Update news and pubs. Add headshots

Author: duncanwp

_news/2023-12-01.md

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+---
+date: "2023-12-01"
+---
+The Climate Analytics Lab had a great time at AGU 2023! We gave two [invited](https://agu.confex.com/agu/fm23/meetingapp.cgi/Paper/1295660) [talks](https://agu.confex.com/agu/fm23/meetingapp.cgi/Paper/1295652), presented a poster on [AerChemMIP2](https://agu.confex.com/agu/fm23/meetingapp.cgi/Paper/1367039) and had a great lunch with Andrew Ng to discuss AI for climate. We're looking forward to AGU 2024!

_news/2024-03-01.md

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+---
+date: "2024-03-01"
+---
+Prof. Watson-Parris gave a talk at the 2024 American Association for the Advancement of Science (AAAS) Annual Meeting in Denver on the use of Generative AI for Climate Science. It was covered by [ACM](https://cacm.acm.org/news/scientific-applications-of-generative-ai/).

_news/2024-05-01.md

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+---
+date: "2024-03-01"
+---
+"Multi-Fidelity Residual Neural Processes for Scalable Surrogate Modeling" has been accepted to ICML 2024! It introduces a novel multi-fidelity neural process and apply it to climate projections - generating a temperature projection of ERA5 out to 2100. The paper is available on [arXiv](https://arxiv.org/abs/2402.18846).

_pages/media.md

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 ## Articles and Interviews
 
+- "Generative AI in Science” in [ACM](https://cacm.acm.org/news/scientific-applications-of-generative-ai/) (2024)
+
+- “How AI is improving climate forecasts” in [Nature](https://www.nature.com/articles/d41586-024-00780-8) (2024)
+
 -   "We're changing the clouds' An unforeseen test of geoengineering is
     fueling record ocean warmth" in [Science
     magazine](https://www.science.org/content/article/changing-clouds-unforeseen-test-geoengineering-fueling-record-ocean-warmth) (2023)

_pages/publications.md

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 \* = (co-)supervised
 
-### Submitted
- -  \*Bouabid, S., Sejdinovic, D., and **Watson-Parris, D.** *FaIRGP: A Bayesian Energy Balance Model for Surface Temperatures Emulation.* Submitted to Journal of Advances in Modeling Earth Systems: [10.22541/essoar.169008319.96252512/v1](https://doi.org/10.22541/essoar.169008319.96252512/v1)
-- Jordan, G., Haywood, J., Malavelle, F., Chen, Y., Peace, A., Duncan, E., Partridge, D. G., Kim, P., **Watson-Parris, D.**, Takemura, T., Neubauer, D., Myhre, G., Skeie, R., and Laakso, A.: *How well are aerosol-cloud interactions represented in climate models? Part 1: Understanding the sulphate aerosol production from the 2014--15 Holuhraun eruption*. Submitted to Atmospheric Chemistry and Physics
-- Fiedler, S., Naik, V., O'Connor, F.M., Smith, C. J., Pincus, R., Griffiths, P., Kramer, R., Takemura, T., Allen, R.J., Im, U., Kasoar, M., Modak, A., Turnock, S., Voulgarakis, A., **Watson-Parris, D.**, Westervelt, D.M., Wilcox, L.J., Zhao, A, Collins, W.J., Schulz, M., Myhre, G., and Forster P.M. *Interactions between atmospheric composition and climate change - Progress in understanding and future opportunities from AerChemMIP, PDRMIP, and RFMIP.* Submitted to Geoscientific Model Development
-- \*Bouabid, S., W**atson-Parris, D.**, Stefanovic, S., Nenes, A., Sejdinovic, D. *AODisaggregation: toward global aerosol vertical profiles.* Submitted to Environmental Data Science
+### 2024
+ - Niu, R., Wu, D., Kim, K., Ma, Y.-A., **Watson-Parris, D.**, and Yu, R.: *Multi-Fidelity Residual Neural Processes for Scalable Surrogate Modeling*, Accepted at ICML 2024, [arXiv](https://doi.org/10.48550/arXiv.2402.18846) 
+ -  \*Bouabid, S., Sejdinovic, D., and **Watson-Parris, D.** *FaIRGP: A Bayesian Energy Balance Model for Surface Temperatures Emulation.* Accepted in Journal of Advances in Modeling Earth Systems: [10.22541/essoar.169008319.96252512/v1](https://doi.org/10.22541/essoar.169008319.96252512/v1)
+- Jordan, G., Haywood, J., Malavelle, F., Chen, Y., Peace, A., Duncan, E., Partridge, D. G., Kim, P., **Watson-Parris, D.**, Takemura, T., Neubauer, D., Myhre, G., Skeie, R., and Laakso, A.: *How well are aerosol-cloud interactions represented in climate models? Part 1: Understanding the sulphate aerosol production from the 2014--15 Holuhraun eruption*. Atmos. Chem. Phys., 24, 1939–1960, [10.5194/acp-24-1939-2024](https://doi.org/10.5194/acp-24-1939-2024)
+- Fiedler, S., Naik, V., O'Connor, F.M., Smith, C. J., Pincus, R., Griffiths, P., Kramer, R., Takemura, T., Allen, R.J., Im, U., Kasoar, M., Modak, A., Turnock, S., Voulgarakis, A., **Watson-Parris, D.**, Westervelt, D.M., Wilcox, L.J., Zhao, A, Collins, W.J., Schulz, M., Myhre, G., and Forster P.M. *Interactions between atmospheric composition and climate change - Progress in understanding and future opportunities from AerChemMIP, PDRMIP, and RFMIP.* Geosci. Model Dev., 17, 2387–2417, [10.5194/gmd-17-2387-2024](https://doi.org/10.5194/gmd-17-2387-2024)
+- \*Bouabid, S., W**atson-Parris, D.**, Stefanovic, S., Nenes, A., Sejdinovic, D. *AODisaggregation: toward global aerosol vertical profiles.* Accepted at Environmental Data Science
 
 ### 2023
   - \*Manshausen, P., **Watson-Parris, D.**, Christensen, M. W., Jalkanen, J.-P., and Stier, P. *Rapid saturation of cloud water adjustments to shipping emissions*. Atmospheric Chemistry and Physics (Highlight Letter) 23, [10.5194/egusphere-2023-813](https://doi.org/10.5194/acp-23-12545-2023)

_pages/teaching.md

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 author_profile: true
 ---
 
+## SIO Courses
+
+### SIO(c) 209 - Deep Learning for Environmental/Geo Science
+
+This course introduces students to the theory and practice of deep learning for environmental and geoscience applications. The course will cover the basics of deep learning, including neural networks, convolutional neural networks, recurrent neural networks, and generative adversarial networks. The course will also cover the use of deep learning for image analysis, time series analysis, and (briefly) natural language processing. The course will include a mix of lectures, hands-on exercises, and a final project.
+
+ - Please see course page [here](/sioc209-2024-sp) for details.
+
 ## HDSI Courses
 
-### DSC 200
+### DSC 200 - Data Science Programming
 
 Computing structures and programming concepts such as object orientation, data structures such as queues, heaps, lists, search trees and hash tables. Laboratory skills include data analysis with pandas and xarray in Jupyter notebooks.
 
  - Please see course canvas page for details: <https://canvas.ucsd.edu/courses/49102>
  - Github repo: <https://github.com/climate-analytics-lab/dsc200-fa23-public/>
 
-## DSC 180A
+### DSC 180A - Data Science Capstone
 
 Data science capstone course. Students work in teams to complete a climate related data science project. Project management, communication, and teamwork skills are emphasized. 
 

_people/Huanyu.md

@@ -3,5 +3,5 @@ lastname: "Anderson"
 firstname: "Isabella"
 role: "Undergraduate Student"
 status: "active"
-image_path: /assets/images/Headshot_PlaceHolder.png
+image_path: /assets/images/headshots/Isabella_Anderson.jpg
 ---

_people/Isabelle.md

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+---
+lastname: "Nicolaou"
+firstname: "Giorgia"
+pub_id: "Nicolaou G"
+role: "PhD Student"
+status: "active"
+image_path: /assets/images/headshots/Giorgia_Nicolaou.jpg
+---

_people/giorgia.md

@@ -3,5 +3,5 @@ lastname: "Radha-krishnan"
 firstname: "Harin"
 role: "Masters Student"
 status: "active"
-image_path: /assets/images/Headshot_PlaceHolder.png
+image_path: /assets/images/headshots/Harin_Radha-krishnan.jpg
 ---