Principal Investigator

Tae Hyun Hwang

I focus on developing novel machine learning and AI algorithms to advance precision oncology and improve outcomes for patients with lethal diseases. I hold the Florida Department of Health Cancer Chair Professorship at Mayo Clinic in the Departments of Artificial Intelligence and Informatics, Immunology, and Cancer Biology, where I lead efforts to establish a globally recognized AI in Oncology program. My research includes pioneering work in 3D and 4D molecular modeling to study cancer progression and treatment response. I have also directed bioinformatics initiatives for cancer biomarkers and predictive models, including leadership roles with NASA’s NSCOR and multiple SPORE programs. Currently, I lead AI-driven research in precision oncology, immuno-oncology, cellular therapy, and 3D/4D molecular modeling.

Education

UNIVERSITY OF MINNESOTA TWIN CITIES
Ph.D., Computer Science, 2011

INHA UNIVERSITY, KOREA
B.E., Computer Science, 2004

Research Scientist

Changjin Hong

I am interested in developing novel algorithms and high-quality informatics pipelines to analyze multi-omic, high-throughput data to make a high impact in aiding disease diagnosis and therapy. My current research projects include:

• Alternative polyadenylation associated with diseases

• Prioritizing disease-causing genes with patient phenotypes

• Analysis of the gut microbiome

Education

UNIVERSITY OF MINNESOTA TWIN CITIES
Ph.D., Electrical Engineering, 2008

HANYANG UNIVERSITY, KOREA
B.S., Electrical and Computer Engineering, 1999

Research Scientist

Sunho Park

My research interest is to develop machine learning models on highly complex bio-medical data related to cancer. For example, I have developed deep Gaussian process (DGP)-based prediction models of microsatellite instability (MSI) status using super-high-resolution Hematoxylin and Eosin (H & E) stained whole slide images (MSI is a known biomarker for immunotherapy in multiple cancer types). I am also interested in spatially resolved transcriptomic (including protein markers) which enables us to capture the genetic information of cancer in the spatial contexts. My goal is to develop not only accurate prediction models but also pattern identification methods which eventually can lead to targetable biomarkers.

Education

POHANG UNIVERSITY OF SCIENCE AND TECHNOLOGY, KOREA
Ph.D., Computer Science, Feb. 2013

KOREA UNIVERSITY, KOREA
B.S., Electrical, Electronics and Radiowave Engineering, 2004

Associate Data Science Analyst

Minji Kim

My research interests are in applying deep learning in biomedical fields.

Education

Texas A&M University-Commerce
M.S., Computer Science, 2021

Hongik University, Korea
B.S., Electronic and Electrical Engineering, 2019

Graduate Student

Jean René Clemenceau

My interests are on translational research focused on utilizing computational techniques to interrogate the tumor-immune microenvironment with the ultimate goal improving patient outcomes. My approaches are based on integrating spatial transcriptomics, proteomics, tissue imaging and single cell technologies in the search for actionable biomarkers.

Education

Cleveland Clinic Lerner College of Medicine
Ph.D., Molecular Medicine, Exp. 2024

Baylor University
B.S., Informatics, 2013

Research Associate

Inyeop Jang

My research interest are in deep learning based Medical Image Processing.

Education

Gwangju Institute of Science and Technology, Korea
Ph.D. 2013, M.S. 2008, Mechatronics

Sejong University, Korea
B.E., Computer Science and Engineering, 2006

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