Jeonghwan Kim

Ph.D. Candidate, BLENDER Lab, UIUC

jk100 [AT] illinois.edu

About

Ph.D. candidate at BLENDER Lab at the University of Illinois Urbana-Champaign (UIUC), advised by Professor Heng Ji. I'm also a recipient of the Capital One Ph.D. Fellowship (2026-2027).

I work on multimodal foundation models, with a focus on building systems that can perceive, ground, and reason over fine-grained visual information in ways that align with human knowledge expressed in language. My research studies how foundation models can move beyond coarse image-text matching toward representations that capture objects, attributes, relations, and dynamics at a level needed for reliable decision-making.

A central challenge in this area is that granular visual signals are difficult to align with language-encoded knowledge: visual evidence is often dense, ambiguous, and spatially localized, while world knowledge translated into language is abstract, compositional, and incomplete. I develop methods that help multimodal models bridge this gap by improving visual grounding, cross-modal alignment, and knowledge integration.

More broadly, I am interested in turning these advances into visually grounded action policies for real-world embodied systems, including robotics, where an agent must connect perception, reasoning, and action under real-world uncertainty.

Specifically, I design models that:

Research Experience

MetaRedmond, WA, USA

Research Scientist Intern May. 2026 - Present

Part-time Student Researcher May. 2025 - Oct. 2025

AmazonBellevue, WA, USA

Applied Scientist Intern May. 2024 - Aug. 2024

University of Illinois Urbana-Champaign (UIUC) Champaign, IL, USA

Graduate Research Assistant (Ph.D.) Aug. 2023 - Present

Advisor: Heng Ji

KAIST IR&NLP Lab Daejeon, Republic of Korea

Research Associate Mar. 2022 - July. 2023

Graduate Research Assistant (M.S.) Feb. 2020 - Feb. 2022

Advisor: Sung-Hyon Myaeng

Selected Publications

For more information, check out my Google Scholar.
* indicates equal contribution.

Pixel-Grounded Retrieval for Knowledgeable Large Multimodal Models

Jeonghwan Kim, Renjie Tao, Sanat Sharma, Jiaqi Wang, Kai Sun, Zhaojiang Lin, Seungwhan Moon, Lambert Mathias, Anuj Kumar, Heng Ji, Xin Luna Dong

Preprint, 2026

Alignment-Aware Training for Generalizable VLAs

Dwip Dalal, Shivansh Patel, Jeonghwan Kim, Utkarsh Mishra, Alex Baratian, Hyeonjeong Ha, Heng Ji, Svetlana Lazebnik, Unnat Jain

Preprint, 2026

Constructive Distortion: Improving MLLMs with Attention-Guided Image Warping

Dwip Dalal, Gautam Vashishtha, Utkarsh Mishra, Jeonghwan Kim, Madhav Kanda, Hyeonjeong Ha, Svetlana Lazebnik, Heng Ji, Unnat Jain

ICLR 2026

PARTONOMY: Large Multimodal Models with Part-Level Visual Understanding

Ansel Blume*, Jeonghwan Kim*, Hyeonjeong Ha, Elen Chatikyan, Xiaomeng Jin, Khanh Duy Nguyen, Nanyun Peng, Kai-Wei Chang, Derek Hoiem and Heng Ji

NeurIPS 2025 (Spotlight)

Infogent: An Agent-Based Framework for Web Information Aggregation

Revanth Gangi Reddy*, Sagnik Mukherjee*, Jeonghwan Kim*, Zhenhailong Wang*, Dilek Hakkani-Tur, Heng Ji

NAACL 2025, Findings

Finer: Investigating and Enhancing Fine-Grained Visual Concept Recognition in Large Vision Language Models

Jeonghwan Kim, Heng Ji

EMNLP 2024

Why So Gullible? Enhancing the Robustness of Retrieval-Augmented Models against Counterfactual Noise

Giwon Hong*, Jeonghwan Kim*, Junmo Kang*, Sung-Hyon Myaeng, Joyce Jiyoung Whang

NAACL 2024, Findings

Exploiting Numerical-Contextual Knowledge to Improve Numerical Reasoning in Question Answering

Jeonghwan Kim, Junmo Kang, Giwon Hong, Kyung-min Kim, Sung-Hyon Myaeng

NAACL 2022, Findings

FinePrompt: Unveiling the Role of Finetuned Inductive Bias on Compositional Reasoning in GPT-4

Jeonghwan Kim*, Giwon Hong*, Sung-Hyon Myaeng, Joyce Jiyoung Whang

EMNLP 2023, Findings

Graph-Induced Transformers for Efficient Multi-Hop Question Answering

Giwon Hong, Jeonghwan Kim, Junmo Kang, Sung-Hyon Myaeng

EMNLP 2022

Have You Seen That Number? Investigating Extrapolation in Question Answering Models

Jeonghwan Kim, Giwon Hong, Kyung-min Kim, Junmo Kang, Sung-Hyon Myaeng

EMNLP 2021

Vitæ

Full CV in PDF.

  • Meta May 2026 - Present
    Research Scientist (intern)
    World-Action Models for Wearables
  • Meta May 2025 - Oct 2025
    Research Scientist (intern)
    Multimodal RAG + MLLM
  • Amazon May 2024 - Aug. 2024
    Applied Scientist (intern)
    Multimodal Representation Learning
  • UIUC Aug. 2023 - Present
    Ph.D. in Computer Science
    BLENDER Lab
  • IR&NLP Lab, KAIST Mar. 2022 - Jul. 2023
    Researcher
    Working on QA and Graph Networks
  • KAIST Feb. 2020 - Feb. 2022
    M.Sc. in School of Computing
    IR&NLP Lab
  • Republic of Korea Marine Corps Mar. 2015 - Dec. 2016
    Honorably Discharged
    Mandatory Military Service
  • Handong Global University Mar. 2014 - Feb. 2020
    B.Sc. in Computer Science & Electrical Engineering
    Magna Cum Laude