Bahare Fatemi

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class="glue-breadcrumbs__list"> <li class="glue-breadcrumbs__item"> <a class="glue-breadcrumbs__link attribution" href="/">Home</a> <svg role="presentation" aria-hidden="true" class="glue-icon "> <use href="/gr/static/assets/icons/glue-icons.svg#chevron-right"></use> </svg> </li> <li class="glue-breadcrumbs__item"> <a class="glue-breadcrumbs__link attribution" href="/people/">People</a> <svg role="presentation" aria-hidden="true" class="glue-icon "> <use href="/gr/static/assets/icons/glue-icons.svg#chevron-right"></use> </svg> </li> </ol> </nav> </div> <h1 class="headline-1 bhoig__headline glue-grid__col--span-10 glue-grid__col--span-9-md glue-grid__col--span-10-lg">Bahare Fatemi</h1> <div class="basic-hero__description bhoig__description glue-grid__col--span-10 glue-grid__col--span-9-md glue-grid__col--span-10-lg"> Bahare Fatemi is a Research Scientist at Google Research in Montreal, specializing in graph representation learning and natural language processing. She received her Ph.D. From the University of British Columbia. Her work has been features in top AI conferences and journals including NeurIPS, ICLR, AAAI, and JMLR. She co-organized Mining and Learning with Graphs workshop at KDD, Women in Machine Learning (WiML) workshop, and the Montreal AI Symposium. She currently serves on the WiML board. </div> <div class="bhoig__cta glue-grid__col--span-10 glue-grid__col--span-9-md glue-grid__col--span-10-lg"> </div> </div> </div> </section> <section class="offset-two-up --research-areas"> <div class="glue-page glue-grid"> <div class="offset-two-up__left-col glue-grid__col glue-grid__col--span-4-sm glue-grid__col--span-12-md glue-grid__col--span-3-lg"> <h3 class="offset-two-up__headline headline-3">Research Areas</h3> </div> <div class="glue-grid__col glue-grid__col--span-4-sm glue-grid__col--span-12-md glue-grid__col--span-9-lg"> <ul class="card-stack--small nested-glue-grid-override"> <li class="glue-grid__col glue-grid__col--span-6-md glue-grid__col--span-4-sm"> <a class="glue-card not-glue glue-card--small" href="/research-areas/data-mining-and-modeling/"> <div class="glue-card__inner"> <div class="glue-card__content"> <div class="content-wrapper"> <p class="glue-headline body js-gt-item-id"> Data Mining and Modeling </p> </div> <div 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glue-grid__col--span-4-sm"> <a class="glue-card not-glue glue-card--small" href="/research-areas/natural-language-processing/"> <div class="glue-card__inner"> <div class="glue-card__content"> <div class="content-wrapper"> <p class="glue-headline body js-gt-item-id"> Natural Language Processing </p> </div> <div class="glue-card__cta glue-card__cta--arrow"> <span class="glue-button glue-button--low-emphasis glue-button--icon"> <svg role="presentation" aria-hidden="true" class="glue-icon "> <use href="/gr/static/assets/icons/glue-icons.svg#arrow-forward"></use> </svg> </span> </div> </div> </div> </a> </li> </ul> </div> </div> </section> <div class="glue-page heading-tabs-wrapper spacer-03-top"> <span class="--as-h4">Authored Publications</span> </div>  <section class="publication-index filtered-list-base hot-swap-base glue-page theme-light spacer-06-top spacer-06-bottom"> <div class="js-configurable-list glue-grid" data-list-id="publications-index-list" data-search-query-param=""> <div 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data-glue-expansion-panel-expand-tooltip="Expand this filter category" data-glue-expansion-panel-collapse-tooltip="Collapse this filter category">  <div class="glue-expansion-panels__header"> <h2 class="glue-headline caption" id="modal-label"> Filter by: </h2>  <div class="glue-filter__modal-controls glue-filter__filter-controls js-gt-filter-clear"> <button class="glue-button glue-button--low-emphasis glue-filter__reset-all" > <span class="js-gt-item-id">Clear</span> </button> </div> <button class="glue-modal__close-btn" title="Close the modal" tabindex="0" aria-label="Close the modal"> </button> </div>  <div class="glue-expansion-panel filter__first-panel"> <h3 class="glue-expansion-panel__toggle"> <span class="glue-expansion-panel__button" id="filter-toggle-type" data-glue-expansion-panel-toggle-for="filter-panel-type"> <span class="glue-expansion-panel__header-text" id="filter-type-label">Publications</span> <svg role="presentation" aria-hidden="true" class="glue-icon 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<label class="filter-option-label" for="filter-type-google"> <span class="js-filter-option-label-text">Google</span> <span class="filter-option-label-count">4</span></label> </div> </div> </li> <li class="glue-filter__category-item js-filter-item js-gt-filter-item"> <div class="mdc-touch-target-wrapper"> <div class="mdc-form-field"> <div class="mdc-checkbox mdc-checkbox--touch glue-checkbox"> <input type="checkbox" class="mdc-checkbox__native-control" id="filter-type-other" name="filter-type-other" value="other" > <div class="mdc-checkbox__background"> <svg class="mdc-checkbox__checkmark" viewBox="0 0 24 24"> <path class="mdc-checkbox__checkmark-path" fill="none" d="M1.73,12.91 8.1,19.28 22.79,4.59"></path> </svg> <div class="mdc-checkbox__mixedmark"></div> </div> <div class="mdc-checkbox__ripple"></div> </div> <label class="filter-option-label" for="filter-type-other"> <span class="js-filter-option-label-text">Other</span> <span class="filter-option-label-count">0</span></label> </div> </div> </li> </ul> </div> </div> </div> <div class="glue-expansion-panel "> <h3 class="glue-expansion-panel__toggle"> <span class="glue-expansion-panel__button" id="filter-toggle-year" data-glue-expansion-panel-toggle-for="filter-panel-year"> <span class="glue-expansion-panel__header-text" id="filter-year-label">Years</span> <svg role="presentation" aria-hidden="true" class="glue-icon glue-expansion-panel__header-arrow"> <use href="/gr/static/assets/icons/glue-icons.svg#arrow-drop-down"></use> </svg> </span> </h3>  <div class="glue-expansion-panel__content" id="filter-panel-year"> <div> <ul class="glue-filter__category glue-no-bullet" data-glue-filter-category="year" role="list" aria-controls="filter-results" aria-labelledby="filter-year-label"> <li class="glue-filter__category-item js-filter-item js-gt-filter-item"> <div class="mdc-touch-target-wrapper"> <div class="mdc-form-field"> <div class="mdc-checkbox mdc-checkbox--touch glue-checkbox"> <input type="checkbox" class="mdc-checkbox__native-control" id="filter-year-2024" name="filter-year-2024" value="2024" > <div class="mdc-checkbox__background"> <svg class="mdc-checkbox__checkmark" viewBox="0 0 24 24"> <path class="mdc-checkbox__checkmark-path" fill="none" d="M1.73,12.91 8.1,19.28 22.79,4.59"></path> </svg> <div class="mdc-checkbox__mixedmark"></div> </div> <div class="mdc-checkbox__ripple"></div> </div> <label class="filter-option-label" for="filter-year-2024"> <span class="js-filter-option-label-text">2024</span> <span class="filter-option-label-count">1</span></label> </div> </div> </li> <li class="glue-filter__category-item js-filter-item js-gt-filter-item"> <div class="mdc-touch-target-wrapper"> <div class="mdc-form-field"> <div class="mdc-checkbox mdc-checkbox--touch glue-checkbox"> <input type="checkbox" class="mdc-checkbox__native-control" id="filter-year-2023" name="filter-year-2023" value="2023" > <div class="mdc-checkbox__background"> <svg class="mdc-checkbox__checkmark" viewBox="0 0 24 24"> <path class="mdc-checkbox__checkmark-path" fill="none" d="M1.73,12.91 8.1,19.28 22.79,4.59"></path> </svg> <div class="mdc-checkbox__mixedmark"></div> </div> <div class="mdc-checkbox__ripple"></div> </div> <label class="filter-option-label" for="filter-year-2023"> <span class="js-filter-option-label-text">2023</span> <span class="filter-option-label-count">3</span></label> </div> </div> </li> </ul> </div> </div> </div> <div class="glue-expansion-panel "> <h3 class="glue-expansion-panel__toggle"> <span class="glue-expansion-panel__button" id="filter-toggle-research_area" data-glue-expansion-panel-toggle-for="filter-panel-research_area"> <span class="glue-expansion-panel__header-text" id="filter-research_area-label">Research Areas</span> <svg role="presentation" aria-hidden="true" class="glue-icon glue-expansion-panel__header-arrow"> <use href="/gr/static/assets/icons/glue-icons.svg#arrow-drop-down"></use> </svg> </span> </h3>  <div class="glue-expansion-panel__content" id="filter-panel-research_area"> <div> <ul class="glue-filter__category glue-no-bullet" data-glue-filter-category="research_area" role="list" aria-controls="filter-results" aria-labelledby="filter-research_area-label"> <li class="glue-filter__category-item js-filter-item js-gt-filter-item"> <div class="mdc-touch-target-wrapper"> <div class="mdc-form-field"> <div class="mdc-checkbox mdc-checkbox--touch glue-checkbox"> <input type="checkbox" class="mdc-checkbox__native-control" id="filter-research_area-general-science" name="filter-research_area-general-science" value="general-science" > <div class="mdc-checkbox__background"> <svg class="mdc-checkbox__checkmark" viewBox="0 0 24 24"> <path class="mdc-checkbox__checkmark-path" fill="none" d="M1.73,12.91 8.1,19.28 22.79,4.59"></path> </svg> <div class="mdc-checkbox__mixedmark"></div> </div> <div class="mdc-checkbox__ripple"></div> </div> <label class="filter-option-label" for="filter-research_area-general-science"> <span class="js-filter-option-label-text">General Science</span> <span class="filter-option-label-count">1</span></label> </div> </div> </li> <li class="glue-filter__category-item js-filter-item js-gt-filter-item"> <div class="mdc-touch-target-wrapper"> <div class="mdc-form-field"> <div class="mdc-checkbox mdc-checkbox--touch glue-checkbox"> <input type="checkbox" class="mdc-checkbox__native-control" id="filter-research_area-machine-intelligence" name="filter-research_area-machine-intelligence" value="machine-intelligence" > <div class="mdc-checkbox__background"> <svg class="mdc-checkbox__checkmark" viewBox="0 0 24 24"> <path class="mdc-checkbox__checkmark-path" fill="none" d="M1.73,12.91 8.1,19.28 22.79,4.59"></path> </svg> <div class="mdc-checkbox__mixedmark"></div> </div> <div class="mdc-checkbox__ripple"></div> </div> <label class="filter-option-label" for="filter-research_area-machine-intelligence"> <span class="js-filter-option-label-text">Machine Intelligence</span> <span class="filter-option-label-count">3</span></label> </div> </div> </li> <li class="glue-filter__category-item js-filter-item js-gt-filter-item"> <div class="mdc-touch-target-wrapper"> <div class="mdc-form-field"> <div class="mdc-checkbox mdc-checkbox--touch glue-checkbox"> <input type="checkbox" class="mdc-checkbox__native-control" id="filter-research_area-software-systems" name="filter-research_area-software-systems" value="software-systems" > <div class="mdc-checkbox__background"> <svg class="mdc-checkbox__checkmark" viewBox="0 0 24 24"> <path class="mdc-checkbox__checkmark-path" fill="none" d="M1.73,12.91 8.1,19.28 22.79,4.59"></path> </svg> <div class="mdc-checkbox__mixedmark"></div> </div> <div class="mdc-checkbox__ripple"></div> </div> <label class="filter-option-label" for="filter-research_area-software-systems"> <span class="js-filter-option-label-text">Software Systems</span> <span class="filter-option-label-count">1</span></label> </div> </div> </li> </ul> </div> </div> </div> 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glue-grid__col--span-6-md glue-filter__result glue-is-hidden" data-glue-filter-="google"> </li> <li class="glue-grid__col glue-grid__col--span-3 glue-grid__col--span-6-md glue-filter__result glue-is-hidden" data-glue-filter-="other"> </li> <li class="glue-grid__col glue-grid__col--span-3 glue-grid__col--span-6-md glue-filter__result glue-is-hidden" data-glue-filter-="2023"> </li> <li class="glue-grid__col glue-grid__col--span-3 glue-grid__col--span-6-md glue-filter__result glue-is-hidden" data-glue-filter-="2024"> </li> <li class="glue-grid__col glue-grid__col--span-3 glue-grid__col--span-6-md glue-filter__result glue-is-hidden" data-glue-filter-="general-science"> </li> <li class="glue-grid__col glue-grid__col--span-3 glue-grid__col--span-6-md glue-filter__result glue-is-hidden" data-glue-filter-="machine-intelligence"> </li> <li class="glue-grid__col glue-grid__col--span-3 glue-grid__col--span-6-md glue-filter__result glue-is-hidden" data-glue-filter-="software-systems"> </li> </ul> 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class="chips-wrapper spacer-03-bottom"> <div class="chipset" id="publications-index-chipset"> <div aria-hidden="true" class="chip-template"> <div class="chip" data-chip-value="template" data-chip-name=""> <div class="chip__label caption">chip template</div> <button class="chip__close" aria-label="Remove chip template"> <div class="glue-visually-hidden">Remove </div> <svg role="presentation" aria-hidden="true" class="glue-icon glue-icon--18px "> <use href="/gr/static/assets/icons/glue-icons.svg#close"></use> </svg> </button> </div> </div> <ul class="chipset-list" title="Applied filters"></ul> </div> </div> <div class="publications-list --theme- row-card-list" data-hot-swap="google-publications-list" data-gt-id="publications_list" data-gt-component-name=""> <div class="row-card"> <div class="row-card__container"> <div class="row-card__body"> <a class="row-card__heading headline-6 glue-link" href=https://research.google/pubs/talk-like-a-graph-encoding-graphs-for-large-language-models/ > Talk like a Graph: Encoding Graphs for Large Language Models </a> <div class="row-card__subheading"> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/108300/"> Bahar Fatemi </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/bryanperozzi/"> Bryan Perozzi </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/jonathanhalcrow/"> Jonathan Halcrow </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> ICLR (2024) </div> </div> </div> <div class="row-card__cta headline-6"> <div class="glue-tooltip" data-glue-tooltip-auto-position="false"> <button class="glue-button glue-button--low-emphasis glue-tooltip__trigger" aria-describedby=tooltip-contentgraphs-are-a-powerful-tool-for-rep tabindex=0 > <span class="js-gt-item-id">Preview</span> </button> <span id="tooltip-contentgraphs-are-a-powerful-tool-for-rep" class="glue-tooltip__content" role="tooltip"> <span data-tooltip-type="simple"> Preview abstract </span> <span data-tooltip-type="rich"> <span class="glue-tooltip__body">Graphs are a powerful tool for representing and analyzing complex relationships in real-world applications such as social networks, recommender systems, and computational finance. Reasoning on graphs is essential for drawing inferences about the relationships between entities in a complex system, and to identify hidden patterns and trends. Despite the remarkable progress in automated reasoning with natural text, reasoning on graphs with large language models (LLMs) remains an understudied problem. In this work, we perform the first comprehensive study of encoding graph-structured data as text for consumption by LLMs. We show that LLM performance on graph reasoning tasks varies on three fundamental levels: (1) the graph encoding method, (2) the nature of the graph task itself, and (3) interestingly, the very structure of the graph considered. These novel results provide valuable insight on strategies for encoding graphs as text. Using these insights we illustrate how the correct choice of encoders can boost performance on graph reasoning tasks inside LLMs by 4.8% to 61.8%, depending on the task.</span> <a class="glue-button glue-button--low-emphasis" href="https://research.google/pubs/talk-like-a-graph-encoding-graphs-for-large-language-models/" > <span class="js-gt-item-id">View details</span> </a> </span> </span> </div> </div> </div> </div> <div class="row-card"> <div class="row-card__container"> <div class="row-card__body"> <a class="row-card__heading headline-6 glue-link" href=https://research.google/pubs/submix-learning-to-mix-graph-sampling-heuristics/ > SubMix: Learning to Mix Graph Sampling Heuristics </a> <div class="row-card__subheading"> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/108580/"> Neslihan Bulut </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/bateni/"> Mohammad "Hossein" Bateni </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/bryanperozzi/"> Bryan Perozzi </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Josh Dillon </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/108300/"> Bahar Fatemi </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/kyriakosaxiotis/"> Kyriakos Axiotis </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Johannes Gasteiger </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/104861/"> Sami Abu-El-Haija </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> 2023 </div> </div> </div> <div class="row-card__cta headline-6"> <div class="glue-tooltip" data-glue-tooltip-auto-position="false"> <button class="glue-button glue-button--low-emphasis glue-tooltip__trigger" aria-describedby=tooltip-contentsampling-subgraphs-for-training-gr tabindex=0 > <span class="js-gt-item-id">Preview</span> </button> <span id="tooltip-contentsampling-subgraphs-for-training-gr" class="glue-tooltip__content" role="tooltip"> <span data-tooltip-type="simple"> Preview abstract </span> <span data-tooltip-type="rich"> <span class="glue-tooltip__body">Sampling subgraphs for training Graph Neural Networks (GNNs) is receiving much attention from the GNN community. While a variety of methods have been proposed, each method samples the graph according to its own heuristic. However, there has been little work in mixing these heuristics in an end-to-end trainable manner. In this work, we design a generative framework for graph sampling. Our method, SubMix, parameterizes subgraph sampling as a convex combination of heuristics. We show that a continuous relaxation of the discrete sampling process allows us to efficiently obtain analytical gradients for training the sampling parameters. Our experimental results illustrate the usefulness of learning graph sampling in three scenarios: (1) robust training of GNNs by automatically learning to discard noisy edge sources; (2) improving model performance by trainable and online edge subset selection; and (3) by integrating our framework into decoupled GNN models improves their performance on standard benchmarks.</span> <a class="glue-button glue-button--low-emphasis" href="https://research.google/pubs/submix-learning-to-mix-graph-sampling-heuristics/" > <span class="js-gt-item-id">View details</span> </a> </span> </span> </div> </div> </div> </div> <div class="row-card"> <div class="row-card__container"> <div class="row-card__body"> <a class="row-card__heading headline-6 glue-link" href=https://research.google/pubs/tpugraphs-performance-prediction-datasets-on-large-tensor-computational-graphs/ > TpuGraphs: Performance Prediction Datasets on Large Tensor Computational Graphs </a> <div class="row-card__subheading"> <div class="row-card__subheading__item extra-small-text"> Mangpo Phothilimthana </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/104861/"> Sami Abu-El-Haija </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Kaidi Cao </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/108300/"> Bahar Fatemi </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Charith Mendis </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/bryanperozzi/"> Bryan Perozzi </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Advances in Neural Information Processing Systems (2023) </div> </div> </div> <div class="row-card__cta headline-6"> <div class="glue-tooltip" data-glue-tooltip-auto-position="false"> <button class="glue-button glue-button--low-emphasis glue-tooltip__trigger" aria-describedby=tooltip-contentprecise-hardware-performance-model tabindex=0 > <span class="js-gt-item-id">Preview</span> </button> <span id="tooltip-contentprecise-hardware-performance-model" class="glue-tooltip__content" role="tooltip"> <span data-tooltip-type="simple"> Preview abstract </span> <span data-tooltip-type="rich"> <span class="glue-tooltip__body">Precise hardware performance models play a crucial role in code optimizations. They can assist compilers in making heuristic decisions or aid autotuners in identifying the optimal configuration for a given program. For example, the autotuner for XLA, a machine learning compiler, discovered 10–20\% speedup on state-of-the-art models serving substantial production traffic at Google. Although there exist a few datasets for program performance prediction, they target small sub-programs such as basic blocks or kernels. This paper introduces TpuGraphs, a performance prediction dataset on full tensor programs, represented as computational graphs, running on Tensor Processing Units (TPUs). Each graph in the dataset represents the main computation of a machine learning workload, eg, a training epoch or an inference step. Each data sample contains a computational graph, a compilation configuration, and the execution time of the graph when compiled with the configuration. The graphs in the dataset are collected from open-source machine learning programs, featuring popular model architectures (eg, ResNet, EfficientNet, Mask R-CNN, and Transformer). TpuGraphs provides 25x more graphs than the largest graph property prediction dataset (with comparable graph sizes), and 770x larger graphs on average compared to existing performance prediction datasets on machine learning programs. This graph-level prediction task on large graphs introduces new challenges in learning, ranging from scalability, training efficiency, to model quality.</span> <a class="glue-button glue-button--low-emphasis" href="https://research.google/pubs/tpugraphs-performance-prediction-datasets-on-large-tensor-computational-graphs/" > <span class="js-gt-item-id">View details</span> </a> </span> </span> </div> </div> </div> </div> <div class="row-card"> <div class="row-card__container"> <div class="row-card__body"> <a class="row-card__heading headline-6 glue-link" href=https://research.google/pubs/geometric-deep-learning-of-particle-motion-by-magik/ > Geometric Deep Learning of Particle Motion by MAGIK </a> <div class="row-card__subheading"> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/108300/"> Bahare Fatemi </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> <a class="row-card__small-link" href="/people/jonathanhalcrow/"> Jonathan Halcrow </a> </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Khuloud Jaqaman </div> <div class="row-card__subheading__spacer"></div> <div class="row-card__subheading__item extra-small-text"> Nature (2023) </div> </div> </div> <div class="row-card__cta headline-6"> <div class="glue-tooltip" data-glue-tooltip-auto-position="false"> <button class="glue-button glue-button--low-emphasis glue-tooltip__trigger" aria-describedby=tooltip-contentwith-modern-fluorescent-probes-and tabindex=0 > <span class="js-gt-item-id">Preview</span> </button> <span id="tooltip-contentwith-modern-fluorescent-probes-and" class="glue-tooltip__content" role="tooltip"> <span data-tooltip-type="simple"> Preview abstract </span> <span data-tooltip-type="rich"> <span class="glue-tooltip__body">With modern fluorescent probes and light microscopes, the possibilities of monitoring the dynamics of cells, organelles, molecular complexes and even single molecules with high spatiotemporal resolution are more than ever [1]. Characterizing the motion of cellular and molecular entities can reveal a great deal of information about their functions and interactions and overall activity landscape [2]. Motion characterization is generally the end product of an image analysis pipeline that starts with identifying and localizing the objects in each image (segmentation/detection), tracking them over time, and then analyzing the resulting trajectories to characterize the objects’ motion. Whether objects are large (e.g. cells or organelles) or small (e.g. molecules or molecular complexes), as long as they can be represented by coordinates (e.g. cell centroid position or molecular position), following them over time in a series of images is effectively a (multiple) particle tracking problem. In their recent publication in Nature Machine Intelligence, Pineda et al. [3] describe a powerful deep learning approach based on graph neural networks (GNNs) for particle tracking or, if desired, for the characterization of object motion without explicit tracking. Particle tracking is often the most challenging step in the analysis pipeline from images to motion characterization. Whenever the analysis involves a relatively high density of heterogeneously moving objects, there is ambiguity in determining which object has gonewent where throughout the image series [4]. In addition, objects may merge with each other – due to crossing paths or interactions – and may undergo splitting, such as during cell division. Despite these challenges, a high density of tracked objects is often desired, because of the rich information that it yields about the system studied [5]. The novel GNN-based approach by Pineda et al. [3], named MAGIK, offers solutions to the tracking problem in two ways: First, MAGIK can be employed to construct the trajectories of the imaged objects from the graph of their connections in space and time. Second, MAGIK can be employed to characterize the motion of the imaged objects directly from their graph of spatiotemporal connections, without explicit tracking. Graphs are ubiquitously used in science to represent complex systems of interacting objects, from molecules to social and transportation networks [6]. GNNs provide a framework for incorporating existing information about the objects, with an inductive bias based on a larger structure relating them, to make predictions about these objects or the system as a whole. In MAGIK [3], spatiotemporal connections between imaged objects, encoded in the structure of the graph, provide this inductive bias , with the premise that objects close in space-time are likely to be the same. MAGIK utilizes this graph representation in a powerful way by employing GNNs [7] to perform various tracking and motion characterization tasks. The GNN model proposed in MAGIK considers both spatial and temporal information in a static graph. This model is enhanced by an adaptive and interpretable attention mechanism. Attention estimates the strength of association among the objects and provides insights into the dynamics of the system for the task. GNNs enable MAGIK to provide a versatile platform for performing multiple tasks from linking coordinates into trajectories to inferring local and global dynamic properties. MAGIK is tested for its flexibility and reliability in real and simulated scenarios corresponding to a variety of biological experiments. The results of the tests show that MAGIK is able to identify which spatiotemporal connections in a graph influence the dynamic properties of each object. They further show that MAGIK accurately constructs trajectories, obtaining outstanding results for cell tracking, including the identification of cell division events, using multiple microscopy techniques and cell types. As in most applications the final goal of tracking is to characterize the dynamics of the system, Pineda et al. [3] have tested MAGIK for quantifying motion parameters without explicit tracking, and they have shown that MAGIK can accurately and sensitively quantify local or global motion properties of the imaged objects. Technically, MAGIK performs these various tasks by tailoring its training to the task: Tracking as a graph edge classification task, local motion motion characterization as a graph node regression task, and global motion characterization as a graph-level regression or classification task. As demonstrated by MAGIK, GNNs offer powerful tools for the analysis of the spatiotemporal connections between objects in biological images. New developments in the fields of graphs and GNNs will further advance this goal. One possibility is to replace the fixed graph and the fully connected graph in MAGIK with a learnable sparse graph [8]. Another possibility is to use hypergraphs, which go beyond binary connections (a fundamental limitation of graphs). This would be a promising approach to characterize the spatiotemporal connections of systems with complex interactions [9]. Furthermore, as the problem studied here is temporal in nature, it may benefit from temporal GNNs [10], which directly incorporate time into the GNN formulation. All in all, the powerful combination of cutting-edge microscopes, fluorescent probes and geometric deep learning analytical tools will aid with the study of the organization, dynamics and interactions of diverse systems, from molecules in a cell, to cells in a tissue, and beyond.</span> <a class="glue-button glue-button--low-emphasis" href="https://research.google/pubs/geometric-deep-learning-of-particle-motion-by-magik/" > <span class="js-gt-item-id">View details</span> </a> </span> </span> </div> </div> </div> </div> </div> <div class="filtered-list-base__pagination" data-hot-swap="pagination"> </div> </div> </div> </section> <section class="google_scholar_search">  <a href="#" class="js-google-scholar-search-link glue-button glue-button--low-emphasis glue-button--icon" title="See more related publications on Google Scholar" target="_blank"> <svg role="presentation" aria-hidden="true" class="glue-icon glue-icon--24px "> <use 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