The Ensemble Podcast, by CrunchDAO

• Occupation: Trader with Jerome Bousquet

  Our Guest: Jerôme BousquetWith over 20 years navigating the dynamic world of macro exotics, Jerome has carved a niche as a leading expert in Hybrid Options trading. Currently, he heads a dedicated team at JP Morgan Chase, based in both London and Paris. His passion lies in crafting and meticulously managing complex hybrid options, instruments that ingeniously combine two or more macro assets like currencies, interest rates, equities, and precious metals.Jerome's journey began in 2002 with the esteemed Man Group, where he honed his analytical and execution skills within a CTA hedge fund environment. This strong foundation propelled him to ABN AMRO, where he delved into the intricacies of EUR rate exotics trading, followed by a stint at Nomura, where he mastered the art of USD rate exotics.Today, at JP Morgan Chase, Jerome spearheads a team of four traders, responsible for quoting Hybrid Options to both real money and fast money accounts within the bank's franchise clientele. His expertise extends beyond mere quoting, encompassing the meticulous risk management of these instruments until their expiry. This requires a deep understanding of the complex dynamics at play within various macro asset classes, coupled with the ability to anticipate and mitigate potential risks.Jerome's extensive experience, combined with his leadership skills and in-depth knowledge of the Hybrid Options landscape, make him a valuable asset to JP Morgan Chase and a respected figure within the wider financial industry. His ability to navigate the ever-evolving world of macro exotics positions him as a key player shaping the future of Hybrid Options trading.Hosted by Ausha. See ausha.co/privacy-policy for more information.

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• Replicability in AI, P-Hacking and implications in Quant. Finance - Prof. Lopez De Prado & Dr. Simon

  During the Awards Ceremony of the ADIA Lab Market Prediction Competition, we discuss Replicability in AI, P-Hacking and implications in Quantitative Finance.  Panel: - Prof. Marcos Lopez de Prado, Global Head - Quantitative R&D at ADIA - Dr. Horst Simon - Director at ADIA Lab - Matteo Manzi, Cofounder & Lead Quant Researcher at CrunchDAO Follow us:  Join the group on Linkedin: https://www.linkedin.com/groups/12920374/ CrunchDAO on Linkedin: https:/linkedin.com/crunchdao-com    CrunchDAO on X https://x.com/CrunchDAO What is CrunchDAO? Crunchdao serves as a secure intermediary, enabling data scientists to keep control of their models while powering financial institutions. Predict & Compete:  Register here: https://crunchdao.com Hosted by Ausha. See ausha.co/privacy-policy for more information.

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• Navigating Crisis as a Quant - With Arnaud de Servigny, CEO of Queensfield AI.

  Explore "Navigating Crisis as a Quant" with industry veteran Arnaud de Servigny at the first-ever Quant Club de Paris event. Arnaud brings a wealth of knowledge from his leadership roles in global finance, including his directorship at BNP Paribas Asset Management and presidency at Queens Field SAS. With a backdrop of experience from Deutsche Bank to Barclays and & Standard & Poors, and authorship in finance, his lecture on "Navigating Crisis as a Quant" is a must-watch for finance professionals and enthusiasts alike. Catch the full talk and stay updated with the latest in finance by subscribing to our channel. (Sorry for the poor Audio Quality - We are working to improve it) Hosted by Ausha. See ausha.co/privacy-policy for more information.

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• Overparameterization, Deep Ensembles and Gaussian Processes with Geoff Pleiss

  Geoff Pleiss is a Postdoctoral researcher at Columbia University, with affiliations in the Department of Statistics and the Zuckerman Institute. He holds a PhD in Computer Science from Cornell University, and is Co-founder and maintainer of the GPyTorch software library. His research places him at the nexus of deep learning, probabilistic modeling, and numerical linear algebra, enabling him to address both of these challenges. One line of his work focuses directly on neural networks, improving their uncertainty estimates while understanding their predictive capabilities through the lens of probabilistic models.  Another line focuses on the inductive biases of Gaussian processes (GP), improving their computational efficiency and ultimately replicating their desirable properties in neural networks.  This research profile ideally situates him to unite these paradigms, transforming today’s powerful models into general reasoning models. In addition, he has a proven record of coupling his findings with performant and easy-to-use software used widely throughout research and industry, facilitating adoption and innovation in this area. Hosted by Ausha. See ausha.co/privacy-policy for more information.

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• A Unified Framework for Sequential Decisions with Warren Powell

  Warren Powell is Chief Analytics Officer of Optimal Dynamics and Professor Emeritus from Princeton University, where he taught and served as a faculty member in the Department of Operations Research and Financial Engineering since 1981.  In the 1980s Powell designed and wrote SYSNET, an interactive optimization model for load planning at Yellow Freight System, where it is still in use after 25 years. He is the founder of Princeton Transportation Consulting Group, which marketed the model as SuperSPIN, stabilizing an industry where 80% of companies went bankrupt in the first post-deregulation decade. SuperSPIN was used in the planning of American Freightways (which became FedEx Freight) and Overnight Transportation (which became UPS Freight). In 1990 Powell founded CASTLE Laboratory which spans research in computational stochastic optimization with applications initially in transportation and logistics. In 2011 he then founded the Princeton laboratory for ENergy Systems Analysis (PENSA) to tackle the rich array of problems in energy systems analysis, and in 2013: this morphed into “CASTLE Labs,” focusing on computational stochastic optimization and learning. In 2017 Powell founded Optimal Dynamics, helping carriers to automate and optimize trucking networks using AI.  Motivated by these applications, he developed a method for bridging dynamic programming with math programming to solve very high-dimensional stochastic, dynamic programs using the modeling and algorithmic framework of approximate dynamic programming. He identified four fundamental classes of policies for solving sequential decision problems, integrating fields such as stochastic programming, dynamic programming (including approximate dynamic programming/reinforcement learning), robust optimization, optimal control and stochastic search (to name a few). This work identified a new class of policy called a parametric cost function approximation. His work in industry is balanced by contributions to the theory of stochastic optimization, and machine learning.Hosted by Ausha. See ausha.co/privacy-policy for more information.

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