Professor Stephen Roberts
Stephen Roberts is Professor of Machine Learning in Information Engineering at the University of Oxford. Stephen’s interests lie in methods for intelligent data analysis in complex problems, especially those in which noise and uncertainty abound. He has successfully applied these approaches to a wide range of problem domains including astronomy, biology, finance, sensor networks, control and system monitoring. His current major interests include the application of intelligent data analysis to huge astrophysical data sets (for discovering exo-planets, pulsars and cosmological models), biodiversity monitoring (for detecting changes in ecology and spread of disease), smart networks (for reducing energy consumption and impact), sensor networks (to better acquire and model complex events) and finance (to provide timeseries and point process models and aggregate large numbers of information streams).
Harsh started his career as a programmer working on various search and pattern recognition algorithms including AI techniques, across radio astrophysics, bioinformatics and speech recognition. He then transitioned to financial risk domain and for the last decade has worked in many regulatory jurisdictions with banks and finance companies as well as consulting firms focussed on quant modelling. In this period he has applied Machine Learning techniques to behavioural modelling for ALM, mortgage risk modelling, derivatives pricing, time series outlier detection and risk data management. He has been a guest faculty with B schools and is currently authoring a book titled ‘Machine Learning for Finance’.
David Jessop, Managing Director, Global Head of Equities Quantitative Research, UBS
David Jessop is the Global Head of Equities Quantitative Research at UBS. His areas of research include portfolio analysis and construction, style analysis and risk modelling. He also helps clients understand, use and implement the quantitative tools available from UBS. David joined UBS in 2002. Prior to this, he spent seven years at Citigroup as Head of Global Quantitative Marketing. Before moving to the sell side he spent six years at Morgan Grenfell Asset Management, where he managed index funds, asset allocation funds and also an option overwriting fund. David graduated from Trinity College, Cambridge with an MA in Mathematics.
Saeed Amen is the founder of Cuemacro. Over the past fifteen years, Saeed Amen has developed systematic trading strategies at major investment banks including Lehman Brothers and Nomura. He is also the author of Trading Thalesians: What the ancient world can teach us about trading today (Palgrave Macmillan) and is the coauthor of The Book of Alternative Data (Wiley), due in 2020. Through Cuemacro, he now consults and publishes research for clients in the area of systematic trading. He has developed many Python libraries including finmarketpy and tcapy for transaction cost analysis. His clients have included major quant funds and data companies such as Bloomberg. He has presented his work at many conferences and institutions which include the ECB, IMF, Bank of England and Federal Reserve Board. He is also a co-founder of the Thalesians.
Juan Manuel Acevedo Valle
Machine Learning Specialist
ABN-AMRO Clearing Bank
Juan Acevedo is a Machine Learning Specialist at ABN-AMRO Clearing Bank. His work is mainly focused on unsupervised learning applied to trade data. He holds a PhD degree in Automatic Control, Robotics and Computer Vision from Technical University of Catalonia, obtaining Cum Laude for his research on bio-inspired artificial intelligence publishing in top conferences and journals of the field. He had two research stages, at the ETH in Zurich and the Humboldt University of Berlin. He also worked as a researcher and lecturer Ramon Llull University in Barcelona. His current interests are Machine Learning, Reinforcement Learning, Evolution Strategies and their applications in finance, robotics, and other fields.
Senior Data Scientist & Founder of London Women in Machine Learning & Data Science Chapter
Roshini has a background in AI and electronics from Edinburgh University. She has more than eight years of experience in applying machine learning techniques to design scalable robust solutions in the fields of e-commerce, travel and finance. She has worked with modelling user behaviour, predictive models, recommendation systems, generative and predictive models with deep learning frameworks and is currently working on models in finance to assess risk. She is very interested in understanding how AI techniques can be applied in various industries to make them more efficient and accurate. She is also very passionate about encouraging more women to enter and lead in this field and runs the London chapter for women in machine learning and data science.
Dr. Max Sipos
Dr. Maksim Sipos began his career at a hedge fund company in Princeton, where he utilised petabyte-size live algorithmic systems managing 100s of millions of USD. He also executed data science initiatives as a consultant at European and Silicon Valley startup companies. As the CTO of causaLens, he is leading the technology team to build a cutting edge, autonomous, real-time system that builds predictive models on time-series data. Maksim is an award winning teacher and communicator, and has more than 250 academic citations. He holds a bachelor degree in Mathematics and Physics and a PHD in Theoretical Statistical Physics from the University of Illinois Urbana-Champaign, USA.