Zegnet Yimer Muhammed
About Zegnet Yimer Muhammed
Zegnet Yimer Muhammed is a Generative AI Data Scientist currently working at Scale AI, with a background in Computational Physics and extensive experience in machine learning and data analysis. He has held positions at various universities and has developed advanced models and automated processes to enhance data quality and prediction accuracy.
Work at ScaleAI
Zegnet Yimer Muhammed currently serves as a Generative AI Data Scientist at Scale AI. He has been in this role since 2022. His responsibilities include utilizing advanced machine learning techniques to enhance data analysis and reporting. He has engineered and deployed machine learning pipelines for various applications, including loan risk prediction and sentiment analysis, across multiple cloud platforms.
Previous Experience at Hollins University
Before joining Scale AI, Zegnet worked as a Computational Physicist at Hollins University from 2022 to 2024. During his tenure, he contributed to various projects involving computational modeling and data analysis, leveraging his expertise in physics and data science.
Education and Expertise
Zegnet Yimer Muhammed holds a Doctor of Philosophy (PhD) in Computational Physics from the University of Arkansas, where he studied from 2016 to 2022. He also earned a Master of Science (MS) in Computational Physics from The University of Texas at El Paso in 2016. His academic background includes additional degrees in Applied and Engineering Physics from the Technical University of Munich and Sustainable Environment and Energy Systems from Middle East Technical University.
Technical Skills and Innovations
Zegnet has developed advanced deep learning models using TensorFlow and PyTorch, achieving a 20% improvement in prediction accuracy. He has also utilized the ChatGPT API for real-time data analysis, significantly accelerating decision-making processes. His work in automating data annotation with ChatGPT led to a 50% reduction in manual effort while improving data quality.
Research and Development Contributions
During his academic career, Zegnet worked as a Computational Physicist at the University of Arkansas from 2016 to 2022 and at The University of Texas at El Paso from 2015 to 2016. He focused on utilizing machine learning techniques for various applications, including enhancing sentiment analysis accuracy by 25% using BERT-based NLP models on Amazon Sagemaker Autopilot.