Data Scientist/AI Engineer/Machine Learning Engineer

Dang Quy Anh

Address: **/** ** ***, ** Liem, Hanoi

E-mail: adyvqf@r.postjobfree.com Telephone number: 077******* Github: https://github.com/QuyAnh2005 Date of birth: 20-05-2001 Education

Applied Mathematics and Computer Science VNU University of Science Bachelor’s degree program September 2019 - June 2023 Teaching Assistant for the Data Structures and Algorithms subject CPA: 3.71/4.0

Thesis title: Some techniques for embedding information in images and applications Work experience

VNU University of Science September 2019 - Present Researcher Hanoi, Vietnam

• Implemented some heuristic algorithms for Traveling Salesman Problem (TSP).

• Built and developed an algorithm to solve the Examination Timetabling problem at VNU Univer- sity of Science.

HUS Applied Mathematics and Informatics Club September 2022 - May 2023 Head of Data Science Department Hanoi, Vietnam

• Organized weekly seminars on topics related to data science field.

• Mentored and trained team members to develop their expertise. Aimesoft November 2022 - June 2023

Natural Language Processing Engineer Hanoi, Vietnam

• Developed TTS system from VITS architecture.

• Joined in building AimeConversion for the company. Advance and Nurture Laboratory (A.N LAB) July 2022 - September 2022 Computer Vision Engineer Internship Hanoi, Vietnam

• Learned and applied some basic CNN models.

• Built a model to detect and classify area code of the Japanese license plate number. Projects

Homemade Machine Learning

Machine Learning

• Implemented and explained the mathematical theory behind Machine Learning algorithms such as: Linear Regression, Logistic Regression, k-means, PCA, SVM, Decision Tree, Naive Bayes, Random Forest, Gradient Boosting.

• Link: https://github.com/QuyAnh2005/homemade-machine-learning Math for Machine Learning

Theory

• Notes on mathematical topics that pertain to machine learning including: linear algebra, calculus, optimization, probability and statistics.

• Link: https://github.com/QuyAnh2005/math-for-machine-learning Grapheme to Phoneme

Natural Language Processing

• An implementation of a G2P conversion model using sequence-to-sequence neural networks. Given a sequence of graphemes, the model predicts the corresponding sequence of phonemes. The model architecture consists of an encoder-decoder with attention mechanism.

• Link: https://github.com/QuyAnh2005/Grapheme-To-Phoneme Text to Speech for Japanese

Natural Language Processing

• Several recent end-to-end text-to-speech (TTS) models enabling single-stage training and parallel sampling have been proposed, but their sample quality does not match that of two-stage TTS systems. In the work, I will introduce a VITS model for Japanese on pytorch version 2.0.0 that customed from VITS model.

• Link: https://github.com/QuyAnh2005/vits-japanese Technical skills

Basic Strong mathematical and statistical skills

Programming Languages Python, R, Java, Scala, Matlab Libraries/Frameworks numpy, pandas, matplotlib, plotly, scikit-learn, tensorflow, keras, pytorch, pyspark, ...

Database Knowledge of database administration systems such as SQL, Mon- goDB to query and store data

Fields Machine Learning, Natural Language Processing, Computer Vi- sion, Big Data

Language proficiencies

English Capable of understanding and writing English documents proficiently Achievements

• Outstanding Academic Achievement Award 2020-2021.

• Outstanding Academic Achievement Award 2021-2022.

• Ranked among the top 6 finalists in the International Data Analysis Olympiad 2022 hosted by Higher School of Economics University and Yandex.

• First prize at the university-level conference for student scientific research 2023. Certificates

• Data Science Track, Developer Circles from Facebook

• Data Science Career Path on Codecademy

• Machine Learning Specialization on Coursera

• Deep Learning Specialization on Coursera

• Generative Adversarial Networks (GANs) Specialization on Coursera

• Natural Language Processing Specialization on Coursera

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