Andreea Anghel

Title

Research Staff Member
Andreea Anghel

Bio

I am a staff research scientist at IBM Research Europe in the Data and AI Systems group, where I am actively involved in building ML and Generative AI-based solutions for IBM's AI products. I am currently leading the design and development of a self-assisted LLM-based agent for the Technology Lifecycle Services organization. I am also responsible for extending IBM's ML library for Z systems with new features as part of various client engagements.

Over the years I have had the opportunity to work on multiple cutting-edge IBM projects and technologies, such as: performance evaluation of LLMs for the watsonX.ai platform, ML modeling for anti-money laundering use cases, high-performance inference serving solutions for ML models, ML libraries for fast and large-scale analytics, accelerated ML-assisted tumor detection in medical images, HPC system modeling methodologies in support of the Square Kilometre Array (SKA), one of the largest radio telescope in the world. 

In 2023 I had the privilege to be selected in the IBM Tech program which recognizes top IBM technical contributors that play a key role in driving innovation, transforming culture and accelerating growth. I have also received 5 plateau invention awards, 5 outstanding technical achievement awards, as well as 2 corporate and special technical awards. Moreover, in 2017, I was nominated for outstanding doctoral theses at ETH Zurich. I have co-authored more than 30 publications and 20 patents.

I hold a Ph.D. degree in Electrical Engineering and Information Technology from ETH Zurich and a M.Sc. degree in Communication Systems from EPF Lausanne. My current main research interests include Generative AI, ML algorithms and high-performance analytics systems.

Publications

Patents

Projects

Snap machine learning

A library that provides high-speed training of popular machine learning models on modern CPU/GPU computing systems.
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AI for IT Infrastructure

AI to improve infrastructure efficiency and user productivity, and to extract valuable insights from business data.

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