Xiaoran Hao is a dedicated Ph.D. candidate at Rutgers University, specializing in machine learning with a focus on developing innovative solutions to enhance existing algorithms. Currently serving as a Graduate Research Assistant, Xiaoran is deeply engaged in a pivotal project involving Coupled Variational Autoencoders (C-VAE)....
Xiaoran Hao is a dedicated Ph.D. candidate at Rutgers University, specializing in machine learning with a focus on developing innovative solutions to enhance existing algorithms. Currently serving as a Graduate Research Assistant, Xiaoran is deeply engaged in a pivotal project involving Coupled Variational Autoencoders (C-VAE). This project not only showcases her technical acumen but also highlights her ability to conduct comprehensive literature reviews that identify critical challenges within current Variational Autoencoder models. By proposing a novel Optimal Transport (OT) based framework, Xiaoran has successfully generalized VAEs, addressing significant limitations that have hindered their performance in various applications.
Her expertise extends beyond theoretical frameworks; she is proficient in programming languages such as Python, R, and SQL, which she employs to implement and document advanced machine learning models, including C-VAE and its variants like WAE and InfoVAE. This hands-on experience is complemented by her strong foundation in statistical analysis, regression analysis, and data visualization, making her a well-rounded candidate for roles that require both analytical rigor and practical application.
Xiaoran's skills in data modeling and quantitative research enable her to extract meaningful insights from complex datasets, positioning her as a valuable asset in any data-driven environment. As she prepares to transition from academia to industry, Xiaoran is eager to leverage her robust understanding of machine learning algorithms and statistical methodologies to create real-world impact, contributing to projects that push the boundaries of technology and innovation.
