Yingchen Ma

Harbin Institute of Petroleum, Carnegie Mellon University

Papers

Total Citations

H-Index

About

Yingchen Ma is a researcher whose work bridges the practical demands of industrial automation with the theoretical frontiers of functional data analysis. In the realm of applied machine vision, Ma’s most cited paper, "The research of material sorting system based on Machine Vision" (2020, 11 citations), addresses a critical challenge in modern manufacturing: the need for precise robotic grasping in sorting systems. This work directly responds to the pressures of rising labor costs and expanding production scales, offering a vision-based solution to enhance accuracy and efficiency in automated sorting—a contribution that resonates with both industry practitioners and robotics researchers. On the theoretical side, Ma’s 2021 paper, "Functional Optimal Transport: Mapping Estimation and Domain Adaptation for Functional Data" (2 citations), introduces a novel formulation of optimal transport for infinite-dimensional function spaces. By representing stochastic maps between functional domains through Hilbert-Schmidt operators, this work opens new pathways for domain adaptation in complex, high-dimensional data settings. Though early in its citation impact, the paper lays foundational groundwork for handling functional data—a growing area in statistics and machine learning. Together, these contributions showcase a researcher adept at solving tangible engineering problems while pushing the boundaries of mathematical methodology, making Ma a versatile voice in both applied and theoretical research communities.