Machine Learning in Finance Hong Kong

This training will gather leading risk, finance, compliance, operations practitioners, machine learning experts, data officers and buy-side professionals to discuss potential benefits and risks for financial stability that should be monitored as machine learning is widely adopted across the business

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Machine Learning in Finance 

19 - 20 September | Hong Kong 

VIEW THE AGENDA    REGISTER NOW

Moving towards a data-driven finance world

Attend this two day training course and learn:

  • In-depth understanding of machine learning applications
  • Understanding fundamentals of machine learning methodology
  • Learn the theory and practise behind machine learning, deep learning and neural networks, and how these methods can be applied in your organization
  • Gain insight into the latest and most widely used industry applications
  • Get a clear view of ML/AI Capabilities in finance, how they can help you solve problems more effectively and drive your business forward

 

VIEW THE AGENDA HERE

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Course highlight
  • In-depth understanding of machine learning applications
  • Understanding fundamentals of machine learning methodology
  • Learn the theory and practise behind machine learning, deep learning and neural networks, and how these methods can be applied in your organization
  • Gain insight into the latest and most widely used industry applications
  • Get a clear view of ML/AI Capabilities in finance, how they can help you solve problems more effectively and drive your business forward
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Who should attend

This workshop is specifically designed for professionals working for financial institutions, buy-side firms, regulatory bodies, advisory firms and technology sector however Risk Training welcomes anyone who would benefit from this training. Specific job titles may include but are not limited to:

  • Machine learning
  • Portfolio management
  • Asset allocation
  • Data science
  • Financial engineering
  • Quantitative analytics
  • Quantitative modelling
  • Innovation
  • Forecasting
  • Infrastructure and technology
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Learning outcomes
  • Understand different types of machine learning models and how they can be applied in practice
  • Learn about capabilities of machine learning tools in portfolio construction, trading and risk management
  • Find out the theory behind machine learning, deep learning and neural networks, and how these methods can be applied in your organisation
  • Hear from expert speakers on case studies and real use-cases of machine learning in finance
  • Ask your questions to our expert speakers during our special one-hour Q&A session!  

Course Tutors

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Adrian Gostick

Chief Revenue Officer

BondIT

Adrian Gostick is Chief Revenue Officer for BondIT, with global responsibility for BondIT sales, business development, marketing, and strategic partnerships. Adrian has over 27 years’ experience working in financial markets across Europe, Asia and the U.S. 

Prior to joining BondIT Adrian spent twelve years in a variety of leadership roles in Thomson Reuters, most recently as Managing Director, China, for the Financial & Risk business. Between 2002 and 2004 Adrian worked in a portfolio and risk management software company in France, and before that spent 11 years as a bond trader for Japanese securities companies in London and Tokyo. 


Adrian has a degree in Geology from the University of Bristol, and an MBA from EDHEC in France.

 

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Hillel Raz

Chief Scientist

BondIT

Hillel is a co-founder of BondIT and holds the position of Chief Scientist. He holds a Ph.D. in mathematical physics from the University of California, Davis. His thesis required solving multi body problems in statistical mechanics, both in classical and quantum frameworks.

Hillel was a postdoctoral fellow at Cardiff University, Wales and a visiting postdoctoral fellow at the Weizmann Institute. His well published paper dealt with graph theory, in both quantum and classical graphs, and explains the way a particular operator behaves in various conditions.

As the Tech Co-founder, he is responsible for many of the proprietary and original algorithms used in BondIT that sets the formulations of the solution for portfolio construction and rebalancing.

Radha Pendyala

Enterprise Data Scientist

THOMSON REUTERS

Radha works as an Enterprise Data Scientist at Thomson Reuters. His work involves applying machine learning and quantitative financial modeling techniques to large datasets in order to solve specific problems in the financial and risk domain. Prior to Reuters, he has worked as a portfolio manager at Goldman Sachs Asset Management. He has more than a decade of experience in building financial and statistical models.

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Johnson Poh

Head Data Science

DBS BANK

Johnson is currently Head Data Science for Big Data Analytics at DBS Bank where he drives the development of core data science capabilities for enhancing decision analysis. He holds an adjunct faculty appointment at SMU School of Information Systems and his focus areas include applied statistical computing, machine learning as well as big data tools and techniques. He was formerly Chief Data Scientist, ASEAN at Booz Allen Hamilton as well as Head Data Science/ Principal Data Scientist at the Ministry of Defence, Singapore.

An avid programmer and data enthusiast, Johnson enjoys developing apps and data products. Most recently, he was awarded first prize in Singapore’s largest coding competition, Smart Nation [email protected] 2015 as well as the CapitaLand Data Challenge 2016.

Johnson completed his bachelor’s degree at University of California, Berkeley, majoring in the subjects of Pure Mathematics, Statistics and Economics. He received his postgraduate degree in Statistics at Yale University.

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Aaron Hallmark

Chief Executive Officer

CATENA TECHNOLOGIES

Aaron is CEO of Catena Technologies, a solutions company that helps financial institutions improve their business capabilities using cutting-edge technologies. Aaron has twenty-three years of technology experience in cross-asset trading, clearing, risk management, accounting, and compliance. His career has spanned across the U.S., Canada, the Middle East, and throughout Asia Pacific, and he has managed projects for such clients as JPMorgan Chase, HSBC, Citibank, Morgan Stanley, HKMA, and SGX. Aaron received his B.S. from Stanford University in Artificial Intelligence, as well as an MBA from the USC Marshall School of Business. Aaron is a frequent lecturer for Singapore Management University's Master of IT in Financial Services program.

 Prior to joining Catena, Aaron spent more than 9 years with Calypso Technology, a global financial software and services company focused on cross-asset trading, risk management, and processing. At Calypso, Aaron led the Professional Services team for the Americas based in New York. He later took on the Singapore-based role of Director of Professional Services, Asia, where he directed the APAC region's first-ever implementation of OTC derivatives clearing.

 

In his current role, Aaron has led the development of Catena TRACE Reporting, a solution that automates financial institutions' workflow for regulatory reporting, as well as TRACE Analytics, a system that consolidates trade data to provide a wide range of descriptive and predictive analytics.