School of Engineering
Showing 101-200 of 223 Results
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Xingyuan Zhang
Ph.D. Student in Chemistry, admitted Autumn 2023
Ph.D. Minor, Computer ScienceBioPhD candidate in Chemistry and Computer Science, Wu Tsai Neurosciences and Sarafan ChEM-H Institute at Stanford University. Working on scientific machine learning, biomolecular modeling, and protein design for drug discovery and disease modeling.
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Zhe Zhang
Postdoctoral Scholar, Electrical Engineering
BioEPFL Ph.D. in Photonics 2024
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Chunmei Zhao
Chief Education Solutions Officer, Stanford Engineering Center for Global and Online Education
Current Role at StanfordChief Education Solutions Officer, Stanford Center for Global & Online Education, School of Engineering.
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Haoqi (Nina) Zhao
Acting Assistant Professor, Civil and Environmental Engineering
BioNina will join Stanford CEE as an Assistant Professor in Summer 2026. Her research bridges computational metabolomics and exposomics to uncover unknown lifestyle chemicals and their impacts on health. Nina earned her B.S. from Peking University, her Ph.D. from the University of Washington, and is currently a postdoctoral scientist at UC San Diego. She received the Pathway to Independence Award (K99/R00) from the National Institutes of Health in 2025.
The Zhao Lab at Stanford will operate at the interface of environmental chemistry, environmental health, and computational mass spectrometry. We aim to identify previously unrecognized chemical drivers that influence human and ecosystem health. We pursue this goal by developing innovative computational and analytical methods centered around untargeted mass spectrometry. Through these efforts, we aim to enable precision environmental health strategies, inform public health policies, and inspire sustainable chemical design.
Current research interests include (but are not limited to):
1.Leveraging mass spectrometry data repositories and advanced informatics tools to systematically map the chemical exposome.
2.Developing tools and resources for mass spectrometry data analysis to advance exposomics research.
3.Investigating host-microbiome-xenobiotic interactions and their impacts on human health.
4.Integrating toxicology with untargeted screening to accelerate the discovery and risk assessment of unknown molecules.
5.Pediatric environmental health: chemical exposures through breastfeeding and their implications for infant development.
6.Traffic-related chemical pollution: environmental fate, occurrence, and relevance to human exposures.
Education
Ph.D., University of Washington, Environmental Engineering (2021)
B.S., Peking University, Environmental Science (2016)
Publications
https://scholar.google.com/citations?user=xW9jBO0AAAAJ&hl=en
Email: hqzhao@stanford.edu -
Renee Zhao
Assistant Professor of Mechanical Engineering and, by courtesy, of Bioengineering and of Materials Science and Engineering
BioRuike Renee Zhao is an Assistant Professor of Mechanical Engineering at Stanford University, where she directs the Soft Intelligent Materials Laboratory. Originally from the historic city of Xi'an, she earned her BS from Xi'an Jiaotong University in 2012. She then pursued Solid Mechanics at Brown University, obtaining her MS in 2014 and PhD in 2016. Following her doctoral studies, she completed postdoctoral training at MIT (2016–2018) before serving as an Assistant Professor at The Ohio State University (2018–2021).
Renee’s research focuses on developing stimuli-responsive soft composites for multifunctional robotic systems with integrated shape-changing, assembly, sensing, and navigation capabilities. By integrating mechanics, material science, and advanced material manufacturing, her work enables innovations in soft robotics, miniaturized biomedical devices, robotic surgery, origami systems, active metamaterials, and general deployable morphing structures.
Her contributions have been recognized with honors and awards, including the Presidential Early Career Award for Scientists and Engineers (PECASE), DARPA Young Faculty Award (YFA, 2025), ARO Early Career Program (ECP) Award (2023), AFOSR Young Investigator Research Program (YIP) Award (2023), Eshelby Mechanics Award for Young Faculty (2022), ASME Henry Hess Early Career Publication Award (2022), ASME Pi Tau Sigma Gold Medal (2022), ASME Applied Mechanics Division Journal of Applied Mechanics Award (2021), NSF CAREER Award (2020), and ASME Applied Mechanics Division Haythornthwaite Research Initiation Award (2018). She is also recognized as a National Academy of Sciences Kavli Fellow and was named one of MIT Technology Review's 35 Innovators Under 35. -
Xiaolin Zheng
Professor of Mechanical Engineering, of Energy Science Engineering, Senior Fellow at the Precourt Institute for Energy and Professor, by courtesy, of Materials Science and Engineering
BioProfessor Zheng received her Ph.D. in Mechanical & Aerospace Engineering from Princeton University (2006), B.S. in Thermal Engineering from Tsinghua University (2000). Prior to joining Stanford in 2007, Professor Zheng did her postdoctoral work in the Department of Chemistry and Chemical Biology at Harvard University. Professor Zheng is a member of MRS, ACS and combustion institute. Professor Zheng received the TR35 Award from the MIT Technology Review (2013), one of the 100 Leading Global Thinkers by the Foreign Policy Magazine (2013), 3M Nontenured Faculty Grant Award (2013), the Presidential Early Career Award (PECASE) from the white house (2009), Young Investigator Awards from the ONR (2008), DARPA (2008), Terman Fellowship from Stanford (2007), and Bernard Lewis Fellowship from the Combustion Institute (2004).
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Zhuo Zheng
Postdoctoral Scholar, Computer Science
BioMy research interests are Earth Vision and AI4Earth, especially multi-modal and multi-temporal remote sensing image analysis and their real-world applications.
First-author representative works:
- Our Change family: ChangeStar (single-temporal learning, ICCV 2021), ChangeMask (many-to-many architecture, ISPRS P&RS 2022), ChangeOS (one-to-many architecture, RSE 2021), Changen (generative change modeling, ICCV 2023)
- Geospatial object segmentation: FarSeg (CVPR 2020) and FarSeg++ (TPAMI 2023), LoveDA dataset (NeurIPS Datasets and Benchmark 2021)
- Missing-modality all weather mapping: Deep Multisensory Learning (first work on this topic, ISPRS P&RS 2021)
- Hyperspectral image classification: FPGA (first fully end-to-end patch-free method for HSI, TGRS 2020) -
Shiyuan Zhou
Postdoctoral Scholar, Materials Science and Engineering
BioShiyuan Zhou is a recipient of the 2026 Stanford Energy Postdoctoral Fellowship. He received his Ph.D. in Energy Chemistry in 2024 through a joint doctoral program between Xiamen University and Argonne National Laboratory, under the supervision of Prof. Shi-Gang Sun, Dr. Gui-Liang Xu, and Dr. Khalil Amine.
His research advances the frontiers of battery chemistry through the development of multimodal operando electrochemical scanning/transmission electron microscopy (EC-S/TEM) integrated with synchrotron X-ray characterization, enabling direct observation of real-time electrochemical and structural dynamics in energy materials. Trained as both a materials chemist and microscopist, his work focuses on visualizing highly sensitive and previously inaccessible electrochemical processes in batteries.
During his doctoral research, he developed in situ liquid-cell transmission electron microscopy techniques to probe real-time reaction dynamics in lithium–sulfur batteries. Following his Ph.D., he continued at Argonne as a postdoctoral fellow, where he expanded his research to multimodal and multiscale imaging approaches, integrating advanced electron microscopy with transmission X-ray microscopy to study all-solid-state batteries. His research has been recognized as one of China’s Top 10 Scientific Advances of 2023, and he has received the Tan Kah Kee Medal as well as the Argonne Impact Award. -
Yihong Zhu
Ph.D. Student in Aeronautics and Astronautics, admitted Summer 2025
Current Research and Scholarly InterestsReduction Order Modeling, Fluid Mechanics, Applied Mathematics
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Richard Zhuang
Masters Student in Computer Science, admitted Autumn 2025
BioI’m broadly interested in understanding and improving the capabilities of Large Language Models (LLMs) in a data-centric way. Specifically, I’m intrigued by how certain data “foster” skills that are essential for LLM agents (e.g. reasoning and planning). I have also had a long-standing passion in Sports Analytics. Outside the realm of AI, you will usually find me playing basketball!
