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Gillmore Centre for Financial Technology

Beyond the Hype: Investment-AI and Machine Learning

This Symposium aims to bring cutting-edge research on financial applications of machine learning and artificial intelligence methods to both academics and practitioners with the aim of inspiring new research directions. Areas addressed are: Explainable AI in its application to finance, the use of AI in investment management and, key tool and approaches.

Our speakers are practitioners and academics from Finance and Computer Sciences. We hope to showcase cutting edge research that will drive quantitative methods in the financial services industry over the next decade.

Friday/Monday format:

Friday 3rd April, 3.5hours of live presentations with Q&A sessions.

Monday's 2 hours of Q&A session and discussion.

Friday April 3, 2020; 13:00-16:30

Beyond the Hype: Investment-AI and Machine Learning

13:00 – 13:10

Opening address

Professor Ram D. Gopal, Warwick Business School

Ram Presentation

The Future of Investment-AI: Explainable AI

Moderator: Dan Philps CFA, Rothko Investment Strategies

To view the recording click here

13:10 – 13:40

Continual Learning, Reasoning and Explainable AI through Knowledge

Extraction from Deep Networks

Prof Artur d’Avila Garcez, City, University of London

Artur Presentation

Artur Paper 

13:40-13:55

Understandable Explanations for Black Box Models

Dr Tillman Weyde, Senior Lecturer, City, University of London

Tillman Presentation

Tillman Paper

13:55-14:10

Improved local Models for Explainability in AI

Dr Adam White, Research Associate, City, University of London

Adam Paper

Adam Presentation

14:10-14:25

Q&A

Artificial Intelligence in Investment Management

Moderator: Dan Philps CFA, Rothko Investment Strategies

To view the recording click here

14:25 – 14:45

AI Fund Managers and Continual learning

Dan Philps CFA, Rothko Investment Strategies

Raj Shah, MMORSE FIA, Rothko Investment Strategies

Dan Presentation

Dan Paper

Raj Presentation

14:45-15:00

Optimisation/Satisficing: Satisfactory is better than the best

Dr Timothy Law, Honorary Senior Research Associate, UCL, Rothko Investment Strategies

Tim Presentation

15:00-15:15

Q&A

Machine Learning in Finance: Tools and Techniques

Moderator: Dan Philps CFA, Rothko Investment Strategies

To view the recording click here

15:15-15:45

Auto Machine Learning: Overview and demonstration

Peter Simon, Lead Data Scientist, Financial Markets, DataRobot

15:45-16:00

Leveraging Cloud, Big Data and ML for FX and Treasury applications.

Dr Christos Papadopoulos – Filelis, Post Doctoral Researcher in High-Performance Computing

Christos Presentation

16:00-16:15

COVID-19: Using News Sentiment in the Time of Crisis

Peter Hafez, Chief Data Scientist RavenPack

Peter H Presentation

16:15-16:30

Q&A

 

Closing Remarks

Prof Ram Gopal, Warwick Business School

Monday April 6, 2020; 13:00-15:00

Participants can ask any further questions directly to dan.philps@rothko.com.

Beyond the Hype: Investment-AI and Machine Learning

To view recording click here

13:00 – 15:00

Q&A with Presenters

Moderator: Dan Philps CFA, Rothko Investment Strategies

Q&A

The Future of Investment-AI: Explainable AI

Dr Tillman Weyde, Dr Adam White

Artificial Intelligence in Investment Management

Dan Philps, Raj Shah, Dr Timothy Law

Machine Learning in Finance: Tools and Techniques

Peter Simon, Dr Christos Papadopoulos - Filelis, Peter Hafez

 

Closing Remarks

Prof Ram Gopal, Warwick Business School

Ram Presentation



Bios of Speakers:

Ram D. Gopal is the Information Systems Society's Distinguished Fellow and a Professor of Information Systems and Management at the Warwick Business School. He previously served as the Head of the Department of Operations and Information Management in the School of Business, University of Connecticut from 2008-2018. As the Department Head, he initiated a new Master of Science degree program in Business Analytics and Project Management in 2011 and an undergraduate business major in Business Data Analytics in 2014. He has a diverse and a rich portfolio of research that spans big data analytics, health informatics, financial technologies, information security, privacy and valuation, intellectual property rights, online market design and business impacts of technology. His research has appeared in Management Science, Management Information Systems Quarterly, Operations Research, INFORMS Journal on Computing, Information Systems Research, Journal of Business, Journal of Law and Economics, Communications of the ACM, IEEE Transactions on Knowledge and Data Engineering, Journal of Management Information Systems, Decision Support Systems, and other journals and conference proceedings. He is currently a Senior Editor of Information Systems Research and has held editorial positions at Decision Sciences, Journal of Database Management, Information Systems Frontiers, and Journal of Management Sciences. He served as the President of the Workshop on Information Technologies and Systems organization from 2016 to 2018.

Timothy Law is a Consultant for Rothko Investment Strategies and an Honorary Senior Research Associate at UCL. He is an experienced quantitative analyst in the financial sector and an artificial intelligence (AI) researcher. He previously held quantitative positions at HSBC and RBS specializing in risk modelling. He holds an MSc in Statistics, an MRes in Financial Computing and a PhD in Computer Science specializing in machine learning and its customization for financial applications.

Dan Philps, CFA, is head of Rothko Investment Strategies and is an artificial intelligence (AI) researcher. He has 20 years of quantitative investment experience. Prior to Rothko, he was a senior portfolio manager at Mondrian Investment Partners. Before 1998, Philps worked as an analyst/programmer at a number of investment banks, specializing in trading and risk models. He has a BSc (Hons) from King’s College London, is a CFA charter holder, a member of CFA Society of the UK, holds a post-graduate research role at London University, and is a member of the AAAI.

Dr. Christos Filelis-Papadopoulos received his Dipl.-Eng. degree from the Electrical and Computer Engineering Department of the Democritus University of Thrace, Greece, in 2010 and his PhD in Numerical Analysis and High-Performance Scientific Computations from the same Department, in 2014. His research interests include preconditioned iterative methods, multigrid and multilevel methods as well as parallel computing, Cloud computing, Big Data and Machine Learning. He has worked in several regional and EU projects and has over 70 publications in the above research areas. He is currently a Research Fellow on the FINTECHNEXT Project.

Peter Simon leads DataRobot’s financial markets data science practice and works closely with fintech, banking, and asset management clients on numerous high-ROI use cases for DataRobot’s industry-leading automated machine learning platform. He has twenty-five years’ experience in senior quantitative research, portfolio management, trading, risk management and data science roles at investment banks and asset managers including Morgan Stanley, Warburg Pincus, Goldman Sachs, Credit Suisse, Lansdowne Partners and Invesco, as well as spending several years as a partner at a start-up global equities hedge fund. Peter has an M.Sc. in Data Science from City, University of London, an MBA from Cranfield University School of Management, and a B.Sc. in Accounting and Financial Analysis from the University of Warwick. His paper, “Hunting High and Low: Visualising Shifting Correlations in Financial Markets”, was published in the July 2018 issue of Computer Graphics Forum.

Raj Shah is a Portfolio Manager for Rothko Investment Strategies. He is an experienced investment professional and an artificial intelligence (AI) researcher. Mr. Shah previously held a senior position at Hymans Robertson as Head of Investments for Workplace Savings and was an investment consultant at Buck Consultants and Mercer. He has a Masters in Mathematics, Operational Research, Statistics and Economics (MMORSE) from the University of Warwick, an MSc in Data Science from

City, University of London and is a fully qualified actuary and a Fellow of the Institute of Actuaries.

   

Tillman Weyde is a Senior Lecturer in the Department of Computer Science, and a member of the Machine Learning Research Centre and the Data Science Institute at City, University of London. He works on machine learning methods and applications with a focus on inductive biases in neural networks as well as deep learning architectures for signal processing and explainable AI. He won prizes for software and research, leads a research group, and received funding for several research projects from the EU, EPSRC, AHRC, and Innovate UK.

Adam White is a Research Associate at City, University of London. His research interests are in explainable AI and causality. Adam received a PhD in Philosophy of Science from the London School of Economics. His PhD thesis was on the causal discovery of nonlinear dynamics in biochemistry. Adam has an MSc in Operational Research from London School of Economics, an MA in Philosophy from Birkbeck College and an MSc in Data Science from City. Adam worked for 17 years as an Operational Research analyst at Barclays Bank and British Airways.

Peter Hafez

Peter Hafez is the head of data science at RavenPack. Since joining RavenPack in 2008, he's been a pioneer in the field of applied news analytics bringing alternative data insights to the world's top banks and hedge funds. Peter has more than 15 years of experience in quantitative finance with companies such as Standard & Poor's, Credit Suisse First Boston, and Saxo Bank. He holds a Master's degree in Quantitative Finance from Sir John Cass Business School along with an undergraduate degree in Economics from Copenhagen University. Peter is a recognized speaker at quant finance conferences on alternative data and AI, and has given lectures at some of the world's top academic institutions including London Business School, Courant Institute of Mathematics at NYU, and Imperial College London.


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