Machine Learning Control Engineering

ABOUT

I graduated from the Hanoi University of Science and Technology with a Bachelor's Degree in

Control Engineering and Automation in October 2022. My areas of expertise include Machine Learning, Deep Learning, Computer Vision (with a focus on research), and Natural Language Processing. I have a genuine interest in understanding how AI can impact the real world. EDUCATION

Co Loa High School 2015 – 2018

Hanoi University of Science and Technology

• Major: Control Engineering and Automation

• Class: Talented program in Control Engineering and Automation

• GPA: 3.33

2018 – 2022

EXPERIENCE

Lab of Development, Recognition, Evaluation and Application in Measurement Science and Technology (Sep 2020 - 2022)

Member

• Research on the topic of using physiological signal to identify emotions, detect stress levels by Machine Learning, Deep Learning algorithms.

• Publication: Driving stress detection using physiological data with machine learning, Journal of Military Science and Technology, https://doi.org/10.54939/1859-1043.j.mst.83.2022.22-29 Computer Vision projects

• Implement image segmentation task on DeepFish dataset (graduate thesis).

• Depth Estimation for Colonoscopy Images with Self-supervised Learning from Videos.

• Implement Stable Diffusion from scratch on Flickr-8k dataset. Natural Language Processing projects

• RAG using Llama 2, Gemma 2b, Langchain and ChromaDB (Kaggle project)

• Fine-tune Embedding models (BGE model) for Retrieval Augmented Generation (RAG)

(Kaggle project)

VinBigData (Jul 2022 - Nov 2022)

• Joined the full-time Vingroup's AI Training Program at VinBigData. Trần Quang Đức

• Date of birth: 04/09/2000

• Address: Hà Nội, Việt Nam

• Tel: 096*******

• E-mail: ************@*****.***

VinBrain (Nov 2022 – May 2024)

Applied Scientist

• Medical Image Research: Focused on Computed Tomography (CT) images, driving advancements in image segmentation and classification.

• Image Segmentation: Developed liver tumor and liver vessel segmentation pipelines, achieving performance improvements over baseline models.

• Image Classification: Leveraged liver tumor segmentation results in creating a tumor classification model, effectively categorizing tumor types.

• Self-Supervised Learning: Utilized unlabeled datasets to train unsupervised models, applying the pretrained models to other tasks with success.

• Active Learning: Supported the labeling process to optimize the workflow.

• Technologies: PyTorch, MONAI, OpenCV, nnUnet, DALI, MedNeXt, Barlow Twins. S-Phenikaa (Jun 2024 – Aug 2024)

Algorithm Engineer

• Drone Detection Model: Developed a custom drone detection model optimized for deployment on a custom chip.

• Technologies: Pytorch, OpenCV, Ultralytics, tracking algorithms. CERTIFICATIONS

• IBM AI Engineering:

https://www.coursera.org/account/accomplishments/specialization/certificate/NGXX4XHFXB 79

• Mathematics for Machine Learning:

https://www.coursera.org/account/accomplishments/specialization/certificate/7K1VYQAYECO R

SKILLS

Programming Language - Python

Frameworks & Libraries - Pytorch, OpenCV, MONAI, NLTK, Huggingface English - Intermediate

Research skill

Contact this candidate