[in-person] Abu Dhabi Machine Learning Meetup Season 5 Episode 1

Register here: https://adgmacademy.com/adgma-research-centre/abu-dhabi-machine-learning

Bring your Emirates ID (for the in-person attendees) in order to be able to check-in.

Talk 1: Mapping Hong Kong's Financial Ecosystem - with Networks and LLMs

Abstract:

In this talk, we present the first study of the Public Register of Licensed Persons and Registered Institutions maintained by the Hong Kong Securities and Futures Commission (SFC) through the lens of complex network analysis. This dataset, spanning 21 years with daily granularity, provides a unique view of the evolving social network between licensed professionals and their affiliated firms in Hong Kong's financial sector. Leveraging large language models, we classify firms (e.g., asset managers, banks) and infer the likely nationality and gender of employees based on their names. This application enhances the dataset by adding rich demographic and organizational context, enabling more precise network analysis. Our preliminary findings reveal key structural features, offering new insights into the dynamics of Hong Kong's financial landscape. We release the structured dataset to enable further research, establishing a foundation for future studies that may inform recruitment strategies, policy-making, and risk management in the financial industry.

Short Bio:
Abdulla AlKetbi is a research specialist in finance, with over 10 years of experience. He is currently pursuing a PhD at Khalifa University, focusing on explainable AI in complex networks. Abdulla is also a CFA charterholder and holds a master's degree in Computational Data Science from Khalifa University. Abdulla also serves as the Chairman of the Operations Board at ADIA Lab.

Talk 2: TelecomGPT: Revolutionizing Telecommunications with Large Language Models

Abstract:

In this talk, we will explore TelecomGPT, a novel framework designed to adapt general-purpose Large Language Models (LLMs) for specialized applications within the telecommunications domain. Leveraging customized pre-training, instruction tuning, and alignment techniques, TelecomGPT addresses unique challenges in telecom, including domain-specific language understanding, code generation, and real-time network optimization. We will delve into the framework's architecture, key benchmarks, and its potential to enhance network management, reduce operational costs, and drive new AI-driven innovations in telecom. The discussion will highlight TelecomGPT's role in transforming the telecommunications landscape, offering unprecedented opportunities for industry advancement and efficiency.

Short Bio:
Mérouane Debbah is a researcher, educator and technology entrepreneur. He has founded several public and industrial research centers, start-ups and held executive positions in ICT companies. He is professor at Khalifa University in Abu Dhabi, United Arab Emirates and founding director of the Khalifa University 6G Research Center.

Learn more about Mérouane Debbah on Wikipedia: https://en.wikipedia.org/wiki/Mérouane_Debbah

Talk 3: Unveiling the Black Box: A Guide to Explainable and Interpretable ML

Abstract:

As machine learning models become more integral to decision-making processes, understanding their inner workings is crucial. This presentation delves into the concepts of explainable and interpretable ML, providing an overview of how complex “black box” models can be made more transparent. Drawing from model-agnostic techniques we explore approaches to ensure that ML systems are not only powerful but also understandable. We'll highlight methods like Partial Dependence Plots, Marginal Plots, ALE and SHAP values, all aimed at balancing model performance with clarity. The goal is to equip participants with practical tools and insights for applying interpretable machine learning in real-world contexts without sacrificing predictive accuracy. Based on Christoph Molnar’s comprehensive work on interpretable machine learning, this session provides both a theoretical foundation and practical examples for enhancing ML transparency.

Short Bio:
Kristof Juhasz is Quantitative Researcher in Abu Dhabi, specializing in signal extraction and predictive modeling for long-short trading strategies in equities. Previously fulfilling similar roles at Goldman Sachs and G-Research in London. With a Mathematics degree from the University of Cambridge, Kristof has expertise in machine learning, data science, and financial modelling.