About Us

Machine Learning & Image Analysis

Services
Resources
Team Members

Services

BICF’s machine learning and image analysis team has expertise in developing state-of-the-art artificial intelligence (AI) models and tackling sophisticated data domains. We create custom solutions tailored to your data and research objectives, bringing together machine learning, image analysis, and advanced visualization techniques to enable complex human-data interactions. The team’s diverse and growing portfolio of engagements includes a growing number of AI-based medical image analyses such as pneumothorax segmentation on X-ray, micro-hemorrhage detection on CT, and brain tumor segmentation on MRI, successfully delivering machine learning algorithms for cancer drug screening, multi-omics data integration, and clinical outcome prediction, and designing custom 3-D image processing pipelines and visualization tools for terabyte-scale image volumes generated by some of the world’s most advanced light sheet microscopes.

Resources

Scientific Community Image Forum (SCIF)

Fiji - image processing package

Open Microscopy Environment

Image Science - software tools

Ilastik - interactive learning and segmentation toolkit

MathWorks - image segmentation and analysis

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Team Members

Andrew Jamieson, Ph.D.
Assistant Professor

Andrew Jamieson

Born in San Diego, California, raised in Plano, Texas and educated in Chicago, Illinois, Andrew leads BICF's image analysis initiatives. His work is focused on accelerating scientific discovery by transforming the diverse and powerful image-analysis tools developed in the lab into an integrated, easy-to-use package available to the community. Prior to joining the Lyda Hill Department of Bioinformatics in 2016, Andrew was a data scientist for a Dallas-based start-up, held various roles at GE Healthcare, including Scientific Lean Leader, and most recently worked in Pharma Services at NeoGenomics/Clarient as part of the MultiOmyx team delivering advanced image-based biomarker analytics to customers. Andrew received his B.A. (Physics) and Ph.D. (Medical Physics) from the University of Chicago. In his free time, Andrew is a dilettante pop cultural critic: listening to a variety of podcasts on a variety of topics-including philosophy, watching David Lynch films, and tuning in to the latest BBC Essential Mix.
Faculty Profile
Publications
LinkedIn Profile

Jeon Lee, Ph.D.
Computational Biologist III

Jeon Lee

Dr. Lee is a Computational Biologist and in collaboration with Dr. Jamieson, leads BICF's machine learning initiatives. He studied Biomedical Engineering, focusing on medical devices and AI-driven diagnostic tools in Yonsei University, South Korea. Prior to joining BICF, Jeon was a senior researcher in KIOM, a Korean national research institutes and holds 20 biomedical patents in Korea. Later, he completed postdoctoral training at Johns Hopkins School of Medicine and worked as a research faculty in the Computer Science and Electrical Engineering Department, University of Maryland Baltimore County. Since joining the BICF, his work has focused on machine learning projects including multi-modal data fusion, deep learning for clinical diagnosis or outcome prediction, and natural language processing for EMR data. Through the Help Desk, he also assists with NGS data analysis for single-cell RNA-seq, bulk RNA-seq, ChIP-seq.
Publications
LinkedIn Profile

Krishna Kanth Chitta, M.S.
Computational Scientist

Krishna Kanth Chitta

Krishna Chitta has a M.S. in Biomedical Engineering and is focused on computational image analysis and deep learning applications. He joined BICF in 2019 to address the increasing demand machine learning. Before this, he worked at A*STAR (Agency for Science and Technology) in Singapore on detecting and classifying brain images for clinical outcome. He is now generalizing these methods for the applications brought to BICF.
LinkedIn Profile

Zhiguo Shang, Ph.D.
Computational Scientist

 Zhiguo Shang

Zhiguo Shang is a Computational Scientist specializing in image analysis. He currently works on PET/CT medical images with diverse machine learning tools, such as 2D/3D segmentation and advance pattern recognition techniques. He also working on developing methods to combine genetic sequencing with medical image processing to address challenges in cancer diagnosis and prognosis.

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