# Raghu Meka
| [HOME](http://www.raghumeka.org) | [RESEARCH](https://raghumeka.github.io/research.html) | [TEACHING](https://raghumeka.github.io/courses.html) |
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| 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)|
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:::spoiler Contact
raghum@cs.ucla.***, Engineering VI 463.
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:::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.
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:::spoiler [CV](https://www.dropbox.com/s/tqcemd42qj6mb86/cv.pdf?dl=0)
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:::spoiler Students
I am fortunate to be working with my current students [Ashutosh Kumar](https://www.ashutoshk.com/) (co-advised with Rafi Ostrovsky) and [Hadley Black](http://web.cs.ucla.edu/~hablack).
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HOME RESEARCH TEACHING Introduction 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. Coursework There will be four assignments for the course that will cover 70% of the credit and a final that will cover 25% of the credit; 5% is for class participation (live-class participation and/or extensive contributions to online discussions). Each assignment will have both written as well as programming components. We'll predominantly use gradescope for the assignments. The final will be as per university schedule. You could potentially discuss how to solve problems with others specifically (a better option would be to just ask questions openly on edStem) but see course policies below and make sure you do not violate them.

2/1/2023Raghu Meka: CS289: Great Theory Hits of 21st Century Winter 2023 HOME RESEARCH TEACHING Introduction We will cover a handful of groundbreaking results across the entire gamut of theoretical computer science discovered in the 21st century! A tentative list of topics can be found below; the topics may change based on student interest as well. Prerequisites: You need background in linear algebra, probability theory, and algorithms (all at a typical undergraduate upper-division level) to make the class fun and interesting for you as well as for me. Check your prerequisites by testing yourself with these problems assignment. You can also check last years' notes to get an idea.

1/10/2023CS181: Introduction to Theoretical Computer Science Fall 2022 HOME RESEARCH TEACHING Introduction The official title for the course is Formal Languages and Automata Theory. This offering will be geared towards introducing you to the beautiful and profundly impactful world of Theoretical Computer Science (TOC). The following three questions encompass our main learning goals: :::spoiler 1. What is computation?

11/29/2022Raghu Meka HOME RESEARCH TEACHING Here are the courses I have taught or will in the future. CS181: Introduction to Formal Languages and Automata Theory Fall 2022, Fall 2021, Fall 2020

11/12/2022
Published on ** HackMD**