Machine Learning Computer Vision

NGUYỄN HOÀI NAM

AI ENGINEER INTERN

086*******

adz7pp@r.postjobfree.com

github.com/namdamri27102000

Ho Chi Minh

SKILLS

Knowledges:

• Calculus, Probability And Statistic, Linear

Algebra.

• Machine Learning: Regression,

Decision Tree, SVMs, Random Forest,

Neural Network

• Computer Vison.

• Exploratory Data Analysis.

Languages: Python, C/ C++, HTML/ CSS.

Fameworks/ Libraries: OpenCV, Numpy,

Pandas, Tensorflow, Keras, Flask.

Source Control: Git, Github.

Soft skills: researching and deploying papers,

problem-solving, critical thinking, self-

learning

INTERESTS

• Football: Manchester United

• Badminton

• Billiards

• Fishing

• Video Game: FIfa Online 4

OBJECTIVES

I am actively pursuing an internship opportunity to harness my fervor for artificial intelligence and leverage my technical prowess in machine learning and deep learning. With an unwavering commitment to innovation and a relentless pursuit of excellence, my aim is to make a substantial contribution to real-world projects, acquire invaluable industry experience, broaden my knowledge base, hone my expertise, and deliver a significant impact in the realm of AI engineering.

EDUCATION

HO CHI MINH CITY UNIVERSITY OF TECHNOLOGY 2018 - 2023 Bachelor of electronics and communication engineering Degree classification: Good

PROJECTS

Application of Deep Learning in Liver Tumor Segmentation on Abdominal CT Images

• Examined and evaluated some U-net family architectures (Recurrent Residual U-Net, U- Net 3+, U-Net with ResNet 50 backbone) for medical image segmentation.

• Processed data to improve segmentation performance, including HU value clipping method, data augmentation, polar transform, 3D cropping method, and morphological transformations.

• Deployed the best model for web applications using HTML, CSS, and Flask frameworks. Application of Deep Learning to Detect Abnormalities in Chest X-ray Images

• Examined and evaluated popular and publicly available datasets of lung X-ray images: NIH Clinical Center–NIHCC, RSNA, Shenzhen, VinBigdata.

• Preprocessed X-ray images by removing the diaphragm and applying GAN and U-Net networks for lung segmentation.

• Examined and evaluated CNN models used in image anomaly classification for Chest X-rays, including VGG16, DenseNet-121, and VGG19.

EXPERIENCE

INEXT TECHNOLOGY 07/2021 - 09/2021

Completed a compulsory bachelor's degree internship under the guidance of an advisor

• Acquired knowledge of DICOM image standards, encompassing DICOM image format components, service object classes, and PACS systems

• Gained expertise in the structural and operational aspects, including image reconstruction, of computed tomography and MRI machines.

• Familiarized with web platforms facilitating DICOM image viewing, such as OHIF and Oviyam.

CERTIFICATES

• TOEIC Listening and Reading 605 (IIG).

• Machine Learning Specialization (Coursera).

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