Seongwoo Choi
Education
- 2020-2022
- MS, Computer Science; University of California, Davis
Courses Taken: Deep Learning, Advanced Computer Architectures, Design and Analysis Of Algorithms, Data Analysis in R and Web Technologies, Machine Learning and Discovery, Distributed Databases, Software Engineering, Advanced Deep Learning
- 2013-2017
- BSc, Computer Science; University of California, Santa Cruz
Courses Taken: Operating Systems, Data Structures and Algorithms, Abstract Data Types, Intermediate Programming, Advanced Programming in C++, Analysis Of Algorithms, Web Programming, Programming Languages
Experience
Intel Corporation (Remote, United States)
Graduate Student Internship in AI & Cloud Computing (June 2022 - October 2022/ Full-time)
AI Research Engineer: Learned about Graph Neural Networks and joined a research to create a new algorithm in Graph Neural Networks in AVA Dataset using PyTorch Geometric.
GPU Solutions Architect / Cloud Application Development Engineer: Learned to use VNC to connect cloud computer to analyze benchmark performance and learned to record benchmark data from multiple configurations of cloud computers and gained experience of constructing virtual machine instances on servers using Linux with different server virtualization options such as SRIOV, and Passthrough.
Completed performance runs on industry standard benchmarks such as 3D Mark, Unigene Superposition, Unigine Valley, Metro and used performance analysis methods and tools (e.g., e.g., vTune Profiler, Intel SoC Watch etc)
Performed GPU analysis on cloud gaming workloads and learned about GPU architecture and features associated with cloud gaming performance.
Created automation methodologies for repetitive performance analysis workloads using macro creator and PowerShell scripting tool and analyzed gaming parameters such as number of cores, GPU driver used, and graphics software impacting workload performance using virsh commands on XML and recorded for building scalable data pipelines/infrastructure.
Shared results of benchmark scores on Excel spreadsheet and used data for visual presentation using Powerpoint and Power BI.
Intel Korea (Hybrid, Korea Republic)
Graduate Technical Internship in Software Consulting (February 2021 - August 2021 / Full-time)
- Software Consulting Engineer: Optimized, debugged, and migrated an OpenCL application to Data Parallel C++ (DPC++) running on Intel CPU and used Intel libraries to increase in runtime performance by 2.5X.
- AI Performance/Optimization Engineering Support: Debugged and optimized deep learning models running in Tensorflow on Linux Server achieving up to 5x speedup in training time, delivered model performance measurement tools using Python for PyTorch NLP application.
United States Army Garrison Korea (Pyeongtaek, Korea Republic)
Korean Augmentation to the United States Army (October 2017 - June 2019 / Mandatory Military Service)
Korean Augmentation to the United States Army (Private-Sergeant): system using Visual Basic for service members, allowing for filing and logging assigned tasks and for superiors to manage equipment for training, leading to improvement of overall performance.
Supply Clerk: Received a Certificate of Achievement for outstanding achievement for designing several important Excel programs to improve the Company’s success rate.
Squad Leader: Led a group of Korean and U.S. Soldiers and planned physical training.
Research Experience
UC Davis Computer Architecture Research Team
◦ C/C++ Programmer: Recompiled and debugged benchmark programs that are specialized in ARM, RISC-V, x86 environments for research purposes at the research center.
◦ Documentation and Code Reviewer: Studied and reviewed each computer architecture and learned how to initiate the Gem5 simulator on multiple architectures.
ResilientDB Distributed Databases (Blockchain) Research Team
◦ DevOps Engineer: Recompiled codebase to run on Linux environment and cloud computing environment.
◦ Documentation Technical Writer: Created a document website using MkDocs to record technical aspects of NexRes - the second generation of the UC Davis Blockchain. ◦ Code Reviewer: Reviewed codes and analyzed further for efficiency in the codebase.
Plug-In Hybrid Electric Vehicles Research Center - UC Davis
◦ Machine Learning to Predict Charge Levels of Electric Vehicles: Conducted a research in studying charging levels of battery electric vehicles and published papers.
◦ Python Programmer: Programmed a Python script that analyzes geocodes in each state to understand which location a charging station is supposed to be built.
Publications
• Paper: Predictive Modeling of Charge Levels for Battery Electric Vehicles using CNN EfficientNet and IGTD Algorithm: Published on ArXiV in June 2022
• Paper: A Machine Learning Approach to Predict the Charge Levels of Battery Electric Vehicles: (Reviewing for submission - March 2022)
• Workshop Paper: Data Analysis on Netflix datasets to observe a trend of an actor in movie genres: Published on Research Gate in March 2022
• Paper: Influence of Communication Among Shared Developers on the Productivity of Open Source Software Projects: Published on ArXiV in March 2022
• Paper: A Deep Learning Technique using a Sequence of Follow Up X-Rays for Disease classification: Published on ArXiV in March 2022
Extra Section
Human Languages:
- English (native speaker)
- Korean
- Spanish
- Mandarin Chinese