CV - Nam Trinh

Education

PhD in Computer Applications, Dublin City University, 11/2021 - present (expected graduate in 11/2025). Topic: Machine learning for brain data characterisation under uncertainty

M.Sc. in Computer Applications, Dublin City University, 2021

B.S. in Electronics Engineering, Danang University of Science and Technology, Danang, Vietnam, 2016

Work experience

16/02/2022 - 16/12/2022: Research Data Scientist Intern - Huawei

Duties included: Researched text-to-speech options for a text-to-video application, built image retrieval applications based on CLIP.
Supervisors: Gonzalo Fiz Pontiveros (Principal Research Scientist) and Tri Kurniawan Wijaya (AI Technical Director)

03/2021 - 11/2021: Machine Learning Engineer - Insight SFI Centre for Data Analytics

Duties included: Develop a full-stack machine learning-based pipeline on AWS for mental health diagnosis from speech.
Supervisors: Prof. Tomas Ward and Dr. Gerard Derosiere

06/2016 - 11/2019: Hardware Design Engineer - eSilicon Corporation (now acquired by Synopsis)

Duties included: Built hardware specialised in deep neural network training

Skills

Machine Learning

Topics: Speech recognition, text-to-speech, large language model, EEG, MRI
ML Frameworks: scikit-learn, TensorFlow, PyTorch, HuggingFace
Cloud: AWS, Azure, GCP

Software Engineering

Frontend: ReactJS, Streamlit
Backend: FastAPI
Programming Languages: Python/C++
Database: NoSQL with MongoDB, SQL

Publications

Generative Adversarial Network-Based Semi-supervised Learning for Pathological Speech Classification

Trinh N.H., O’Brien D. (2020) Generative Adversarial Network-Based Semi-supervised Learning for Pathological Speech Classification. In: Espinosa-Anke L., Martín-Vide C., Spasić I. (eds) Statistical Language and Speech Processing. SLSP 2020. Lecture Notes in Computer Science, vol 12379. Springer, Cham. https://doi.org/10.1007/978-3-030-59430-5_14

Task-Related and Resting-State EEG Classification of Adult Patients with ADHD Using Machine Learning

N. Trinh, R. Whelan, T. Ward and G. Derosiere, Task-Related and Resting-State EEG Classification of Adult Patients with ADHD Using Machine Learning, 2023 IEEE 19th International Conference on Body Sensor Networks (BSN), Boston, MA, USA, 2023, pp. 1-4, doi: 10.1109/BSN58485.2023.10331185.

Nam Trinh

CV