# Raghu Meka | [HOME](http://www.raghumeka.org) | [RESEARCH](https://raghumeka.github.io/research.html) | [TEACHING](https://raghumeka.github.io/courses.html) | | -------- | -------- | -------- | --- | 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](http://web.cs.ucla.edu/~theory). | ![](https://i.imgur.com/thwetyX.jpg =1000x)| | -------- | -------- | :::spoiler Contact raghum@cs.ucla.***, Engineering VI 463. ::: :::spoiler 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. ::: :::spoiler [CV](https://ucla.box.com/s/1w93q0jyxnfhzk8dmceh855y9ybcgjs2) ::: :::spoiler Students I am fortunate to be working with my current students [Ashutosh Kumar](https://www.ashutoshk.com/), [Hadley Black](http://web.cs.ucla.edu/~hablack), and Yimeng Wang. :::
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Raghu Meka
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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.

4/24/2024
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.

2/29/2024
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.

2/23/2024
Teaching

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

2/23/2024
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