Abstract: 

Machine Learning has been making waves in the tech sector for decades, but only recently are we seeing an acceleration in adoption of Machine Learning technologies by finance companies. In this talk, I will discuss the application of machine learning to a broad range of use cases in portfolio and risk management, as well as common pitfalls to look out for when working with financial data.

Learning Outcomes & Takeaways for Attendees
Understand how Machine Learning can be applied to the financial sector in various ways:
Detection of black swan events
Bet sizing & alpha capture
Portfolio construction
Learn about key challenges in applying machine learning to financial data
Non-stationarity of time series data
Backtest overfitting

Bio: 

Min Yan is a Quantitative Researcher at Credit Suisse, where she utilises cutting-edge machine learning and statistical models to optimise automated trading strategies. Before that, she worked in various Artificial Intelligence startups such as SWAT Mobility and KeyReply, on geospatial optimisation problems and multilingual Natural Language Processing models for context-aware chatbots. She was also part of Yahoo’s Search and Advertisement Platforms team in Silicon Valley, where she built distributed algorithms trained with deep neural networks on Tensorflow and Spark.

During her graduate studies at Carnegie Mellon University, Min Yan’s research was focused on developing AI-driven innovative sensing systems under the Human-Computer Interaction Institute. She has authored papers on extending the capabilities of wearable sensors and computer vision systems for health sensing, and using clustering algorithms to provide semi-automated feedback on programming assignments.

Currently, Min Yan also leads the team of Data Science Instructors at Heicoders Academy, a renowned technology education institution based in Singapore. She has a deep passion for education and seeks to make AI education accessible to the layperson.