Alternative Data in Investment Management with Petter Kolm from New York University

Thought Leadership Webinar: Complimentary to the PRMIA network! As AI and Machine learning enters financial services firms pervasively, and with the new regulatory efforts in US and in the EU, there is increased interest on what risk management is going to look like when AI and ML models are integrated with current frameworks.

Exploring the landscape of training and inference, we cover a myriad of tricks that step-by-step improve the efficiency of most deep learning pipelines, reduce wasted hardware cycles, and make them cost-effective.

Limited data access continues to be a barrier to data-driven product development. In this talk, we explore if and how generative adversarial networks (GANs) can be used to incentivize data sharing by enabling a generic framework for sharing synthetic datasets with minimal expert knowledge.

The advent of AI and ML algorithms has led to opportunities as well as challenges. In this paper, we provide an overview of bias and fairness issues that arise with the use of ML algorithms.

Challenges and Frontiers in Deploying Transparent Machine Learning with Umang Bhatt, Mozilla, University of Cambridge

This talk will go over common machine learning security attacks and the remediation steps an organization can take to deter these pitfalls.

Transforming Model Risk Management is key to providing robust, timely and effective risk management of a growing uses models, especially for machine learning models.

Dr.Reha Tutuncu from Point 72 shared his expertise and thoughts on the challenges and issues in Asset management from a practitioner's perspective. Reha discussed issues associated with Factor investing and multi-period models and discuss how investors should strategize in the day of Covid19

Dr Joseph Simonian introduced an approach towards model validation which we call modular machine learning (MML) and used it to build a methodology that can be applied to the evaluation of investment.

Dr. Agus Sudjianto focused the discussion on machine learning explainability and robustness. Explainability is critical to evaluate conceptual soundness of models particularly for the applications in highly regulated institutions such as banks. There are many explainability tools available and the focus in this talk is how to develop fundamentally interpretable models.

Dr.Giulia Fianti explored if and how generative adversarial networks (GANs) can be used to incentivize data sharing by enabling a generic framework for sharing synthetic datasets with minimal expert knowledge.

Tony Guido first introduced the concept of supervised learning. He covered the practitioner angle for constructing non linear multi factor signals using stock characteristics. He showed the added value of ML based signals over traditional linear stale factors blend in equity.

Dr Matthew Dixon, Dr.Igor Halperin and Dr. Paul Bilokon talks about key trends in Machine Learning in finance.

Stu Kozola presented about Managing Machine Learning Models in the Financial Industry. From Model Risk Management for AI and Machine Learning to Rapid Prototyping Quant Research ML Models for Algorithmic Auditing using the QuSandbox.

Jennifer Jordan, Kareem Saleh, Anthony Habayeb, and Slater Victoroff discussed about AI explainability and Bias from an entrepreneur and investor perspective. and discussed about what the opportunities and challenges are and what the future looks like for explainable AI

Dr. Jorg Kientz oultined the use of Machine Learning Algorithms and their portential application. Specifically focused on Deep Neural Networks. Ben Steiner discussed the challenges of Deep Learning and focussed on the key aspects of Model Risk Management for Deep Learning and Alpha Strategies.

Alexander Denev introduced Alternative Data and discussed its uses from his book, The Book of Alternative Data. Saeed Amen discussed use cases in Alternative Data.

The banking industry has rapidly adopted machine learning for various applications. Large banks in the US are typically more cautious in adopting the methodology for high risk and regulated areas such as credit underwriting.

In today's workshop, we will go over a scenario using CodeNet, a large scale code sample dataset, to demonstrate how to use MLX to manage and analyze datasets, to training and hosting ML models using the same platform.

In this talk, David will cover the need for an understanding of explainability and interpretability in AI from the perspective of experimental psychology.

In our first session of the QU Fall School, Rob May will discuss investment trends in AI and the outlook of AI investing.

To responsibly deploy ML solutions we need good methods, software and processes.

Gautier Marti will discuss Generative Adversarial Networks (GANs) and discuss applications in synthetic data generation and other quantitative finance applications. He will also discuss his work on CORRGANS, Sampling Realistic Financial Correlation Matrices Using Generative Adversarial Networks.

As companies and customers change their behaviors, we are seeing changes on how companies are addressing new challenges. Join Fintech experts, D.Shahrawat and Sarah Biler for a not to be missed conversation on Fintech in the Post-Covid age.

As AI and ML penetrate the financial industry, there are growing concerns about ethical use of AI in Finance. In this talk, Dan Liebau will focus on how the AI can be operationalized to help industry professionals and executive teams alike think about opportunities, risks as well as required actions factoring in ethics in our data-driven world.

As the discussion on AI ethics and adoption of Responsible AI grows, there is confusion of what Responsible AI actually means for an enterprise. Is it regulation? Is it having a moral stance? Is it policy? Is it to prevent bad actors? As we delegate more and more decision making to machines, we need to not only bring policy, but also have pragmatic ways to adopt these practices within the enterprise.

This talk aims to unwrap the black box of deep ReLU networks through exact local linear representation, which utilizes the activation pattern and disentangles the complex network into an equivalent set of local linear models (LLMs)

Ian will discuss a new paper that unifies a large portion of the literature using a simple idea: simulating feature removal. The new class of "removal-based explanations" describes 20+ existing methods (e.g., LIME, SHAP) and reveals underlying links with psychology, game theory and information theory.

Stefan shows how to create synthetic time-series data using generative adversarial networks (GAN). GANs train a generator and a discriminator network in a competitive setting so that the generator learns to produce samples that the discriminator cannot distinguish from a given class of training data.

With Alternative Data becoming more and more popular in the industry, quants are eager to adopt them into their investment processes. However, with a plethora of options, API standards, trying and evaluating datasets is a major hindrance to adoption of datasets.