Ph.D. Student, Biomedical Engineering
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Ryan joined the lab in 2013 after graduating from Georgia Tech with a B.S. in Biomedical Engineering. His research areas include health informatics and biomedical image processing.
Ying Sha earned her bachelor’s degree in biology from Peking University and her master’s degree in bioinformatics from Georgia Tech. Currently working on her doctoral degree in bioinformatics at Georgia Tech, she is conducting research pertaining to temporal data mining using intensive care unit (ICU) data. Find more details in my homepage.
Ph.D. Student, Biomedical Engineering
Before joining Georgia Tech, I graduated with a bachelor’s degree in engineering from the Department of Biomedical Engineering, Peking University, Beijing, China. My primary research focuses on medical imaging informatics and biomedical data integration. For medical imaging informatics, I have worked on image classification and segmentation for pathological whole slide images, CT images, and endomicroscopic images, aiming to improve the performance using semi-supervised or weakly-supervised deep learning methods. For biomedical data integration, I am mainly working on improving the prediction by learning modality-invariant representation from multi-modal data sets.
Ph.D. Student, Machine Learning (Home Dept. BME)
Before joining Georgia Tech, I graduated with a bachelor degree in Engineering from Department of Automation, Tsinghua University, Beijing, China. My research focus is on causal/counterfactual inference and its applications in health informatics. I’ve also worked on projects on biomedical imaging and public health.
Electrical and Computer Engineering
I am working on applying deep learning techniques on medical image processing and data-driven health informatics. For medical imaging, my research interest is weakly-supervised learning on whole-slide images. For health informatics, I prefer to topic on exploring the causal inference between diagnosis codes and non-diagnosis information (such as demographic, healthcare plan and payment) on the mortality consideration.
Master Student, Biomedical Engineering
Qihang earned his Bachelor degree in Biomedical Engineering from Northeastern University, China, having two-year experience as research assitant in Chineses Academy of Sciences, prior to joining in Georgia Tech. His past reseach projects mainly conern physiological signal analysis. He is currently working on clinical event prediction based on clinical and physiologcial time series analysis.
Je-Hoon Michael Oh
Master student, Bioinformatics
I am currently researching on causal inference. My focus is applying variations of Granger Causality algorithm to analyze causal relationships among time series data.
M.S. Student, Computational Science and Engineering
Anirudh Choudhary is a second year graduate student, pursuing M.S. in Computational Science and Engineering. He received his Bachelors degree in Electrical Engineering from IIT Kharagpur and completed his MBA from IIM Calcutta, India. His research interests include machine learning and medical image processing. His work focuses on developing efficient deep learning and causal inference models for personalized and predictive healthcare. Previously, he has worked on developing predictive algorithms for oral cancer detection, liver tumor segmentation, and cerebellum segmentation in MRI images.
B.S. Student, Computer Science and Biomedical Engineering
Mohammed is an undergraduate student who is double majoring in BME and CS. He is currently researching on EEG waveform processing and prediction (seizure, patient phenotypes) and is interested in subjects in both CS and BME (i.e. scalability of technology, bioinformatics, clinical informatics, machine learning).
B.S. Student, Nuclear & Radiological Engineering
Tarun is an undergraduate student majoring in Nuclear & Radiological Engineering with minors in Physics and Computer Science. He is interested in the fields of medical image processing and machine learning and is currently working on a project utilizing convolutional neural networks to preform weakly supervised learning on whole-slide images.
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