Publications
Current research projects:
- Interpretable Inference of Heterogeneous Treatment Effects (1 methodological paper, 3 applied papers, 1 software paper) at National Studies on Air Pollution and Health Software at Harvard;
- Development and release of Bayesian Causal Forest with Instrumental Variable algorithm package (1 software paper) at National Studies on Air Pollution and Health Software at Harvard;
- Deep Learning for Causal Modeling and interpretation of acoustic subsurface data for anomaly detection and hazard prevention at Schlumberger-Doll Research;
- Upper-body Posture Detection using Deep Learning at Intelligent Global Health Lab at EPFL.
Towards Robust and Adaptive Motion Forecasting: A Causal Representation Perspective (long)
IEEE Conference on Computer Vision and Pattern Recognition, 2022
Learning behavioral patterns from observational data has been a de-facto approach to motion forecasting. Yet, the current paradigm suffers from two shortcomings: brittle under covariate shift and inefficient for knowledge transfer. In this work, we propose to address these challenges from a causal representation perspective. We first introduce a causal formalism of motion forecasting, which casts the problem as a dynamic process with three groups of latent variables, namely invariant mechanisms, style confounders, and spurious features. We then introduce a learning framework that treats each group separately: (i) unlike the common practice of merging datasets collected from different locations, we exploit their subtle distinctions by means of an invariance loss encouraging the model to suppress spurious correlations; (ii) we devise a modular architecture that factorizes the representations of invariant mechanisms and style confounders to approximate a causal graph; (iii) we introduce a style consistency loss that not only enforces the structure of style representations but also serves as a self-supervisory signal for test-time refinement on the fly. Experiment results on synthetic and real datasets show that our three proposed components significantly improve the robustness and reusability of the learned motion representations, outperforming prior state-of-the-art motion forecasting models for out-of-distribution generalization and low-shot transfer.
2022
Towards Robust and Adaptive Motion Forecasting: A Causal Representation Perspective (short)
Workshop in Distribution Shifts at NeurIPS, 2021
Learning behavioral patterns from observational data has been a de-facto approach to motion forecasting. Yet, the current paradigm suffers from two fundamental shortcomings: brittle under covariate shift and inefficient for knowledge transfer. In this work, we propose to address these challenges from a causal representation perspective. We first introduce a causal formalism of motion forecasting, which casts the problem as a dynamic process with physical mechanisms, style confounders, and spurious correlations. We then propose two components that explicitly promote the robustness and reusability of the learned motion representations: (i) unlike the common practice of merging datasets collected from different locations, we exploit their subtle distinctions by means of an invariance loss function, which encourages the model to suppress spurious correlations and capture physical mechanisms; (ii) we devise a modular architecture that factorizes the representations of physical laws and motion styles in a structured way, and progressively prune their dense connections during training to approximate a sparse causal graph. We empirically validate the strength of the proposed method for robust generalization in controlled real-world experiments. We finally discuss the challenges and opportunities in the presence of style shifts through synthetic simulations.Quantification of the available area for rooftop photovoltaic installation from overhead imagery using convolutional neural networks
Journal of Physics: Conference Series, 2021
The integration of solar technology in the built environment is realized mainly through rooftop-installed panels. In this paper, we combine state-of-the-art Machine Learning and computer vision techniques together with high-resolution overhead images to provide a geo-localization of the available rooftop surfaces for solar panel installation. We further associate them to the corresponding buildings by means of a geospatial post-processing approach. The stand-alone Convolutional Neural Network used to segment suitable rooftop areas reaches an intersection over union of 64% and an accuracy of 93%, while a post-processing step using building database improves the rejection of false positives. The model is applied to a case study area in the canton of Geneva and the results are compared with another recent method used in the literature to derive the available area.