Lauren Takahashi

Assistant Professor

Hokkaido University

Japan

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Lauren Takahashi is an Assistant Professor at Hokkaido University in Japan. Her research areas include materials informatics, automated materials feature engineering, and the development of advanced catalysts for chemical processes. Recent articles authored by her focus on topics such as AI and automation in materials science, graphical user interfaces for experimental design, and the synthesis of novel materials using machine learning techniques.

Articles (19)

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2025

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2021

Collaborators (2)

Itsuki Miyazato

Assistant Professor(Specially Appointed)

Hokkaido Daigaku

JAPAN

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Keisuke Takahashi

Professor

Hokkaido University

JAPAN

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Lauren Takahashi

Research Interests

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Articles (19)

Web-based graphical user interface for automated materials feature engineering for machine learning

Development of an Open-Source 3D-Printed Material Synthesis Robot FLUID: Hardware and Software Blueprints for Accessible Automation in Materials Science

Multidimensional Catalysis Data Representation for Designing Oxidative Coupling of Methane Catalysts

AI and automation: democratizing automation and the evolution towards true AI-autonomous robotics

Design of low temperature La2O3 oxidative coupling of methane catalysts using feature engineering and automated sampling

Designing and synthesizing perovskites with targeted bandgaps via tailored descriptors

Web-based graphical user interface for visualizing and analyzing chemical reaction networks

Development of graphical user interface for design of experiments via Gaussian process regression and its case study

Unveiling the relation between multiple chemical products and process conditions for trichloroethylene and perchloroethylene production via catalysis network analysis

Toward the Golden Age of Materials Informatics: Perspective and Opportunities

Catalysts informatics: paradigm shift towards data-driven catalyst design

Improving scientific image processing accessibility through development of graphical user interfaces for scikit-image

Designing Catalyst Descriptors for Machine Learning in Oxidative Coupling of Methane

Synthesis of Heterogeneous Catalysts in Catalyst Informatics to Bridge Experiment and High-Throughput Calculation

Unveiling the reaction pathways of hydrocarbons via experiments, computations and data science

Catalysis Gene Expression Profiling: Sequencing and Designing Catalysts

Unveiling gas‐phase oxidative coupling of methane via data analysis

Learning Catalyst Design Based on Bias-Free Data Set for Oxidative Coupling of Methane

Representing Catalytic and Processing Space in Methane Oxidation Reaction via Multioutput Machine Learning

Collaborators (2)

Itsuki Miyazato

Keisuke Takahashi

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