Sungjin Park

Jin Park

Senior Applied Scientist

Microsoft Research, Cambridge, United Kingdom

About

I'm Sungjin (Jin) Park, a Senior Applied Scientist at Microsoft Research Cambridge. I received my PhD from KAIST, advised by Edward Choi, and my Master's degree from KAIST under the guidance of Sooyoung Lee. During my studies, I had the pleasure of interning at Microsoft Research Asia in Beijing with Xiao Liu and in Vancouver with Yuhang He.

My current research interests lie in multimodal learning and reinforcement learning for remote collaboration environments.

Multimodal Learning Reinforcement Learning Large Language Models Complex Reasoning Healthcare AI

News

  • Apr 2026 Joined Microsoft Research Cambridge as a Senior Applied Scientist.
  • Feb 2026 Received PhD in Artificial Intelligence from KAIST.
  • 2025 Two papers currently under review.

Publications

Conference

Lifelong Chart-to-Code Generation via LLM-Driven Multimodal Data Synthesis

Sungjin Park, Soheil Abbasloo, Qi Chen, Yuhang He

Under Review

Reward-Guided Decoding as Shallow Fusion

Sungjin Park, Yeyun Gong, Edward Choi, Xiao Liu

Under Review

Ensembling Large Language Models with Process Reward-Guided Tree Search for Better Complex Reasoning

Sungjin Park, Xiao Liu, Yeyun Gong, Edward Choi

NAACL 2025 PDF

Multimodal Transformer With A Low-Computational-Cost Guarantee

Sungjin Park and Edward Choi

ICASSP 2024 PDF

FactKG: Fact Verification via Reasoning on Knowledge Graph

Jiho Kim, Sungjin Park, Yeonsu Kwon, Yohan Jo, James Thorne, Edward Choi

ACL 2023 PDF

Do Language Models Understand Measurements?

Sungjin Park, Seungwoo Ryu, Edward Choi

EMNLP 2022 Findings PDF

Unconditional Image-Text Pair Generation with Multimodal Cross Quantizer

Hyungyung Lee, Sungjin Park, Edward Choi

BMVC 2022 PDF

Graph-Text Multi-Modal Pre-training for Medical Representation Learning

Sungjin Park, Seongsu Bae, Jiho Kim, Tackeun Kim, Edward Choi

CHIL 2022 PDF

Journal

Unsupervised Multi-sense Language Models For Natural Language Processing Tasks

Jihyeon Roh, Sungjin Park, Bo-Kyeong Kim, Sang-Hoon Oh, Soo-Young Lee

Neural Networks, Vol. 142, 2021 PDF

Preprint

Semi-supervised Disentanglement with Independent Vector Variational Autoencoders

Bo-Kyeong Kim, Sungjin Park, Geonmin Kim, Soo-Young Lee

arXiv:2003.06581 PDF

Experience

Work

Microsoft Research

Cambridge, UK

Senior Applied Scientist

2026 – Present

Microsoft

Vancouver, Canada

Research Intern

Apr 2025 – Jul 2025

  • Applied reinforcement learning to enhance system domain coding in large language models.
  • Enhanced chart-to-code capabilities of vision-language models through LM-driven data synthesis.

Microsoft Research Asia

Beijing, China

Research Intern

Sep 2024 – Mar 2025

  • Developed reward-guided tree search for complex reasoning via LLM ensembling.
  • Investigated the effect of fine-grained rewards on math reasoning.

Education

KAIST

Daejeon, Korea

Ph.D. in Artificial Intelligence

Sep 2020 – Feb 2026

  • Advisor: Prof. Edward Choi

KAIST

Daejeon, Korea

M.S. in Electrical Engineering

Sep 2018 – Aug 2020

  • Advisors: Prof. Sooyoung Lee, Prof. Daeshik Kim

KAIST

Daejeon, Korea

B.S. in Electrical Engineering

Mar 2014 – Aug 2018

Academic Service

Reviewer

CHIL 2023, EMNLP 2023, ACL 2024, ICLR 2025-2026, NeurIPS 2025-2026, ARR 2024-2026

Teaching Assistant

AI504 Programming for AI — Fall 2020, 2021, 2022, 2023

AI612 Machine Learning for Healthcare — Spring 2021, 2022, 2023

Talks

Electronic Health Records and Medical Text — Tutorial @ KOSAIM 2021 Summer School

K-MOOC

Introduction to Medical AI — 2021