Keynote Speakers

• Julia Bingler (Fellow, Council on Economic Policy)

Julia Anna Bingler is a fellow with Council on Economic Policy (CEP) which is an international non-profit non-partition economic policy think-tank for sustainability. At CEP, Julia focuses on integrating climate and environmental risks in financial regulation and monetary policy. Prior to joining CEP, Julia did her PhD in Economics at ETH Zurich, where she focused on climate transition risk metrics and climate risk disclosures, and developed a strong interest in Machine Learning and Natural Language Processing. Before starting the PhD, Julia worked at a think tank in Germany on sustainable finance regulation and the implementation of Art. 2.1c of the Paris Agreement. Julia regularly participates in the UNFCCC climate conferences as an observer on finance-related topics. She holds a M.Sc. in Economics from Leipzig University and in Environmental Economics and Climate Change from London School of Economics and Political Science.

• Budha Bhattacharya (Institute of Financial and Technology, University College London)
  

Budha is an Industrial Professor at Institute of Finance and Technology (IFT) at University College London. He has worked in Capital Markets for almost twenty years, having held key positions at tier-one investment banks such as UBS as an Executive Director, Goldman Sachs, JPMorgan, and Bank of America Merrill Lynch, as well as for KPMG. He currently works as Chief Product Officer of ESG IQ, KPMG's flagship ESG Big Data analytics platform, developed by data scientists and engineers in conjunction with Google. ESG IQ ingests and analyses ESG and Climate-related enormous structured datasets from multiple sources and millions of unstructured data points to assess several asset classes, enabling firms to understand the drivers behind their ESG standings. Budha's doctoral research at UCL's Institute of Finance and Technology focuses on big data-driven ESG analytics, NLP models, and sustainable finance. He pursued his EMBA from INSEAD and other advanced degrees in Finance and Economics from the University of Exeter and London Business School.

• Matt Goldklang (Climate Scientist, Man Numeric)

Matt Goldklang is a climate scientist at Man Numeric which he joined in 2021. Prior to Man Numeric, he worked in climate risk analytics at Rhodium Group, where he gained experience working with climate impact modeling. Matt received a bachelor’s degree in geology and geophysics and a certificate in energy studies from Yale University. He later received a master’s degree in climate change from the University of Copenhagen, where he worked on climate change impacts and machine learning research.

At Man Numeric, he has been working on interdisciplinary climate analysis integrating climate change risk into decision-making.

• Edwin Simpson (Lecturer, Dept of Computer Science, Bristol University)

Edwin joined the University of Bristol in early 2020 as a lecturer (assistant professor), working at the join between natural language processing (NLP) and machine learning. Previously, he was a postdoc at the Ubiquitous Knowledge Processing (UKP) Lab at TU Darmstadt, Germany, from 2016 to 2020, where he developed experience in NLP. Prior to that Edwin was a PhD student and then postdoc at the Machine Learning Research Group at the University of Oxford, working on Bayesian machine learning methods. Before his PhD, he was a research engineer at HP Labs. He has extensive experience collaborating with industry partners, including Man-AHL and Aleph Insights, as well as research organisations such as The Zooniverse.

Edwin is responsible for teaching new courses on Text Analytics, Dialogue and Narrative at Bristol University and is a member of the Intelligent Systems research group at Bristol. Among various applications of his work, he is particularly interested in disaster response, disaster risk reduction and sustainability, where large amounts of valuable information are often stored in unstructured data sources, such as social media, analysts’ reports or satellite imagery. These are prime use cases for interactive methods, as models must adapt to new locations and situations in limited time.