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Register your interest for 2024

Learn New Skills with Machine Learning for Finance Tools

Quantitative Finance is a necessity for any institution looking to be a major player in the industry. From chatbot assistants to fraud detection and task automation, the machine learning-enabled transformation in finance requires serious software engineering. These projects of the future promise to be some of the most exciting jobs in software engineering.


A Quantitative Analyst works at the intersection of finance and data science. At ODSC, learn essential quantitative modeling frameworks as well as how to use machine learning and reinforcement techniques to invest, trade, and manage risk in finance.

Register your interest for 2024

What You'll Learn

Talks + Workshops + Special Events on these topics:

Topics

  • Recommendation System for Trading

  • Machine Learning for Algorithmic Trading

  • Deep Reinforcement Learning for Quant Trading

  • Streaming Analytics for Quant Trading

  • Alternative Data For Quant Trading

  • Sentiment Analysis for Quant Trading

  • Artificial Intelligence in Finance

  • and more…

Past ML for Finance Speakers

The AI Quant | CEO The Python Quants & The AI Machine | Adjunct Professor of Computational Finance

Dr. Yves J. Hilpisch

Dr. Yves J. Hilpisch is founder and CEO of The Python Quants (http://tpq.io), a group focusing on the use of open source technologies for financial data science, artificial intelligence, algorithmic trading, and computational finance. He is also founder and CEO of The AI Machine (http://aimachine.io), a company focused on AI-powered algorithmic trading based on a proprietary strategy execution platform.

Yves has a Diploma in Business Administration, a Ph.D. in Mathematical Finance and is Adjunct Professor for Computational Finance at Miami Herbert Business School.

AI-Powered Algorithmic Trading with Python(Training)

Visiting Lecturer | CEO and Founder Imperial College London | Thalesians Ltd

Dr Paul A. Bilokon

Bio Coming Soon!

Iterated and Exponentially Weighted Moving Principal Component Analysis(Talk)

Founder and Core Developer of sktimesktime

Franz Kiraly, PhD

Franz Kiraly is the founder and a core developer of the open source framework sktime. His research is focused on software engineering for open source and data science, machine learning for structured learning tasks such as time series tasks, and robust empirical and statistical evaluation of algorithms in deployment. Franz held a faculty position at University College London 2013-2020, before he moved to industry R&D in principal data scientist roles.

sktime – Python Toolbox for Machine Learning with Time Series(Training)

Managing Director | Adjunct Lecturer | AuthorDSI Analytics | Amsterdam Business School

David Stephenson, PhD

David Stephenson has over 20 years of experience leading analytics initiatives, including as Head of Global Business Analytics at eBay Classifieds Group. Since founding DSI Analytics in 2014, he has worked directly with dozens of companies across a wide range of industries (Adidas, Miro, Janssen Pharmaceuticals, ABN Amro, Sky Broadcasting, etc). Dr. Stephenson also serves as part time faculty at the University of Amsterdam Business School, has published two books, and has developed and delivered data science trainings for hundreds of analytics professionals around the globe.

Equipping your analytics professions with the most critical business skills (Business Talk)

Professor, Head Institute of Wealth & Asset ManagementZHAW Zurich University of Applied Sciences

Peter Schwendner, PhD

Peter Schwendner leads the Institute of Wealth & Asset Management at Zurich University of Applied Sciences, School of Management and Law, Switzerland. His interests are financial markets, asset management and machine learning applications. With the European Stability Mechanism (ESM), he has been developing analytics for primary and secondary bond markets and tools for optimizing the issuance process. Currently, he is working on the BRIDGE Discovery project “Spatial sustainable finance: Satellite-based ratings of company footprints in biodiversity and water”. Within the European COST Action «Fintech and AI in Finance», he leads the working group «Transparency into Investment Product Performance for Clients».

ML Applications in Asset Allocation and Portfolio Management(Talk)

Founder and CEO | Advisory Board MemberHedged Capital LLC | AIKON

Deepak Kanungo

Deepak Kanungo is the founder and CEO of Hedged Capital LLC, an AI-powered, proprietary trading and analytics firm built around probabilistic machine learning technologies. In 2005, long before machine learning was an industry buzzword, Deepak invented a probabilistic machine learning method and software system for managing the risks and returns of project portfolios. It is a unique framework that has been cited by IBM and Accenture, among others. Previously, Deepak was a financial advisor at Morgan Stanley, a Silicon Valley fintech entrepreneur, and a director in the Global Planning Department at Mastercard International. He was educated at Princeton University (astrophysics) and the London School of Economics (finance and information systems).

Probabilistic Machine Learning for Finance and Investing(Talk)

Senior Quantitative ResearcherBloomberg LP

Chakri Cherukuri

Chakri Cherukuri is a senior researcher in the Quantitative Research group within the CTO office at Bloomberg LP. His research interests include quantitative portfolio management, algorithmic trading strategies, applied machine learning and numerical methods. Previously, he built analytical tools for the trading desks at Goldman Sachs and Lehman Brothers. Before that he worked in the Silicon Valley for startups building enterprise software systems. He is a core contributor and steering council member of bqplot, a 2D plotting library for the Jupyter notebook. He has extensive experience in numerical computing and software development. He holds an undergraduate degree in mechanical engineering from Indian Institute of Technology, Madras, and an MS in computational finance from Carnegie Mellon University.

Fast Option Pricing Using Deep Learning Methods(Talk)

Data ScientistElectrolux

Marc Rovira, PhD

Marc Rovira is a data scientist at Electrolux Group in Stockholm, with a strong focus on forecasting and time series analysis. He actively contributes to the sktime community as a council member and user representative. Prior to his industry experience, Marc completed a Ph.D. that explored the intersection of computational fluid mechanics, chemical engineering, and machine learning, with the aim of mitigating air pollution. His educational background also includes a master’s degree in aerospace engineering.

sktime – Python Toolbox for Machine Learning with Time Series(Training)


Why Attend

Immerse yourself in talks and workshops on AI in Quant Finance

With numerous introductory level workshops, get hands-on experience to quickly build up your skills

Post-conference, get access to recorded talks online and learn from over 100+ high-quality recording sessions that let you review content at your own pace 

Take time out of your busy schedule to accelerate your knowledge of the latest advances in data science

Learn directly from world-class instructors who are the authors of and contributors to many of the tools and frameworks used in quant finance today

Meet hiring companies ranging from hot startups to Fortune 500s looking to hire professionals with data science skills at all levels

Get speaker insights and training in AI frameworks such as TensorFlow, MXNet, PyTorch, Spark, Storm, Drill, Keras, and other AI platforms

Get access to other focus area content, including ML/DL, Data Visualization Big Data, and Open Data Science

More Reasons To Attend?

Download the why attend guide

Who should attend

  • Data scientists looking to model, monitor, measure, and attribute financial risk

  • Data scientists seeking to build robust quantitative tools

  • Anyone interested in learning how to understand, maintain, and improve infrastructure supporting investment process 

  • Business professionals and industry experts looking to understand data science ethics in practice

  • Software engineers and technologists who need to develop algorithms to solve algorithmic trading problems

  • CTO, CDS, and other managerial roles that require a bigger picture view of data science

  • Technologists in the field of Quant Finance and others looking to develop and enhance quantitative and statistical models using Python, R, and SQL

  • Students and academics looking for more practical applied training in data science tools and techniques

ODSC EUROPE Hybrid Conference 2024

Register your interest for 2024

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Stay current with the latest news and updates in open source data science. In addition, we’ll inform you about our many upcoming Virtual and in person events in Boston, NYC, Sao Paulo, San Francisco, and London. And keep a lookout for special discount codes, only available to our newsletter subscribers!


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