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.
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.
In this training, you will develop a basic understanding of quantum computing and how it can be used in machine learning models, with special emphasis on generative models. We will focus on a particular architecture, the quantum circuit Born machine (QCBM), and use it to generate a simple dataset of bars and stripes.
Join Sri Krishnamurthy,CFA as we kickoff the QuantUniversity’s Winter school 2021. We will introduce you to the upcoming programs and have a masterclass on 10 innovations in AI and ML you need to know in 2021!
This talk will outline a new approach to “incident response” specifically tailored to AI and it will present a free and open sample AI incident response plan. Participants will leave understanding when and why AI creates liability for the organizations that employ it, and how organizations should react when their AI causes major incidents.
Join QuantUniversity for a complimentary fall 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.
Insights about the decision making are mostly opaque for humans. Particularly understanding the decision making in highly sensitive areas such as healthcare or finance, is of paramount importance. Join Nadia Burkart and Dr. Marco Huber in a discussion on Explainability of Supervised Learning.
Daniel will, in the first part of this talk, overviews RKHS (Reproducing Kernel Hilbert Space) methods and some of their applications. Jean-Marc will then present and discuss a Python library called codpy (curse of dimensionality - for Python), and implementing RKHS methods.
In this talk, Sri will introduce Algorithmic auditing and discuss why Algorithmic auditing will be a formal process industries using AI will need. Sri will also discuss the emerging risks in the adoption of AI and discuss how QuSandbox, his company is building, will address the emerging needs of formal Algorithmic auditing practices in enterprises.
Jon will discuss what if a financial firm decided to delete its entire set of models and redevelop them from scratch? What might it do differently in the process of rebuilding its entire model ecosystem in order to avoid and leverage from some of its previous mistakes?
In this session, Francesca Lazzeri, PhD, will provide students with comprehensive and approachable explanation and treatment of the application of machine learning algorithms (such as ARIMA and LSTMs) to time series forecasting.
Privacy-Preserving Analytics and Machine Learning with Differential Privacy with Andreas Kopp and Lucas Rosenblatt from Microsoft
AI Regulation Panel with Patrick Hall from bnh.ai & The George Washington University, Agus Sudjianto from Wells Fargo, Nick Schmidt from BLDS LLC and Tulsee Doshi from Google
Monitoring Machine Learning Systems in Production with Elena Samuylova & Emeli Dral Founders of Evidently AI
Making your Models Explainable with Oege Dijk from ThoughtWorks
Serverless Data Ops: from data to documentation with Jacopo Tagliabue from Coveo
With Shea Brown from BABL AI inc. and Ryan Carrier from ForHumanity. As automation increases and AI permeates our world, there is concern on how the lack of guardrails and scrutiny could affect humanity. Algorithmic auditing and governance are mechanisms to enhance transparency for the responsible adoption of AI
Visualizing Data with Rami Krispin from Apple
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.
With the complexity of Deep Learning models growing, there is concern on transparency and explainability aspects of the model. For example, many facial recognition algorithms which are implemented using Deep Learning models are undergoing renewed scrutiny.
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.
Mathematical Finance & Financial Data Science Seminar at NYU Courant with Sri Krishnamurthy, CFA
