PyData Nights Vol.1 The Revival

Hallo Münchners,

PyData Munich is back to the future with in-person events 😉

We would like to invite you to our meetup with two exciting talks at a very cool location, for which we want to thank JetBrains for hosting us this evening along with drinks and pizza.

This event is brought to you in collaboration with the Munich🥨NLP community. Join their Discord to discuss the latest developments and also stimulate exchange on research and innovation around NLP.

Hurry up we have limited spots and see you on the other side after a long time.

Best,
Muhtasham and Nithish

=== Agenda ===
18:30 - Doors open
19:00 - Welcome and Introduction
19:10 - Talk 1: Practical Text Search or how to beat Elasticsearch (when you have to) by Egor Labintcev, Alyne GmbH
19:50 - [Break] Networking and refreshment
20:00 - Talk 2: Fraud Detection with Graph Features and GNN by Nikita Iserson, S&P Global
20:40 - Networking with Drinks

=== Talks ===
Talk 1: Practical Text Search or how to beat Elasticsearch (when you have to)
Speaker: Egor Labintcev, Alyne GmbH

Abstract:
In this talk, he is going to guide you through the process of building a good text search engine when it’s hard to do so. He’ll start by defining what actually makes the text search good (it’s not that trivial), continue with a colourful description of the struggle the speaker went through while building a (relatively) good text search, and culminate by stating the best practices and approaches.

Speaker Bio
Currently, Egor is a Senior ML Engineer in the RegTech industry. He builds various text engines ranging from low-resource extreme multi-label classification to ensemble-based text search in complex domains. His current research interest lies in modern retrieval approaches and NLP model interpretability.

Talk 2: Fraud Detection with Graph Features and GNN
Speaker: Nikita Iserson, S&P Global

Abstract: Identifying fraudulent behaviors is becoming increasingly more complex as technology advances and fraudsters constantly evolve new ways to exploit people, companies, and institutions. The complexity grows as companies introduce new channels, platforms, and devices for customers to engage with their brand, manage their accounts, and make transactions. Graph neural networks (GNN) are increasingly being used to identify suspicious behavior. GNNs can combine graph structures, such as email accounts, addresses, phone numbers, and purchasing behavior to find meaningful patterns and enhance fraud detection.

Speaker Bio
Nikita is a Lead Machine Learning Engineer at S&P Global with over 10 years of experience in software engineering, data warehouse development, data analytics, and machine learning. He has built demand forecasting, network analysis, recommender systems, digital twins, and much more covering a wide range of industries, including telecom, retail, and banking.

PS: If you would like to speak or host us in one of the upcoming events, please reach out to one of us.