Ji Hwan Park
Ji Hwan Park

Ji Hwan Park is an assistant professor in the School of Computer Science at the University of Oklahoma (OU). Before joining OU, he was an assistant computational scientist at Brookhaven National Lab. He earned his Ph.D. under prof. Arie Kaufman at Stony Brook University. His research interests are visual analytics, data visualization (including VR/AR), machine learning/deep learning, and human computer interaction.

Curriculum Vitae,Google Scholar

Prospective students:

I am seeking undergraduate and graduate (MS/PhD) students at CS@OU. If you are interested in data visualization (including VR/AR), visual analytics, machine learning/deep learning, human-computer interation, and data science, please contact me with your resume.

News

  • 10/2022: Our paper "A Bayesian Deep Learning Approach to Near-Term Climate Prediction" has been published in Journal of Advances in Modeling Earth Systems.
  • 10/2022: Our proposal "Identifying Protein Motifs in Cas9 Essential for Bacterial Virulence" has been selected for funding by the DoD.
  • 10/2022: Our paper "Survival Analysis of Localized Prostate Cancer with Deep Learning" has been accepted to Scientific Reports.
  • 10/2022: We received an OU DISC seed funding award. Thank you, DISC!
  • 07/2022: Our paper "Ambienizer: Turning Digital Photos into Ambient Visualizations" has been accepted for presentation at VIS 2022 Posters.

Projects

Aesthetically Pleasing Charts for Casual Information Visualization

Graphoto
Graphoto: a framework that automatically generates a photo or adjusts an existing one to match a line graph. More INFO

Ji Hwan Park, Arie Kaufman, and Klaus Mueller, "Graphoto: Aesthetically Pleasing Charts for Casual Information Visualization", IEEE Computer Graphics and Application, 2018 Paper

Saliency-Aware Compression

compression
A transfer-function-guided 3D blockbased saliency-aware compression scheme for volumetric data that is both content- and spatially-scalable. More INFO

Ji Hwan Park, Ievgeniia Gutenko, and Arie Kaufman, "Transfer Function-Guided Saliency-Aware Compression for Transmitting Volumetric Data", IEEE Transaction on Multimedia, 2020 Paper

Crowd Consensus Analytics for Virtual Colonoscopy

c2a
C2A: a visual analytic framework to help clinical technicians obtain the best results from a crowdsourced VC application More INFO

Ji Hwan Park, Seyedkoosha Mirhosseini, Saad Nadeem, Joseph Marino, Arie Kaufman, Kevin Baker, and Matthew Barish, "Crowdsourcing for Identification of Polyp-Free Segments in Virtual Colonoscopy Videos", SPIE Medical Imaging, 101380V, 2017 (oral presentation) Paper

Ji Hwan Park, Saad Nadeem, Seyedkoosha Mirhosseini, and Arie Kaufman, "C2A: Crowd Consensus Analytics for Virtual Colonoscopy", IEEE Conference on Visual Analytics Science and Technology (VAST), pp. 21-30, 2016 Paper

Turning Digital Photos into Ambient Visualizations

ambienizer
Ambienizer: a visual encoding approach that seeks to convey a user's personal data in a casual non-technical way

Ji Hwan Park, Arie Kaufman, and Klaus Mueller, "Ambienizer: Turning Digital Photos into Ambient Visualizations", IEEE VIS Poster, 2022 Paper

Crowd Analytics for Medical Imaging Data

cmed
CMed: a visual analytic framework to allow crowdsourcing application analysts/developers to observe patterns and gather insights into the crowdsourced medical data More INFO

Ji Hwan Park, Saad Nadeem, Saeed Boor Boor, Joseph Marino, and Arie Kaufman, "CMed: Crowd Analytics for Medical Imaging Data", IEEE Transactions on Visualization and Computer Graphics, 2021 Paper

Ji Hwan Park, Saad Nadeem, Joseph Marino, Kevin Baker, Matthew Barish, and Arie Kaufman, "Crowd-Assisted Polyp Annotation of VirtualColonoscopy Videos", SPIE Medical Imaging 2018 (oral presentation) Paper

Exploration of Spatio-Temporal Data

geobrick
GeoBrick: a visual framework to explore multivariate spatio-temporal data for domain experts

Ji Hwan Park, Saad Nadeem, and Arie Kaufman, "GeoBrick: Exploration of Spatio-Temporal Data", The Visual Computer, 2018 Paper

Recent Publications

-Xin Dai, Ji Hwan Park, Shinjae Yoo, Nicholas D'Imperio, Benjamin H. McMahon, Christopher T. Rentsch, Janet P. Tate, and Amy C. Justice, "Survival Analysis of Localized Prostate Cancer with Deep Learning", Scientific Reports, 12, 17821, 2022

-Xihaier Luo, Balu Nadiga, Ji Hwan Park, Yihui Ren, Wei Xu, and Shinjae Yoo, "A Bayesian Deep Learning Approach to Near-Term Climate Prediction", Journal of Advances in Modeling Earth Systems, 14, e2022MS003058, 2022

-Ji Hwan Park, Arie Kaufman, and Klaus Mueller, "Ambienizer: Turning Digital Photos into Ambient Visualizations", IEEE VIS Poster, 2022

-Wei Xu, Xihaier Luo, Yihui Ren, Ji Hwan Park, Shinjae Yoo, and Balasubramanya T. Nadiga, "Feature Importance in a Deep Learning Climate Emulator", AI: Modeling Oceans and Climate Change Workshop (AIMOCC) at International Conference on Learning Representations (ICLR), 2021

-Xin Dai, Ji Hwan Park, Nicholas D’imperio, Shinjae Yoo, "Longitudinal deep learning study on MIMIC-III dataset", ISC Workshop on HPC Applications in Precision Medicine, 2021

-Ji Hwan Park, Saad Nadeem, Saeed Boorboor, Joseph Marino, and Arie Kaufman, "CMed: Crowd Analytics for Medical Imaging Data", IEEE Transactions on Visualization and Computer Graphics, 27(6), pp 2869-2880, 2021

-Sandeep Madireddy, Ji Hwan Park, Sunwoo Lee, Prasanna Balaprakash, Shinjae Yoo, Wei-keng Liao,Cory D. Hauck, M. Paul Laiu, Richard Archibald, "In Situ Compression Artifact Removal in ScientificData Using Deep Transfer Learning and Experience Replay", Machine Learning: Science and Technology, 2 (2), 025010, 2020

-Ji Hwan Park, Han Eol Cho, Jong Hun Kim, Melanie Wall, Yaakov Stern, Hynsun Lim, Shinjae Yoo,H young-Seop Kim, Jiook Cha, "Machine learning prediction of incidence of Alzheimer’s disease using large-scale administrative health data", npj Digital Medicine, 3, 46, 2020

-Siwu Liu, Ji Hwan Park, and Shinjae Yoo, "Efficient and Effective Graph Convolution Networks", SIAM International Conference on Data Mining (SDM), pp 388-396, 2020

-Ji Hwan Park, Ievgeniia Gutenko, and Arie Kaufman, "Transfer Function-Guided Saliency-Aware Compression for Transmitting Volumetric Data", IEEE Transactions on Multimedia, 22(9), pp 2262–2277, 2020

-Yun Wang, Chenxiao Xu, Ji-Hwan Park, Seonjoo Lee, Yaakov Stern, Shinjae Yoo, Jong Hun Kim, Hyoung Seop Kim, Jiook Cha, The Alzheimer's Disease Neuro imaging Initiative, "Diagnosis and prognosis of Alzheimer's disease using brain morphometry and white matter connectomes", NeuroImage: Clinical Vol.23, 101859, 2019

-Ji Hwan Park, Saad Nadeem, and Arie Kaufman, "GeoBrick: Exploration of Spatio-Temporal Data", The Visual Computer, 35(2), pp 191–204, 2018

-Ji Hwan Park, Saad Nadeem, Joseph Marino, Kevin Baker, Matthew Barish, and Arie Kaufman, "Crowd-Assisted Polyp Annotation of Virtual Colonoscopy Videos", SPIE Medical Imaging, 105790M, 2018 (oral presentation)

-Saeed Boorboor, Saad Nadeem, Ji Hwan Park, Kevin Baker, Arie Kaufman, "Crowdsourcing Lung Nodules Detection and Annotation", SPIE Medical Imaging, 105791D, 2018

-Ji Hwan Park, Arie Kaufman, and Klaus Mueller, "Graphoto: Aesthetically Pleasing Charts for Casual Information Visualization", IEEE Computer Graphics and Application, 38(6), pp 67-82, 2018

-Ji Hwan Park, Seyedkoosha Mirhosseini, Saad Nadeem, Joseph Marino, Arie Kaufman, Kevin Baker, and Matthew Barish, "Crowdsourcing for Identification of Polyp-Free Segments in Virtual Colonoscopy Videos", SPIE Medical Imaging, 101380V, 2017 (oral presentation)

-Ji Hwan Park, Saad Nadeem, Seyedkoosha Mirhosseini, and Arie Kaufman, "C2A: Crowd Consensus Analytics for Virtual Colonoscopy", IEEE Conference on Visual Analytics Science and Technology (VAST), pp. 21-30, 2016