Our research interests cover a wide range of topics in ML inspired by our dataset and business needs. Our most popular dataset is composed of over 600`000 construction project schedules. These are DAGs, 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.Since nPlan’s products are based on forecasting on project plans, our research efforts have always been focused on probabilistic modelling and uncertainty estimation.

In the past we have also built in-house language models, however, with the rise of large pre-trained models we have shifted our focus on graph neural networks on temporal DAGs.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 recommendations.

On Forecasting Project Activity Durations with Neural NetworksP Zachares, V Hovhannisyan, C Ledezma, J Gante, A Mosca International 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 Brilakis Advanced Engineering Informatics 52, 101625
‍Determining Construction Method Patterns to Automate and Optimise Scheduling–A Graph-based ApproachY Hong, V Hovhannisyan, H Xie, I Brilakis 2021 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 Mosca Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pages 3499–3500, 2020.