Education, Administrative Tech, AI & Machine Learning

Avela envisions a world where opportunities are more equitably and efficiently allocated, starting with education



  • Year founded: 2019
  • Fully remote team, with HQ in San Francisco 
  • 11-50 FTEs
  • Key clients/partners: Teach for America (TFA), the Inter-American Development Bank (IDB), Tulsa Public Schools (TPS), Oakland Enrolls, My Schools Newark,  New Orleans Public Schools (NOLA-PS, Jersey City Board of Education (JCBOE)
  • Key executives: Greg Bybee, co-founder and CEO, social entrepreneur and co-founder of a communications platform for policymakers; Joshua A. Angrist, co-founder and board member, MIT economics professor, 2021 Nobel Laureate; Parag Pathak, co-founder and board member, MIT economics professor and Clark Medalist


Avela is using technology to reduce bias, inequality, and inefficiencies in assessment processes. Their team, including a Nobel Laureate in economics and a Clark Medalist, have developed a set of matchmaking algorithms. Based on a given set of inputs, these algorithms determine the fairest distribution of outcomes. Avela’s platforms have already had real-world impact, including determining the most efficient allocation of ventilators during the pandemic and matching kidney donors to patients in most urgent need.

Allocating students to schools is notoriously biased by factors such as a student’s socioeconomic status or the training resources available to them. Avela’s Explore platform works to combat this bias by enabling parents to compare schools and express their preferences. Research suggests that students who attend a school of their choice have better nonacademic outcomes (e.g., fewer disciplinary actions) and in some cases better academic outcomes as well.

Research conducted by the MIT economists found that school districts saw a 90% reduction in the number of dissatisfied families when using Avela’s platform, with 50% more students placed in their preferred school. Avela underscores the tangible effects this has on learning outcomes: MIT research suggests that students who enrolled in their first choice school improved maths achievement by 0.4 standard deviations.

Avela’s Match platform is also being used by the US Army to match soldiers to the brigades where their skills are most needed.

plans for 2022

Launch Avela Enroll, a platform for school enrollment.

who should connect with this company

Organisations such as school districts and public sector authorities that need to manage a large number of employees, participants, or other stakeholders.

case study

Teach for America, a nonprofit, recruits recent college graduates to teach in low-income schools for at least two years. The organisation used Avela Match to assign teachers to partner schools. 

Dr. Jonathan Davis, an economist at the University of Oregon, studied the programme and found that by improving the teacher match, teacher satisfaction increased 33% while teacher retention increased 16%. Davis calculates that this impact is “comparable in magnitude to the change from raising new teachers’ salaries by $8,000 per year.” These positive effects, Davis argues, come from the increased influence teachers get over their assigned match by using Avela’s platform.

Listen to co-founder and MIT economist Parag Pathak explain his work on ventilator allocation during the Covid-19 pandemic on the Freakonomics podcast

stateup view

Insights from Dr. Paolo Turrini, StateUp Expert and Associate Professor in Computer Science at the University of Warwick

The use of algorithms for conflict resolution is well established, and there are many success stories. These procedures can construct allocations to optimise pre-defined notions of fairness and are backed by Nobel prize winning research, such as the famous Gale-Shapley "stable marriage" algorithm. 

Avela shows great promise. Its platform is currently applied to small-scale contexts, and scaling up to more complex scenarios involving uncertainty and incomplete knowledge will be the next hurdle. 

It can also be challenging to adapt the definitions of optimality and fairness to the context studieddetermining what people really want or need is not always easy. AI researchers studying reinforcement learning could help to make Avela’s algorithms effective in uncertain conditions, further differentiating Avela’s work from other matching algorithms in the market.


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