nPlan's Experimental Research Department

Research is the core of nPlan

Research has always been one of the foundations of nPlan since we began in 2017.

We are 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 also publish research contributing to broader scientific audiences in AI/Machine Learning, Project Controls, and Construction Management.


nERD’s mission is to advance the state of the art in AI for project controls, enabling robust and predictable projects. We rapidly transfer and deploy innovative technologies into nPlan products ensuring that nPlan products have a future. Your research here will be applied quickly to real world problems with huge impact.

Technical Brief

Our research interests cover a wide range of topics in Machine Learning inspired by our datasets and business needs. For example, our most well-known dataset is composed of over 600`000 construction project schedules, with over 2 billion individual activities. These are DAGs - Directed Acyclic Graphs, where each node represents an activity and edges are constraints between activities. Each node has numerical and textual features, while edges have types, weights and directions. In addition, our graphs have a temporal component representing how each project changed over time.

Doing research at nERD means going deep on Large Language Models (LLMs), Graph Neural Networks (GNNs), Forecasting Science, Stochastic Machine Learning, Uncertainty Modelling, and lots more. We are deeply techincal, and extremely rigorous. We solve difficult problems at large scale and make sure that what we say is backed by heavy evidence and strong science.

Another area of active research in nERD is studying how humans interact with, trust and take action using forecasts given by AI. This includes explainable forecasting and creating recommendations for risk mitigation, and generative AI that suggests hundreds of alternative execution and delivery options for our clients to choose from.


On Forecasting Project Activity Durations with Neural NetworksP Zachares, V Hovhannisyan, C Ledezma, J Gante, A MoscaInternational Conference on Engineering Applications of Neural Networks, 103-114
A graph-based approach for unpacking construction sequence analysis to evaluate schedulesY Hong, H Xie, V Hovhannisyan, I BrilakisAdvanced Engineering Informatics 52, 101625
Determining Construction Method Patterns to Automate and Optimise Scheduling–A Graph-based ApproachY Hong, V Hovhannisyan, H Xie, I Brilakis2021 European Conference on Computing in Construction
How to Calibrate your Neural Network Classifier: Getting True Probabilities from a Classification ModelN Culakova, D Murphy, J Gante, C Ledezma, V Hovhannisyan, A MoscaProceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 3499–3500, 2020.