Ahmed Awadallah
Partner Research Manager
About
I lead multidisciplinary initiatives, with teams of researchers and engineers at the cutting edge of artificial intelligence. Our mission is to develop AI models and platforms that redefine the boundaries of AI in terms of capability, efficiency, control, and safety.
My current focus lies in the training, fine-tuning, and specialization of large language models, as well as streamlining the orchestration, automation, and optimization of multi-agent AI systems. Our objective is to meld groundbreaking research with practical application, ensuring our innovations address real-world challenges related to control, safety, and the balance between efficiency and capability.
In my earlier work, I explored model compression, few-shot learning, summarization, semantic parsing, and web search. I have co-authored over 100 peer-reviewed publications spanning Machine Learning, Natural Language Processing, and Information Retrieval, and I am a co-inventor on more than 50 patents. I frequently serve as a (senior) committee member, (senior) area chair, guest editor, and editorial board member at premier ML, NLP, and IR venues. My contributions to NLP and IR were recognized with the 2020 Karen Spärck Jones Award (opens in new tab).
Featured content
AI Frontiers: The future of scale with Ahmed Awadallah and Ashley Llorens
What’s the driving force behind AI’s recent, rapid progress? Research manager Ahmed Awadallah shares his insights on this, the two-stage approach to training large-scale models, and the need for better model evaluation in this episode of the #MSRPodcast.
AutoGen: Enabling next-generation large language model applications
Microsoft researchers are introducing AutoGen, a framework for simplifying the orchestration, optimization, and automation of workflows for large language model (LLM) applications—potentially transforming and extending what LLMs can do.
Orca: Progressive Learning from Complex Explanation Traces of GPT-4
Recent research has focused on enhancing the capability of smaller models through imitation learning, drawing on the outputs generated by large foundation models (LFMs). A number of issues impact the quality of these models, ranging from limited imitation signals from shallow LFM outputs;…
AI Explainer: Foundation models and the next era of AI
The release of OpenAI’s GPT-4 is a significant advance that builds on several years of rapid innovation in foundation models. GPT-4, which was trained on the Microsoft Azure AI supercomputer, has exhibited significantly improved abilities across many dimensions—from summarizing lengthy…
Microsoft Research Summit 2022: What’s Next for Technology and Humanity?
Today, we are experiencing waves of breakthroughs in computing that are transforming just about every aspect of our lives. Artificial intelligence is changing the way we develop and create. Human language technologies are revolutionizing the workflows of healthcare professionals. Deep…