Research is in the foundations of nPlan. It is currently done in nERD - nPlan’s Experimental Research Department. We operate as an independent multidisciplinary team, with our long term goals aligned with nPlan’s business objectives. We contribute to shared progress through publication in academic venues.
The first results nPlan’s users see about their projects is the forecasted project end date distribution. We have solved this problem by posing it as an activity duration forecasting task combined with large-scale simulations. The next and more interesting step is recommending mitigating actions. Here we are exploring a range of potential solutions including treatment effect analyses, stochastic schedule optimisation, generation of alternatives, and reinforcement learning. Our first solution, called Intervention Recommender, is currently one of the most powerful tools in nPlan’s project risk mitigation product.
During our first few years, we created language models that understand the specifics of construction management. We then created ways to represent any activity from a graph within an embedded space, making use of the textual description, numeric features, and graph structure. This enabled us to research our first forecasting models which are now used in production every day.
In recent years Graph Neural Networks have grown from a niche topic in ML to a prominent and growing research area with a wide range of applications. Our largest dataset is a collection of DAGs where nodes represent individual tasks in the project and edges represent dependency constraints. Although this dataset is unique to nPlan and very different from academic and other industrial applications, we have built highly accurate node and edge forecasting models for nPlan’s product. Our current research interests are around expressive autoregressive models for temporal flow networks.
An important area of nPlan’s research is studying how humans interact with AI-driven forecasts.
In particular, we aim to give comprehensive answers to the following questions:
1. What makes individuals and teams trust or challenge AI driven forecasts?
2. Who are the decision makers in the team and when do they act upon or dismiss AI driven recommendations?
3. What is their utility function? How does the presentation of forecasts and risks affect decision makers’ trust in AI?
Our Machine Learning Paper Club began in 2018 and has since grown to a community of over 1,500 members. We host a free weekly (remote) meetup on Thursday lunchtimes, where we discuss and keep up to date with the latest advances in Machine Learning. We present papers that are interesting to us, and we'll give you opportunities to present too.
Please click the Meetup link below to join our community!
