Tomorrow, October 24th at 18:00 CET, will take place the virtual “Estimathon” hosted by Jane Street: 13 unique estimation problems in 30 minutes, combining maths and trivia, to be solved as a team.
It’s good fun, a good challenge, and a great opportunity to learn more about Jane Street! Sign up here or follow the poster below to obtain the Zoom link.
The Quant Finance Workshop by G-Research took place last Wednesday at EPFL, followed by networking and pizza.
Thanks to more than 50 EPFL PhD students who joined, and congratulations to the winners of the cointegration challenge! 🏆
Stay tuned for more events like this in the future! 😉
The Professional Journey Showcase took place this Tuesday, October 8th. Thanks to everyone who attended, and special thanks to Dirk Hartmann from Siemens for an engaging presentation and answering our questions!
Thanks as well to the EPFL mathematics department and Agepoly for their financial support.
Are you an EPFL PhD student and curious about the world of Quantitative Finance?
You are invited to participate in G-Research’s Interactive Quant Finance Workshop on Wednesday, Oct. 16, from 18:00 to 22:00 at EPFL room GC B3 31.
🔍 What to Expect: You will take part G-Research’s brand new cointegration challenge, where you’ll test your strategies on unseen, real world datasets. Play for fun, or bring your competitive spirit for the chance to win prizes! Following the challenge you’ll have the chance to network with G-Research’s researchers over some food and drink!
👥 The workshop is a challenge that can be enjoyed solo, or in teams of up to three, so feel free to share the registration link with your friends and colleagues.
👉 Sign up now: qrco.de/EPFL2024 (or the QR code in the poster below)
Interested in a career in Applied Mathematics, Computational Science or Data Science, but still have some doubts?
Join us on Tuesday, October 8th, for the Professional Journey Showcase with Dirk Hartmann! The event will take place at EPFL in room MA B1 11, starting from 17h.
The workshop will consist in a short presentation, a career Q&A, and will be followed by a networking apéro. Registration is recommended (this link, or the QR code in the poster below).
Dirk Hartmann is an industrial mathematician, intrapreneur, and thought leader in the field of Simulation and Digital Twin. In his career, he took several leading roles in research, innovation, and development across Siemens including the lead of a multi-million Siemens R&D program and the technical leadership for the Simulation & Digital Twin field at Siemens Technology with more than 120 scientists across the globe. Many of his innovations have led to novel products and are regularly showcased at Siemens innovation events. For these achievements, he has received numerous Siemens awards, including the prestigious Werner-von-Siemens Top Innovator Award in 2019 and the Siemens Inventor of the Year Award in 2021.
Join us for an online seminar on Monday, June 10th at 4PM, given by Emilia Magnani, a Ph.D. candidate at the University of Tübingen. She will present her work on “Learning Solution Operators for PDEs with Uncertainty”.
Abstract: We provide a Bayesian formulation of the problem of learning solution operators of PDEs in the formalism of Gaussian processes. We consider neural operators, recent deep architectures that have shown promising results in tackling the task of learning PDE solution operators. The current state of the art for these models lacks explicit uncertainty quantification. Our approach offers a practical and theoretically sound way to apply the linearized Laplace approximation to neural operators to provide uncertainty estimates. Moreover, we introduce a new framework for Bayesian uncertainty quantification in neural operators using function-valued Gaussian processes.
Bio: Emilia Magnani is a Ph.D. candidate at the University of Tübingen under the supervision of Philipp Hennig. She is also part of the ELLIS program and spent part of her Ph.D. in Genoa working with Lorenzo Rosasco. Before that, Emilia obtained her Master’s degree in Mathematics from ETH Zurich. Her research interests span various areas of machine learning such as probabilistic numerics, Gaussian processes, and operator learning.
Zoom Link: https://epfl.zoom.us/j/63925499984?pwd=GOEI1rAQrMOXaIgFLG5B3IYle4Funr.1
This Wednesday (April 17th), we are pleased to host a talk by Philippe Trunschke (PostDoc at Centrale Nantes & Nantes Université), which should be interesting to many of our members!
The seminar will take place online at the zoom link below, and will also be projected live in the room CM 1 517. https://epfl.zoom.us/j/61353461236?pwd=MnI2VkRMWlE2WUJxalRmNVJwc2JGQT09
Title: Optimal sampling for stochastic gradient descent
Abstract: Approximating high-dimensional functions often requires optimising a loss functional that can be represented as an expected value. When computing this expectation is unfeasible, a common approach is to replace the exact loss with a Monte Carlo estimate before employing a standard gradient descent scheme. This results in the well-known stochastic gradient descent method. However, using an estimated loss instead of the true loss can result in a “generalisation error”. Rigorous bounds for this error usually require strong compactness and Lipschitz continuity assumptions while providing a very slow decay with increasing sample size. This slow decay is unfavourable in settings where high accuracy is required or sample creation is costly. To address this issue, we propose a new approach that involves empirically (quasi-)projecting the gradient of the true loss onto local linearisations of the model class through an optimal weighted least squares method. The resulting optimisation scheme converges almost surely to a stationary point of the true loss, and we investigate its convergence rate.
Philipp TRUNSCHKE studied Mathematics at the Humboldt University in Berlin, specialising in statistical learning theory. He completed his doctoral studies focusing on tensor product approximation at the Technical University of Berlin 2018. Currently, he is working with Anthony NOUY in Nantes on compositional function networks and optimal sampling.
We were honored to host Francisco Ruiz, Research Scientist @ Google DeepMind, for an online seminar on AlphaTensor, last Tuesday 05/12 evening.
AlphaTensor is a Deep Reinforcement Learning agent designed to automatically discover fast algorithms for matrix multiplication, a mathematical operation ubiquitous in science and engineering. It discovered new improved algorithms, with heavy impact on theory and practice.
Francisco spoke about its development and shared firsthand insights into its design.
Memento link
On Thursday 3rd November 2022, G-Research came to the EPFL campus for a “Quant Finance Challenge”, an algorithmic trading-based game. In teams of 2-3, over 100 Master, PhD students and postdocs tried their hands (and their Python skills) at a few problems inspired from quantitative finance.
The Challenge was followed by pizza and drinks, and the opportunity to discuss with Quant Researchers and Machine Learning Specialists from G-Research.
We hosted two events organized by the trading company Jane Street, on Thursday 10th March 2022, in collaboration with CLIC and EPFelles (two student associations from EPFL). Slightly over 130 participants joined for
This was followed by pizza and a Q&A with a Jane Street Trader, Software Engineer, and Recruiters.
