School of Engineering

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  • Xianghao Zhan

    Xianghao Zhan

    Ph.D. Student in Bioengineering, admitted Autumn 2019
    Student Employee, DASH

    BioXianghao Zhan is a 5th -year Ph.D. candidate at Stanford Bioengineering. He obtained his M.S in Bioengineering in 2021 and his M.S in Statistics in 2023 both at Stanford. Before that he got B. Eng. in Control Science and Engineering (Automation) and his B. Art in English Language and Literature with Summa Cum Laude at Chu Kochen Honors College, Zhejiang University, China, in 2019.

    Under the guidance of Prof. Oliver Gevaert and Prof. David B. Camarillo, he mainly focuses on the optimization of computational modeling of traumatic brain injury with machine learning and animal modeling based on biomechanical and radiological data. His research interests and projects also extend to the data mining of free-text clinical notes with natural language processing, biomedical data fusion for COVID-19 patient outcome prediction, machine learning reliability quantification with conformal prediction, reliability-based semi-supervised learning, and domain adaptation for biomedical sensory systems (with artificial olfaction systems and surface electromyography systems). He has published 16 peer-reviewed articles as a first/co-first author in such journals as NPJ Digital Medicine, IEEE Transactions on Biomedical and Health Informatics, IEEE Transactions on Biomedical Engineering, Journal of Sport and Health Science, with 6 first-author journal articles under review. He has been a peer reviewer for 16 journals including Annals of Biomedical Engineering, Journal of Neurotrauma, Computer methods in biomechanics and biomedical engineering.

    In addition to his research, he has two master degrees while pursuing his Ph.D. degree: BIOE 2021 and STATS 2023. He has taken more than 10 data science and machine learning courses at Stanford with course project experiences and technical background with UNet-based image segmentation, BERT, Transformer-XL, DeepSEA, BPNet, VAE/SSVAE, flow model, energy-based model cycle-GAN, CNN-based image classification, LSTM-based clinical event prediction, Bi-LSTM-based neural machine translation, BERT, DCT/DWT/STFT, PCA, DRCA, NFL, convex optimization.

    His research is recognized by the field and he was awarded with IET Postgraduate Research Award for an Outstanding Researcher (one awardee across the globe, first Chinese), Siebel Scholar Class of 2024, IET Healthcare Technology William James Award (one awardee across the globe), Stanford Interdisciplinary Graduate Fellowship (highest honor for interdisciplinary Stanford graduates), Pfeiffer Research Foundation Fellow, AMIA Trainee Award (six awardees, the only Chinese), American Society of Neurotrauma Trainee Award (20 awardees, the only Chinese), Chu Kochen Scholarship (12/23,000), Ten most Preeminent Students of Zhejiang University (10/36,000), Chinese National Scholarship (Top 0.2%).

    He is dedicated to support underrepresented minorities. He has been a program leader for Stanford Summer Research Program and mentored 3 undergrads from the underrepresented minorities. He has been a research mentor at Foothill College for two years and mentored latino students from local community college. Additionally, he is a sports fan with 12 Stanford Intramural champions (9 volleyball, 3 tennis) and two medals from regional volleyball tournaments. He enjoys the sport passion and team spirits as a captain.

  • Anqi Zhang

    Anqi Zhang

    Postdoctoral Scholar, Chemical Engineering

    BioDr. Anqi Zhang is currently an American Heart Association (AHA) postdoctoral fellow advised by Professor Zhenan Bao in the Department of Chemical Engineering and Professor Karl Deisseroth in the Department of Bioengineering at Stanford University. She received her Ph.D. degree under the supervision of Professor Charles M. Lieber in the Department of Chemistry and Chemical Biology at Harvard University in 2020, and her B.S. degree in Materials Chemistry from Fudan University in 2014. She is interested in combining novel electronic, chemical, and genetic tools to monitor and modulate neural circuits in a minimally invasive manner.