Machine Learning New York
This two day training course will provide delegates with a comprehensive understanding of machine learning applications. Sessions will cover in-depth the technical aspects of machine learning and provide suggestions and strategies for integrating it within your organization.
This training course will provide delegates with an in-depth, technical understanding of machine learning applications, models, and more advanced tools and solutions through a quantitative approach.
Sessions across the two days will delve into a variety of key theories and topics including portfolio construction, trading, risk management and other business areas.
The course is delivered by a variety of expert practitioners working in the machine learning field, and held in an open and discussion based learning environment.
Who Should Attend
This course is primarily aimed at those working in financial institutions; as well as regulatory bodies, advisory firms and technology vendors. However Risk Training welcomes anyone who would benefit from this training. Specific job titles may include:
- Machine Learning
- Portfolio Management
- Asset Allocation
- Data Science
- Financial Engineering
- Quantitative Analytics
- Quantitative Modelling
- Infrastructure and Technology
What Will You Learn?
- Best practice approaches to machine learning applications in finance from a quantitative viewpoint
- The capabilities of machine learning tools in portfolio construction, trading, risk management and beyond
- Insight into the big data revolution and the building blocks of machine learning tools in finance
- The theory behind machine learning, deep learning and neural networks, and how these methods can be applied in your organization
- A clear view of ML/AI capabilities, how they can help you solve problems more effectively and drive your business forward
- Get to grips with modern data analysis, structured and unstructured data and new models
Global Fixed Income
BNP Paribas Asset Management
Co-Founder, New York Quantum Computing Meet-up and Director, XVA Quant Core Lead
Steve Yalovitser is a Director of Quantitative Strategies Group, Global Banking and Markets, within Bank of America Merrill Lynch's Counterparty Portfolio Management (CPM) Group. Yalovitser has been a lead architect for the bank, with exposure to a wide variety of asset classes, for the past 13 years. He has delivered multiple innovation-driven technology solutions for the bank, including its first equity exotics booking platform, its first equity back-testing platform and its first ad hoc scenario platform for Capital Calculations.
Yalovitser founded, led and delivered the Quartz Equity Derivatives Risk eco-system, currently running a portion of Bank of America Merrill Lynch's end-of-day risk reporting function. He is currently working on building out a Strategy Platform for CPM, covering Counterparty Valuation Adjustment (CVA), Capital Valuation Adjustment (KVA), Funding Valuation Adjustment (FVA), and Initial Margin (IM) posting.
Before joining Bank of America Merrill Lynch, Yalovitser founded Integrasoft LLC, creating the first product to address data aspects of grid computing and implementing it for use in derivative pricing applications for potential client sites. Prior to that, he held lead architect roles at several firms, including DoubleClick, Morgan Stanley and Dow Jones.
Director of the Mathematics in Finance Masters Program
New York University
Petter Kolm, Director of the Mathematics in Finance Masters Program and Clinical Professor, Courant Institute of Mathematical Sciences, NEW YORK UNIVERSITY
Petter Kolm is the Director of the Mathematics in Finance Masters Program and Clinical Associate Professor at the Courant Institute of Mathematical Sciences, New York University and the Principal of the Heimdall Group, LLC. Previously, Petter worked in the Quantitative Strategies Group at Goldman Sachs Asset Management where his responsibilities included researching and developing new quantitative investment strategies for the group's hedge fund. Petter coauthored the books Financial Modeling of the Equity Market: From CAPM to Cointegration (Wiley, 2006), Trends in Quantitative Finance (CFA Research Institute, 2006), Robust Portfolio Management and Optimization (Wiley, 2007), and Quantitative Equity Investing: Techniques and Strategies (Wiley, 2010). He holds a Ph.D. in mathematics from Yale, an M.Phil. in applied mathematics from Royal Institute of Technology, and an M.S. in mathematics from ETH Zurich.
Petter is a member of the editorial boards of the International Journal of Portfolio Analysis and Management (IJPAM), Journal of Investment Strategies (JOIS), Journal of Portfolio Management (JPM), and the board of directors of the International Association for Quantitative Finance (IAQF). As a consultant and expert witness, he has provided his services in areas such as algorithmic and quantitative trading strategies, econometrics, forecasting models, portfolio construction methodologies incorporating transaction costs, and risk management procedures.
Consultant in Risk and Quantamental Investing, CRO
Formerly of Och Ziff
Ken created the Risk Management department at Och Ziff and served as Chief Risk Officer for over 13 years. He led the Firm through a five-fold increase in AUM and headcount, the transition from private to public company, and managed major and minor financial/business crises and the introduction of new strategies and products.
He pioneered the use of quantitative techniques for portfolio construction and analysis at a fundamentally oriented firm, anticipating the “quantamental” revolution. Most recent activities have focused on how Artificial Intelligence may adapted to Finance.
Exeter Consulting and Capital Management
Baruch College & New York University
Gordon Ritter completed his PhD in mathematical physics at Harvard University in 2007, where his published work ranged across the fields of quantum computation, quantum field theory, differential geometry and abstract algebra. Prior to Harvard he earned his Bachelor's degree with honours in Mathematics from the University of Chicago. Prof. Ritter is currently a Professor at NYU, Rutgers, and the award-winning Baruch MFE program, where his research interests are focused on portfolio optimization and statistical machine learning.
Prof. Ritter is also a leader in the quantitative trading industry. He is preparing to launch his own company which will manage money for institutional clients by means of high-Sharpe pure alpha systematic trading strategies. He has ten years' experience doing this; most recently he built a successful trading system from scratch at GSA Capital, a firm which won the Equity Market Neutral & Quantitative Strategies category at the Eurohedge awards four times. Prior to GSA, Gordon was a Vice President of Highbridge Capital and a core member of the firm's statistical arbitrage group, which although less than 20 people, was responsible for billions in profit and trillions of dollars of trades across equities, futures and options with low correlation
Director, Head of Model, Library, and Tools Development for Corporate Model Risk
Bernhard Hientzsch, Director, Head of Model, Library, and Tools Development for Corporate Model Risk, WELLS FARGO
Bernhard Hientzsch is the Head of Model, Library, and Tool Development in the Corporate Model Risk Management Group at Wells Fargo. His group is responsible for the implementation of models, libraries, components, and tools for the validation, benchmarking, and oversight of models at Wells Fargo. Prior to joining Wells Fargo, he was a postdoctoral scientist at New York University in several DoE supported projects and consulting on mathematical, financial, and computer modelling in the USA and Germany. Bernhard received his PhD in applied mathematics from the Courant Institute at New York University.