This project aims to translate streams of data from individual sensors into a shared manifold-space for joint understanding and processing. This work includes an investigation of computational topology and contrastive learning for manifold learning.
The goal of this project is to advance the point-cloud post-processing using deep learning method to understand the global and local manifolds of a 3D object.
The objective of this project is to provide an accurate electricity day ahead price forecasting system in presence of congestions; using data comprising of power generation from various energy plants, weather conditions, and past nodal prices; by adoption of modern deep learning techniques.
The goal of this project is to advance the point-cloud post-processing using deep learning method to understand the global and local manifolds of a 3D object.
The objective of this project is to provide an accurate electricity day ahead price forecasting system in presence of congestions; using data comprising of power generation from various energy plants, weather conditions, and past nodal prices; by adoption of modern deep learning techniques.
The goal of this work is to translate streams of data from individual sensors into a shared manifold-space for joint understanding and processing. This work includes investigation of computational topology for manifold learning, data summarization, and intrinsic dimensionality estimation. In practice, for a given application, processing chains are generally developed for a particular sensor or set of sensors.
We propose to tackle denoising problem using deep learning into two folds: outlier removal and denoising. We used two stage deep learning pipeline where first stage acts as a binary classifier that classifies 3d points either as outlier or non-outlier. The second stage receives non-outlier and noisy points from the first stage and learns the underlying manifold to produce residual noise from the reference(true) surface.
The objectives of this research are: (1) Develop a unified and accurate deep learning based regression model for predicting energy usage of different building types. (2) Develop a zero shot learning approach (using an ontology of building types) to accurately predict energy usage of building types whose data are not available during training. (3) Evaluate the deep learning model against traditional machine learning models. If successful, the proposed research can accurately model the energy usage of a variety of building types for architects and engineers (at design time).
The goal of this work is to translate streams of data from individual sensors into a shared manifold-space for joint understanding and processing. This work includes investigation of computational topology for manifold learning, data summarization, and intrinsic dimensionality estimation. In practice, for a given application, processing chains are generally developed for a particular sensor or set of sensors.
Enable efficient 3D sensing and information sharing for auto-driving and smart city with 3D point cloud denoising and end-to-end compression using deep learning architectures. We would explore deep learning architecture for point cloud processing. We formulate a multi-scale CNN based 3d point cloud feature extraction technique.