Alexander Kim

• Led a team of junior developers, providing mentorship, training, and oversight to foster their professional growth and ensure project standards.
• Collaborated with external companies to integrate their technologies into our platform, ensuring seamless interoperability and expanding our feature set.
• Engineered an educational web platform to enhance artificial intelligence learning, incorporating a Docker-based online development environment for accessible and interactive user experiences.
• Implemented the frontend using Next.js with TypeScript and styled with Tailwind CSS, ensuring a modern and responsive design.
• Developed the backend services using Next.js API and FastAPI, and established GraphQL as the communication protocol for seamless data exchange.
• Managed the database architecture with MongoDB for document storage and InfluxDB for time-series data handling.
• Deployed the entire platform on Docker, optimizing for scalability and ease of updates.
• Configured Nginx for efficient request routing and load balancing.
• Set up a comprehensive monitoring suite with Grafana, Prometheus, and Node-exporter to track system performance and user metrics.

Technical Proficiencies:

• Frontend: Next.js, GraphQL, TypeScript, Tailwind CSS
• Backend: Next.js API, FastAPI
• Database: MongoDB, InfluxDB
• Deployment: Docker
• Network: Nginx
• Monitoring: Grafana, Prometheus, Node-exporter

• Developed a Next.js web application for medical image segmentation, labeling, and calculation. Utilized a U-net model for image processing, deployed on a GPU server.
• Managed medical images in DICOM format using Orthanc server integrated into an image viewer behind a Nginx server for CORS requests.
• The frontend was deployed in Docker containers on AWS servers.
• Developed a FastAPI-based web server for AI model inferencing, enhancing the system’s efficiency and scalability.
• Implemented a load scheduler using Celery to manage task queues and a monitoring system with Flower to oversee task execution and resource usage.

Technical Proficiencies:

• Frontend: NextJS, Redux, Saga
• Backend: ExpressJS, FastAPI, Socket.io
• Task Queue: Celery
• Monitoring: Flower
• Database: MySQL
• Deployment: Docker, AWS
• Network: Nginx
• Image server: Orthanc
• Image processing: U-net
• Demo Video: https://youtu.be/dxHxjgpG3-0

• Developed a sophisticated React web application for detecting rust on reused scaffolding, leveraging GPU servers and a Mask R-CNN-based semantic segmentation model.
• Implemented REST API for efficient data requests and utilized Socket.io for real-time progress updates from the GPU server.
• Enhanced user interface with Bootstrap and custom CSS, and developed features for file upload/download, as well as 2D and 3D visualization capabilities.
• Fortified application security by integrating a JWT token-based authentication system.
• Managed React state using the Mobx library and employed Mask R-CNN with Dice Loss for precise segmentation and rust area calculation.
• Authored a scientific paper on the application’s technology, contributing to the field’s literature.

Technical Proficiencies:

• Frontend: ReactJS, Mobix, Bootstrap
• Backend: ExpressJS, Socket.io
• Database: PostgreSQL
• Image Processing: Mask R-CNN
• Demo Video: https://youtu.be/j6_R4cZ_sm4
• Code: https://github.com/aksovius/scaffolding.git
• Paper: https://doi.org/10.3390/app121910097

• Spearheaded the development of a concrete crack detection application utilizing the U-net semantic segmentation model, enhancing the ability to identify structural issues in concrete efficiently.
• Designed and implemented a unique and innovative web system that automated the inspection of construction scaffolding as part of a scientific research project under the Ministry of Land, Infrastructure, and Transport of Korea.
• Contributed to the development of an intelligent artificial intelligence system equipped with defect detection functionality through the application of semantic segmentation technology.
• Authored and successfully published a paper in an international SCI journal, based on the research achievements, contributing to the body of knowledge in the field of construction management and artificial intelligence.

Technical Proficiencies:

• Frontend Development: Streamlit
• Image Processing: Specialized in U-net architecture
• Machine Learning Framework: Proficient in TensorFlow
• Code: https://github.com/aksovius/fms.gitn

• Expertly assembled and calibrated X-ray and Bone Mineral Density (BMD) systems, ensuring technical precision and operational excellence.
• Performed delicate soldering of electrical microcircuits for X-ray equipment, demonstrating meticulous attention to detail and commitment to high-quality workmanship.
• Carried out comprehensive testing on medical imaging devices to validate functionality and adherence to industry standards.
• Managed calibration of Toshiba X-ray tubes and generators, as well as the installation of Toshiba and Bontech digital detectors, emphasizing accuracy and reliability.
• Facilitated effective communication between Korean and Russian technical teams during the installation, calibration, and maintenance of medical equipment.
• Directed the import/export documentation for medical devices, maintaining strict compliance with international trade laws and regulations.