4+ Experience
Nearly 3 years of industry experience and 2 years of graduate student research assistant experience.
~ 30 Projects
Conducted nearly 30 projects across industry, research, and academic settings.
100K+ Lines of Code
Written and maintained 100K+ lines of code for production systems, ML/DL model development, deployment pipelines, rapid prototyping, and algorithm design.
About Me
I’m an Applied Data Scientist and AI professional with over 5+ years of experience across different industries and academic environments.
I’m an applied data scientist and AI engineer focused on building ML systems that get used in real settings. My work spans industry, healthcare, and research, from deploying predictive and computer vision models in manufacturing to developing ML and LLM-based pipelines for clinical and public health applications at Johns Hopkins.
I enjoy working where modeling meets real-world constraints, turning complex data into reliable, understandable systems that support better decisions. I’m currently pursuing an MSE in Data Science at Johns Hopkins while continuing applied research and hands-on development.
EXPERIENCE
Nearly 3 years of industry experience at Hankook Tire and 2+ years of research experience across healthcare and and applied AI at CAI Research Lab and Johns Hopkins–affiliated institutions.
Johns Hopkins-Affilated
sep 2025 – present
AI Research Assistant | Johns Hopkins Bloomberg Public Health Medical Center
Developing LLM pipelines to extract and analyze large-scale policy and regulatory documents for public health research.
Apr 2025 – aug 2025
AI-Intern | Johns Hopkins Bayveiw Medical Center
Built scalable ML for complication prediction and LLM systems for clinical message triage, focusing on safe evaluation, clinician validation, and real-world healthcare workflows.
Hankook Tire
feb 2022 – jan 2025
AI-Engineer
Built and deployed production ML and computer vision systems used by R&D engineers to predict tire performance, durability, and risk at scale, replacing manual analysis and accelerating design decisions across hundreds of internal users.
CAI Research Lab, Hanyang Univeristy
Sep 2019 – Aug 2021
Graduate Research Assistant
Led the development of real-time driver recognition systems for ADAS; conducted in-depth research and proposed novel methods in computer vision and deep learning.
Works/Project
Below is a selection of projects I’ve worked on across industry, research, and academic settings, focusing on AI-driven performance prediction, real-time computer vision, and LLM-based applications using machine learning and deep learning tools.
An intelligent real time driver activity recognition system using spatio-temporal feature
In this project a long-term recurrent convolutional network (LRCN) l is developed to detect up to nine various distracted driver activities includ-ing driving, drinking, texting, smoking, talking with rising hands, adjusting the navigation system, looking outside, nodding off to sleep, faint-ing inside a real vehicle cabin during the daytime and nighttime conditions.
Prompt Based Data Analysis and Visualization Chatbot APP
A prompt-based data analysis and visualization chatbot has been developed. The chatbot utilizes a large language model (LLM) to analyze data, create visualizations based on user requests, and display the results accordingly.
Hybrid Deep Learning Frame work for Distracted Driver Detection
In this project, we proposed a computationally efficient frame work that comprises a generative adversarial network (GAN) and convolutional neural network (CNN) framework for real time distracted driver detection during both day and nighttime conditions. The proposed system is composed of two consecutive steps.
Text-to-SQL AI Agent App
In this project, a Text-to-SQL chatbot powered by Google Gemini is developed. The chatbot allows users to input plain text queries and generate corresponding SQL query code, along with an example of the output and an explanation of the generated code.
Vehicle License Plate Detection Using YOLO Frame Work
This project implements a robust vehicle license plate detection system utilizing the YOLO (You Only Look Once) framework. The model is fine-tuned and leveraged to efficiently detect and localize license plates from vehicle images or video streams. The solution demonstrates high precision and speed, making it suitable for practical applications such as traffic monitoring, automated toll collection, and vehicle surveillance systems.
Contactless-Face-Mask-Recognition
In this project, a pretrained convolutional neural netwrok (CNN) is utilized for real time contactless face mask detection. The system effectively identifies faces with mask and non-mask regardless of the color of the mask. CNN is adopted to extract the meaningful features of the image input and perform the detection task.
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