Kim Bente

• Using Data Science, particularly probabilistic machine learning methods

  
  to address challenging Climate Science problems in Antarctica. Polar research is heating up but don't be fooled because "what happens in the poles, doesn't stay in the poles."

• Bayesian Optimisation
• Spatial statistics
• Human-Centred Technology (HCT)
• Causal Inference

My name is Kim Bente and I am a second year Data Science PhD student at the School of Computer Science, University of Sydney, supervised by Prof Fabio Ramos from NVIDIA, Dr Roman Marchant from CSIRO's DATA61, and Prof Judy Kay. 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 am a member of the DARE (Data Analytics for Resources and Environments) ARC Training Centre, an Industrial Transformation training centre, led by the University of Sydney. 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.

I am also an HDR student rep at the Faculty of Engineering and a co-founder and Vice President of PRESS, the Postgraduate Research in Engineering Student Society.

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

• Data Science PhD student (current), The University of Sydney, Australia

  
  'Uncertainty quantification and communication in environmental modelling', supervised by Professor Fabio Ramos, Dr Roman Marchant and Professor Judy Kay

  
  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'

• 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 B. 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 B. 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.

• AAAI 2022 Fall Symposium: The Role of AI in Responding to Climate Challenges hosted by Climate Change AI

  
  17-19 November 2022, Arlingtion, Virginia, USA

• Information Resilience PhD School 2022 hosted by the ARC Industrial Transformation Training Centre in Information Resilience [CIRES]

  
  12-14 September 2022, The University of Queensland (UQ), Brisbane, Queensland, Australia

• 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
• Designing and sewing timeless garments and other textile creations
• Cooking healthy food, 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.

• 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@sydney.edu.au [university email]
• kimbente@hotmail.com [personal email]
• Find me in J12 [School of Computer Science]
• The University of Sydney, 2006 NSW, Australia