Monday 16th November: Machine Learning in Quantitative Finance
The Importance of Soft Skills in a Quantitative World
Quantitative finance often requires substantial “hard skills”–programming, machine learning, data science, AI. But what about the “soft skills” required to collaborate with others, obtain buy-in for ideas, and lead cross-functional teams?
This fast-paced workshop will highlight the importance of these skills within the industry. Attendees will learn:
- What the DISC personality assessment tool is and how can it improve self-awareness
- Ways to improve communication skills to increase your influence
- How to obtain support for your ideas
- Steps to build effective business relationships
- How to work with “difficult” people
Founder and CEO, Section 810 Communications, LLC,
Jeff Scott: Founder and CEO, Section 810 Communications, LLC,
Jeff Scott is the Founder and CEO of Section 810 Communications, LLC, a global training firm focused on communications, leadership and sales skills. Prior to founding Section 810, Jeff’s diverse experience included leading a global crowdsourcing initiative in quantitative finance that grew to 130,000 participants in just five years. During this time he also provided leadership and communication training to hundreds of leaders in quantitative finance.
An internationally recognized speaker, certified DISC personality consultant and published author, Jeff has spoken to tens of thousands of people across dozens of countries. Section 810 Communications has one primary goal: to help people increase their level of influence through improved communication and greater self-awareness.
- Computation: Classical versus quantum logic gates
- Why quantum computing is more powerful
- Quantum Neural Network (QNN)
- Born Machine and Boltzmann Machine
- Quantum machine learning in practice
Managing Director, Head of Data Analytics, Standard Chartered Bank
Alexei Kondratyev: Managing Director, Head of Data Analytics, Standard Chartered Bank
In his role as Managing Director and Head of Data Analytics at Standard Chartered Bank, Alexei is responsible for providing data analytics services to Financial Markets sales and trading.
He joined Standard Chartered Bank in 2010 from Barclays Capital where he managed a model development team within Credit Risk Analytics. Prior to joining Barclays Capital in 2004, he was a senior quantitative analyst at Dresdner Bank in Frankfurt.
Alexei holds MSc in Theoretical Nuclear Physics from the University of Kiev and PhD in Mathematical Physics from the Institute for Mathematics, National Academy of Sciences of Ukraine.
Tackling Nonlinear High Dimensional Problems in Finance with Low Rank Tensor Techniques and Deep Neural Networks
Lecturer in Financial Mathematics, Queen Mary University of London
Kathrin Glau: Lecturer in Financial Mathematics, Queen Mary University of London
Kathrin Glau currently is a Lecturer in Financial Mathematics at Queen Mary University of London & FELLOW co-founded by Marie Skłodowska Curie at École Polytechnique Fédérale de Lausanne. Between 2011 and 2017 she was Junior Professor at the Technical University of Munich. Prior to this she worked as a postdoctoral university assistant at the chair of Prof. Walter Schachermayer at the University of Vienna. In September 2010 she completed her Ph.D. on the topic of Feynman-Kac representations for option pricing in Lévy models at the chair of Ernst Eberlein.
Her research is driven by the interdisciplinary nature of computational finance and reaches across the borders of finance, stochastic analysis and numerical analysis. At the core of her current research is the design and implementation of complexity reduction techniques for finance. Key to her approach is the decomposition of algorithms in an offline phase, which is a learning step, and a fast and accurate online phase. The methods range from model order reduction of parametric partial differential equations to learning algorithms and are designed to facilitate such diverse tasks as uncertainty quantification and calibration, real-time pricing, real-time risk monitoring, and intra-day stress testing.
In the finance sector, the potential for innovation with advanced machine intelligence is significant. But often, new and complex models are not being fully leveraged due to latency issues and compute restraints. Enter the IPU – a completely new processing architecture designed for machine intelligence, capable of running advanced financial models up to 26x faster. Graphcore’s Alex Tsyplikhin explains how the IPU’s unique architecture can power such incredible breakthroughs – and what this means for the future of finance and trading.
What you’ll learn:
- How the IPU is able to achieve faster financial model accelerations than other hardware available on the market
- How to use IPUs for financial modelling training and inference
- Insights into advanced models, use cases and IPU benchmarks
Senior AI Engineer, Graphcore
Alexander Tsyplikhin: Senior AI Engineer, Graphcore
Alexander Tsyplikhin is a Senior AI Engineer at Graphcore. Qualified to PhD level in Speech Biometrics, Alexander has worked in the field of machine learning for 19 years. He is passionate about leveraging technology and innovation to improve lives and generate results for businesses. At Graphcore, Alexander is focused on AI use cases in industry, using his expertise in machine intelligence to help drive customer success.
- What is the current state of utilisation of machine learning in finance?
- What are the distinct features of machine learning problems in finance compared to other industries?
- What are the best practices to overcome these difficulties?
- What’s the evolution of a team using machine learning in terms of day to day operations?
- Are we becoming more software engineers than quants?
- What is a typical front office ‘Quant’ skillset going to look like in three to five years time?
- How do we deal with model risk in machine learning case?
- How is machine learning expected to be regulated?
- Is there a way to make it more explainable?
- Where do you think alternative data fits in with the vogue for machine learning?
- Have you used alternative data?
- Is it more for buy side or sell side.
- What applications can you list among its successes?
- How much value is it adding over and above the “classical” techniques such as linear regression, convex optimisation, etc.?
- Do you see high-performance computing (HPC) as a major enabler of machine learning?
- What advances in HPC have caused the most progress?
- What do you see as the most important machine learning techniques for the future?
- What are the main pitfalls of using Machine Learning currently in trading strategies?
- What new insights can Machine Learning offer into the analysis of financial time series?
- Discuss the potential of Deep Learning in algorithmic trading?
- Do you think machine learning and HPC will transform finance 5-10 years from now?
- If so, how do you envisage this transformation?
- Can you anticipate any pitfalls that we should watch out for.
- Discuss quantum computing in quant finance:
- Future uses