- GPA: 3.91/4
- I’ve taken courses in AI, data analytics, machine learning, deep learning, computer vision, scientific visualization, bioinformatics, numerical linear algebra, and probability and statistics in R.
I use databases regularly with ORMs or SQL. I've used MS SQL, SQLite, postgreSQL, Oracle and mySQL.
I've experience with Linux/Ubuntu, Windows and Mac OS. I've also deployed applications with digitalocean and googleplay.
I'm experienced in web, mobile, desktop and game developments. I've also made CLI applications, REST APIs and libraries.
Research, Teaching, Leadership, Time management, Responsibility, Problem Solving, Working Under Pressure, Flexibility
AI & machine/deep learning, computer vision, computer graphics, visualization, data analysis, bioinformatics, linear algebra, probability & statistics
I made a moving cube with shading and lighting. For scalar field visualization: slice visualization, marching squares and marching cubes, volume rendering using raycasting and iso ray casting. As for vector field visualization: glyphs, streamlines, pathline visualization, and line integral convolution.
The project aimed at making a model that combines classical computer vision techniques with deep learning methods. This was achieved by using CNN for the deep learning part and interest point descriptors with ORB, which was then turned into BoVW via K-means and trained with MLP model. The CNN model and the MLP models were then trained jointly.
The project aimed at retrieving similar kanji by training a siamese network with triplet loss. Then comparing retrieval results of the siamese model with an autoencoder results.
I’ve made 13 projects. In each, many different algorithms must be applied. such as HMM, suffix arrays, clustering and more. The aim is to answer biological questions. Such as motif finding, antibiotic sequencing, genomes assembly, comparing biological sequences, evolutionary trees and so on.