diff --git a/content/blog/atmosphere.md b/content/blog/atmosphere.md index 9350a2833..291ac660f 100644 --- a/content/blog/atmosphere.md +++ b/content/blog/atmosphere.md @@ -22,4 +22,4 @@ Much of the uncertainty in climate-model projections for surface precipitation a ##### Parameterization of the boundary layer ###### People involved: Alexander Connolly, Pierre Gentine -Boundary layer turbulence parameterization remains a major source of uncertainties in climate models, including for low-level clouds. We aim to develop a new approach to the boundary layer parameterization by targeting high-order closure terms in the turbulence representation, leveraging Large-Eddy Simulations and machine learning/symbolic regression. +Boundary layer turbulence parameterization remains a major source of uncertainties in climate models, including for low-level clouds. We aim to develop a new approach to the boundary layer parameterization by targeting high-order closure terms in the turbulence representation, leveraging Large-Eddy Simulations and machine learning/symbolic regression. diff --git a/content/blog/climate.md b/content/blog/climate.md index 9eb819b23..f2f1cee84 100644 --- a/content/blog/climate.md +++ b/content/blog/climate.md @@ -10,8 +10,7 @@ heroBackground: '/images/retrosupply-jLwVAUtLOAQ-unsplash.jpeg' Comprehensive climate models typically include **atmosphere, ocean, sea ice, and land components** and the **coupling** between them. These models can also incorporate **land ice, atmospheric chemistry and terrestrial and marine biogeochemistry**, enabling carbon cycle simulations. Early climate models were developed in the 1970s and have increased in complexity over the years, with more process interactions, more sophisticated parameterizations of subgridscale processes, and higher spatial resolution being incorporated over time. These models have been skillful at predicting anthropogenic climate change, and even early models accurately simulated aspects of the spatial pattern of warming. However, there is still considerable uncertainty associated with model structure, and climate models which incorporate different parameterizations can differ greatly in many characteristics of future projected change. Because of this, it is imperative that there are continued developments and improvements of these modeling systems. Bringing new approaches, such as **Machine Learning**, to this challenge has the potential to rapidly accelerate progress. -Studies across M²LInES are using **scientific and interpretable Machine Learning** to gain new insight on parameterization development across the atmosphere, ocean, and sea ice systems. Development and testing of these parameterizations is underway in component model configurations and work is planned to incorporate these into a number of climate models. This includes, among others, improved parameterizations of: -* The simulated conductive heat fluxes through sea ice ([Zampieri et al, 2024](https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023GL106760)), -* Ocean mixing processes, -* Moist convection in the atmosphere - +Studies across M²LInES are using **scientific and interpretable Machine Learning** to gain new insight on parameterization development across the atmosphere, ocean, and sea ice systems. Development and testing of these parameterizations is underway in component model configurations and work is planned to incorporate these into a number of climate models. This includes, among others, improved parameterizations of: +* The simulated conductive heat fluxes through sea ice ([Zampieri et al, 2024](https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023GL106760)), +* Ocean mixing processes, +* Moist convection in the atmosphere diff --git a/content/blog/climateprocess.md b/content/blog/climateprocess.md index 0e6670f4e..dd8c63eaf 100644 --- a/content/blog/climateprocess.md +++ b/content/blog/climateprocess.md @@ -10,7 +10,7 @@ heroBackground: '/images/retrosupply-jLwVAUtLOAQ-unsplash.jpeg' Despite drastic improvements in climate model development, current simulations have difficulty capturing the interactions among different processes in the atmosphere, oceans, and ice and how they affect the Earth’s climate; this can hinder projections of temperature, rainfall, and sea level. -**M²LInES will be focusing on understanding these key climate processes using two types of data:** +**M²LInES will be focusing on understanding these key climate processes using two types of data:** ### High-resolution simulation and observations * Atmospheric convection and clouds (O’Gorman, Mooers, Yuval) (see [Atmosphere](/blog/atmosphere)) diff --git a/content/blog/coupledphysics.md b/content/blog/coupledphysics.md index b571d7b53..a2cc66afe 100644 --- a/content/blog/coupledphysics.md +++ b/content/blog/coupledphysics.md @@ -8,13 +8,13 @@ heroSubHeading: '' heroBackground: '/images/retrosupply-jLwVAUtLOAQ-unsplash.jpeg' --- -The earth system is complex with many different distinct subsystems interplaying with each other. Our approach in modeling the earth system is generally to develop separate models for each component and to include additional couplers that model their interactions. For example, Earth System Models typically include models of: +The earth system is complex with many different distinct subsystems interplaying with each other. Our approach in modeling the earth system is generally to develop separate models for each component and to include additional couplers that model their interactions. For example, Earth System Models typically include models of: * Atmosphere * Land * Land Ice * Ocean * River Runoff -* Sea Ice +* Sea Ice * Wave In addition to improving each component independently, it is also important to better understand and model the coupling of components. @@ -37,9 +37,8 @@ Take the air-sea interaction as an example. The large-scale dynamics of coupled * ENSO represents complex feedback between the tropical Pacific Ocean and the atmosphere, which significantly influences global climate patterns. ### Climate Regulation and Teleconnections: -* These interactions contribute to teleconnections, where climate changes in one region can affect distant regions. -* Large-scale coupling helps balance heat and energy across latitudes, playing a critical role in Earth's climate regulation. +* These interactions contribute to teleconnections, where climate changes in one region can affect distant regions. +* Large-scale coupling helps balance heat and energy across latitudes, playing a critical role in Earth's climate regulation. In M²LInES, there are projects using observational data to better model these air-sea fluxes, in collaboration with scientists from the NSF STC LEAP. Several other projects also focus on the study of large-scale processes, which enhance our understanding of climate variability and change. This ultimately leads to improvements in climate model accuracy and better predictions for the earth system as a whole. - diff --git a/content/blog/projectgoals.md b/content/blog/projectgoals.md index 8b1b02b92..c2f78874b 100644 --- a/content/blog/projectgoals.md +++ b/content/blog/projectgoals.md @@ -14,7 +14,7 @@ heroBackground: '/images/retrosupply-jLwVAUtLOAQ-unsplash.jpeg' Two leading sources of errors contribute to climate model biases: missing processes and numerics. The missing or inadequate representation of multiscale ocean, sea-ice, and atmosphere processes (e.g., clouds, mixing, turbulence), are not resolved by the current generation of climate models due to computational limitations. Another error source arises from the climate models' numerics, which include spatial and temporal discretizations and numerical dissipation. These errors can accumulate or compensate for each other, making improving climate models intricate and requiring a range of approaches. -To tackle these biases and reduce the potential sources of error, **M²LInES’ strategy is to leverage advances in machine learning & "interrogate” the data to**: +To tackle these biases and reduce the potential sources of error, **M²LInES’ strategy is to leverage advances in machine learning & "interrogate” the data to**: 1. Develop data-informed, interpretable & generalizable subgrid physics models (ocean, ice, atm); 2. Produce error corrections derived from observational products for climate model components. @@ -22,10 +22,10 @@ To tackle these biases and reduce the potential sources of error, **M²LInES’ By improving model physics, this strategy ensures a more faithful representation of feedbacks and sensitivities under different climates. ### Our vision -💡 **_Generate new scientific knowledge in climate science_** from innovative use of data and machine learning: e.g., which physics did we overlook that might be important for scale interaction? +💡 **_Generate new scientific knowledge in climate science_** from innovative use of data and machine learning: e.g., which physics did we overlook that might be important for scale interaction? -💻 **_Accelerate end-to-end, from development to delivery, for a new generation of climate models_**; this includes learning and testing parameterizations in global frameworks to tackle significant biases in climate models. +💻 **_Accelerate end-to-end, from development to delivery, for a new generation of climate models_**; this includes learning and testing parameterizations in global frameworks to tackle significant biases in climate models. -⚙️ **_Drive a change of direction in the field by building models and tools centered around data-driven methods_** for the community to advance climate science discovery. +⚙️ **_Drive a change of direction in the field by building models and tools centered around data-driven methods_** for the community to advance climate science discovery. 👩‍🏫 **_Enable a new generation of versatile scientists working at the interface of machine learning, climate science & numerical modeling._** diff --git a/content/blog/seaice.md b/content/blog/seaice.md index 46774f50d..42c7ba181 100644 --- a/content/blog/seaice.md +++ b/content/blog/seaice.md @@ -22,7 +22,7 @@ Although sea ice models are continuously improving, there is still work to do. W

Figure: Sea ice mean state (1979-2010) of the GFDL SPEAR large ensemble (historical simulation). Thin grey lines represent individual ensemble members and the black line is the ensemble mean. Shown for (top) Arctic, (bottom) Antarctic.

-Biases in the sea ice mean state are due to a myriad of factors, and are often difficult to isolate. Biases may originate from missing physics within the sea ice model itself (e.g., absence of a sea ice ridging scheme), or from errors in sea ice model parameters (e.g., sea ice albedo or snow thermal conductivity). Furthermore sea ice is strongly coupled to the atmosphere and ocean, hence biases in either one of these components can imprint on the sea ice. +Biases in the sea ice mean state are due to a myriad of factors, and are often difficult to isolate. Biases may originate from missing physics within the sea ice model itself (e.g., absence of a sea ice ridging scheme), or from errors in sea ice model parameters (e.g., sea ice albedo or snow thermal conductivity). Furthermore sea ice is strongly coupled to the atmosphere and ocean, hence biases in either one of these components can imprint on the sea ice.

M²LInES is working to improve sea ice model biases by developing new data-driven sea ice model parameterization schemes. Recent highlights include work from M²LInES members Lorenzo Zampieri and Marika Holland, who used in-situ data from the recent MOSAiC expedition ( Multidisciplinary drifting Observatory for the Study of Arctic Climate) to derive a new parametric correction to sea ice and snow conductive heat fluxes within the CICE sea ice model. This work showed that simulations which do not account for local-scale sea ice and snow heterogeneity can under-estimate conductive heat fluxes through sea ice by up to 10% (see this work in GRL).

diff --git a/content/jobs/_index.md b/content/jobs/_index.md index 65dca2c5c..9ff49f796 100644 --- a/content/jobs/_index.md +++ b/content/jobs/_index.md @@ -5,13 +5,12 @@ heroSubHeading: '' heroBackground: 'images/SouthAtlantic.A2002157.1055.250m.jpg' --- -Updated on 01/31/2025 - The links for applications will be updated as they become available. -M²LInES affirms the value of differing perspectives in Sciences. As such, we strongly encourage applications from women, racial and ethnic minorities, and other individuals who are under-represented in the profession, across color, creed, race, ethnic and national origin, physical ability, gender and sexual identity, or any other legally protected basis. +Updated on 01/31/2024 - The links for applications will be updated as they become available. + ### Princeton University/GFDL * Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning. [Apply here](https://puwebp.princeton.edu/AcadHire/apply/application.xhtml?listingId=37582) * Postdoctoral researcher or more senior scientist for Ocean Surface Boundary Layer Mixing Parameterizations using Machine Learning. [Apply here](https://puwebp.princeton.edu/AcadHire/apply/application.xhtml?listingId=36662) - diff --git a/content/news/2411Shi.md b/content/news/2411Shi.md index fcb38c871..324ffe796 100644 --- a/content/news/2411Shi.md +++ b/content/news/2411Shi.md @@ -9,4 +9,3 @@ images: ['images/news/2411-Shi.png'] link: 'https://doi.org/10.1038/s41558-024-01958-8' --- In this [paper](https://doi.org/10.1038/s41558-024-01958-8), Jia-Rui Shi and co-authors present scientific evidence indicating that a human-caused signal has already emerged in the seasonal cycle amplitude of Sea Surface Temperature (SST), distinct from the background noise of natural variability. The geographical patterns of the forced SST seasonal cycle change reveal two notable features: an increase in mid-latitudes of the Northern Hemisphere, which is associated with alterations in mixed-layer depth, and a robust dipole pattern in the Southern Hemisphere mid-latitudes, primarily driven by changes in surface winds. These observed changes in the SST seasonal cycle have significant implications for marine ecosystems. - diff --git a/content/news/2411Zampieri.md b/content/news/2411Zampieri.md index 5549e6697..67b30d226 100644 --- a/content/news/2411Zampieri.md +++ b/content/news/2411Zampieri.md @@ -8,4 +8,4 @@ thumbnail: 'images/news/2411-Zampieri.png' images: ['images/news/2411-Zampieri.png'] link: 'https://doi.org/10.5194/tc-18-4687-2024' --- -Lorenzo Zampieri contributed to a [new paper](https://doi.org/10.5194/tc-18-4687-2024), published in the Cryosphere and led by Francesco Cocetta at the CMCC Foundation, where the representation of Arctic sea ice in ocean and sea ice reanalyses is evaluated against observations from different sources. In the upcoming years, these reanalyses will play an important role when used as training datasets from data-driven models of sea ice, which motivates evaluation studies such as this one. \ No newline at end of file +Lorenzo Zampieri contributed to a [new paper](https://doi.org/10.5194/tc-18-4687-2024), published in the Cryosphere and led by Francesco Cocetta at the CMCC Foundation, where the representation of Arctic sea ice in ocean and sea ice reanalyses is evaluated against observations from different sources. In the upcoming years, these reanalyses will play an important role when used as training datasets from data-driven models of sea ice, which motivates evaluation studies such as this one. diff --git a/content/news/2412AGU.md b/content/news/2412AGU.md index 4b2d5940c..c0364bcca 100644 --- a/content/news/2412AGU.md +++ b/content/news/2412AGU.md @@ -8,4 +8,4 @@ thumbnail: 'images/newlogo.png' images: ['images/newlogo.png'] link: 'https://mailchi.mp/235bd85ebd4d/m2lines-dec2024' --- -Find all the posters and talks from our team at the AGU 24 conference in our [December newsletter](https://mailchi.mp/235bd85ebd4d/m2lines-dec2024). If you are going, make sure to add them to your schedule. \ No newline at end of file +Find all the posters and talks from our team at the AGU 24 conference in our [December newsletter](https://mailchi.mp/235bd85ebd4d/m2lines-dec2024). If you are going, make sure to add them to your schedule. diff --git a/content/news/2502Jupyterbook.md b/content/news/2502Jupyterbook.md index 41edbd64f..226e2cd94 100644 --- a/content/news/2502Jupyterbook.md +++ b/content/news/2502Jupyterbook.md @@ -14,6 +14,3 @@ Photo (credit: Will Chapman): students at the Future Earth Research School on d The M²LInES team is proud to share this **[article](https://doi.org/10.21105/jose.00241)** and **[Jupyter Book](https://m2lines.github.io/L96_demo/intro.html)** published in the Journal of Open Science Education (JOSE) and led by Dhruv Balwada. Developed by our team, it aims to introduce Machine Learning (ML) methods to climate scientists and also climate modeling to machine learning experts. The book presents **a wide range of ML applications for climate modeling, focusing on hybrid AI+Physics methods using the Lorenz-96 model**. We hope this book can be used as a pedagogical tool for self-learning, a reference manual, or for teaching modules in an introductory class on ML or hybrid climate modeling. - - - diff --git a/content/news/2502YTCarlos.md b/content/news/2502YTCarlos.md index 068131730..5614fd018 100644 --- a/content/news/2502YTCarlos.md +++ b/content/news/2502YTCarlos.md @@ -8,4 +8,4 @@ thumbnail: 'images/news/2502YTCarlos.png' images: ['images/news/2502YTCarlos.png'] link: 'https://www.youtube.com/@cfgranda/playlists' --- -If you are looking for clear material to understand statistics, probabilities, and mathematical tools for data science, check out the **[Youtube Channel](https://www.youtube.com/@cfgranda/playlists)** of Carlos Fernandez-Granda where he shares recordings of his lectures at NYU! \ No newline at end of file +If you are looking for clear material to understand statistics, probabilities, and mathematical tools for data science, check out the **[Youtube Channel](https://www.youtube.com/@cfgranda/playlists)** of Carlos Fernandez-Granda where he shares recordings of his lectures at NYU! diff --git a/content/news/2502Zhang.md b/content/news/2502Zhang.md index e6ebc45ac..03e2cbe2c 100644 --- a/content/news/2502Zhang.md +++ b/content/news/2502Zhang.md @@ -8,4 +8,4 @@ thumbnail: 'images/news/2502Zhangetal.png' images: ['images/news/2502Zhangetal.png'] link: 'https://doi.org/10.48550/arXiv.2411.01138' --- -This [study](https://doi.org/10.48550/arXiv.2411.01138), led by Cheng Zhang, improves machine-learned models for simulating ocean mesoscale eddies **by addressing artifacts near coastlines caused by out-of-sample predictions using convolutional neural networks (CNN)**. Comparing two different strategies for treating Boundary Conditions (BCs) in CNN models - zero and replicate padding - they show that replicate padding significantly reduces these artifacts, enhancing the accuracy and stability of ocean modeling near complex coastal regions. \ No newline at end of file +This [study](https://doi.org/10.48550/arXiv.2411.01138), led by Cheng Zhang, improves machine-learned models for simulating ocean mesoscale eddies **by addressing artifacts near coastlines caused by out-of-sample predictions using convolutional neural networks (CNN)**. Comparing two different strategies for treating Boundary Conditions (BCs) in CNN models - zero and replicate padding - they show that replicate padding significantly reduces these artifacts, enhancing the accuracy and stability of ocean modeling near complex coastal regions. diff --git a/content/pages/code-of-conduct/_index.md b/content/pages/code-of-conduct/_index.md index 0b108b5a3..f2f3e31a5 100644 --- a/content/pages/code-of-conduct/_index.md +++ b/content/pages/code-of-conduct/_index.md @@ -12,12 +12,12 @@ _____ - This code of conduct applies to all members of M²LInES without exception. We also expect external collaborators to comply with this code when interacting with M²LInES, and we extend guidelines (such as inclusion for authorship) to external collaborators. -- Key aspects of this document: Inclusivity, professionalism and conduct, unethical and/or unacceptable behavior, authorship, scientific integrity, and conflicts of interest. +- Key aspects of this document: Respect, professionalism and conduct, unethical and/or unacceptable behavior, authorship, scientific integrity, and conflicts of interest. -- Management committee (Laure, Johanna, Alistair, Carlos, Ryan) will deal with any issues arising: +- Management committee (Laure, Johanna, Alistair) will deal with any issues arising: -#### Inclusivity +#### Respect - Enjoyable, high-quality research can only be conducted when you **feel safe, secure, and supported**. All group members are thus dedicated to a **harassment-free experience for everyone, regardless of gender identity and expression, sexual orientation, disability, physical appearance, body size, race, age, and/or religion [^1] (or lack thereof), family status or socio-economic status**. The group members also recognize that some of those biases can be unconscious and creep into different aspects of Academic life and research, such as meetings, publications, citations, hiring, etc. Members should strive to consciously combat those biases and bring awareness to others. @@ -41,11 +41,11 @@ _____ - Meetings and interactions: - + Be inclusive of all present, listen, invite input, be constructive. + + Be considerate of all present, listen, invite input, be constructive. + Be mindful of dominant personalities, including yourself. - + Employ appropriate language inclusive of all backgrounds. + + Employ appropriate language respectful of all backgrounds. + Follow appropriate etiquette: * In virtual meetings, raise hands in your video stream or use the software equivalent. diff --git a/content/publications/_index.md b/content/publications/_index.md index 6e09906c4..3bf739314 100644 --- a/content/publications/_index.md +++ b/content/publications/_index.md @@ -7,7 +7,7 @@ heroBackground: 'images/susan-q-yin-2JIvboGLeho-unsplash.jpg' ## M²LInES research and other relevant publications -M²LInES educational [Jupyter Book](https://m2lines.github.io/L96_demo) is out. Learn about about the interface between machine learning & climate modeling whether you are an ML expert or a climate scientist. +M²LInES educational [Jupyter Book](https://m2lines.github.io/L96_demo) is out. Learn about about the interface between machine learning & climate modeling whether you are an ML expert or a climate scientist. You can also check all our publications on our **[Google Scholar profile](https://scholar.google.com/citations?hl=en&user=iY8RO4QAAAAJ)** diff --git a/content/research/talks/_index.md b/content/research/talks/_index.md index 674a67cea..7b97841d2 100644 --- a/content/research/talks/_index.md +++ b/content/research/talks/_index.md @@ -193,7 +193,7 @@ p {

Laure Zanna, Sara Shamekh, Pierre Gentine
Navigating the Academic Search Process
- LEAP Professional Development Series + LEAP Professional Development Series

@@ -488,7 +488,7 @@ p {

Laure Zanna and Steven Brunton
Machine learning in fluid dynamics and climate physics
- Nature Reviews Physics + Alan Turing Institute - October 5th 💻 🎆 + Nature Reviews Physics + Alan Turing Institute - October 5th 💻 🎆

diff --git a/content/resources/_index.md b/content/resources/_index.md index 47d4ff593..8186d5901 100644 --- a/content/resources/_index.md +++ b/content/resources/_index.md @@ -6,14 +6,14 @@ heroBackground: '/images/SouthAtlantic.A2002157.1055.250m.jpg' --- ### Open Storage Network Pod -M²LInES has an Open Storage Network (OSN) Pod! +M²LInES has an Open Storage Network (OSN) Pod! What can you get out of the Pod as a member of the team: * A project for a specific bucket (authenticated or public) to work with your team * Move data (needs LEAP DCT team admin) to the publication bucket (m2lines-pubs) before publishing a paper. * Ingest publicly available datasets into Analysis Ready Cloud Optimized formats. Start by adding an issue [here](https://github.com/leap-stc/data-management/issues/new?template=new_dataset.yaml) and work with the LEAP DCT on the recipe. More info in the [docs](https://leap-stc.github.io/_preview/206/guides/data_guide.html#ingesting-datasets-into-cloud-storage) You can find the relevant guide to the pod [here](https://leap-stc.github.io/_preview/206/guides/team_guide.html#) -Non-members can access publicly available data from our team. As with all the OSN Pod, 20% of our space is reserved for public use. +Non-members can access publicly available data from our team. As with all the OSN Pod, 20% of our space is reserved for public use. ### Learning Machine Learning with Lorenz-96 diff --git a/content/team/AakashSane.md b/content/team/AakashSane.md index 552063327..d155713c3 100644 --- a/content/team/AakashSane.md +++ b/content/team/AakashSane.md @@ -9,4 +9,4 @@ Website: https://aakashsane.gitlab.io/ tags: [Ocean, Machine Learning, Climate Model Development] --- -Princeton University, Affiliate NOAA-GFDL +Princeton University, Affiliate NOAA-GFDL diff --git a/content/team/AntoineNasser.md b/content/team/AntoineNasser.md index 656f230e0..e6e9e5087 100644 --- a/content/team/AntoineNasser.md +++ b/content/team/AntoineNasser.md @@ -4,7 +4,7 @@ draft: false image: "/images/team/AntoineNasser.jpg" jobtitle: "Postdoc" promoted: true -weight: 54 +weight: 54 Website: Position: Topography tags: [Ocean, Machine Learning, Climate Model Development] diff --git a/content/team/CemGultekin.md b/content/team/CemGultekin.md index afa5cae5a..d96ff44bc 100644 --- a/content/team/CemGultekin.md +++ b/content/team/CemGultekin.md @@ -11,4 +11,4 @@ tags: [Machine Learning] --- -NYU \ No newline at end of file +NYU diff --git a/content/team/JiarongWu.md b/content/team/JiarongWu.md index d18c9f761..20bce3d35 100644 --- a/content/team/JiarongWu.md +++ b/content/team/JiarongWu.md @@ -10,4 +10,4 @@ tags: [Ocean, Coupled Physics] --- -NYU \ No newline at end of file +NYU diff --git a/content/team/NoraLoose.md b/content/team/NoraLoose.md index c68182292..1ceab727c 100644 --- a/content/team/NoraLoose.md +++ b/content/team/NoraLoose.md @@ -11,4 +11,4 @@ tags: [Ocean, Machine Learning] --- -[C]Worthy +[C]Worthy diff --git a/content/team/RyanAbernathy.md b/content/team/RyanAbernathy.md index bc7ae1f04..6bfd25956 100644 --- a/content/team/RyanAbernathy.md +++ b/content/team/RyanAbernathy.md @@ -11,4 +11,4 @@ tags: [Ocean] --- -Earthmovers \ No newline at end of file +Earthmovers diff --git a/content/team/SuryaDheeshjith.md b/content/team/SuryaDheeshjith.md index 007d2f4ee..481f6dd96 100644 --- a/content/team/SuryaDheeshjith.md +++ b/content/team/SuryaDheeshjith.md @@ -11,4 +11,4 @@ tags: [Machine Learning] --- -NYU \ No newline at end of file +NYU diff --git a/content/team/former/AuroraBasinki.md b/content/team/former/AuroraBasinki.md index 80044e1e1..e5568146c 100644 --- a/content/team/former/AuroraBasinki.md +++ b/content/team/former/AuroraBasinki.md @@ -9,4 +9,4 @@ weight: 13 --- -Scripps, UCSD \ No newline at end of file +Scripps, UCSD diff --git a/content/team/former/ElizabethYankovsky.md b/content/team/former/ElizabethYankovsky.md index f9efa23d7..cc5d95e8a 100644 --- a/content/team/former/ElizabethYankovsky.md +++ b/content/team/former/ElizabethYankovsky.md @@ -9,4 +9,4 @@ Website: --- -Yale University \ No newline at end of file +Yale University diff --git a/content/team/former/FriedrichGinnold.md b/content/team/former/FriedrichGinnold.md index 79d19999a..75444f1ff 100644 --- a/content/team/former/FriedrichGinnold.md +++ b/content/team/former/FriedrichGinnold.md @@ -9,4 +9,4 @@ Website: --- -Netlight \ No newline at end of file +Netlight diff --git a/content/team/former/LeoLiu.md b/content/team/former/LeoLiu.md index 5ec28f49a..8df0aafc4 100644 --- a/content/team/former/LeoLiu.md +++ b/content/team/former/LeoLiu.md @@ -8,5 +8,3 @@ weight: 45 Website: tags: [] --- - - diff --git a/content/team/former/MarioHerreroGonzalez.md b/content/team/former/MarioHerreroGonzalez.md index 0f9a49b28..709568144 100644 --- a/content/team/former/MarioHerreroGonzalez.md +++ b/content/team/former/MarioHerreroGonzalez.md @@ -9,4 +9,4 @@ Website: --- -The University of Edinburgh +The University of Edinburgh diff --git a/content/team/former/PatriciaLuc.md b/content/team/former/PatriciaLuc.md index 228fab00d..63648bb60 100644 --- a/content/team/former/PatriciaLuc.md +++ b/content/team/former/PatriciaLuc.md @@ -9,4 +9,4 @@ Website: --- -Microsoft +Microsoft