Stanford University
Showing 341-350 of 447 Results
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Genevieve Smith
Postdoctoral Scholar, Comparative Literature
BioI am a Postdoctoral Fellow at Stanford University in the Clayman Institute for Gender Research. I completed my doctoral degree at the University of Oxford, where I studied societal impacts of artificial intelligence in low- and middle-income countries, focusing on gender. As a social scientist with a disciplinary background of science and technology studies (STS) and devleopment studies, I examine the impacts of AI on inequality and society, as well as explore more equitable and responsible paradigms for AI technologies globally. I founded the Responsible AI Initiative at the Berkeley Artificial Intelligence Research Lab and teach on responsible AI. I am a research affiliate at the Minderoo Centre for Technology & Democracy at Cambridge University and at the Technology & Management Centre for Development at University of Oxford. Prior, I served as the Responsible AI Fellow at the United States Agency for International Development and as Interim Co-Director of the UC Berkeley AI Policy Hub.
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Aamir Sohail
Graduate Visiting Researcher Student, Psychology
BioI am a PhD student in Psychology based at the Centre for Human Brain Health (CHBH), University of Birmingham, and a Visiting Student Researcher at the Departments of Psychology and Sociology at Stanford University.
My research interests involve using experimental tasks and computational modeling to understand social decision-making in humans and AI. I am also more broadly interested in the impact of AI on society, and towards how science is conducted. -
Siamak Sorooshyari
Postdoctoral Scholar, Statistics
BioMy research lies at the intersection of AI/ML, statistics, biology, and engineering. I was initially trained as an electrical engineer, with a focus on signal processing and statistical algorithms. I then pursued my PhD in a neuroscience laboratory studying stress and the blood-brain barrier, where I gained experience with biological systems, experimental design, instrumentation, and data collection. My current work brings these perspectives together as I develop computational and statistical methods to better understand and predict biological processes.
A major focus of my research is understanding how aging affects the brain and how these changes are reflected across biological scales and measurement modalities. I have studied signals recorded from individual brain regions, communication between brain networks, and changes in functional connectivity across the lifespan. An important question in this work is whether quantitative properties of biological signals, such as monotonicity, exhibit consistent relationships with age. I am particularly interested in determining how different modalities capture changes associated with aging in both healthy and diseased systems, and what these measurements reveal about the underlying biological processes. This perspective can also provide insight into the reliability and interpretability of different recording modalities as measures of biological aging. I have recently begun extending these questions beyond the brain to the brain-gut-heart axis in healthy humans. By examining relationships and coordinated changes among measurements from multiple organs, I aim to develop a more integrated understanding of healthy aging and, ultimately, of how these relationships are altered in disease, specifically neurodegeneration. This work represents a broader effort to study aging as a multidimensional biological process rather than as a phenomenon confined to a single organ or measurement modality.
In parallel, I develop statistical methods for assessing the reliability and reproducibility of unsupervised learning results. In particular, I am interested in understanding how methodological choices - including the clustering algorithm, model parameters, and the number of clusters - can affect the conclusions drawn from noisy, high-dimensional datasets. This work has led to ERICA (evaluating replicability via iterative clustering assignments), a framework for evaluating whether clustering structure can be reproduced under repeated analyses without requiring a predefined ground truth. I am applying this framework to biological datasets, including cancer and neurodegenerative diseases, where clustering is frequently used to identify molecular or phenotypic subgroups. More broadly, this work seeks to develop rigorous statistical tools that can help distinguish reproducible structure from patterns that may depend strongly on methodological choices.
