Joy Gabriel
Joy is a data scientist, software engineer, product manager, project manager, tech leader, consultant, and innovator who has delivered many creative and engaging solutions in massive-scale technologies. As a technology professional, his experience demonstrated in various industries such as technology, transportation, education, e-commerce, finance, telco, macroeconomics, health, etc. He also actively participates and involves in various technology communities and events.
Has 2x experiences as a technical advisor (Equivalent to Echelon II) to the ministerial and head of the ministerial-level agency. Has 3x led digital transformation in government and State-Owned Enterprises (BUMN). Also, he was rewarded as an Alibaba Cloud Most Valuable Professional in Big Data starting from 2019.
Joy is known for his work during the COVID-19 pandemic in Indonesia. He developed the Sistem Informasi Satu Pintu, which is a combination of several important information systems in handling COVID-19, such as the All Record TC-19, RS Online, SiLacak, and Aplikasi Satu Data Kesehatan. In addition, he developed Tele Sehat Desa, a system that is an important infrastructure for the expansion of village community health services, and Tele Detection, a digital tracing system in the context of handling COVID-19 in Indonesia.
Session
In an era where energy volatility meets climate urgency, Python is quietly powering some of the most critical infrastructure decisions at the edge. This talk dives into how Python can be used to build real-time AI pipelines for smart energy forecasting, using edge devices and microservices to process data from IoT sensors, weather APIs, and grid telemetry.
We’ll walk through a cutting-edge case study: deploying a Python-based forecasting system for microgrid load balancing in a tropical urban environment. The system leverages probabilistic modeling (via PyMC), streaming data (via Apache Kafka and Faust), and lightweight deployment (via FastAPI and Docker) to deliver sub-second predictions that inform automated switching, battery storage decisions, and demand response.
This isn’t just about code, it’s about architecture, trade-offs, and the human impact of getting it right. You’ll leave with a blueprint for building resilient, interpretable, and scalable Python systems that don’t just analyze data, they act on it.