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In this episode, we discuss with Pierre-Philippe Ste-Marie, a seasoned practitioner with over 25 years of experience in quantitative finance. We start by tracing his educational path and early career moves in fixed income trading, including his decision to return to school at Carnegie Mellon to study computational finance.

Pierre emphasizes the importance of focusing on the problem rather than the tools, sheds light on applying stochastic calculus to capture randomness in financial models, and discusses the roles of alpha and beta managers, among other things. We close with a rapid-fire segment, where Pierre reveals his passion for Kendo and offers advice for the next generation of quantitative traders. Enjoy!


Timeline

01:24 Early Career and Transition to Finance
10:15 Reflections on Career Path and Opportunities
13:03 Understanding Jump Diffusion and Mean Reversion Models
14:46 Defining Quantitative Finance
17:37 Buy Side vs Sell Side: A Quantitative Perspective
19:23 The Role of Machine Learning in Quant Finance
23:11 Model Implementation: Balancing Simplicity and Complexity
28:02 The Evolution of Programming in Quant Finance
29:50 Cross-Disciplinary Applications of Quant Finance
31:25 Understanding Uncertainty in Financial Markets
33:05 The Role of Beta and Alpha in Investment Management
35:39 Life as a Monte Carlo Simulation
37:38 Navigating Incomplete Information in Trading
38:23 Rapid Fire Insights and Personal Reflections

The podcast team is excited to announce the McGill Artificial Intelligence Society’s 3rd podcast episode of the 2023-2024 school year featuring Yves Hilpisch. 

Yves is the founder and CEO of The Python Quants and has written several books, including Python for Finance. 

In recent years, AI has been in the forefront of computational finance and algorithmic trading. As the accessibility of AI increases, building our own financial models has become easier with programming languages like Python and the support of various available libraries.

In this episode, we examine AI’s rise in the world of finance, exploring its applications across various facets of financial analysis. We discuss the possibility of AI models predicting market movement and what this means in terms of the Efficient Market Hypothesis.  Yves also talks about the implications of AI for smaller companies as they try to compete with the bigger players in the industry, as well as impacts of the growing accessibility of individual finance and trading. 

The McGill AI podcast is available on Apple Podcast and Spotify.

The podcast team is excited to announce the McGill Artificial Intelligence Society’s 2nd podcast episode of the 2023-2024 school year featuring Ignacio Cofone. 

Ignacio Cofone is a professor at McGill’s Faculty of Law and is the Canada Research Chair in AI Law and Data Governance. With the rapid evolution of artificial intelligence in recent years, there are also many concerns at the forefront. This episode tackles current topics like the effects of bias in AI, the intersection between AI and privacy law, as well as AI regulations. We will examine how the transition between the current privacy regulation and the new Bill C-27 addresses these concerns and also its potential blind spots, while covering the challenges and uncertainties around Canada’s first comprehensive attempt at AI regulation. 

The McGill AI podcast is available on Apple Podcast and Spotify.

Artem Kirsanov is a graduate student in neuroscience at New York University Center for Neural Science and a YouTuber with a following of over 100k subscribers. In this episode, we talk about modelling the human brain with artificial intelligence, how AI is being used to enhance neuroscience, and what a future alongside AI can look like for scientists.

Embark on an enlightening journey into the world of reinforcement learning with Prof. Doina Precup in this captivating podcast episode. As a distinguished professor at McGill University and the Head of DeepMind Montreal, Prof. Precup brings a wealth of expertise to our discussion. Join us as we delve into the intricacies of hierarchical reinforcement learning, navigating the complexities of decision-making processes. Explore the realm of continual reinforcement learning, where adaptability harmonizes with efficiency, and gain invaluable insights into new possibilities and challenges posed by large models. Whether you're an AI aficionado or newcomer, this episode promises an illuminating expedition into the forefront of reinforcement learning, guided by a true trailblazer in the field.

The advent of powerful generative AI models - particularly in the art space - has enabled limitless creative opportunities. In this episode, we discuss both the usage benefits and the importance of regulating generative AI in this space from a diverse stakeholder perspective. Chloë Ryan, CEO and founder of Acrylic Robotics - a fast-growing pre-seed art-tech startup, Katherine Beaulieu, a BCL/JD Candidate at the McGill Faculty of Law & member of Tech Law, and Dr. Kussil Oumedjbeur, a current MSc candidate in experimental surgery at McGill and popular digital artist, join us in a panel to discuss the ethical and regulatory challenges involved in the usage of generative AI in the art space. 

What does it mean for an artificial intelligence model to do the right thing? What are researchers and governments doing to ensure that the use of AI technologies does not infringe on human rights? These are two of the questions that we touch on in this conversation with Dr. Su Lin Blodgett and Prof. Fenwick McKelvey. The world of AI has seen an explosion of progress in recent years and this has opened up exciting new applications for these models. However, the issue of reflecting human biases, which has long been plaguing these models, is far from solved. On top of this, the applications in which such models can be used remain poorly regulated, at least within Canada. In this episode, spanning across fields like computer science, media, and technology policy, we try to understand why these questions are essential to address, and how we can start thinking about solutions that take into account a plurality of perspectives.

Yoshua Bengio, co-laureate of the 2018 ACM AM Turing Award for his pioneering contributions in deep learning and recognized worldwide as one of the leading experts in artificial intelligence, joins us on the McGill AI Podcast to discuss his experience in pioneering deep learning, the work towards achieving human-level artificial intelligence, and advice to students wanting to do research in AI.

He is a Full Professor at Université de Montréal, and the Founder and Scientific Director of Mila – Quebec AI Institute. He co-directs the CIFAR Learning in Machines & Brains program as Senior Fellow and acts as Scientific Director of IVADO.

In 2019, he was awarded the prestigious Killam Prize and in 2022, became the computer scientist with the highest h-index in the world. He is a Fellow of both the Royal Society of London and Canada, Knight of the Legion of Honor of France and Officer of the Order of Canada.

Dr. Dan Cervone is the principal data scientist at Zelus Analytics where they are building the world leading sports analytics platform. Prior to this, Dan spent three seasons with the Los Angeles Dodgers, most recently as Director of Quantitative Research. He completed his PhD in Statistics at Harvard University, and was then a Moore-Sloan Data Science Fellow at NYU. His work focuses on spatiotemporal data and hierarchical models, with particular application to sports analytics and player tracking data. Dan joins us today to talk about the field of sports analytics, his own research using machine learning in sports, and the future of sports analytics.

Music by Aria Khiabani with other music under the name SATRAP