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Hang Chang

Computer Research Scientist

Biological Systems and Engineering

  • BioEngineering & BioMedical Sciences

Secondary Affiliation: Molecular Biophysics and Integrated Bioimaging

  • Cellular and Tissue Imaging

Building: 977, Room 291
Mail Stop: 977
Phone: (510) 495-2262
HChang@lbl.gov
http://bmihub.org/users/hang-chang

Research Interests

The research interests in my lab are primarily centered at interfaces between engineering, computation and biology. Our current research focus is on knowledge discovery and inference from large scale scientific data with applications to computational biology and biomedical informatics, including,

  • Identification of imaging bio-markers towards personalized therapy; and,
  • Development of a big data oriented open-source Information Technology (IT) solution for domain adaptive biomedical informatics.

Selected Publications

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Genetic Background Influences Cancer Risk of Thirdhand Smoke Exposure

June 7, 2023

A new study investigating the effect of thirdhand smoke (THS) in a mouse model system specially designed to mimic the genetic diversity of human populations has shed new light on how genetic predispositions contribute to an individual’s cancer risk. This work is an instrumental step towards building a more realistic understanding of how tobacco smoke residue could impact cancer risk in people.

Machine Learning Helps Link Chemical Exposure and Obesity

May 1, 2023

Scientists at Berkeley Lab and their collaborators developed a machine learning technique to discover obesity-related mixed chemical exposure patterns associated with environmental health risk in the general U.S. population. To assess this, they used indicators like body mass index and waist circumference.

Researchers at Berkeley Lab Advance Cancer Research Using Artificial Intelligence   

July 22, 2022

BSE Researchers recently published two studies that will help oncologists more precisely understand the state of their patients’ disease or their risk for cancer relapse. As with many diseases, cancer can be challenging to predict and in some cases, impossible to treat. This work, however, is pushing the boundaries of how science and artificial intelligence (AI) can be used to better understand the risks and outcomes of cancer in human health. 

For more Hang Chang news items »

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