DoangJoo "Alan" Synn

I am a PhD Student in Computer Science at Georgia Institute of Technology. My research explores the intersection of generative AI and computational design, particularly in creative processes like building automata and kinematics. I investigate how foundation models can revolutionize mechanical and computational design by enabling rapid production of diverse solutions while preserving human creative intent.

My work addresses three key challenges in AI-assisted design: (1) developing computational design frameworks that help users effectively collaborate with AI while maintaining their creative vision, (2) preserving human intent throughout the AI-assisted design process, and (3) creating specialized tools for non-expert users working on mechanical design projects.

Throughout my career, I have developed and implemented a wide range of solutions, from government-led programming education initiatives to advanced ubiquitous computing stacks. My background includes significant experience as a cloud architect, where I have designed social robots, optimized cloud-native software and managed DevOps infrastructures for various companies.

I received M.Eng and B.Eng in Electrical and Computer Engineering from Korea University advised by Prof. Jong-Kook Kim. My research at Korea University focused on Machine Learning Systems and Distributed Systems.

📧 Contact me at: alansynn [at] gatech [dot] edu

Research Interests

- My research explores the intersection of generative AI and computational design, particularly in creative processes like building automata and kinematics. I investigate how foundation models can revolutionize mechanical and computational design by enabling rapid production of diverse solutions while preserving human creative intent.
- I focus on three key challenges: (1) developing educational frameworks that help designers effectively collaborate with AI while maintaining their creative vision, (2) preserving human intent throughout the AI-assisted design process, and (3) creating specialized tools for non-expert users working on mechanical design projects.

Education

2022 -
Georgia Institute of Technology – PhD in Computer Science
- Advised by Prof. HyunJoo Oh and Prof. Sehoon Ha
- AI in computational design, automata, and kinematics
2020 - 2022
Korea University – M.Eng in Electrical and Computer Engineering
- Advised by Prof. Jong-Kook Kim
- GPA: 4.25 (4.5 scale)
- Thesis: On Efficient Data Delivery for Machine Learning Systems
2012 - 2020
Korea University – B.Eng. in Electrical Engineering
- GPA: 3.56 (scale 4.5)
- On leave from 2016 Spring to 2019 Spring to serve alternative military service

Work Experience

2024
PhD Research Intern @Dolby Research
- Atlanta, GA, USA
- Experience Delivery Lab
- Topic: Time-series Multi-modal Data Store for Multi-Media
2023
MLOps Engineering Intern @LG Uplus Corp.
- Seoul, Korea
- Project: DeepCrunch, a model compression library with support for multiple back-ends for efficient model serving in MLOps in edge devices.
2021 - 2022
Research Scientist @Protopia AI inc.
- Austin, Texas, USA
- CEO: Dr. Eiman Ebrahimi
- CTO: Prof. Hadi Esmaeilzadeh
- R&D on:
  
  Privacy-preserving Deep Learning
  
  Development on internal ML framework for privacy
  
  Development and maintenance of Cloud Learning
- Published at NeurIPS 2021 Demo.
2019 - 2022
Cloud Architect
- Design cloud native softwares and consulted 4 different companies to manage the DevOps infrastructure
- Field : Cloud Computing, Cloud Applications, Data Pipeline
2020 - 2022
Amazon Web Services(AWS) Educate Cloud Ambassador
- The AWS Educate Cloud Ambassador Program is for top-tier cloud educators who serve as ambassadors and evangelists for AWS Educate.
2019
Server Engineer @Flysher inc.
- Global #10 social game
2017-2019
System Engineer @AKA Intelligence inc.
- Develop and Produce Embedded board & S/W for AKA INTELLIGENCE's social robot, Musio.

Selected Publications

CHI

Tangible-MakeCode: Bridging Physical Coding Blocks with a Web-Based Programming Interface for Collaborative and Extensible Learning

Jin Yu, Poojita Garg, DoangJoo Synn, and HyunJoo Oh

In Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems, Apr 2025

DOI Bib

@inproceedings{yu2025tangible,
  title = {Tangible-MakeCode: Bridging Physical Coding Blocks with a Web-Based Programming Interface for Collaborative and Extensible Learning},
  author = {Yu, Jin and Garg, Poojita and Synn, DoangJoo and Oh, HyunJoo},
  booktitle = {Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems},
  year = {2025},
  month = apr,
  publisher = {ACM},
  address = {Yokohama, Japan},
  doi = {10.1145/3706598.3713260},
  isbn = {979-8-4007-1394-1/25/04},
}

KIPS
ADaPT: An Automated Dataloader Parameter Tuning Framework using AVL Tree-based Search Algorithms

MyungHoon Ryu, XinYu Piao, JooYoung Park, DoangJoo Synn, and Jong-Kook Kim

The Transactions of the Korea Information Processing Society, Jan 2025

Abs DOI Bib

Recently deep learning has become widely used in many research fields and businesses. To improve the performance of deep learning, one of the critical challenges is to determine the optimal values of many parameters that can be adjusted. This paper focuses on the adjustable dataloader parameters that affect the overall training time and proposes an automated dataloader parameter tuning framework, called ADaPT, to determine the optimal values of the dataloader parameters. The proposed ADaPT uses the characteristic of the AVL tree and attempts to determine optimal dataloader parameters in a small amount of time to accelerate the data loading. The results show that the proposed method effectively accelerates the data loading speed compared to the default parameter values and values that are recommended by PyTorch and the speed is comparable to the optimal.
@article{ryu2025adapt, title = {ADaPT: An Automated Dataloader Parameter Tuning Framework using AVL Tree-based Search Algorithms}, author = {Ryu, MyungHoon and Piao, XinYu and Park, JooYoung and Synn, DoangJoo and Kim, Jong-Kook}, journal = {The Transactions of the Korea Information Processing Society}, volume = {14}, number = {1}, pages = {1--8}, year = {2025}, month = jan, doi = {10.3745/TKIPS.2025.14.1.1}, url = {https://tkips.kips.or.kr/digital-library/101925}, publisher = {Korea Information Processing Society} }
IEEE Access

Enabling large batch size training for dnn models beyond the memory limit while maintaining performance

XinYu Piao, DoangJoo Synn, JuYoung Park, and Jong-Kook Kim

IEEE Access, Jan 2023

DOI

NIPS Demo

Protopia AI: Taking on Missing Link in Inference Privacy

Byung Hoon Ahn, DoangJoo Synn, Masih Derkani, Eiman Ebrahimi, and Hadi Esmaeilzadeh

In Neural Information Processing Systems Demonstrations, Jan 2021

Bib Website

@inproceedings{ahn2021nips,
  title = {Protopia AI: Taking on Missing Link in Inference Privacy},
  author = {Ahn, Byung Hoon and Synn, DoangJoo and Derkani, Masih and Ebrahimi, Eiman and Esmaeilzadeh, Hadi},
  booktitle = {Neural Information Processing Systems Demonstrations},
  year = {2021},
  organization = {Neural Information Processing Systems},
}

arXiv

Micro Batch Streaming: Allowing the Training of DNN models Using a large batch size on Small Memory Systems

DoangJoo Synn, XinYu Piao, JuYoung Park, and Jong-Kook Kim

arXiv, Nov 2021
IEEE Access

Feature Based Sampling: A Fast and Robust Sampling Method for Tasks that Use Point Cloud Data

Jung-Woo Han, DoangJoo Synn, Tae-Hyeong Kim, Hae-Cheon Jung, and Jong-Kook Kim

IEEE Access, Apr 2022

DOI
ACK
Num Worker Tuner: An Automated Spawn Parameter Tuner for Multi-Processing DataLoaders

DoangJoo Synn, and Jong-Kook Kim

In Proceedings of ACK 2021, Oct 2021

Abs Bib

In training a deep learning model, it is crucial to tune various hyperparameters and gain speed and accuracy. Although the hyperparameters that mathematically affect the convergence significantly affect the training speed, the system parameters that affect the host-to-device data set transmission time also occupy a specific part in the overall time acceleration. Therefore, it is important to properly tune and select parameters that can affect the data loader as a system parameter in overall time acceleration. We propose an automated framework called Num Worker Tuner (NWT) to address this problem. This method finds the appropriate number of multi-processing subprocesses through the search space and accelerates the learning through the number of subprocesses. Furthermore, this method allows memory efficiency and speed-up by tuning the system-dependent parameter, the number of multi-process spawns.
@inproceedings{synn2021nwt, title = {Num Worker Tuner: An Automated Spawn Parameter Tuner for Multi-Processing DataLoaders}, author = {Synn, DoangJoo and Kim, Jong-Kook}, booktitle = {Proceedings of ACK 2021}, year = {2021}, month = oct, organization = {Korea Information Processing Society} }

KIPS

A Survey on the Advancement of Virtualization Technology

JooYoung Park, DoangJoo Synn, and Jong-Kook Kim

In Proceedings of Korea Information Processing Society 2021, Mar 2021

Bib

@inproceedings{park2021survey,
  title = {A Survey on the Advancement of Virtualization Technology},
  author = {Park, JooYoung and Synn, DoangJoo and Kim, Jong-Kook},
  booktitle = {Proceedings of Korea Information Processing Society 2021},
  year = {2021},
  month = mar,
  organization = {Korea Information Processing Society}
}

KIPS
SG-Drop: Faster Skip-Gram by Dropping Context Words

DongJae Kim, DoangJoo Synn, and Jong-Kook Kim

Proceedings of Korea Information Processing Society 2020, Nov 2020

Abs Bib PDF

Many natural language processing (NLP) models utilize pre-trained word embeddings to leverage latent information. One of the most successful word embedding model is the Skip-gram (SG). In this paper, we propose a Skipgram drop (SG-Drop) model, which is a variation of the SG model. The SG-Drop model is designed to reduce training time efficiently. Furthermore, the SG-Drop allows controlling training time with its hyperparameter. It could train word embedding faster than reducing training epochs while better preserving the quality.
@article{kim2020sg, title = {SG-Drop: Faster Skip-Gram by Dropping Context Words}, author = {Kim, DongJae and Synn, DoangJoo and Kim, Jong-Kook}, journal = {Proceedings of Korea Information Processing Society 2020}, year = {2020}, month = nov, publisher = {Korea Information Processing Society} }
KIPS
Distributed Training of Deep Learning Models Using Micro Batch Streaming

DoangJoo Synn, XinYu Piao, JuYoung Park, and Jong-Kook Kim

KIPS Transactions on Software and Data Engineering, Mar 2023

Abs DOI Bib

Deep learning models have been growing in size and complexity, requiring more computational resources for training. Distributed training has emerged as a solution to this challenge, allowing models to be trained across multiple devices. However, traditional distributed training approaches often face challenges related to memory constraints, especially when dealing with large batch sizes. In this paper, we propose a novel approach called Micro Batch Streaming (MBS) for distributed training of deep learning models. MBS addresses memory limitations by dividing large batches into smaller micro-batches and processing them sequentially while accumulating gradients. We evaluate our approach on various deep learning tasks and demonstrate its effectiveness in enabling distributed training with large batch sizes on memory-constrained systems. Our results show that MBS can achieve comparable or better performance than traditional distributed training methods while using less memory per device.
@article{synn2023distributed, title = {Distributed Training of Deep Learning Models Using Micro Batch Streaming}, author = {Synn, DoangJoo and Piao, XinYu and Park, JuYoung and Kim, Jong-Kook}, journal = {KIPS Transactions on Software and Data Engineering}, volume = {12}, number = {3}, pages = {101--108}, year = {2023}, month = mar, doi = {10.3745/KTSDE.2023.12.3.101}, url = {https://tkips.kips.or.kr/digital-library/101925}, publisher = {Korea Information Processing Society} }

Honors and Awards

Fellowships
- Brain Korea 21(BK21) Research Fellowship (2020-2022)
Awards
- Dean's List, Spring 2012, Fall 2020, Department of Electrical Engineering
- Prize Winner, Creative Challenge Program @Korea University Center for Teaching and Learning (2016)
- Prize Winner, Creative Challenge Program @Korea University Center for Teaching and Learning (2015)

Student Activities

2023 - 2024
Georgia Tech Korean Student Association
- President of Korea Students in College of Computing
2014 - 2019
Hardware and Software, Korea University
- President
- The engineering community at Korea University grew from 10 members to over 200 active participants, encompassing both hardware and software specialties.
2015 - 2016
TEDxKoreaUniversity, Korea University
- Founder & Chief-Organizer
- I organized a TEDx event with 200 guests, 7 speakers, 47 team members, and a budget of $4k. My responsibilities included fundraising, sponsorships, editorial decisions, communication planning, and production and media.

Teaching Experience

2021 - 2022
Lecturer at Korea University Science & Technology School
- Taught Java for 10+ Undergraduate students
2021
Teaching Assistant at Korea University
- Digital System Design
- Data Structure and Algorithm
2012 - 2014
Lecturer at KAIST Engineering School
- Created comprehensive coding education programs in collaboration with the Ministry of Science and ICT of South Korea
- Develop 20+ hardware and software development curriculum for programming education
- Emphasized hands-on projects utilizing Arduino to teach the principles of mobile and ubiquitous computing
- Guided highschool and college-level students in exploring the third generation of computing through practical projects addressing real-world problems

Technical Projects

Privacy-Preserving Deep Learning @ Protopia AI
- Protopia generates curated noise around the source signal, safeguarding data from unauthorized inference.
  Illustration of our technology on Mask Detection task on Baby Faces
  Demo video for our technology on Face Detection task
  NetApp Post: Responsible AI and ethics by design: Is your privacy governance-ready for AI at scale?
  
  Tasks at Protopia AI inc.:
  
  Development of Protopia AI's noise training technology for inference privacy.
  
  Development of Internal framework and system for noise training technology for privacy.
  
  Research on Face Recognition System with Privacy Preserving with noise training
  
  Development of Scalable training and Workflow management system for noise training
  
  (2021.1 – 2022.8) NeurIPS-D'21
High scalable node.js game server framework @Flysher inc.
- Game server framework for Typescript developers which provides the basic development framework and many related components, including libraries and tools
- The former framework, which is pomelo has been deprecated. Therefore, we need to make a new framework based on the former architecture in order to achieve fast and adoptable framework.
Musio @AKA Intelligence inc.
- Musio is a social robot designed by AKA Intelligence, powered by MUSE, a deep learning based AI quantum engine for communication developed by AKA. Using deep learning algorithms, Musio converses with people, recognises objects and also understands people's facial expressions.
- For two years, I was an engineer at AKA Intelligence inc., where I worked on running various applications on embedded devices. I tackled the problem from various levels of the system stack.
  - Develop and Optimize the MusioOS based on Android framework and device drivers to support the system
  - Implement circuit defect detection system
  - Stabilize the power consumption among the AP with sleep control
  - Design in-house devices for flashing
  - Implement in-house software for production stage

References

Professor HyunJoo Oh
- Assistant Professor
- Georgia Institute of Technology
- School of Industrial Design & School of Interactive Computing
Professor Sehoon Ha
- Assistant Professor
- Georgia Institute of Technology
- College of Computing, School of Interactive Computing
Dr. ByungHoon Ahn
- Senior Software Engineer, Apple inc.
- [email protected]
Dr. Eiman Ebrahimi
- CEO, Protopia AI inc.
- eimanebrahimi.com
Professor Hadi Esmaeilzadeh
- CTO, Protopia AI inc.
- Associate Professor, University of California San Diego
- cseweb.ucsd.edu/~hadi

DoangJoo "Alan" Synn

Research Interests

Education

Georgia Institute of Technology – PhD in Computer Science

Korea University – M.Eng in Electrical and Computer Engineering

Korea University – B.Eng. in Electrical Engineering

Work Experience

PhD Research Intern @Dolby Research

MLOps Engineering Intern @LG Uplus Corp.

Research Scientist @Protopia AI inc.

Cloud Architect

Amazon Web Services(AWS) Educate Cloud Ambassador

Server Engineer @Flysher inc.

System Engineer @AKA Intelligence inc.

Selected Publications

Honors and Awards

Student Activities

Georgia Tech Korean Student Association

Hardware and Software, Korea University

TEDxKoreaUniversity, Korea University

Teaching Experience

Lecturer at Korea University Science & Technology School

Teaching Assistant at Korea University

Lecturer at KAIST Engineering School

Technical Projects

Privacy-Preserving Deep Learning @ Protopia AI

High scalable node.js game server framework @Flysher inc.

Musio @AKA Intelligence inc.

References

Professor HyunJoo Oh

Professor Sehoon Ha

Dr. ByungHoon Ahn

Dr. Eiman Ebrahimi

Professor Hadi Esmaeilzadeh