studies, surveys, courses, assessments and all of the administrative work that goes along with their roles — they also have to stay up to date with the volume of research being conducted and published across the globe.
And academic software is notoriously clunky, plus nearly every research database works differently. The time it takes to simply arrive at the journals and articles that a researcher needs can be a lot to handle.
Academia.edu is an edtech company using AI and machine learning to try and free up some of that time. Staff Software Engineer Greg Atkin shared that the engineers at Academia.edu are building machine learning models that can look through millions of documents to pinpoint the ones that a researcher really needs.
“[It can] identify papers and data that support or contradict a hypothesis, greatly speeding up their research process,” Atkin noted.
A press release from 2022 on the company’s website also compares the platform to the next big tool that can share researcher's work widely. “What Spotify means for podcasters, SoundCloud for musicians, Etsy for artists and YouTube for content creators, Academia.edu is inspiring the world's academics to have their work read and cited.”
At the time of the press release, the platform was adding 30,000-60,000 papers each day and had 28 million monthly visitors, representing over 16,000 universities around the world.
Built In spoke with Atkin about what it’s like to work with such a massive amount of data and how the company supports him and his team in continuous learning.
Academia.edu is a platform for researchers and scholars to share their work, discover research and connect with fellow academics globally.
How is your team integrating AI and ML into the product development process, and what specific improvements have you seen as a result?
Many engineers use AI-powered coding assistants to speed up our development time. We have also used AI tools to quickly analyze and label data allowing us to perform analyses that uncover key business insights or fine-tune and deploy ML models much more rapidly than we could before.
What strategies are you employing to ensure that your systems and processes keep up with the rapid advancements in AI and ML?
At Academia.edu, we are always constantly building and learning. Engineers are encouraged to take time to explore, learn and keep up to date on the latest developments in AI/ML.
We also have a very strong demo culture at Academia.edu where we are encouraged to take time to build small-scale demos of promising new technologies we find to validate the promise of those technologies. Many of our most exciting products and features started as small-scale demos an engineer built after learning about how a new technology could be applied at Academia.edu.
Professional Development Perks at Academia.edu
- Continuing education stipend
- Job training and conferences
- Mentorship program
- Paid industry certifications
- Promote-from-within culture
- Continuing education available during work hours
- Personal development training
- Virtual coaching services
Can you share some examples of how AI/ML has directly contributed to enhancing your product line or accelerating time-to-market?
We have used AI/ML to build advanced search tools that allow researchers to search among millions of academic papers to identify papers and data. We also use AI to identify key researchers to invite to publish in our academic journals. Additionally, we use AI/ML to power reading recommendations that ensure our users can stay up to date on the latest research.
