Check out the upcoming speakers at: https://quspeakerseries.splashthat.com/

Subscribe to this podcast at www.anchor.fm/qupodcast or on Apple Podcast at https://podcasts.apple.com/us/podcast/qupodcast/id1510865003

Slides and video at: https://academy.qusandbox.com/#/market/5f062b8455fd05416e7fd285

A conversation with Quants, Thinkers and Innovators all challenged to innovate in turbulent times!

Join QuantUniversity for a complimentary summer speaker series where you will hear from Quants, innovators, startups and Fintech experts on various topics in Quant Investing, Machine Learning, Optimization, Fintech, AI etc.

Topic: Practical Issues in Asset Management

The markets have seen significant volatility in the past few months. With the Factor investing boom of the nineties to the whiplash we have seen during #Covid19, Quants and asset management institutions are questioning accepted best practices in asset management.

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

Check out the upcoming speakers at: https://qufallschool.splashthat.com/

Subscribe to this podcast at www.anchor.fm/qupodcast

Or

On Apple Podcast at https://podcasts.apple.com/us/podcast/qupodcast/id1510865003

Slides and video at: https://academy.qusandbox.com/#/market/5fd0ff0e99aa4a24691da8d2

A conversation with Quants, Thinkers and Innovators all challenged to innovate in turbulent times!

Join QuantUniversity for a complimentary summer speaker series where you will hear from Quants, innovators, startups and Fintech experts on various topics in Quant Investing, Machine Learning, Optimization, Fintech, AI etc.

Topic: Machine Learning Interpretability -  Self-Explanatory Models: Interpretability, Diagnostics and Simplification

The deep neural networks (DNNs) have achieved great success in learning complex patterns with strong predictive power, but they are often thought of as "black box"models without a sufficient level of transparency and interpretability. It is important to demystify the DNNs with rigorous mathematics and practical tools, especially when they are used for mission-critical applications. 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). We develop a convenient LLM-based toolkit for interpretability, diagnostics, and simplification of a pre-trained deep ReLU network. We propose the local linear profile plot and other visualization methods for interpretation and diagnostics, and an effective merging strategy for network simplification. The proposed methods are demonstrated by simulation examples, benchmark datasets, and a real case study in credit risk assessment. The paper that will be presented in this talk can be found here.

Check out the upcoming speakers at: https://qufallschool.splashthat.com/

Subscribe to this podcast at www.anchor.fm/qupodcast

Or

On Apple Podcast at https://podcasts.apple.com/us/podcast/qupodcast/id1510865003

Slides and video at: https://academy.qusandbox.com/#/market/5fc8293199aa4a24691da8c1

A conversation with Quants, Thinkers and Innovators all challenged to innovate in turbulent times!

Join QuantUniversity for a complimentary summer speaker series where you will hear from Quants, innovators, startups and Fintech experts on various topics in Quant Investing, Machine Learning, Optimization, Fintech, AI etc.

Topic: Responsible AI in Action

As the discussion on AI ethics and adoption of Responsible AI grows, there is confusion of what does Responsible AI actually mean 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 entrprise. Join industry leaders Dr. Valeria Sadovykh, Dr. Reid Blackman, and Jun Wu on a discussion on what it means to adopt Responsible AI in the enterprise.

Check out the upcoming speakers at: https://qufallschool.splashthat.com/

Subscribe to this podcast at www.anchor.fm/qupodcast

Or

On Apple Podcast at https://podcasts.apple.com/us/podcast/qupodcast/id1510865003

Slides and video at: https://academy.qusandbox.com/#/market/5fad64f599aa4a24691da862

A conversation with Quants, Thinkers and Innovators all challenged to innovate in turbulent times!

Join QuantUniversity for a complimentary summer speaker series where you will hear from Quants, innovators, startups and Fintech experts on various topics in Quant Investing, Machine Learning, Optimization, Fintech, AI etc.

Topic: Ethical Use of AI in Financial Markets

As AI and ML penetrate the financial industry, there are growing concerns about ethical use of AI in Finance. In this talk, Dan focused 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.

Check out the upcoming speakers at: https://qufallschool.splashthat.com/

Subscribe to this podcast at www.anchor.fm/qupodcast

Or

On Apple Podcast at https://podcasts.apple.com/us/podcast/qupodcast/id1510865003

Slides and video at: https://academy.qusandbox.com/#/market/5fa42ceb99aa4a24691da856

A conversation with Quants, Thinkers and Innovators all challenged to innovate in turbulent times!

Join QuantUniversity for a complimentary summer speaker series where you will hear from Quants, innovators, startups and Fintech experts on various topics in Quant Investing, Machine Learning, Optimization, Fintech, AI etc.

Topic: Fintech in the Post-Covid Age

The world has changed in the last six months with COVID-19! There have been a shakeup in business models and funding. 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

Check out the upcoming speakers at: https://qufallschool.splashthat.com/

Subscribe to this podcast at www.anchor.fm/qupodcast

Or

On Apple Podcast at https://podcasts.apple.com/us/podcast/qupodcast/id1510865003

Slides and video at: https://academy.qusandbox.com/#/market/5f9834e199aa4a24691da81e

A conversation with Quants, Thinkers and Innovators all challenged to innovate in turbulent times!

Join QuantUniversity for a complimentary summer speaker series where you will hear from Quants, innovators, startups and Fintech experts on various topics in Quant Investing, Machine Learning, Optimization, Fintech, AI etc.

Topic: GANS with Apps in Synthetic data

With various innovations in neural networks, GANs are becoming popular as a means of generating synthetic data.  In this master class, Gautier 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.[1]  

Reference:

Check out the upcoming speakers at: https://qufallschool.splashthat.com/

Subscribe to this podcast at www.anchor.fm/qupodcast

Or

On Apple Podcast at https://podcasts.apple.com/us/podcast/qupodcast/id1510865003

Slides and video at: https://academy.qusandbox.com/#/market/5f58f95999aa4a24691da778

A conversation with Quants, Thinkers and Innovators all challenged to innovate in turbulent times!

Join QuantUniversity for a complimentary summer speaker series where you will hear from Quants, innovators, startups and Fintech experts on various topics in Quant Investing, Machine Learning, Optimization, Fintech, AI etc.

Topic: Validation and Machine Learning - Some thoughts on Deep Neural Networks

Lecture 1: Dr.Jorg Kientz

In this talk we outline the use of Machine Learning algorithms and their potential application. We focus on Deep Neural Networks. The aim is to outline different network architectures. Then, we wish to find a way of choosing the architecture that best fits from a model validation perspective. This approach is illustrated with examples.

Lecture 2: Ben Steiner

In this talk we will discuss the challenges of Deep learning and focus on the key aspects of Model Risk Management for Deep Learning and Alpha Strategies

Check out the upcoming speakers at: https://qufallschool.splashthat.com/

Subscribe to this podcast at www.anchor.fm/qupodcast

Or

On Apple Podcast at https://podcasts.apple.com/us/podcast/qupodcast/id1510865003

Slides and video at: https://academy.qusandbox.com/#/market/5f4fe5bb99aa4a24691da76e

A conversation with Quants, Thinkers and Innovators all challenged to innovate in turbulent times!

Join QuantUniversity for a complimentary summer speaker series where you will hear from Quants, innovators, startups and Fintech experts on various topics in Quant Investing, Machine Learning, Optimization, Fintech, AI etc.

Topic: Explainable AI and Bias in Machine Learning: A Financial Industry perspective

In the last year or so, there has been a significant interest in AI explainability, fairness, and bias. Many regulatory efforts are being proposed to reign in the uncontrolled deployment of AI. Companies on the other hand are grappling with complex black boxes and are figuring out how to build models that are explainable, fair, and bias-free. Many startups are working on interesting technologies to address these issues. In this session, we will discuss AI explainability and Bias from an entrepreneur and investor perspective. and have a discussion on what the opportunities and challenges are and what the future looks like for explainable AI

Check out the upcoming speakers at: https://qufallschool.splashthat.com/

Subscribe to this podcast at www.anchor.fm/qupodcast

Or

On Apple Podcast at https://podcasts.apple.com/us/podcast/qupodcast/id1510865003

Slides and video at: https://academy.qusandbox.com/#/market/5f467c5499aa4a24691da707

A conversation with Quants, Thinkers and Innovators all challenged to innovate in turbulent times!

Join QuantUniversity for a complimentary summer speaker series where you will hear from Quants, innovators, startups and Fintech experts on various topics in Quant Investing, Machine Learning, Optimization, Fintech, AI etc.

Topic: Managing Machine Learning Models in the Financial Industry

Lecture 1: Model Risk Management for AI and Machine Learning

Lecture 2: Rapid Prototyping Quant Research ML Models for Algorithmic Auditing using the QuSandbox

Check out the upcoming speakers at: https://qusummerschool.splashthat.com/

Subscribe to this podcast at www.anchor.fm/qupodcast

Or

On Apple Podcast at https://podcasts.apple.com/us/podcast/qupodcast/id1510865003

Slides and video at: https://academy.qusandbox.com/#/library?tagId=5f06326c55fd05416e7fd28a

A conversation with Quants, Thinkers and Innovators all challenged to innovate in turbulent times!

Join QuantUniversity for a complimentary summer speaker series where you will hear from Quants, innovators, startups and Fintech experts on various topics in Quant Investing, Machine Learning, Optimization, Fintech, AI etc.

Topic: Machine Learning and Model Risk (With a focus on Neural Network Models)

All models are wrong and when they are wrong they create financial or non-financial risks. Understanding, testing and managing model failures are the key focus of model risk management particularly model validation.

For machine learning models, particular attention is made on how to manage model fairness, explainability, robustness and change control. In this presentation, I will focus 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 my focus in this talk is how to develop fundamentally interpretable models.

Neural networks (including Deep Learning), with proper architectural choice, can be made to be highly interpretable models. Since models in production will be subjected to dynamically changing environments, testing and choosing robust models against changes are critical, an aspect that has been neglected in AutoML.