Dr. Benyamin Ahmadnia is a tenure-track Assistant Professor of Computer Science at California State University, Dominguez Hills. His academic background is in Computer Science, with a research focus on Natural Language Processing, Large Language Models, Neural Machine Translation, low-resource language technologies, and applications of Artificial Intelligence in multilingual, educational, medical, and domain-specific settings.
He received his Ph.D. in Computer Science from the Autonomous University of Barcelona (2017), where his research focused on Artificial Intelligence and Natural Language Processing. He also completed postdoctoral research appointments at the Autonomous University of Barcelona (Spain), Tulane University (USA), the University of California, Davis (USA), and Harvard Medical School / Brigham and Women’s Hospital (USA). He also served as a Visiting Scholar at Monash University (Australia), where his work focused on Natural Language Processing.
Dr. Ahmadnia has held faculty appointments at several USA-based institutions. In addition to his current tenure-track appointment at California State University, Dominguez Hills, his previous faculty roles have included Visiting Assistant Professor and Adjunct Lecturer at Claremont McKenna College, Adjunct Lecturer at California State University, San Bernardino, Adjunct Lecturer at California State University, Long Beach, Visiting Assistant Professor at Occidental College, Visiting Assistant Professor at Wentworth Institute of Technology, and Adjunct Lecturer at Tulane University.
His research interests include Artificial Intelligence, Large Language Models, Neural Machine Translation, low-resource machine translation, multilingual NLP, medical language technologies, educational applications of AI, and domain adaptation for language technologies. His work has appeared in peer-reviewed journals, book chapters, conference proceedings, and workshops in areas related to NLP, machine translation, AI, and applied machine learning.
Dr. Ahmadnia has taught a broad range of undergraduate and graduate Computer Science courses, including programming, data structures and algorithms, software engineering, databases, digital logic design, compilers, data mining, computer vision, deep learning, natural language processing, machine learning, and applied computing. His teaching emphasizes conceptual clarity, structured problem-solving, hands-on practice, and research-informed learning.
He also mentors graduate students on research and applied computing projects involving machine learning, natural language processing, large language models, software engineering, educational technologies, and AI-driven systems. His student-centered mentoring supports technical development, research writing, presentation skills, and publication-oriented project design.
For a current list of publications and research outputs, please refer to the Publications page and linked academic profiles.