Kickstart Your Career: GenAI & Telco-Cloud
Guest Speaker · ITI – Information Technology Institute
A career-focused session on building GenAI and Telco-Cloud skills for telecom roles, with practical guidance for graduates entering the industry.
Applied Data Scientist
I turn complex operational data into decisions that hold up outside the notebook.
Applied Data Scientist working where AI, data science, and telecommunications meet — network analytics, anomaly detection, forecasting, root-cause analysis, and the production engineering that keeps those systems running once they ship.
Companies & Institutions
Selected Work
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APPLIED AI · TELECOM
AI-Powered Hybrid RAG Platform for Telecom Optimization
A production-oriented Retrieval-Augmented Generation system combining hybrid retrieval, cross-encoder re-ranking, and grounded evaluation for telecom network intelligence.
View case studySuperset Production Deployment & Unified Analytics Platform
A production analytics platform built around Apache Superset to unify multi-database reporting and reduce custom dashboard development for engineering teams.
Featured Publications
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A Compact Microwave CSRR Resonator for Non-Invasive Glucose Sensing Enhanced by Machine Learning
License Plate Image Analysis Empowered by Generative Adversarial Neural Networks (GANs)
Multimodal Biosensing Platforms for Sensor-Computing Integration towards Intelligent Healthcare
Impact
7
Publications
2
Technical Talks
7
Companies & Institutions
6
Professional & Teaching Roles
Talks & Media
Introduction to Telco Cloud and AI-Driven Networks
Guest Speaker · ITI – Information Technology Institute
Suez Canal University Branch
An introductory session on cloud computing and AI in telecom networks, emerging roles in the field, and practical career guidance.
Principles
Start with the decision, not the model.
The problem, constraints, and action that follows should determine the technical approach—not the other way around.
Interrogate the data before trusting the metric.
Definitions, granularity, missingness, population bias, and aggregation can invalidate an otherwise correct analysis.
A model is not finished when the notebook works.
Reliability, scalability, monitoring, reproducibility, and integration are part of the solution.
Be precise about what the evidence supports.
I prefer an explainable limitation over a confident conclusion that the data cannot justify.
The strongest solutions cross disciplinary boundaries.
Domain knowledge, data engineering, machine learning, and operational context should reinforce each other rather than operate in isolation.
How I Approach Problems
Validate the question
Check the KPI, population, denominator, time window, and aggregation before trusting the result.
Understand the data
Understand how the data was generated, filtered, and aggregated before trusting patterns.
Choose the method
Then select the statistical, ML, or engineering approach that fits the problem.
The Journey
Read the Journey