Computer Science / CMUQatar Teaching Track Candidate
Location:
In Person and Virtual - ET
-
Reddy Conference Room, Gates Hillman 4405 and Zoom
Speaker:
OMAR KHATTAB
,
Ph.D. Candidate, Computer Science Department, Stanford University
https://omarkhattab.com/
Parallel Thinking and Modularity at Very Large Scale in Information Retrieval Applications
TEACHING DEMO CMU 15-210 places strong emphasis on parallel thinking and on modularity (e.g., through clearly delineating problems vs. algorithms). In this teaching demo, I will demonstrate how achieving parallelism and modularity is key to designing practical algorithms and systems at very large scales. I will draw on applications from Information Retrieval (IR), the field concerned with building search engines. Through these applications, I will also highlight how the space of algorithms can be particularly rich when the problem statement involves optimizing an objective (like in machine learning tasks) or a service-level agreement (SLA), rather than delivering a deterministic output.
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Omar Khattab is a fifth-year CS Ph.D. candidate at Stanford NLP and an Apple Scholar in AI/ML. He is interested in Natural Language Processing (NLP) at scale, where systems capable of retrieval and reasoning can leverage massive text corpora to craft knowledgeable responses efficiently and transparently. Omar is the author of the ColBERT retrieval model, which has helped shape the modern landscape of neural information retrieval (IR), and author of several early multi-stage retrieval-based LM systems like ColBERT-QA and Baleen. Omar's work has been funded by an Apple Fellowship and Eltoukhy Family Graduate Fellowship, and his research has received industry grants from Azure, IBM, Oracle, and Virtusa. His recent work includes the DSPy programming model for building and optimizing reliable language model systems—by bringing structure, composition, and new forms of optimization into the space of prompting, finetuning, and chaining retrieval and language models. Much of Omar's work forms the basis of influential open-source projects, and his lines of work on ColBERT and DSPy have sparked applications at dozens of academic research labs and leading tech companies, including at Google, Meta, Amazon, IBM, VMware, Baidu, Huawei, AliExpress, and many others. Omar obtained his B.S. in Computer Science from CMU-Qatar. In Person and Zoom Participation. See announcement.