Linda Petzold

About

Linda Petzold is a faculty member affiliated with the Department of Mathematics at the University of California, Santa Barbara. She is associated with the BioEngineering department and her office is located in South Hall, Room 6607. Her contact information includes an email address (petzold@engineering.ucsb.edu), a phone number (805-893-5362), and office hours from Monday to Friday, 9-12 and 1-4. The page does not provide specific details about her research focus, background, or key contributions.

Research topics

• Biology
• Computer Science
• Computational biology
• Botany
• Biotechnology
• Physiology
• Physics
• Biochemistry
• Genetics
• Neuroscience

Selected publications

• Multiscale modeling meets machine learning: What can we learn?

  SUNY Digital Repository Support (State University of New York System) · 2020-02-17 · 346 citations

  article

  Machine learning is increasingly recognized as a promising technology in the biological, biomedical, and behavioral sciences. There can be no argument that this technique is incredibly successful in image recognition with immediate applications in diagnostics including electrophysiology, radiology, or pathology, where we have access to massive amounts of annotated data. However, machine learning often performs poorly in prognosis, especially when dealing with sparse data. This is a field where c…

  PublisherDOI

• Functional neuronal circuitry and oscillatory dynamics in human brain organoids

  Nature Communications · 2022 · 220 citations

  Human brain organoids replicate much of the cellular diversity and developmental anatomy of the human brain. However, the physiology of neuronal circuits within organoids remains under-explored. With high-density CMOS microelectrode arrays and shank electrodes, we captured spontaneous extracellular activity from brain organoids derived from human induced pluripotent stem cells. We inferred functional connectivity from spike timing, revealing a large number of weak connections within a skeleton o…

  DOI

• Experimentally Validated Reconstruction and Analysis of a Genome-Scale Metabolic Model of an Anaerobic Neocallimastigomycota Fungus

  mSystems · 2021 · 67 citations

  Beyond identifying a trove of lignocellulolytic enzymes, we use this genome to construct the first genome-scale metabolic model of an anaerobic gut fungus. The model is experimentally validated and sheds light on unresolved metabolic features common to gut fungi. Model-guided analysis will pave the way for deepening our understanding of anaerobic gut fungi and provides a systematic framework to guide strain engineering efforts of these organisms for biotechnological use.

  DOI

• An empirical study on the robustness of the segment anything model (SAM)

  Pattern Recognition · 2024-06-12 · 45 citations

  articleOpen accessSenior author

  The Segment Anything Model (SAM) is a foundation model for general image segmentation. Although it exhibits impressive performance predominantly on natural images, understanding its robustness against various image perturbations and domains is critical for real-world applications where such challenges frequently arise. In this study we conduct a comprehensive robustness investigation of SAM under diverse real-world conditions. Our experiments encompass a wide range of image perturbations. Our ex…

  PublisherDOI

• A Survey on Large Language Models for Critical Societal Domains: Finance, Healthcare, and Law

  arXiv (Cornell University) · 2024-05-02 · 16 citations

  preprintOpen access

  In the fast-evolving domain of artificial intelligence, large language models (LLMs) such as GPT-3 and GPT-4 are revolutionizing the landscapes of finance, healthcare, and law: domains characterized by their reliance on professional expertise, challenging data acquisition, high-stakes, and stringent regulatory compliance. This survey offers a detailed exploration of the methodologies, applications, challenges, and forward-looking opportunities of LLMs within these high-stakes sectors. We highlig…

  PublisherOA PDFDOI

Recent grants

• StochSS: A Next-Generation Toolkit for Simulation-Driven Biological Discovery

  NIH · $598k · 2012–2015

• IGERT: Graduate Education Program in Computational Science and Engineering with Emphasis on Multiscale Problems in Fluids and Materials

  NSF · $3.4M · 2002–2013

• StochSS: A Next-Generation Toolkit for Simulation-Driven Biological Discovery

  NIH · $3.3M · 2012–2023

Frequent coauthors

• Brian Drawert

  University of North Carolina at Asheville

  32 shared

• Jeffrey W. Shupp

  MedStar Washington Hospital Center

  29 shared

• Andreas Hellander

  29 shared

• Bernie J. Daigle

  26 shared

• Mitchell J. Cohen

  University of Colorado Denver

  23 shared

• Yang Cao

  21 shared

• Stefan Hellander

  18 shared

• Lauren T. Moffatt

  Georgetown University

  17 shared

Similar researchers at University of California, Santa Barbara

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• Zheng Liuh-59
• Zhenghan Wangh-56