Track / Overview

The recent advancement of deep learning has had a profound influence on many fields, including finance. Mathematical and quantitative finance provide a plentitude of challenging prediction problems that can be used as benchmarks for deep and reinforcement learning algorithms. Specifically, financial markets represent a complex interplay of agents interacting through auction-market mechanisms at different time scales and with different objectives. Therefore, it is not surprising that it continues to receive attention from computer scientists, physicists, social scientists, and others interested in addressing a multitude of challenging prediction and decision problems. 

In this track, we will highlight recent ML advancements like transformers, physics-informed neural networks, graph neural networks, and complexity tools and their impact on decision making, data-driven analysis, and time series predictions in finance.

The purpose of this track is to enable the exchange of recent research and insights amongst researchers interested in machine learning approaches for decision making and times series analysis of financial markets. We aim to bring together world-class presenters from academia and industry working on topics such as:

  • Deep learning for financial time series
  • Reinforcement learning and data-driven optimal control for financial decision making
  • Transformer-based and related NLP approaches for financial sentiment and event analysis
  • Graph-based neural network techniques in finance

We anticipate this track will result in a most vibrant and fruitful exchange of ideas and information from researchers from different disciplines such as machine learning, complex systems, physics, mathematics and quantitative finance.

Track / Schedule

Introduction & Workshop Overview: Recent Developments in Deep Representation Learning and its Applications in Finance

With Petter Kolm & Nino Antulov-Fantulin

Modelling Time Varying Interactions in Financial Markets

With Fabrizio Lillo

Deep Order Flow Imbalance: Extracting Alpha at Multiple Horizons from the Limit Order Book

With Nicholas Westray

Market Making and Incentives Design in the Presence of a Dark Pool: A Deep Reinforcement Learning Approach

With Mathieu Rosenbaum


Do Word Embeddings Really Understand Loughran-McDonald’s Polarities?

With Charles-Albert Lehalle

Machine Learning for Predicting Stock Return Volatility

With Amir Khalilzadeh

Lightning Talk: Explaining Graph Neural Networks

With Kenza Amara

Lightning Talk: xFraud: Explainable Fraud Transaction Detection

With Susie Xi Rao

Lightning Talk: A Deep Generative Model for Clickstream Analysis

With Yilmazcan Özyurt

Lightning Talk: StockTwits Classified Sentiment and Stock Returns

With Marc-Aurèle Divernois


Panel Discussion

With Nicholas Westray, Thomas Wolf, Fabrizio Lillo, Robert Almgren, Lisa Huang, Petter Kolm, Matthew Dixon, Nino Antulov-Fantulin & Mathieu Rosenbaum

Track / Speakers

Petter Kolm

Prof. and Director of Mathematics in Finance Masters Program, Courant Institute, New York University

Charles-Albert Lehalle

Global Head - Quantitative Research & Development, Abu Dhabi Investment Authority (ADIA)

Thomas Wolf

Co-founder and Chief Science Officer, Hugging Face

Nino Antulov-Fantulin

ETH, Aisot Technologies AG

Nicholas Westray

Head of Execution Research, Alliance Bernstein Multi Asset Solutions

Marc-Aurèle Divernois

PhD Candidate, EPFL

Yilmazcan Özyurt

PhD Candidate, ETH Zürich

Amir Khalilzadeh

Data Scientist, EPFL Extension School

Fabrizio Lillo

Professor, Università di Bologna and Scuola Normale Superiore, Pisa

Kenza Amara

Doctorate, ETHZ

Matthew Dixon

Assistant Professor, Illinois Tech

Robert Almgren

Chief Scientist

Susie Xi Rao

PhD Researcher, ETH Zürich

Lisa Huang

Head of AI Investment Management and Planning , Fidelity

Mathieu Rosenbaum

Professor, École Polytechnique

Vaiva Vasiliauskaite


Track / Co-organizers

Nino Antulov-Fantulin

ETH, Aisot Technologies AG

Petter Kolm

Prof. and Director of Mathematics in Finance Masters Program, Courant Institute, New York University

Vaiva Vasiliauskaite


Aisot Technologies


AMLD EPFL 2022 / Tracks & talks

AMLD Keynote Session – Monday morning

Marcel Salathé, Lenka Zdeborová, Carmela Troncoso, Chiara Enderle, Patrick Barbey, Thomas Wolf, Gunther Jansen, Laure Willemin, Simon Hefti, Arthur Gassner

10:00-12:00 March 28Auditorium A

AI & Physics

Francesca Mignacco, Gert-Jan Both, Michael Unser, Thomas Asikis, Dalila Salamani, Pietro Rotondo, Tom Beucler, Giulio Biroli

12:30-18:00 March 285BC

AI & Pharma

Asif Jan, Jonas Richiardi, Patrick Schwab, Naghmeh Ghazaleh, Alexander Büsser, Carlos Ciller, Caibin Sheng, Silvia Zaoli, Félix Balazard, Giulia Capestro, Marianna Rapsomaniki, Martijn van Attekum

13:30-17:30 March 281BC

AMLD / Global partners