SeongEun Cho is a Doctoral student at Brigham Young University studying applied mathematics. Under the mentorship of esteemed advisor Tyler Jarvis, his research delves into cutting-edge domains such as Deep Learning, backpropagation, and Language Models.

 

Seong’s Master's thesis on "Language Modeling Using Image Representations of Natural Language" showcased his interdisciplinary approach to problem-solving. As an Instructor at Brigham Young University, he has contributed to teaching courses such as Calculus and Linear Algebra, and he is focusing on his deep learning research in various areas such as language, music, and tensor calculus.

 

His role as a Machine Learning Engineer Intern at Pulse Labs and a Machine Learning Research Intern at Cobalt Speech honed his skills in Natural Language Processing (NLP) and machine learning techniques, enabling him to enhance user experiences and develop innovative solutions for real-world challenges.

 

In his research endeavors, he has contributed to several projects addressing critical issues in the field, including the development of novel autoencoder architectures for language image encoding and the creation of deepfake detection models using advanced neural network methodologies. His research to advance knowledge and push the boundaries of technological innovation underscores his commitment to academic excellence and societal impact.

 

As he continues his academic and professional journey, Seong tries to remain poised to make significant contributions to mathematics and machine learning, shaping the future of AI-driven technologies and computational sciences.