Research

I am currently an EU Marie Curie Postdoctoral Research Fellow at the Institute of Science and Technology Austria (ISTA). My research concerns the use of high-resolution and parameterised atmospheric simulations to study deep convection and climate change, i.e. the formation of clouds and they would change in a warming climate. I was previously a postdoctoral research at the Climate Change Research Centre at UNSW in Sydney, Australia.

I completed my Doctoral studies at the Australian Centre for Astrobiology (ACA) at the University of New South Wales, Australia. My research concerned gathering empirical evidence for the processes, impact and outcomes of social media science communication by means of computational techniques. The science topics that I focused on were space science and climate change related. Given the amount of time, money and effort spent on social media science communication, it striked me as odd and particularly curious that little concrete evidence has been provided in terms of the effectiveness of these activities. I was especially interested in the effects of social media engagement on public trust in science. Does online engagement with scientists improve public trust in science? How do we communicate science better on social media to build public trust? These were the questions I aimed to answer. My PhD Thesis can be downloaded from here.

I have a special interest in big data and the potential to glean meaningful insights from them to inform decision making. As I have a background in engineering and computer science, I decided to use machine learning to approach my research questions. The techniques that I have used include supervised and unsupervised learning, sentiment analysis and topic modelling. I wrote a blog post to explain my findings so far and linked to the source code for my experiments. Ultimately, I am interested in using the knowledge and results gained from my analysis to develop intuitive and useful tools to help scientists communicate their research to the public.

In a previous life I was a European Commission funded Marie Curie Fellow at the European Organisation for Nuclear Research (CERN), working as a data acquisition engineer for the Compact Muon Solenoid (CMS) experiment of the Large Hadron Collider (LHC) accelerator. The CMS collaboration is one of the two experiments that detected the Higgs Boson, a discovery that won its eponymous predictor Professor Peter Higgs a Nobel Prize in Physics in 2013.

My Google scholar profile can be found here.

 

Selected Publications

Hwong, Y. L., Sherwood, S. C., & Fuchs, D. (2022). Can We Use 1D Models to Predict 3D Model Response to Forcing in an Idealized Framework?. Journal of Advances in Modeling Earth Systems, 14(4), e2021MS002785.

Hwong, Y. L., Song, S., Sherwood, S. C., Stirling, A. J., Rio, C., Roehrig, R., … & Touzé‐Peiffer, L. (2021). Characterizing convection schemes using their responses to imposed tendency perturbations. Journal of Advances in Modeling Earth Systems, 13(5), e2021MS002461.

Hwong, Y.L. (2018). Communicating space science on social media: A study of engagement and trust in science. Doctoral dissertation, Faculty of Science, University of New South Wales. Available for download here.

Hwong, Y. L., Oliver, C., Van Kranendonk, M., Sammut, C., & Seroussi, Y. (2017). What makes you tick? The psychology of social media engagement in space science communication. Computers in Human Behavior, 68, 480-492.

Hwong, Y. L., Kusters, V. J., Willemse, T. A., Keiren, J. J.A., Leemans, S. (2013). Formalising and analysing the control software of the Compact Muon Solenoid Experiment at the Large Hadron Collider. Science of Computer Programming, Volume 78, Issue 12 (pp. 2435-2452).

Hwong, Y. L., Kusters, V. J., & Willemse, T. A. (2011). Analysing the control software of the compact muon solenoid experiment at the large hadron collider. In International Conference on Fundamentals of Software Engineering (pp. 174-189). Springer, Berlin, Heidelberg.

Hwong, Y. L., Willemse, T., Kusters, V., Bauer, G., Beccati, B., Behrens, U., … & Cano, E. (2011). An analysis of the control hierarchy modelling of the CMS detector control system. In Journal of Physics: Conference Series (Vol. 331, No. 2, p. 022010). IOP Publishing.

 

Selected Conference Proceedings

Colin, M., Hwong, Y. L., Nie, J., Wu, C. M., Dixit, V. (2022, Aug). Co-convener for accepted session proposal (Idealised Frameworks, Modelling, and Observations to Understand Moist Convective Processes at Various Scales). 19th Annual Meeting Asia Oceania Geosciences Society (AOGS).

Hwong, Y. L., Sherwood, S. C., Fuchs, D. (2022, May). Can We Use Single-Column Models to Predict 3D Model Response to Forcing in an Idealized Radiative-Convective Equilibrium Framework? 35th Conference on Hurricanes and Tropical Meteorology. Abstract accepted for oral presentation.

Hwong, Y. L., Sherwood, S. C., Fuchs, D. (2021, Dec). Comparing the Physics of 1D vs. 3D Atmospheric Models Using Their Linearised Responses. 24th International Congress on Modelling and Simulation (MODSIM2021). Extended abstract accepted for oral presentation.

Hwong, Y. L., Sherwood, S. C., Fuchs, D. (2021, Dec). Can We Use 1D Models to Predict 3D Physics? AGU Fall Meeting. Abstract accepted for poster presentation.
Raupach, T., Hwong, Y. L., Sherwood, S. C. (2021, Dec). Simulated convection responses to temperature and moisture perturbations in large eddy simulations. 24th International Congress on Modelling and Simulation (MODSIM2021). Extended abstract accepted for oral presentation.

Hwong, Y. L., Song, S., Sherwood, S. C., Stirling, A., Rio, C., Roehrig, R., … & Touzé-Peiffer, L. (2021, Apr). Characterising Convection Schemes Using Their Linearised Responses to Convective Tendency Perturbations. Improvement and calibration of clouds in models (virtual). Oral presentation.
Colin, M., Sherwood, S. C., Hwong, Y. L. (2021, Mar). Comparing convective memory in different schemes with imposed fixed large-scale state. Atmospheric Modelling virtual workshop. Oral presentation.

Hwong, Y. L., Sherwood, S. C., Song, S. (2019, Jul). Using WRF Single-Column Model as a Testbed for Convective Parameterisation. Convection Parametrization: Progress and Challenges workshop, MetOffice, UK. Poster presentation.

Hwong, Y. L., Oliver, C. (2018). How Should We Communicate Science on Social Media? A Machine Learning Approach to Science Communication Research. Australian Science Communicators Tenth National Conference 2018. Sydney, Australia.

Hwong, Y. L., Oliver, C., Van Kranendonk, M. (2018). In Science We Trust: Does Social Media Engagement with Scientists Improve Public Trust in Science? Astrobiology Australasia Meeting 2018. Rotorua, New Zealand.

Hwong, Y. L., Oliver, C., Van Kranendonk, M. (2017). We Engage, Therefore They Trust? A Study of Social Media Engagement and Public Trust in Science. American Geophysical Union Fall Meeting 2017. New Orleans, LA, USA

Hwong, Y. L., Oliver, C., Van Kranendonk, M. (2017). To Trust or Not to Trust? What Drives Public Trust in Science in Social Media Engagement. American Geophysical Union Fall Meeting 2017. New Orleans, LA, USA

Hwong, Y. L., Oliver, C., Van Kranendonk, M., Sammut, C. (2017). Keep it real and visual: dissecting social media engagement and its potential to influence trust in space science. Astrobiology Science Conference 2017. Phoenix, AZ, USA

Hwong, Y. L., Oliver, C., Van Kranendonk, M., Sammut, C. (2016). What Makes You Tick? An Empirical Study of Space Science Related Social Media Communications Using Machine Learning. American Geophysical Union Fall Meeting 2016. San Francisco, USA.

Hwong, Y. L., Oliver, C., Van Kranendonk, M., Sammut, C. (2016). Will this tweet be retweeted? An empirical study of astrobiology related social media communications using machine learning. Australasian Astrobiology Conference 2016. Perth, Australia.