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I am a research-minded engineer passionate about co-designing algorithm and system for efficient multimodal and large language models. I have experience building automated and portable distributed ML systems with a focus on parallelism, operator fusion, and graph optimizations. I currently work on buidling a compiler for efficient distributed Transformer inference in TensorRT at NVIDIA. I received my Ph.D. in Computer Science at Carnegie Mellon University with a dissertation titled "Automated and Portable Machine Learning Systems". At CMU, I was fortunate to be advised by Prof. Tianqi Chen and Prof. Zhihao Jia as a member of the Automated ML System group.
Email: soojeonml [at] gmail.com | | | | |
Teaching
CMU 15-884 Machine Learning Systems
Teaching Assistant, Spring 2021, Instructors: Tianqi Chen
CMU 10-403 Deep Reinforcement Learning and Control
Teaching Assistant, Spring 2020, Instructors: Katerina Fragkiadaki
CMU 10-701 Machine Learning (PhD)
Teaching Assistant, Spring 2019, Instructors: Leila Wehbe, Aaditya Ramdas
Award
Qualcomm Innovation Fellowship 2022
One of 19 winners in US, awarded $100K to the team (Byungsoo Jeon, Sunghyun Kim from MIT)
- Project title: Holistic Distributed Deep Learning Compilation with Automated Cross-stack Optimization
Kwanjeong Scholarship 2017 - 2021
One of ~50 nationwide outstanding PhD students in STEM, awarded $30K per year
Publication
> Automated and Portable ML System
GraphPipe: Improving the Performance and Scalability of DNN Training with Graph Pipeline Parallelism
Byungsoo Jeon*, Mengdi Wu*, Sunghyun Kim*, Shiyi Cao*, Sunghyun Park, Neeraj Aggarwal, Colin Unger, Daiyaan Arfeen, Peiyuan Liao, Xupeng Miao, Mohammad Alizadeh, Gregory R. Ganger, Tianqi Chen, Zhihao Jia
ASPLOS 2025
PDF
Collage: Seamless Integration of Deep Learning Backends with Automatic Placement
Byungsoo Jeon*, Sunghyun Park*, Peiyuan Liao, Sheng Xu, Tianqi Chen, Zhihao Jia
PACT 2022 - Integrated to Apache TVM Project (in TVM v0.9.0) / Presented in GTC 2022
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Slides
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Code
SRTuner: Effective Compiler Optimization Customization by Exposing Synergistic Relations
Sunghyun Park, Salar Latifi, Yongjun Park, Armand Behroozi, Byungsoo Jeon, Scott Mahlke
CGO 2022
PDF
> Applied ML / RL
OBP-RL: Exploring Deep Reinforcement Learning Methods for Online Binpacking Problem
Byungsoo Jeon, Bharathan Balaji, Saurabh Gupta, Chun Ye
Amazon Machine Leanring Conference 2020
FactoredRL: Leveraging factored graphs for deep reinforcement learning
Bharathan Balaji*, Petros Christodoulou*, Xiaoyu lu*, Byungsoo Jeon, Jordan Bell-Masterson
NeurIPS 2020 (Deep RL Workshop)
PDF
Dropout Prediction over Weeks in MOOCs by Learning Representations of Clicks and Videos
Byungsoo Jeon*, Namyong Park*
AAAI 2020 (AI4Edu Workshop)
PDF
Dropout Prediction over Weeks in MOOCs via Interpretable Multi-Layer Representation Learning
Byungsoo Jeon*, Namyong Park*, Seojin Bang*
AAAI 2020 (AI4Edu Workshop)
PDF
Time-series Insights into the Process of Passing or Failing Online University Courses using Neural-Induced Interpretable Student States
Byungsoo Jeon, Eyal Shafran, Luke Breitfeller, Jason Levin, Carolyn P. Rose
EDM 2019 (Short oral presentation)
PDF
Attentive Interaction Model: Modeling Changes in View in Argumentation
Yohan Jo, Shivani Poddar, Byungsoo Jeon, Qinlan Shen, Carolyn P. Rose, Graham Neubig
NAACL 2018 (Poster)
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Code
Music Emotion Recognition via End-to-End Multimodal Neural Networks
Byungsoo Jeon, Chanju Kim, Adrian Kim, Dongwon Kim, Jangyeon Park, Jungwoo Ha,
RecSys 2017 (Poster)
PDF
> Distributed System & Algorithm for Tensor Algebra
BIGtensor: Mining Billion-Scale Tensor Made Easy
Namyong Park*, Byungsoo Jeon*, Jungwoo Lee, U Kang
CIKM 2016 (Demo paper)
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Web (open source)
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Web (paper)
SCouT: Scalable Coupled Matrix-Tensor Factorization - Algorithm and Discoveries
Byungsoo Jeon, Inah Jeon, U Kang
ICDE 2016 (Long oral presentation)
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Web
TeGViz: Distributed Tera-Scale Graph Generation and Visualization
Byungsoo Jeon, Inah Jeon, U Kang
ICDM 2015 (Demo paper)
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Web