Jaeho Bang
AI Computing Software Engineer at 리벨리온 Rebellions
Seoul, South Korea
Summary
🤩
Rockstar🎓
Top SchoolJaeho Bang is an AI computing software engineer with 10 years of experience at the intersection of deep learning and hardware, currently building highly efficient DL operators and an AI compute library for Rebellions' custom processors. His background blends academic research in accelerating video analytics and domain-drift recovery at Georgia Tech with hands-on work in model training and image-based vehicle classification contributed to the popular EvaDB project. He has practical experience bridging compiler-style operator development (TensorRT-like) with data-science pipelines, and has researched explainable anomaly detection during an Adobe internship. Based in Seoul, he combines rigorous ECE training from Carnegie Mellon with PhD-level research, uniquely positioning him to optimize end-to-end ML stacks from algorithms to silicon-aware implementations.
11 years of coding experience
5 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Science (On Leave), Doctor of Philosophy - PhD, Computer Science (On Leave) at Georgia Institute of TechnologySpanish, Spanish at Daewon Foreign Language High SchoolMaster of Science - MS, Electrical and Computer Engineering, Master of Science - MS, Electrical and Computer Engineering at Carnegie Mellon University
English, Korean
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (16)
scikit10
scikit-learn10
keras10
machine-learning10
python10
image-processing10
data-analysis10
computer-vision9
databases9
database9
object-detection8
data-structures8
data-structure8
ai7
data-engineering7
Programming languages (1)
Github contributions (5)
georgia-tech-db/evadb
Oct 2018 - Oct 2019
Database system for AI-powered apps
Role in this project:
Back-end Developer / Data Scientist
Back-end Developer / Data ScientistContributions:36 commits, 9 PRs, 11 pushes in 1 year
Contributions summary:Jaeho contributed to the development of a pipeline structure and the creation of a filters folder. Code changes include the implementation of data loading, dataset labeling, and model training using libraries like scikit-learn and Keras. The primary focus appears to be on image processing and vehicle type classification utilizing machine learning algorithms. The user's commits show hands-on experience with data manipulation, model training, and evaluation within the context of an AI-powered database system.
aidatabaseevavideo-analyticsserving
jaehobang/Eva
Aug 2019 - Apr 2020
Exploratory Video Analytics System
Contributions:59 pushes in 7 months
video-analyticsanalyticsexploratorymachine-learningvideo
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