neuromatch: Algorithms to match scientists

To tackle the matching problem, we set it up as an optimization problem aimed at minimizing a global topical distance between matched attendees. We add constraints where each attendee has to meet exactly N other attendees. Here, we also ask the preference of the meeting, ranging from casual to collaborative (range of number 0 to 1). We slightly modify the distance matrix so that people with similar preference are more likely to be matched.

Algorithm details. Natural language processing (NLP) has a rich history of describing the content of scientific publications in topic spaces where distance approximates the difference in meaning (Kong et al., 2016; Lopes et al., 2010). For neuromatch we use a very simple standard approach, latent semantic analysis (LSA) along with linear programming (Achakulvisut et al., 2016; Taylor, 2008). We apply LSA on the document-terms matrix based on submitted abstracts of the matches. Then, we create a cosine distance matrix between pairs of attendees. A linear programming problem is formed to find an assignment matrix b that minimizes the total distance between pairs of attendees under given constraints such as the number of meetings (Achakulvisut et al., 2018). There is little doubt that matching could be improved by moving to more advanced methods. At the same time, the current algorithm works well and having some noise in the algorithm leads to more diverse meetings which participants may enjoy more.

These neuromatch conversations were some of the highest quality I’ve had out of any conference, in-person or otherwise. I began to see how good collaborations emerge naturally from great, energetic conversations, if you can find them. Thanks to neuromatch for making that happen.

– Feedback from neuromatch attendee

In legacy conferences, we typically have a job board for attendees to look at, which neuromatch also makes available in an online format. We have a public job board for the conference similar to how legacy conferences work. Furthermore, we are now experimenting with job matching. The algorithm is a variant of mind-matching algorithm, where we instead solve an optimal matching problem between job seekers and job posters. Using the text and keywords of a pool of position announcements, and also abstracts from papers published by a pool of job candidates, it is possible to define a shared topic space and match individuals to the jobs they are most appropriate for.

The way students find supervisors, and in general the way people move across labs, is today largely dominated by networks of personal acquaintances. Providing good academic matchmaking possibilities promises to reduce friction in the academic job market. For example, it should be easy for a student to shortlist potentially appropriate labs, and for labs to know who may be a good fit for a new position. There is a good opportunity to use neuromatch and similar technologies to make these processes more efficient and less biased.

Generally, legacy conferences provide a number of social hours where attendees can mingle and chat about specific topics. In this online format, we replace the coffee break or research over beer by running a clustering technique using hierarchical clustering to group people together. We apply hierarchical clustering where we minimize average distance between clusters while preserving the minimum distance by order. Here, we can group the attendees in a group of N people and then ask them to do a group discussion within a timeslot during the conference.

In the future, to improve the matching experience, the outcomes of mind-matching can be studied to understand the factors that make it enjoyable and useful in an online meeting format. This includes adjusting meeting lengths, acquiring additional information from attendees, e.g. from their Mendeley profile, and improving the algorithm itself (Bhagavatula et al., 2018). Furthermore, we can include location or time zone as an input for our optimisation problem if we want to constraints locations of the match by time zone.

1. Visit our GitHub repository to find out more

We believe that machine learning can be used to improve social components when we move online. Online conferences can facilitate meetings and collaborations between attendees. We find algorithms such as mind-matching can lower the barrier of how people can meet during the conference. We show here that machine learning can help us establish social components during the conference.