Machine Learning and AI Session for Business School and Economics

Thursday 24 November 2022 (16:00-17:00)

Thursday 24th November 2022 at 4pm

Advanced Research Computing (or the BEAR team!) and researchers in the Business School and Economics will be presenting a hybrid 1 hour session around the use of machine learning and AI techniques in relation to their research areas.

Session Plan

List of speakers and timings
 16:00-16:20 Dr Jenny Wong - Introduction to Machine Learning and AI 
 16:20-16:30 Duiyi (Claire) Dai - Textual Analysis and Machine Learning in my Economics Research: BlueBEAR Helps
 16:30-16:40 Binzhi Chen - How BlueBEAR helps Business School Research? From the Perspective of Econometrics
 16:40-16:50 Dr Bowen Liu - When Econometrics meets Machine Learning: Application in Environmental Economics
 16:50-17:00 Q & A

Speaker Information

Dr Jenny Wong (Senior Research Software Engineer in Advanced Research Computing) will provide an overview of machine learning and AI techniques and how you can access the Baskerville Tier 2 supercomputer. 

Duiyi (Claire) Dai (PhD student in Economics), will be presenting her research on measuring Brexit uncertainty, applying textual analysis and machine learning techniques.  She will introduce how BlueBEAR has helped in her research. More information about her research project can be found here:

Binzhi Chen (PhD student from Economics), will discuss advantages of the BlueBEAR to Birmingham Business School students, especially to data analysis oriented research during the PhD study. I will also talk about how  the Bluebear helps my currnet research and why the Bluebear is a powerful tool to both applied and theoretical econometricians.

Dr Bowen Liu (Research Fellow in the Birmingham Business School), will introduce how the combination of machine learning and econometrics can help better understanding the effectiveness of air pollution control policies around the world, he will discuss his recent work on "Winter Heating and Air Pollution in China". More information can be found here:

How to Join

This session will be delivered in-person at University House, room G12 and can also be joined online via Zoom: 

Join Zoom Meeting

Meeting ID: 824 6715 7765

Passcode: 684637


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