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Raghu Meka

HOME RESEARCH TEACHING
I am an Associate Professor in the CS department at UCLA. I am broadly interested in complexity theory, learning and probability theory. See my publications and talks for more details. I am part of UCLA's theory group.
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Contact

raghum@cs.ucla.***, Engineering VI 463.

Short bio

I got my PhD at UT Austin under the (wise) guidance of David Zuckerman in 2011. After that I spent two wonderful years in Princeton as a postdoctoral fellow at the Institute for Advanced Study with Avi Wigderson and at DIMACS, Rutgers. After that I spent an enjoyable year as a researcher at Microsoft Research, Silicon Valley Lab. I had a great time on sabbatical visiting the MIT math department from 2018-2020.

CV
Students

I am fortunate to be working with my current students Ashutosh Kumar, Hadley Black, and Yimeng Wang.

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Algorithmic Machine Learning Spring 2025

In this course we will look at a handful of ubiquitous algorithms in machine learning. We will cover several classical tools in machine learning but more emphasis will be given to recent advances and developing efficient and provable algorithms for learning tasks. A tentative syllabus/schedule can be found below; the topics may change based on student interests as well. You can also check last year's course notes for a more details about what's to come.

Mar 14, 2025
Teaching

Here are the courses I have taught or will in the future.

Feb 28, 2025
Research

My main interests are in complexity theory, learning theory, algorithm design. More generally, I like probability and combinatorics related things. The best resource for up to date publications really is to look up my profile on Google scholar.

Aug 27, 2024
Algorithmic Machine Learning

In this course we will look at a handful of ubiquitous algorithms in machine learning. We will cover several classical tools in machine learning but more emphasis will be given to recent advances and developing efficient and provable algorithms for learning tasks. A tentative syllabus/schedule can be found below; the topics may change based on student interests as well. You can also check last year's course notes for a more details about what's to come.

May 20, 2024
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