Enthusiastic Student Researcher experienced in conducting and supporting research into Neuroinformatics with deep neural networks. Presently a Medical student finalizing an M.D. degree. Eager to support discovery and promote artificial intelligence(AI).
I am very interested in how consciousness arises from neural activity. Based on this philosophical problem, I want to deepen my insights through the development of products related to AI.
I am also working as a part-time product manager, engineer, and data scientist for an educational company, and now running several projects developing educational apps, including one which I proposed to the senior manager.
My forte is to think big and try to realize my idea. I am ambitious to break our common sense through massive innovation in technology.
I am working for an education company as a student product manager. I suggested the senior manager to create a gamified task management App for students and currently running the project.
I am in charge of the production of CBT tests for the recruitment of student part-time software engineers. The test consists of Github, which stores problems and submitted files and automatically conduct brief checks of the files by Github Actions, and Lambda, which automatically conducts scoring the submitted files and post the results to DB and slack. Also, I am working as the product manager of study App with gamification for junior high school students, which is currently under designing of the architecture.
I am running a team of 1000 part-timers which is responsible for scoring dozens of thousands of high school students. I created the I substantially improved the leader team from scratch and arranged the team to automatically operates. Also, I substantially improved the quality of the scoring by implementing assessment system of the scorers and assigning the tasks to scorers who were highly rated.
Contributed to OSS for microscopy cell tracking GNN.
・Implemented preprocessing of microscopy videos into a graph.
・Refactored inference module of previously released GNN pipeline to be easily used by end-users.
・Currently working to develop a new GNN structure to capture cell features.
・The work done during the GSoC period is available at: https://github.com/watarungurunnn/GSoC2022_submission
Python