Stanford University
Showing 2,081-2,090 of 2,711 Results
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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. -
Alexander Spangher
Postdoctoral Scholar, Computer Science
BioAlexander Spangher is a post-doctoral researcher advised by Daniel Ho, Sanmi Koyejo and Diyi Yang. His research focuses on modeling human decision-making in creative domains, especially in contexts where data is limited and rewards and goals are less clear. He is building out a new domain of learning, called emulation learning, with the goal of training the next generation of reasoning-oriented language models to be more proficient in these domains. His research has been used at technology organizations like OpenAI, Google and EleutherAI. He is especially passionate about helping journalists and has framed tasks and trained reasoning LLMs to help journalists find stories and sources, structure narratives and track information updates. These tools have been incorporated into newsrooms at the New York Times, Bloomberg and Stanford Big Local News, impacting thousands of journalists; and his work is also informing the next generation of journalistic education at USC Annenberg. His work has received numerous awards including two outstanding paper awards at EMNLP 2024, one spotlight award at ICML 2024, one outstanding paper award at NAACL 2022 and a best paper award at CJ2023; and he has been supported by a 4-year Bloomberg PhD Fellowship. His work is broad: in addition to his work in NLP and computational journalism, he has studied misinformation at Microsoft Research and collaborated with the MIT Plasma Science and Fusion Center to model plasma fusion processes.
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Sean Paul Spencer, MD,PhD
Assistant Professor of Medicine (Gastroenterology and Hepatology)
BioSean Spencer, MD,PhD is a Gastroenterologist and Physician Scientist at Stanford University working to uncover the role of dietary intake on the gut microbiome and mucosal immune system. Sean obtained his medical degree University of Pennsylvania, earning his PhD studying nutritional immunology with Yasmine Belkaid,PhD at the National Institutes of Health (NIH), after which he moved to Boston for residency training at Massachusetts General Hospital and completed his Gastroenterology training at Stanford University. Sean’s career goal is to study mechanisms by which dietary intake influences our microbiome and immune system to better understand and treat gastrointestinal disease. Sean has launched a microbiome-focused clinical practice at Stanford where he is working to develop novel microbiome diagnostics and microbial medicines.
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Chelse Spinner, PhD, MPH
Postdoctoral Scholar, Neonatal and Developmental Medicine
BioAn Ohio native, Dr. Chelse Spinner obtained a Bachelor of Science in Biological Sciences (Biomedical Studies) with a minor in Health Education from the University of Cincinnati. She earned a Master of Public Health in Epidemiology and Maternal & Child Health (MCH) from the University of South Florida. She received a Doctor of Philosophy in Public Health Sciences with a concentration in Behavioral Sciences from the University of North Carolina at Charlotte. Dr. Spinner is certified in public health and has experience working across health systems. Her research interests include health disparities, women’s health, social determinants of health, domains of structural racism, and oral-systemic health within the MCH population. She employs quantitative and qualitative methods in the hopes of providing innovative and evidence-informed research to improve health outcomes for marginalized and underserved communities. Her research agenda intends to focus on the exploration of social and structural factors that impact health and well-being.
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Catherine Spurin
Postdoctoral Scholar, Energy Resources Engineering
BioI am a postdoctoral researcher in the Energy Science & Engineering department. My current research is focused on understanding how subsurface heterogeneity can be exploited to increase the amount of CO2 that is residually trapped. This increases storage security and minimizes the spread of the CO2 plume. This research makes up part of the GeoCquest consortium with Melbourne University, Cambridge University and CO2CRC. My supervisors are Prof. Hamdi Tchelepi and Prof. Sally Benson.
I obtained my PhD from Imperial College London in 2021. My PhD thesis "Intermittent flow pathways for multiphase flow in porous media: a pore-scale perspective" explored how flow phenomena not included in the framework of Darcy's law extended to multiphase flow influence the propagation and trapping of fluids. My supervisors were Prof. Sam Krevor and Prof. Martin Blunt. My research was funded by the President's PhD scholarship at Imperial. -
Griffin Srednick
Postdoctoral Scholar, Oceans
BioGriffin Srednick, PhD, is an NSF Postdoctoral Research Fellow at Stanford Oceans and a community ecologist specializing in the spatiotemporal dynamics of marine communities. His postdoctoral research investigates how coral reef communities recover from disturbance and respond to the effects of climate change. Conducted within the National Science Foundation's Moorea Coral Reef (MCR) Long Term Ecological Research (LTER) program, his work examines how spatiotemporal heterogeneity in coral communities can promote ecological resilience. By integrating oceanographic modeling with coral reef ecology, his research aims to reveal the mechanisms underpinning coral recovery following disturbance. His broader scientific interests focus on understanding the complex architecture of ecosystems and how a holistic view of ecological systems can inform and enhance conservation and restoration strategies.
