In this work, we formulate the problem of team formation amidst conflicts. The goal is to assign individuals to tasks, with given capacities, taking into account individuals' task preferences and the conflicts between them. Using dependent rounding schemes as our main toolbox, we provide efficient approximation algorithms. Our framework is extremely versatile and can model many different real-world scenarios as they arise in educational settings and human-resource management. We test and deploy our algorithms on real-world datasets and we show that our algorithms find assignments that are better than those found by natural baselines. In the educational setting we also show how our assignments are far better than those done manually by human experts. In the human resource management application we show how our assignments increase the diversity of teams. Finally, using a synthetic dataset we demonstrate that our algorithms scale very well in practice.

Alexa Joyce, future skills director at Microsoft, discusses how the education sector can go about unlocking its'black box' of data As the UK education system recovers from the effects of the global pandemic, there has been a significant rise in attention paid to the increased use of technology in teaching and learning. Two billion learners will use digital learning services by 2050, while the education technology (edtech) market is projected to triple by 2025, with global spending reaching $404bn. As investment in edtech continues to grow, students, parents, and teachers face an array of solutions -- from digital personalised learning platforms, devices and accessories, through to multiple online courses. Alongside the opportunities technology provides for increasing accessibility, the wealth of data that edtech offers is unsurmountable. But, how can technology allow policymakers, school leaders and sector experts to unlock this'black box' of education data and use it for improved learning outcomes?