September 2024 Events

📢 Discover commonalities between Quantum chemistry and Fintech and learn about the posterior in Bayesian inference.

🗓️ September 19, 2024 6pm - 8pm
📍Perpay, 2400 Market St, Suite 300, Philadelphia, PA

Event Schedule:

Doors opens at 6:00 pm ET
Introductions and networking: 6:00 - 6:30
JD Herr, Head of Data Science and Data Engineering at Perpay, presents “Things can be two things: Quantum chemistry and Fintech“ (25-30 minutes) followed by Q&A
Dante Gates, Data Scientist at Perpay, presents “Bayesian Inference: What is the posterior and why does it matter?” (25-30 minutes) followed byQ&A
Networking time

We are back at Perpay!
Perpay already hosted us for a February event and a lot of you might remember their super stylish office with unparalleled views and living plant wall at the entrance.\

Speakers

JD Herr, Head of Data Science and Data Engineering at Perpay
Things Can Be Two Things

Bio: Science nerd, wood worker, brewer, and new father

Abstract: In the seemingly disparate worlds of theoretical chemistry and financial technology (fintech), a profound connection lies within the mathematical and modeling methods that underpin both fields. This presentation explores the fascinating parallels between quantum mechanics and fintech, demonstrating how foundational concepts in one domain can be applied in the other. Drawing from my background in theoretical and computational chemistry, where I specialized in quantum mechanics, and my seven years of experience in fintech, I will illustrate how three key mathematical methods—quantum states, potential energy optimization and wave functions, molecular dynamics and path integral molecular dynamics (PIMD)—bridge the gap between these fields

Dante Gates, Data Scientist at Perpay
Bayesian Inference: What is the posterior and why does it matter?

Bio: Current data scientist at Perpay, with 10+ years of experience in industries such as Finance, AdTech and Healthcare

Abstract: We've all seen it. That neon sign hanging on the wall of some math department somewhere. Bayes' Rule. An expression of conditional probability. And when it's a model conditioned on data, it's Bayesian Inference. Evaluating the right hand side of that equation, transforming the left into a posterior distribution. Though often reduced to a point estimate or confidence interval, when left as a distribution interesting possibilities abound...
The primary goal of this talk is to answer the second half of its title: why should you care? After providing a brief overview of how the posterior fits into Bayesian Inference we'll focus on application. Examples from domains such as finance, investing, E-commerce and baseball will drive the content and demonstrate the versatility this approach affords. Attendees should leave with an understanding of the role of the posterior distribution and ideas of how to apply this to their own work.