Alternative Data in Investment Management

Alternative Data in Investment Management
Alternative Data in Investment Management with Petter Kolm from New York University
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AI and Risk Management in Finance

AI and Risk Management in Finance
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.
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Best Practices in AI

Best Practices in AI
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.
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Computing Platforms for Trustworthy, Responsible AI in Financial Services

Computing Platforms for Trustworthy, Responsible AI in Financial Services
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.
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Bias, Fairness, and Accountability with AI and ML Algorithms

Bias, Fairness, and Accountability with AI and ML Algorithms
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.
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Challenges and Frontiers in Deploying Transparent Machine Learning

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

Secure Machine Learning
This talk will go over common machine learning security attacks and the remediation steps an organization can take to deter these pitfalls.
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Modernizing Model Risk Management

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

Practical Issues in Asset Management
Dr. Reha Tutuncu from Point72 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.
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Modular Machine Learning for Model Validation

Modular Machine Learning for Model Validation
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.
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Model Validation and Machine Learning

Model Validation and Machine Learning
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.
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Generating High-fidelity, Synthetic Time Series Datasets with DoppelGANger

Generating High-fidelity, Synthetic Time Series Datasets with DoppelGANger
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.
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Machine Learning for Factor Investing

Machine Learning for Factor Investing
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.
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Machine Learning in Finance - Key Trends

Machine Learning in Finance - Key Trends
Dr Matthew Dixon, Dr. Igor Halperin, and Dr. Paul Bilokon talk about key trends in Machine Learning in finance.
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Managing Machine Learning Models in the Financial Industry

Managing Machine Learning Models in the Financial Industry
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.
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Explainable AI and Bias in Machine Learning: A Financial Industry Perspective

Explainable AI and Bias in Machine Learning: A Financial Industry Perspective
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.
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