Completed HIDA Visiting Research Fellowship
Completed a 3-month research fellowship at the Materials Data Science and Informatics (IAS-9) at Forschungszentrum Jülich, Germany
About
I am a materials engineer and PhD candidate at the Australian National University, specialising in Applied Machine Learning for Materials Design. During my PhD, I’ve worked as a software engineer at the Software Innovation Institute and Instaclustr by NetApp, and spent time as a research intern at the Materials Data Science and Informatics (IAS-9) in Germany and Nanocube in Australia. I also had the opportunity to participate in Google Summer of Code 2021 with AiiDA, where I developed the AiiDA-RESTAPI. I hold a Bachelor’s and Master’s degree from the Indian Institute of Technology, Bombay.
If you would like to get in touch with me, feel free to message me on LinkedIn or email me at bhat.ninadmb@gmail.com.
Interests
- Software Development
- Materials Design
- Machine Learning
Education
Doctor of Philosophy (PhD)
Australian National University
Master of Technology (M.Tech)
Indian Institute of Technology, Bombay
Bachelor of Technology (B.Tech)
Indian Institute of Technology, Bombay
Recent News
Traveling to Germany for HIDA Visiting Research Fellowship
Received the HIDA Visiting Research Fellowship to conduct research at the Materials Data Science and Informatics (IAS-9) at Forschungszentrum Jülich, Germany
Attended AIMSE 2023
Presented a poster at the Artificial Intelligence in Materials Science and Engineering 2023 in Saarbrücken, Germany
Attended PyCon AU 2023
Demonstrated applications developed at Software Innovation Institute (SII)
Attended Light Metals Technology 2023
Presented a poster at the Light Metal Technology 2023 in Melbourne, Australia
Recent Publications
(2024).
Inverse design of aluminium alloys using multi-targeted regression.
Journal of Materials Science.
(2023).
Improving the prediction of mechanical properties of aluminium alloy using data-driven class-based regression.
Computational Materials Science.
(2023).
Unsupervised machine learning discovers classes in aluminium alloys.
Royal Society Open Science.
(2020).
On the in-situ characterisation of metastable pitting using 316L stainless steel as a case study.
Corrosion Science.