School of Humanities and Sciences
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Persis S. Drell
Provost, Emerita, James and Anna Marie Spilker Professor, Professor of Materials Science and Engineering and of Physics
BioPersis Drell is the James and Anna Marie Spilker Professor in the School of Engineering, a professor of materials science and engineering, and a professor of physics. From Feb 1, 2017 to Sept. 30, 2023, Drell was the provost of Stanford University.
Prior to her appointment as provost in February 2017, she was dean of the Stanford School of Engineering from 2014 to 2017 and director of U.S. Department of Energy SLAC National Acceleratory Laboratory from 2007 to 2012.
She earned her bachelor’s degree in mathematics and physics from Wellesley College and her PhD in atomic physics from UC Berkeley. Before joining the faculty at Stanford in 2002, she was a faculty member in the physics department at Cornell University for 14 years. -
Justin Du Bois
Henry Dreyfus Professor of Chemistry and Professor, by courtesy, of Chemical and Systems Biology
BioResearch and Scholarship
Research in the Du Bois laboratory spans reaction methods development, natural product synthesis, and chemical biology, and draws on expertise in molecular design, molecular recognition, and physical organic chemistry. An outstanding goal of our program has been to develop C–H bond functionalization processes as general methods for organic chemistry, and to demonstrate how such tools can impact the logic of chemical synthesis. A second area of interest focuses on the role of ion channels in electrical conduction and the specific involvement of channel subtypes in the sensation of pain. This work is enabled in part through the advent of small molecule modulators of channel function.
The Du Bois group has described new tactics for the selective conversion of saturated C–H to C–N and C–O bonds. These methods have general utility in synthesis, making possible the single-step incorporation of nitrogen and oxygen functional groups and thus simplifying the process of assembling complex molecules. To date, lab members have employed these versatile oxidation technologies to prepare natural products that include manzacidin A and C, agelastatin, tetrodotoxin, and saxitoxin. Detailed mechanistic studies of metal-catalyzed C–H functionalization reactions are performed in parallel with process development and chemical synthesis. These efforts ultimately give way to advances in catalyst design. A long-standing goal of this program is to identify robust catalyst systems that afford absolute control of reaction selectivity.
In a second program area, the Du Bois group is exploring voltage-gated ion channel structure and function using the tools of chemistry in combination with those of molecular biology, electrophysiology, microscopy and mass spectrometry. Much of this work has focused on studies of eukaryotic Na and Cl ion channels. The Du Bois lab is interested in understanding the biochemical mechanisms that underlie channel subtype regulation and how such processes may be altered following nerve injury. Small molecule toxins serve as lead compounds for the design of isoform-selective channel modulators, affinity reagents, and fluorescence imaging probes. Access to toxins and modified forms thereof (including saxitoxin, gonyautoxin, batrachotoxin, and veratridine) through de novo synthesis drives studies to elucidate toxin-receptor interactions and to develop new pharmacologic tools to study ion channel function in primary cells and murine pain models. -
John Duchi
Associate Professor of Statistics, of Electrical Engineering and, by courtesy, of Computer Science
Current Research and Scholarly InterestsMy work spans statistical learning, optimization, information theory, and computation, with a few driving goals: 1. To discover statistical learning procedures that optimally trade between real-world resources while maintaining statistical efficiency. 2. To build efficient large-scale optimization methods that move beyond bespoke solutions to methods that robustly work. 3. To develop tools to assess and guarantee the validity of---and confidence we should have in---machine-learned systems.
