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Who am I?

Hello! My name is Ehab A. AlBadawy. I hold a PhD in Electrical and Computer Engineering from the University at Albany, SUNY and a B.Eng. in Computer Engineering from Ain Shams University in Egypt.

What I Do

I’m currently an Applied Research Scientist at Meta, where I work on Text-to-Speech (TTS) technologies, particularly focusing on deep learning models to improve TTS quality and performance.

Academic Research

Throughout my doctoral studies at the University at Albany, SUNY, I had the privilege of collaborating with esteemed professors. My initial research, guided by Prof. Yelin Kim, focused on affective computing and automatic emotion recognition using deep learning techniques. When Prof. Kim transitioned to Amazon, my research shifted under the mentorship of Prof. Ming-Ching Chang, with co-advisement from Prof. Siwei Lyu. During this period, I delved into speech synthesis and AI-generated speech detection. I also had the opportunity to collaborate with Prof. Hany Farid from UC Berkeley on a specialized project in media forensics.

I also had the privilege of being a Visiting Research Scholar at Duke University’s RAILabs, where I worked with Dr. Maciej A. Mazurowski. My research there focused on leveraging deep learning for medical applications, particularly in brain tumor and breast cancer segmentation.

What’s This Blog About?

This is my personal space on the internet where I explore a range of Machine Learning topics. Here’s what you can anticipate:

  • Small Projects: From building your first neural network to more advanced projects.
  • Deep Dives: In-depth analysis on certain Machine Learning topics.
  • Tutorials: Step-by-step guides to help you grasp complex algorithms.
  • Case Studies: Practical applications of Machine Learning in different industries.


Feel free to get in touch for any collaborations, queries, or just to say hi!


The thoughts and opinions expressed on this blog are solely my own and do not reflect the views of my employer or any other organizations with which I am associated.

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