Doctor of Philosophy (Ph.D.)
Applied Mathematics and Statistics
SUNY at Stonybrook
2006
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Faculty
Faculty
Yeona Kang, Ph.D.
Associate Professor
Department/Office
- Mathematics
School/College
- College of Arts & Sciences
Additional Positions
-
Data Science Program
Biography
Yeona Kang, Ph.D., is an associate professor in the Department of Mathematics at Howard University in Washington, D.C. She joined Howard University in 2018 after serving as instructor of mathematics in the Department of Radiology at Weill Cornell Medical College. Prior to her faculty appointment at Howard, Kang held research and visiting scientist positions at institutions including Brookhaven National Laboratory and the Department of Materials Science at Stony Brook University. She earned her doctorate in applied mathematics and statistics from SUNY Stony Brook and holds a master’s and bachelor’s degree from Pusan National University in the Republic of Korea.
Kang’s research focuses on scientific computing and the mathematical and statistical analysis of dynamic brain positron emission tomography imaging, as well as machine learning and neural network modeling applied to biological and medical problems. She develops mathematical models using differential equations, spectral analysis and optimization techniques to describe complex physiological processes, and her work has appeared in peer-reviewed journals and collaborative scientific projects. In addition to her individual research, she plays a key role in multidisciplinary efforts funded by agencies such as the National Institutes of Health, the Simon Foundation and the National Science Foundation.
At Howard University, Kang teaches courses in probability, statistics and mathematical modeling and mentors undergraduate and graduate students in research and independent study. She serves on departmental committees, advises students on applied mathematics projects and contributes to curriculum development. Her scholarship and teaching reflect a commitment to advancing quantitative methods in science and expanding opportunities for students in mathematical and data sciences.
Education & Expertise
Education
Master of Science (M.S.)
Mathematics
Pusan National University
2001
Bachelor of Science (B.S.)
Mathematics
Pusan National University
1999
Academics
Academics
MATH 189 - Probability and Statistics I
Probability and Statistics for Data Science
Research
Research
Specialty
Quantification and bio-math modeling for the PET brain imagingFunding
Co-Principal Investigator, "Multi-modal assessment of cognitive dysfunction and resilience in Multiple Sclerosis" Sponsored by NIH, Federal. (July 2024 - June 2029)
Co-Principal Investigator, "Quantification of the innate immune activity within chronic lesions as a novel treatment biomarker in Multiple Sclerosis," Sponsored by NIH, Federal. (April 2024 - March 2029)
Principal Investigator, "Targeted Positron Emission Tomography Imaging of Head and Neck Squamous Cell Carcinoma," Sponsored by Howard University/Siteman Cancer Center Collaborative Research Initiative, Other, $50,000.00. (January 2023 - December 2023).
Co-Principal Investigator, "Excellence in Research: PathoRadi ‒ an interactive web server for AI-assisted radiologic-pathologic image analysis, correlation and visualization," Sponsored by NSF, Federal, $651,130.00. (September 1, 2022 - August 31, 2025).
Kang, Y.(Principal), "Mathematical modeling in brain dynamic PET image and application with machine learning," Sponsored by Simon Foundation, Private, $85,000.00. (September 2022 – August 2027).
Co-Principal Investigator, "ML, AI, data Science, Teaching, Education, and Research (MASTER) Consortium on Health Disparities Training Core," Sponsored by NIH, Federal, $15,000,000.00. (August, 2021 - March, 2025).
Kang, Y., "Cornell Math REU 2021," Sponsored by NSF-Cornell, Private, $5,000.00. (June 2021 - July 2021).
Kang, Y., "Summer Faculty Fellowship," Sponsored by Howard University, Private, $10,000.00. (2019).
Publications and Presentations
Publications and Presentations
Validating the Utility of supervised clustering algorithms for precise DPA713 PET brain image quantification
The reliance of quantitative PET imaging on the arterial input function makes brain PET challenging to perform in certain populations, limiting the sample size. To address this challenge, a supervised clustering algorithm (SVCA) has been introduced as an alternative. Our objective was to validate SVCA's performance for brain PET with [11C]DPA-713 that targets a putative marker of brain injury and repair.
Extended-release Pre-Exposure prophylaxis and drug resistant HIV
Extended-release Pre-Exposure prophylaxis and drug resistant HIV
The pharmacologic tail of long-acting cabotegravir (CAB-LA), an injectable pre-exposure prophylaxis (PrEP), allows for months-long intervals between injections, but it may facilitate the emergence of drug-resistant human immunodeficiency virus (HIV) strains during the acute infection stage. In this chapter, we present a within-host, mechanistic ordinary differential equation model of the HIV latency and infection cycle in CD4 T-cells to investigate the impact of CAB-LA on drug-resistant mutations in both humans and macaques. We develop a pharmacokinetic/pharmacodynamic model for CAB-LA to correlate the inhibitory drug response with the drug concentration in plasma.
Investigation of [11C]carfentanil for mu opioid receptor quantification in the rat brain
Investigation of [11C]carfentanil for mu opioid receptor quantification in the rat brain
[11C]Carfentanil ([11C]CFN) is the only selective carbon-11 labeled radiotracer currently available for positron emission tomography (PET) imaging of mu opioid receptors (MORs). Though used extensively in clinical research, [11C]CFN has not been thoroughly characterized as a tool for preclinical PET imaging. As we were occasionally observing severe vital sign instability in rat [11C]CFN studies, we set out to investigate physiological effects of CFN mass and to explore its influence on MOR quantification. In anesthetized rats (n = 15), significant dose-dependent PCO2 increases and heart rate decreases were observed at a conventional tracer dose range (IV, > 100 ng/kg).
Analysis of the Convolutional Neural Network Model in Detecting Brain Tumor
Analysis of the Convolutional Neural Network Model in Detecting Brain Tumor
Detecting brain tumors is an active area of research in brain image processing. This paper proposes a methodology to segment and classify brain tumors using magnetic resonance images (MRI). Convolutional Neural Networks (CNN) are one of the effective detection methods and have been employed for tumor segmentation. We optimized the total number of layers and epochs in the model. First, we run the CNN with 1000 epochs to see its best-optimized number. Then we consider six models, increasing the number of layers from one to six. It allows seeing the overfitting according to the number of layers.