Daniel Cho is a highly skilled machine learning engineer with a Master's degree in Computer Science specializing in Machine Learning from Sungkyunkwan University. Cho is an expert in deep learning, machine vision, smart factory, and smart farms. He has worked on image data and product development, with a focus on the manufacturing domain, including semiconductors, displays, and motors. Cho is skilled in web programming and web-based services as a backend and frontend developer with experience in various web technologies like Flask, Python, ORM, Java, Spring framework, Springboot, jsp, thymeleaf, html, javascript, jQuery, AWS, Nginx, Tomcat, JWT, JPA, ORM, hibernate, mybatis, rest api, MSA, docker, Android, MySQL, Oracle, MSSQL, and Redis.

Some of his research includes winner prediction of a curling game supported by a Hybrid Machine Learning model, recognition of anaerobic based on ML using Smart Watch Sensor Data, and Apparatus for predicting the result of the curling game. Cho is proficient in TensorFlow, Keras, and Pytorch. He has experience with Tizen and machine learning on the Samsung smartwatch platform.

Cho's interests include understanding different ML concepts like Reinforcement Learning, Meta Learning, and GAN, skillfully implementing Deep Learning papers with high-level understanding, and writing. In addition, he also wants to develop his skills in recommendation systems (Collaborative Filtering Approach / Content-based Approach), finance automation, prediction, and recognition, especially in the domains of beauty, fashion, and finance.

Cho has worked for various organizations such as ChameleonSoft as a founder and architect, Osstem as a team member, and currently as a machine learning researcher at DIAT. He was a visiting scholar during the summer session at the University of Southern California, where he studied data informatics project management.