School of Engineering
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Sho Takatori
Associate Professor of Chemical Engineering
BioPeople say that a picture is worth a thousand words. We think that an equation is worth a thousand pictures. Literally. By collecting and processing data-rich images of complex fluids and matter, we develop “picture-perfect” equations to learn structure-property relationships for new material innovation.
In the Takatori lab, we combine theory, simulation, and experiment to discover mathematical models for complex fluids in engineered and natural environments. We use advanced microscopy and analyze pictures with data-driven methods to understand material properties that bridge the microscopic-to-continuum scales. Our research encompasses soft squishy materials like polymers and liquid crystals, as well as granular matter like sand, powders, and foams.
Outside of research, I have had a strong passion for public speaking since high school, taking speech courses in college and competing in speech contests in Toastmasters International (a professional organization to improve public speaking and leadership skills) for several years as a PhD student. More recently, as a professor and educator, I have channeled my passion for speaking towards science education and technical communication. I have always believed that effective science communication can make broad impacts to society by building public trust in science, promoting data-driven decisions in government and industry, and improving the accessibility of science to all communities. I look forward to continue working on effective science communication skills and storytelling techniques with Stanford graduate students and researchers. -
Thierry Tambe
Assistant Professor of Electrical Engineering and, by courtesy, of Computer Science
BioThierry Tambe is an Assistant Professor of Electrical Engineering and, by courtesy, of Computer Science, and the William George and Ida Mary Hoover Faculty Fellow at Stanford University. His research centers on co-designing algorithms and hardware—from high-level models down to custom silicon—to enable efficient execution of AI and data-intensive workloads, with memory efficiency as a central theme. His work has been recognized through an NSF CAREER Award, the inaugural Google ML and Systems Junior Faculty Award, an NVIDIA Graduate PhD Fellowship, an IEEE SSCS Predoctoral Achievement Award, and several distinguished paper awards. Previously, Thierry was a visiting research scientist at NVIDIA and an engineer at Intel. He received a B.S. and M.Eng. from Texas A&M University, and a PhD from Harvard University, all in Electrical Engineering.
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Li-Yang Tan
Associate Professor of Computer Science
Current Research and Scholarly InterestsTheoretical computer science, with an emphasis on complexity theory
