Kai-Chih Huang
Decoding Tissue Microenvironments with Spatial Omics and Deep Learning.
From Electrical Engineering to Computational Biology: Advancing Precision Medicine through Multimodal Data Integration and Translational Insights.
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Dr. Kai-Chih Huang is a Senior Computational Biologist specializing in precision medicine and reverse translation.
He holds a Bachelor's degree in Electrical Engineering from National Taiwan University and a Ph.D. in Biomedical Engineering from Boston University, bringing over 12 years of cross-disciplinary experience in bioinformatics, spatial omics, and deep learning algorithm development.
He excels at integrating single-cell data, digital pathology imaging, and cutting-edge machine learning models to build integrated tissue maps and extract key translational insights. Committed to cross-functional communication, he leverages his expertise to fully support early-stage drug discovery, target identification, and clinical patient stratification.
Experience
Led advanced spatial omics research and pipeline development for spatial transcriptomics, proteomics, and digital pathology at Pfizer I&I.
Spearheaded spatial biology algorithms and Graph Neural Network (GNN) models to construct integrated tissue maps for autoimmune indications.
Delivered translational insights from clinical biopsy datasets to identify spatial determinants of therapeutic non-response.
Represented Pfizer at in-person high-stakes industry consortium meetings, including the FNIH AMP-AIM.
Streamlined computational platforms and deep learning architectures (e.g., DeepNeurite) to accelerate target identification and structural pathology quantification.
Education
Boston University, Ph.D. in Biomedical Engineering
Boston University, M.S. in Biomedical Engineering
National Taiwan University, B.S. in Electrical Engineering
Journey
Journey
Dr. Kai-Chih Huang is a passionate scientist specializing in precision medicine and reverse translation. He brings over 12 years of cross-disciplinary collaboration experience working alongside biologists and handling high-dimensional data processing.
His academic journey began with a Bachelor's degree in Electrical Engineering from National Taiwan University, followed by advanced studies at Boston University, where he earned both his Master's and Ph.D. degrees in Biomedical Engineering. During his academic research phase, he successfully developed the fastest label-free chemical image cytometry system of its time—achieving a throughput of up to 100 cells per second—and leveraged it to high-content screen and identify biomarkers for drug-resistant cancer cells, demonstrating his deep foundation in optical imaging, image processing, and machine learning.
Upon graduation, he entered the biotech and pharmaceutical industry as a computational biology platform scientist, utilizing single-cell RNA sequencing (scRNA-seq) data and deep learning models to analyze the tumor microenvironment and neuro-cancer interactions. Currently, he serves as a Senior Computational Biologist in the Inflammation & Immunology (I&I) department of a leading global pharmaceutical company. In this role, he leads a team utilizing spatial omics and digital pathology to drive drug discovery, clinical patient stratification, and reverse translational medicine. Possessing a unique cross-disciplinary skill set that spans bioinformatics analysis, deep learning algorithm development, and cross-functional communication, he remains dedicated to advancing next-generation precision medicine.
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