Abu Dhabi Machine Learning Season 3 Episode 2
19.01.2023 - Abu Dhabi Machine Learning - ~4 Minutes
When?
- Thursday, January 19, 2023 from 3:00 PM to 5:00 PM (Abu Dhabi Time)
Where?
- ADGM Academy, 20F, Al Maqam Tower, Al Maryah Island, Abu Dhabi
The Meetup page of the event:
Abu Dhabi Machine Learning Meetup Season 3 Episode 2
You can find the slides for the 3 speakers here.
Programme:
Talk 1: Quantum Machine Learning
Abstract: Quantum Machine Learning (QML) is an area of active research with, arguably, the highest chances of demonstrating quantum advantage on the near-term quantum computers. The main competitive advantage of QML models is their larger expressive power in comparison with the comparable classical counterparts and strong regularization properties of the quantum neural networks.
Speaker: Oleksiy Kondratyev is Quantitative Research & Development Lead at Abu Dhabi Investment Authority (ADIA). Prior to joining ADIA in July 2021, he held quantitative research and data analytics positions at Standard Chartered, Barclays Capital and Dresdner Bank. Oleksiy holds a MSc in Theoretical Physics from Taras Shevchenko National University of Kiev and PhD in Mathematical Physics from the Institute for Mathematics, National Academy of Sciences of Ukraine. He was the recipient of the 2019 Risk magazine Quant of the Year award.
Talk 2: Machine Learning meets Statistical Physics: a Web3 perspective
Abstract: CrunchDAO’s Machine-Learning-enabled ensemble framework builds on top of traditional econometric risk models, requiring a number of steps in the data preparation: features orthogonalization, standardization, model order reduction and data obfuscation will be discussed. It is discussed how, in the context of ensemble learning and bagging in particular, combining a variety of orthogonal models yields more accurate estimates of expectations. Moreover, the statistics of the set of predictions can be used to infer a measure of risk in the portfolio management process. We discuss how to integrate this in modern portfolio theory. We briefly discuss the necessary relation between these design choices and the ergodic hypothesis on financial data.
Speaker: Matteo is the lead quant researcher of CrunchDAO. With a background in machine learning and dynamical systems theory. He graduated in Space Flight with a Talent Scholarship from TU Delft, worked as a researcher in the Horizon2020 program by the European Commission and worked as a flight dynamics software engineer for the European Space Agency. Together with working in quantitative finance, he is one of the developers of CrunchDeSci, a Decentralized Science platform used to perform research in a reproducible manner.
Talk 3: Project Evaluation, Real Options, and Investment Decisions: A data driven approach
Abstract: Corporate and industrial strategic decisions have evolved tremendously in the last decades towards a higher degree of quantitative analysis. Such decisions require taking into account a large number of uncertain variables and volatile scenarios, much like financial market investments. Furthermore, they can be evaluated by comparing the decisions to portfolios of investments in financial assets such as in stocks, derivatives and commodity futures. This revolution led to the development of a new field of managerial science known as Real Options.
The use of Real Options incorporates also the value of flexibility and gives a broader view of many business decisions that brings in tools from quantitative finance and risk management. Such techniques are now part of the decision making process of many corporations, especially in the energy sector. When conjoined with the recent developments of data driven analysis and statistical inference, they can provide a powerful machinery for investment and strategic decisions. Yet, they require a substantial amount of mathematical and statistical background thus making the field a natural breeding ground for multidisciplinary collaborations.
In this talk, after a short presentation on the topic of Real Options, we shall discuss the contributions that data science, machine learning and quantitative finance can make to the area. We shall present some research lines that we have developed in the past. They make use of alternatives to risk neutral pricing and are highly data driven. Thus, they are suitable for evaluation of projects in a realistic context with special attention to projects dependent on commodities and non-hedgeable uncertainties. We shall conclude with some current research topics that are being developed at the Mathematics Department of Khalifa University on this subject.
Speaker: Jorge P. Zubelli obtained his PhD in Applied Mathematics from the University of California at Berkeley (1989), his MSc from the National Institute for Pure and Applied Mathematics (IMPA – Brazil) in 1984, and his Electrical Engineering degree from IME-RJ in 1983 with specialization on Telecommunications Engineering. He has previous experience as Professor of Mathematics at IMPA and heading the Laboratory for Analysis and Mathematical Modeling in the Physical Sciences (LAMCA – IMPA).
Video Recording of the ADML Meetup on YouTube
- YouTube videos:
ADML S3E2 - Abu Dhabi Machine Learning - ADGM Academy
