William Peria is a staff scientist. He received a BS in physics  from the University of Minnesota. As an undergraduate, he worked in Laurence J. Cahill's laboratory, managing and participating in the building of a rocket payload to measure the electrodynamic properties of the auroral ionosphere. The payload (ARCS4) flew successfully in 1991.
Bill began a PhD program at the University of New Hampshire in 1989 in the laboratory of Roy Torbert, where he built and calibrated charged particle detectors to study plasma physics in several contexts, including the auroral ionosphere, a barium release experiment, and an artificial HF heating experiment. In the latter context, he showed that the nonlinear ponderomotive force, an unavoidable driver of currents, present whenever an electromagnetic wave has an intensity gradient, most likely drives the formation of cavity structures in plasmas, not only at previously predicted and observed meter scales, but also at scales ranging from tens of meters to tens of kilometers. He was a NASA Graduate Student Fellow from 1992-1995.
Bill joined the UC Berkeley Space Sciences lab as a postdoc in 1996 to work on results from the FAST (Fast Auroral SnapshoT) spacecraft, a polar orbiter with extremely high burst-mode resolution, configurable on-orbit. He built a database of all auroral overflights by FAST, indexed by probable traversals of field-aligned auroral current sheets. Bill implemented and deployed the software to extract these traversals from the raw magnetometer data. He used this database to index and explore other aspects of the FAST data set. For example, he discovered that the energetic electrons in upgoing electron beam (UEB) events have a greater spatial inhomogeneity than their downgoing counterparts, a clear signature of two distinct acceleration mechanisms for these two particle populations.
In 2002, he began to apply his overarching interest in the organizing principles of complex systems to the study of living cells. In 2005, he began to research the idea that macromolecules ought to produce detectable acoustic radiation, or ultrasound, when undergoing conformational changes or binding, and he began the quest to detect such sounds. He holds a patent on the use of sound and an apparatus to detect these conformational changes (US8402828 B2).
In 2006, Bill began work as a research scientist in the laboratory of Geoffrey Loftus, at the Department of Cognitive Psychology at the University of Washington. Here he contributed to the study of the human visual memory system, designing and implementing experiments with human subjects. In these experiments, he presented to human participants series of visual stimuli, for varying controlled durations ranging from 17 ms to 537 ms. The stimuli were grayscale images of human faces and naturalistic scenes. After the stimuli, participants took a recognition test; such a test can determine the minimal stimulus required to form a useful memory of a naturalistic image. Bill discovered that the strength of memory formation appeared to follow a power law, i.e. the likelihood of failing to form a recognition memory decreased as an inverse power of stimulus duration. These stimuli were presented in a mixture of three ways: (1) unfiltered, (2) filtered so as to include only low-frequency spatial information, and (3) filtered so as to include only high-frequency spatial information. (Different spatial frequency bands are known to be processed in different locations within the mammalian brain, see e.g. Enroth-Cugell and Robson, 1966, or Schiller et al., 1976.) He found that unfiltered images were more effective in forming memories than one would predict from a model in which the high and low frequency bands are processed independently, thus discovering that the processing of distinct spatial frequency bands is in fact synergistic.
In 2011, Bill joined at the Brent lab, where he began work on physical and computational approaches to the study of cell signaling. He has contributed to continual improvements in microscopy, data analysis and modeling, and understanding of the structure of cell signaling systems. In late 2015, he began to explore independently the burgeoning world of machine learning, and since 2017 has been helping produce interpretable deep learning models for addressing biological questions.
Enroth-Cugell, C, and Robson, J. G The contrast sensitivity of retinal ganglion cells of the cat. J Physiol. 1966 Dec;187(3):517-52
P. H. Schiller, P. H., Finlay, B. L. and Volman, S. F.. Quantitative studies of single-cell properties in monkey striate cortex. III. Spatial frequency. Journal of Neurophysiology 39, 1334-1351 (1976)