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Computer Vision Algorithm Evaluation Engineer at Apple
Experienced Software Engineer in the field of Computer Vision, Machine Learning, Deep Learning, Artificial Intelligence. In combining a rigorous academic experience with professional roles in the public and private sector, I have exemplified my keen ability to program creatively, develop intelligent software, and utilize computer vision, machine learning, and deep learning at an expert level. Colleagues and professors describe me as a progressive, driven, down-to-earth, engineering and creative expert who can be relied on to offer superior solutions that deliver profitable results on time and under budget. In-depth understanding of the entire software development process (design, development, and deployment) from working in teams to develop a project from scratch. R&D on a broad range of computer vision, data modeling and machine learning topics leveraging various funding sources including government contracts, private funding, and external commercial partners. I am always interested in hearing from former colleagues, managers, or just interesting creative folk, so feel free to contact me if you would like to connect through this profile, or by phone at 424-320-1150
Udacity
Computer Vision Nanodegree, Computer Vision
January 1, 2018 – January 1, 2018
Udacity
MACHINE LEARNING ENIGNEER NANODEGREE, MACHINE LEARNING
January 1, 2017 – January 1, 2017
edX
DAT101X: MICROSOFT PROFESSIONAL ORIENTATION , DATA SCIENCE
January 1, 2017 – January 1, 2017
Udacity
DEEP LEARNING NANODEGREE FOUNDATION, Deep Learning
January 1, 2017 – Present
UCLA
M.S. in Electronic Engineering , COMPUTER VISION AND MACHINE LEARNING
January 1, 2015 – January 1, 2017
Kyonggi University
B.S. in Electronic Engineering, SIGNAL & SYSTEM
January 1, 2006 – January 1, 2013
Apple
Computer Vision Algorithm Evaluation Engineer, Apple vision pro
May 1, 2020 – Present
United States
Atollogy, Inc.
Computer vision specialist
August 1, 2017 – May 1, 2020
United States
Bio-and Nano-Photonic Laboratory (Ozcan group)
Computer Vision Engineer / Machine learning Engineer (UCLA Graduate Student Researcher)
September 1, 2015 – March 1, 2017
Los Angeles, CA 90095
Oaks Pacific Blue Resort, NSW, Australia
Housekeeper
February 1, 2010 – October 1, 2010
NSW, Australia
Theater Seorabeol., Anseong-si, Gyeonggi-do, Korea
Actor
November 1, 2009 – January 1, 2010
Anseong-si, Gyeonggi-do, Korea
Green Fitness Gym., Seoul, Korea
Personal Trainer
February 1, 2009 – October 1, 2009
Seoul, Seoul, South Korea
TensorFlow 2.0 CNN and Transfer Learning
April 1, 2019 – July 1, 2019
• Image Classification with Convolution Neural Network: Build and train a neural network to classify images of clothing like sneakers and shirts. Tools are TensorFlow 2.0 and Run in Google Colab • TensorFlow Hub and Transfer Learning: Use a TensorFlow Hub model for Dogs vs. Cat dataset and Do simple transfer learning with TensorFlow Hub
Advanced Computer Vision with Deep Learning
September 1, 2018 – December 1, 2018
• Image Captioning: Training CNN-RNN model to predict cations for a given image with an implementation of an effective RNN decoder for a CNN encoder • Landmark Detection and Tracking (SLAM): Implement SALM, a robust method for tracking an object over time and mapping out its surrounding environment, using elements of probability, motion models, and linear algebra.
Machine Learning
May 1, 2017 – August 1, 2017
•Train a Smartcab to Drive: Applied reinforcement learning to build a simulated vehicle navigation agent. •Creating Customer Segments: Reviewed unstructured data to understand the patterns and natural categories that the data fits into (K-Means, GMM) •Finding Donors for CharityML: Employed several supervised (GaussianNB, XGBoost, SVM) algorithms to accurately model individuals' incomes using data collected from the 1994 U.S. Census. •Bag of Words: NLP (Natural Language Processing) technique to load and clean the IMDB movie reviews, then applying a simple Bag of Words model to get accurate predictions. •Predicting Boston Housing Prices: Predicted real estate prices based on the house features with a decision tree regressor analysis. •Titanic Survival Exploration: Built a decision tree model for predicting the survival of each passenger aboard the RMS Titanic.
DEEP LEARNING
March 1, 2017 – August 1, 2017
•Golf swing analysis from video: Convert golf swing video file to data distribution matrix using CVPR'17 pose estimation model (14 body joint features), MLP predicts success swing or fail swing. •Style Transfer using VGG-16 Model: Create mixed-image which has the contours of the content-image and the colours and texture of the style-image (DeepDreaming) •Dog Breed Classifier: Built CNN to identify an estimate of canine breed of a given an image of a dog using Transfer Learning (InceptionV3) •Train a CNN and MLP on augmented images from the CIFAR-10 database to classify multiple image sets. •Build CNN using TensorFlow and Python to recognize handwriting letter using notMNIST data. •Create Neural Network to predict daily bike rental ridership along with exploratory data analysis and data modeling.
UCLA, 360 Degree Camera Module
April 1, 2016 – June 1, 2016
•Designed a device that can capture 360-degree snapshots or videos using multiple web cameras. •Provided the advanced viewing interface has playback controls for changing viewing direction which allows a user to perceive a seamless 360-degree view, as a form of virtual reality. •Camera Calibration, Computed Scale-invariant feature transform (SIFT), Random sample consensus (RANSAC), Homograph calculation, Seamless Stitching, Cylindrical projection, and Blinding.cal projection, and Blinding.
GRAPHS&NETWORK FLOW, PATTERN RECOGNITION
January 1, 2016 – December 1, 2016
•POPULARITY PREDICTION ON TWITTER: Build linear model used to predict popularity on the tweets along with a method of Regression, Classification, and Singular Value Decomposition. •EXPERIMENTS ON DEEP LEARNING WITH CONVOLUTION NEURAL NETWORKS: Trained and tested a typical convolutional neural network (CNN) structure in a task of classifying 10 object classes. •PCA AND FLD FOR ANALYZING HUMAN FACES AND DETECTION BY BOOSTING TECHNIQUES: Developed ASM and AAM model for face reconstruction. •FACE SOCIAL TRAIT AND POLITICAL ELECTION ANALYSIS BY SVM: Showed the correlations between facial attributes, social attributes, and election outcomes.
UDACITY Computer Vision Nanodegree
Udacity
June 24, 2026 – Present
DAT101x: Data Science Orientation
edX
June 24, 2026 – Present
UDACITY - MACHINE LEARNING ENGINEER NANODEGREE
Udacity
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's project portfolio is diverse, covering various aspects of computer vision and machine learning, from 360-degree cameras and image captioning to medical imaging and industrial automation. The experience at Apple and Atollogy, Inc. suggests exposure to industry-standard practices and product development cycles. The academic background and continuous learning through Nanodegrees demonstrate a proactive approach to skill development. The breadth of projects and roles indicates a strong interest and capability in applying CV/ML across different domains, which generally aligns well with dynamic, innovation-driven environments. The non-technical roles earlier in their career (Housekeeper, Actor, Personal Trainer) are not directly relevant to cultural fit for a technical role but show a diverse personal background.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a strong problem-solving orientation and a hands-on approach to developing complex systems. The experience at Atollogy, Inc. highlights an ability to work on diverse projects, from research and prototyping to API development and deployment, suggesting adaptability and a full-stack mindset within the CV domain. The role at UCLA as a Graduate Student Researcher also points to strong research and analytical skills. However, without specific psychometric test results or interview data, it is difficult to assess collaboration, stress handling, or work attitude directly.