Kim Bente

• I am interested in developing application-driven machine learning innovations, particularly for geospatial, geophysical, and Antarctic science.
• Geospatial modelling
• Physics-informed ML
• Climate, Earth Science, and Geophysics applications
• Antarctica and the Cryosphere
• Earth observation (EO)
• EO foundation models
• Active learning with Bayesian Optimisation (BO)

My name is Kim Bente and I am a postdoctoral research fellow in Computational Antarctic Geophysics at the School of Natural Sciences, University of Tasmania, in the Compute Antarctic research group, led by Professor Anya Reading. I completed my PhD in Machine Learning at the School of Computer Science, University of Sydney, where I was supervised by Prof Fabio Ramos and A/Prof Roman Marchant, and a member of the DARE (Data Analytics for Resources and Environments) ARC Training Centre, an Industrial Transformation training centre. My research interests are centred around using probabilistic machine learning and statistical computing methods to address Climate Science problems. I am particularly interested in the quantification of uncertainty to inform high-stakes decision in the climate domain. I am currently working on the application Bayesian Optimisation to Antarctic research problems, including sensor network design, data fusion and ice core drilling site determination. I have gained extensive experience on interdisciplinary research projects, most notably collaborating with Nutrition and Dietetics research, and also working on educational data, as well as criminology data.

I have completed the Master of Data Science [with high distinction] from the University of Sydney in 2020 and I hold a Bachelor of Science from the Technical University Munich, in Management & Technology, specialising in Chemical Engineering and Finance.

Please contact me via kim.bente@utas.edu.au

Check out my GitHub repositories for preprocessing pipelines of Antarctic datasets (e.g. Bedmap3 points, Bedmap maps, BedMachine, RACMO 2.4, GRACE-FO, MODIS MOA, NASA's MEaSUREs datasets), Physics-Informed ML models built in PyTorch, Uncertainty Quatification using GPyTorch and BOTorch for example, and much more.

• ML4Cryo (Machine Learning for the Cryosphere) is a research community initiative that connects researchers and practitioners working at the intersection of machine learning and cryospheric science. It was co-founded in 2024 by Julia Kaltenborn, Andrew McDonald, and myself.
• Visit the ML4Cryo website.
• Join the ML4Cryo community Slack channel.
• Submit to the `Machine Learning for the Cryosphere' session (CR6.1) at EGU 2026 in Vienna, Austria (3–8 May 2026).
• Submit to the `Artificial Intelligence and Machine Learning in Antarctic Science' parallel session at the 12th SCAR Open Science Conference in Oslo, Norway (8–19 August 2026).

I organise our weekly Machine Learning & Robotics reading group at the University of Sydney, led by Prof Fabio Ramos. If you're interested in joining you can sign up to the USYD ML reading group mailing list.

• Kim Bente, Roman Marchant, Fabio Ramos. "Mass Conservation on Rails - Rethinking Physics-Informed Learning of Ice Flow Vector Fields." In NeurIPS 2025 Tackling Climate Change with Machine Learning Workshop (2025). Available on arXiv.
• Contribution to Moreno-Rodenas et al. "Applications of artificial intelligence for water management." Publication via the UNESCO Intergovernmental Hydrological Programme (2025).
• Kim Bente, Roman Marchant, Fabio Ramos. "Antarctic Bed Topography Super-Resolution via Transfer Learning." In NeurIPS 2023 Tackling Climate Change with Machine Learning Workshop.
• Kim Bente, Judy Kay, Roman Marchant. "Probabilistic Machine Learning in Polar Earth and Climate Science: A Review of Applications and Opportunities." In AAAI 2022 Fall Symposium: The Role of AI in Responding to Climate Challenges (2022).
• Kim Bente*, Emma Nassif*, Alyse Davies*, Lyndal Wellard-Cole, Jisu Jung, Judy Kay, Clare Hughes et al. "The Contribution of Nutrients of Concern to the Diets of 18-to-30-Year-Old Australians from Food Prepared Outside Home Differs by Food Outlet Types: The MYMeals Cross-Sectional Study." Nutrients 14, no. 18 (2022): 3751.
  * equal contributions
• Lyndal Wellard-Cole, Alyse Davies, Juliana Chen, Jisu Jung, Kim Bente, Judy Kay, Wendy L. Watson et al. "The contribution of foods prepared outside the home to the diets of 18-to 30-year-old Australians: the MYMeals study." Nutrients 13, no. 6 (2021): 1761.

• COMING UP  —  EGU 2026. I will be attending EGU26 to present my accepted abstract, Bayesian Optimisation for Antarctic Survey Planning (poster, Wednesday 6 May 2 PM, Hall X5 X5.182) and to co-convene session CR6.1, Machine Learning for Cryospheric Sciences on Tuesday 5 May 10.45 AM –12.30 PM Room L3.
  3 - 8 May 2026, Vienna, Austria
• COMING UP  —  Climate Informatics 2026. I will be attending CI26 to present my accepted paper, Deep Learning Ice Shelf Basal Melt Rates via Differentiable Physics (presentation, Thursday 30 April 11.15 AM, Aula).
  27 - 30 April 2026, Lausanne, Switzerland
• NeurIPS 2025 Tackling Climate Change with Machine Learning workshop hosted by Climate Change AI. I will be attending NeurIPS to present my accepted workshop paper, Mass Conservation on Rails – Rethinking Physics-Informed Learning of Ice Flow Vector Fields (poster presentation, Sunday 7 December, Upper Level Room 24ABC).
  1 - 7 December 2025, San Diego, California, USA
• Polar Data Forum VI hosted by the Australian Antarctic Data Centre — I gave a talk within the Machine Learning / Artificial Intelligence theme.
  20 - 24 October 2025, Hobart, Tasmania, Australia
• EGU 2025 — I served as co-convener of the 'Machine Learning for Cryospheric Sciences' session, and also presented at session EOS4.6.
  27 April - 2 May 2025, Vienna, Austria (virtual attendance)
• Climate Informatics 2024 — I attended and presented my poster virtually at this conference.
  22 - 24 April 2024, London, UK (virtual attendance)
• EGU 2024 — I presented my poster virtually at the 'Understanding cryospheric processes in the past, present and future using data assimilation and machine learning' session.
  14 - 19 April 2024, Vienna, Austria (virtual attendance)
• NeurIPS 2023 Tackling Climate Change with Machine Learning workshop hosted by Climate Change AI. I attended NeurIPS and presented my poster.
  10 - 16 December 2023, New Orleans, Louisiana, USA
• AAAI 2022 Fall Symposium: The Role of AI in Responding to Climate Challenges hosted by Climate Change AI. I attended the symposium and gave a talk.
  17 - 19 November 2022, Arlingtion, Virginia, USA
• Information Resilience PhD School 2022 hosted by the ARC Industrial Transformation Training Centre in Information Resilience (CIRES). I presented my research as a poster and in a 3MT.
  12 - 14 September 2022, The University of Queensland (UQ), Brisbane, Queensland, Australia

• Seminar at the Scripps Polar Center, Scripps Institution of Oceanography, University of California San Diego (UCSD)
  5/12/2025 University of California San Diego, San Diego, California, USA
• Seminar at the Geology-Geophysics seminar series, School of Geosciences, University of Sydney
  5/11/2025 University of Sydney, Sydney, Australia
• Seminar at the Centre for Research in Mathematics and Data Science at Western Sydney University, hosted by Associate Professor Oliver Obst
  25/09/2025 Western Sydney University, Sydney, Australia
• Seminar at the Compute Antarctic research group at University of Tasmania, hosted by Professor Anya Reading
  13/06/2025 University of Tasmania, Hobart, Australia
• Panelist at the Information Resilience PhD School 2024 hosted by the ARC Industrial Transformation Training Centre in Information Resilience (CIRES)
  31/10/2024 University of Queensland, Brisbane, Australia
• Research talk & Panel at the Google Deepmind event organised by USYD GDSC (Google Developer Student Club) at Google Sydney
  28/06/2024 Google, Sydney, Australia
• Seminar at the National Institute for Applied Statistics Research Australia (NIASRA) at the University of Wollongong
  6/04/2024 University of Wollongong, Wollongong, Australia

• EGU's Geoscientific Model Development (GMD) (journal)
• NeurIPS 2025 Tackling Climate Change with Machine Learning workshop
• Conference on Uncertainty in Artificial Intelligence (UAI) 2025
• ICLR 2025 Tackling Climate Change with Machine Learning workshop
• Conference on Uncertainty in Artificial Intelligence (UAI) 2024
• ICLR 2024 Tackling Climate Change with Machine Learning workshop

• Machine Learning PhD, The University of Sydney, Australia
  'Uncertainty Quantification and Physical Priors for Geospatial Models of Antarctica', supervised by Professor Fabio Ramos and A/Prof Roman Marchant
  member of the DARE (Data Analytics for Resources and Environments) ARC Training Centre
• Master of Data Science, The University of Sydney, Australia
  completed with High Distinction in 2020
  Final research project (Capstone) 'New Perspectives on the Validation of a Mobile App for Dietary Assessment using Data Mining', supervised by Professor Judy Kay
  Faculty of Engineering Summer Research Program Scholarship 2019/2020 for the project 'Data mining of dietary patterns', supervised by Prof Judy Kay, A/Prof Irena Koprinska and A/Prof Kalina Yacef
• Graduate Certificate in Data Science, The University of Sydney, Australia
  completed with High Distinction in 2019
• Bachelor of Science in Management & Technology, Technical University of Munich, Germany
  Specialisation in Chemical Engineering
  Bachelor's Thesis about 'Knowledge Management in a Dynamic Organisation'

• Tutor for Computer Science Research Methods [INFO5993], School of Computer Science
  Semester 2 2024, postgraduate research education
• Tutor for Computer Science Research Methods [INFO5993], School of Computer Science
  Semester 1 2023, postgraduate research education
• Tutor for Human-in-the-Loop Data Analytics [DATA3406], School of Computer Science
  Semester 2 2022, undergraduate level tutoring
• Tutor for Data Analysis in the Social Sciences [DATA5207], United States Study Centre
  Intensive February 2021, postgraduate level tutoring
• Tutor for Algorithms [COMP9007], School of Computer Science
  Semester 2 2019, postgraduate level tutoring
• Course analytics for Human-in-the-Loop Data Analytics [DATA3406], School of Computer Science
  Educational data analytics, student performance feedback with SRES, development of a computer-based marking program for Semester 2 2020

• Customer Analytics and Customer Relationship Management at FOURSOURCE, a tech start-up in apparel sourcing, Berlin, Germany
• Innovation Consulting and Ideation, Junior Innovation consultant, HYVE, Munich, Germany
• Management Consulting, media sector, working student, goetzpartners advisory, Munich, Germany
• Research & Development (R&D) and International Marketing, internships, consumer goods sector, Schwarzkopf (Henkel), Hamburg, Germany
• Product development and product management, internship, consumer goods sector, Kneipp, Würzburg, Germany

• Running, Cycling, Tennis, Hiking, Swimming, Skiing, Pilates, Yoga
• Designing and sewing timeless garments and other textile creations
• Cooking, eating, travelling, exploring art

The Daily Data Dose [DDD] is a compilation of my favourite data-related resources, both informative & entertaining. My shortlist includes podcasts, websites, talks and other useful things that I came across. Most of these I have recommended to a plethora of data-enthusiasts, so it was time to put a list on the web. I thank everyone who has introduced me to any of these gems. Links are included below.

• 3D ICE data visualisation

  
  Superb tool built by Yu Wang to explore polar datasets.

• PolarGlobe 4D climate data visualisation

  
  Amazing nowcasts as well as 12 day forecasts of global wind and ocean currents for the whole globe.

• The Vanishing Gradients podcast

  
  This is my new favourite Data Science podcasts!

• The Dataframed podcast

  
  This is my previous favourite Data Science podcasts! I have listened to every of the 60 episodes at least once, soaking up insights from industry to academia. I can't recommend this enough, and if you know me I will have likely already recommended it to you.

• My compilation of Antarctic data sets

  
  A repository on my GitHub with an overview of icy data sets, frosty models and other high-latitude data collections. I focus on the Antarctic ice sheet.

• 'AI for Earth' projects by Microsoft

  
  I learnt about some of the projects from 'AI for Earth' at a very inspiring talk by Jennifer Marsman at the Microsoft reactor in Sydney. I was very fascinated by the creative use of AI, especially by the World Mosquito Program and the Whalebook. I recommend checking out the projects.

• Process Mining Coursera course by Professor Wil van der Aalst

  
  I took this course on Process Mining which is not only interesting from an algorithmic point of view but very relevant for any temporal or procedural data set. Great course!

• "Environment Variables: how software impacts climate change" - Merrin Macleod (PyCon AU 2019)

  
  This is an outstanding talk which was recommended to me by a colleague (Thanks Ben!). If you care about the environment and you work in tech, you will surely learn something new here.

• The Pudding

  
  The Pudding is website which publishes visual essays about societal and cultural topics. They are pushing the boundaries of data journalism and therefore always worth a digital visit.

• Gapminder

  
  Gapminder is a great organisation that promotes a more fact-based worldview. They also have an extensive collection of open access data sets. Furthermore, I highly recommend reading the book 'Factfulness' by Hans Rosling.

• Information is Beautiful

  
  A collection of informative and visually appealing data visualisations.

• Google dataset search tool

  
  A search engine for data sets. A very useful tool to find openly available data sets.

• Data Science by John D. Kelleher and Brendan Tierney

  
  I recommend this book to family and friends who want to understand what I do, but I also like going back to this pocket book by MIT Press to reset my own perspective for the big picture.

• Papers with code

  
  Professor Caren Han's NLP course [COMP5046] introduced me to this site, which is a great resource to stay on top of the latest ML research in different fields.

• Datasaurus, the new Anscombe's quartet

  
  An updated and more creative version of the Anscombe's quartet, including animated visualisations. This demonstrates once again that summary statistics are not enough to describe data.

• Spurious correlations blog by Tyler Vigen

  
  A good reminder that correlation ≠ causation.

• The True size of ...

  
  This is a fun web tool to understand the distortion of the size of countries, as they are displayed on the world map.

• kim.bente@utas.edu.au
• Find me in Maths & Physics (Sandy Bay Campus)
• The University of Tasmania, 7005 Sandy Bay, Tasmania, Australia