# A Self-Learning, Modern Computer Science Curriculum

## 1 Introduction

"The great idea of constructive type theory is that there is no distinction between programs and proofs… Theorems are types (specifications), and proofs are programs" (Robert Harper)

This is a collection of modern resources on various undergrad level computer science topics, for someone with an interest in theory. Use LibGen if you can't buy these books. You don't have to do everything here, just the topics of interest to you. If for any reason you want a condensed version, watch the Theory of Computation lectures to see what interests you in the field. Almost all the material in each resource is self contained so will cover necessary background which if you don't have see the preliminaries.

### 1.1 How to Learn

On Isaac Newton's iteration method to self-learn geometry:

"He bought Descartes' Geometry and read it by himself .. when he was got over 2 or 3 pages he could understand no farther, than he began again and got 3 or 4 pages father till he came to another difficult place, than he began again and advanced farther and continued so doing till he made himself master of the whole without having the least light or instruction from anybody" (King's Cam.,Keynes MS 130.10,fol. 2/v/)

Cal Newport has some anecdotes on how he was able to get the best grade in his Discrete Mathematics class, and the rest of the site is full of advice on studying, how to schedule yourself and deliberate practice. Further anecdotes exist on the importance of deep work and deliberate practice. There is a free course Learning How to Learn on coursera.org which suggest the same techniques plus Cornell style note taking and how to read a research paper.

## 2 Preliminaries

### 2.1 Intro to Computation

The book *How to Design Programs (HtDP)* has proven success teaching an introduction to programming while remaining rigorous. One of it's authors is now running a public school curriculum project called Bootstrap and gave a talk why this style of teaching programming has the most success, and how it's curriculum transfers to other fields such as
how the students writing games are actually writing differential equations, as the behavior of a game over time is the integral. If you really want to understand programming you should start with this book. If you liked HtDP, it's sequel is PAPL (original Racket version also exists, but Pyret and Racket are essentially the same languages just the author was tired of parenthesis complaints from other professors).
A very rigorous introduction exists as the classic MIT book *Structural Interpretation of Computer Programs* (SICP) but the more programming you do before reading SICP, the more you get out of it.

#### 2.1.1 Some Additional Optional Intros

It's possible to do (or at least model) every course in this list using Common Lisp, such as building your own domain-specific machine code while completing 15-213 *Computer Systems*, or hacking together purely functional data structures, or writing your own pattern matcher, or simulating a turing machine, ect.
Of course you will do the courses using their recommended languages and tools, but in addition to this, building your own toy prototypes for the topics here in Lisp can help you learn this material as you discover it for yourself with active learning.
Another shill for Common Lisp as a good language choice is it hasn't changed much in decades, so an old library you found that hasn't been updated in 15 years will still work. There is also the joy of programming Lisp as a system.

- (Book) Common Lisp - A Gentle Introduction to Symbolic Computation
- This book assumes no knowledge of programming and starts from pen and paper to writing complete programs.
- The Road to Common Lisp is another introduction with 2017 Common Lisp development recommendations
- A good sequel is PAIP by Norvig and On Lisp by PG
- For example the straight-forward description of OOP compared to other confusing large books attempting to teach Object-Oriented Programming

The Little Schemer series books are a Q&A format/Socratic method for learning the basics of computation (read the Preface of each book). You can do the Little Schemer with pencil and paper in a weekend though the authors recommend at least 3 sittings. The first in the series is *The Little Schemer* which teaches you to think recursively.
The second is the *Seasoned Schemer* covering higher-order functions, which will help understand calculus. The differential operator is a higher-order function: given a function on the real line it yields its first derivative as a function on the real line.

- (Book) The Little MLer - Matthias Felleisen, Daniel P. Friedman
- Self contained but will make much more sense if you've already done the first book,
*The Little Schemer*covering cons, lists and recursion. - Teaches you to think recursively, and provides an introduction to the principles of types, computation, and program construction.
- Applies to any functional language, almost all the exercises can be done in OCaml or another language with a small amount of syntactic adjustment.

- Self contained but will make much more sense if you've already done the first book,

Students with direct entry to this CMU course already have a background in programming. Good information here on style, design and efficiency, with an introduction to complexity otherwise you would learn all of this in HtDP.

- (Full Course) 15-112 Fundamentals of Programming and Computer Science
- Click on each topic, there are short youtube videos for lectures.
- The homework assignments (Python) are self-correcting, with assert statements and a linter doing static analysis
- Some notes on editors and version control (git), how to write a good commit comment.

### 2.2 Elementary Math

"So I went to Case, and the Dean of Case says to us, it’s a all men’s school, “Men, look at, look to the person on your left, and the person on your right. One of you isn’t going to be here next year; one of you is going to fail.” So I get to Case, and again I’m studying all the time, working really hard on my classes, and so for that I had to be kind of a machine. In high school, our math program wasn’t much, and I had never heard of calculus until I got to college. But the calculus book that we had was (in college) was great, and in the back of the book there were supplementary problems that weren’t assigned by the teacher. So this was a famous calculus text by a man named George Thomas (second edition), and I mention it especially because it was one of the first books published by Addison-Wesley, and I loved this calculus book so much that later I chose Addison-Wesley to be the publisher of my own book. Our teacher would assign, say, the even numbered problems, or something like that (from the book). I would also do the odd numbered problems. In the back of Thomas’s book he had supplementary problems, the teacher didn’t assign the supplementary problems; I worked the supplementary problems. I was scared I wouldn’t learn calculus, so I worked hard on it, and it turned out that of course it took me longer to solve all these problems than the kids who were only working on what was assigned, at first. But after a year, I could do all of those problems in the same time as my classmates were doing the assigned problems, and after that

I could just coast in mathematics, because I’d learned how to solve problems" (Don Knuth )

Start with Po-Shen Loh's new site Expii.com which presents topics in a brief intuitive manner, then feeds you problems in that domain and makes decisions based on your answers if you need more practice problems. The Advanced Topics have all kinds of great tutorials on CS Theory as well as Number Theory. While you go through Expii, do a survey of The Better Explained Math and Calculus series books, which explain basic math such as the natural logarithm. Gilbert Strang also has a lecture series Highlights of Calculus for more intuition and a free book.

#### 2.2.1 Calculus With Theory

Optional, though doing the exercises of Apostol's *Calculus I* however is a great way to build mathematical maturity.
This alternative Calculus w/Theory lecture series only uses a simple algebraic framework, eliminating limits/infinity. Linear algebraic ideas and the Discrete Calculus are both highly relevant to the material here. There is also a full course available.

- MIT 18-014 Calculus w/Theory
- Uses Apostol's
*Calculus, Volume 1: One-Variable Calculus, with An Introduction to Linear Algebra*(buy the international version on Amazon for $20) - Read Common Errors in Undergrad Math like undistributed cancellations, sign errors, confusion about notation ect.

- Uses Apostol's

## 3 Discrete Mathematics

Resources to learn undergrad Discrete Mathematics.

### 3.1 Introduction to Pure Mathematics (How to do Proofs)

- (Book) An Infinite Descent into Pure Mathematics
- Collection of excellent lecture notes used in CMU's 15-151/21-127 Concepts of Mathematics class
- Curriculum based on findings in evidenced based teaching
- Author's motivations for not including answers are explained here: "people learn more when they discover something for themselves than they do if someone tells them about it."
- You may also find as you write your questions on math.stackexchange, that half way through writing you discover the answer yourself

### 3.2 Discrete Math with Standard ML

The merging of proof and program is what makes this a great book. If you can't get this book *Discrete Mathematics*, by L. Lovász, J. Pelikán, and K. Vesztergombi is normally used or just skip to 15-251 *Great Theoretical Ideas in CS*.
If you can finish this book you've essentially done most of 15-150 *Principles of Functional Programming* (such as proving programs correct) and most of 15-215 *Great Theoretical Ideas in Computer Science*.

- (Book/Lectures) Discrete Mathematics and Functional Programming - Thomas VanDrunen
- Lectures exist on the author's homepage, this book is used for a one semester university course with additional elective chapters in Graph Theory, Complexity Theory, Automata, ect.
- List of Common Errors in Undergrad Math like undistributed cancellations, sign errors, confusion about notation ect. if you missed it from the prelims section of this guide
*The Art of Computer Programming Vol 1: Fundamental Algorithms*by Donald E. Knuth also offers an excellent but terse introduction to Discrete Math

### 3.3 Great Theoretical Ideas in Computer Science

This is a crash course in multiple topics such as Probability, Linear Algebra, Modular Arithmetic, Polynomials, Cryptography and Complexity Theory. 15-251 assumes students have completed *An Infinite Descent into Pure Mathematics* though it is self contained. "This course teaches the mathematical underpinnings of computation and explores some of the central results and questions regarding the nature of computation."

- (Full Course) 15-251 Great Theoretical Ideas in Computer Science
- Recorded lectures if you click each topic, backup YouTube lectures and course notes
- Complete lecture notes on discrete probability
- Some more material on the most recent course schedule such as induction pitfalls
- (Optional) MIT 6.042j
*Mathematics for Computer Science*recorded lectures and course notes

- (Optional) MIT 6.042j

## 4 Algebra

It is possible in a functional language like SML to do algebra with types, proving two types are isomorphic with the desired properties of reflexivity, symmetry, and transitivity. It's also possible to abstract Lists and Trees into polynomials, as every polynomial looks like a sum of terms.
As you will learn in 15-150 *Principles of Functional Programming* "most functional datastructures have constant time access near the outer layer of their structure, ie: the head of a list or the root of a tree. However, access at some random point inside the structure is typically linear since looking at some element of a list is
linear in the length of the list, and looking at some element of a tree is linear in the depth of the tree. Datatype derivatives allow constant time access to the entire structure."

### 4.1 Linear Algebra

- (Full Course) CS053 - Coding the Matrix
- Covers interesting applications like what was shown in
*Great Theoretical Ideas in Computer Science*when you learned how a parity bit works. - No formal prerequisites except assumes you know how to do basic proofs

- Covers interesting applications like what was shown in

- (Book) Linear Algebra: An Introduction to Abstract Mathematics - Robert J. Valenza
- Similar to Valenza's text is this free book
*Linear Algebra - As an Introduction to Abstract Mathematics*which you can use if you can't find Valenza's text - These YouTube presentations here help with understanding the topics on a geometric level. Watch the Essence of Linear Algebra to see what this means
- Other Resource: Sheldon Axler has recorded lectures for his book
*Linear Algebra Done Right*aka Down With Determinants! - Yet Another Resource: NJ Wildberger has a Linear Algebraic Geometry series of lectures

- Other Resource: Sheldon Axler has recorded lectures for his book

- Similar to Valenza's text is this free book

### 4.2 Abstract Algebra

- (Full Course) Math-371 Abstract Algebra
- Has recorded lectures which are essential as you can quickly get lost in the texts at this level of abstraction
- Uses readings from three texts
- There is also this Harvard course with excellent recorded lectures and uses readings from
*Algebra - M. Artin*(blue book) as a great compliment to Math-371 lectures

- There is also this Harvard course with excellent recorded lectures and uses readings from
- The history of Abstract Algebra by Prof Lee Lady

## 5 Principles of Functional Programming

The "functional" part in functional programming refers to mathematical functions, meaning functions where each input has a single output. The 2012 version by Dan Licata has the best lecture notes, optionally combine with most recent course notes. Robert Harper keeps a blog and a follow up post on the success of teaching this course. SML is used because it's syntax and module system is simple, much like Scheme's easy syntax was once preferred for introductory courses so the complexity of the language gets out of the way and you learn the fundamentals. This course introduces you to programming with types, proving properties of programs, writing programs for parallel evaluation, and you'll find it surprisingly very intuitive making it easy to grok large programs.

- (Lecture Notes) 15-150 Principles of Functional Programming
- "PSML Chapters" are assigned reading from this book Programming in Standard ML an excellent book.
- Can pair these notes with the recorded lectures from Unit 1 through 4 (specific to SML) at CSE341 (UW)
- Do the labs and homework plus any exercises you find in the PSML book. Try the CSE341 homework
- Note emacs setup also here (if you use emacs)
- Read this post about what recursion is if you struggle with it then complete The Little MLer or
*The Little Schemer*book

## 6 Computer Systems

"[Computer science] is not really about computers - and it's not about computers in the same sense that physics is not really about particle accelerators, and biology is not about microscopes and Petri dishes…and geometry isn't really about using surveying instruments. Now the reason that we think computer science is about computers is pretty much the same reason that the Egyptians thought geometry was about surveying instruments: when some field is just getting started and you don't really understand it very well, it's very easy to confuse the essence of what you're doing with the tools that you use." (Hal Abelson)

This covers computer architecture from a programmer's perspective, such as how to write cache friendly code, and other optimizations for the x86-64 arch. You learn how to manually write loops in assembly
and see how recursion works at a lower abstraction. You learn machine code instructions, how compilers work, return oriented programming (ROP) to bypass stack protections, the memory hierarchy, and networks. You could read K&R's
*The C Programming Language* for a brief intro, though this course will explain C as you go anyway and fully covers pointers at the assembly language level making it self contained.

- (Full Course) 15-213 Intro to Computer Systems (CMU)
- Recorded lectures here
- Get the CS:APP book, 3rd version used or the global version (the global version has a lot of errata, try to get the reg version with the memory mountain graph on the cover)
- The labs (Data Lab, Bomb Lab ect) are on the book website

- Read this post on Boolean Blindness
- You can also do Unix programming in Standard ML(pdf) or OCaml
- Try the embedded security CTF here after you finish the Attack Lab
- (Optional) A series of posts on writing an OS in Rust lang. Programming Rust is also an excellent systems book

## 7 Database Systems

Interesting post on the future of database systems by Andy Pavlo.

- (Full Course) 15-445 Database Systems (CMU)
- Recorded lectures on youtube
- Uses readings from this book, assumes you have taken 15-213 Intro to Computer Systems and know some basic set theory
- (Optional) An advanced class w/recorded lectures on building your own dbms, that uses autonomous dbms Peloton.

## 8 Practical Data Science

- (Full Course) 15-388 Practical Data Science
- Provides a practical introduction to the “full stack” of data science analysis: data collection and processing, data visualization and presentation, statistical model building using machine learning, and big data techniques for scaling these methods.

## 9 Meta-Linguistic Abstraction

A classic introductory computer science book on thinking about the big picture of programs with abstraction: finding general patterns from specific problems and building programs based on these patterns. An applied example of this is the package manager GNU Guix and distro GuixSD, which is a GNU implementation of the NixOS functional software deployment model. Package builds, including entire system builds, are declared in one text file. The resulting software deployment is functional: build inputs go in such as compilers, customizations, environments ect, and a reproducible, immutable build comes out with a hashed identifier meaning you can do roll backs to previous successful builds. A recent GUixSD feature gexp (g-expressions) is a good example what can be achieved through syntactic extensions of the Scheme language.

You are more likely to benefit from this book after having some programming experience, but no matter what level of programmer you are you'll still benefit from SICP.

- (Full Course) 6.037 Structure and Interpretation of Computer Programs
- Recorded lectures from the first edition of SICP here
- Read the excellent forward from SICP on what programming is
- There is a unit test library for SICP as well as an online tutorial
- SICP in TeXinfo so you can read it in emacs
- Optional read: The Art of the Propagator for further adventures in symbolic programming
- If you like Lisp, see Norvig's old AI book for a really great programming craft text

## 10 Programming Language Theory

Read these reasons for studying programming languages.

- (Book) Practical Foundations for Programming Languages - Robert Harper
- Recorded lectures to accompany the book Programming Languages Background — Robert Harper and Dan Licata
- More info on Judgements (logic) here and a series of recorded lectures here by Frank Pfenning on Basic Proof Theory but all this is self contained in the PFPL book
- Read these recitations and try the assignments here

## 11 Isolating Software Failure, Proving Safety and Testing

How to verify software, and strategies of programming that minimize catastrophe during failure. The Little Prover is a good introduction in determining facts about computer programs. Prof Rob Simmons can be hired as a tutor to teach you Coq, the curriculum follows the Software Foundations book series below.

- (Lecture Notes) 15-316 Software Foundations of Security and Privacy
- Read about tests, strategies to safe program design (in OCaml), and proving safety
- Clone this repository to get the full course lecture notes w/homework assignments: github.com/jeanqasaur/cmu-15316-spring17.git

- (Book) Verified Functional Algorithms
- Some recorded lectures by Andrew Appel here
- Part 3 of the Deep Specifications interactive book series by Andrew Appel, learn by doing
- Assumes you have read these chapters of Software Foundations Part I: Preface, Basics, Induction, Lists, Poly, Tactics, Logic, IndProp, Maps, (ProofObjects), (IndPrinciples)

### 11.1 Physical Systems Software Security

- (Full Course) 15-424 Foundations of Cyber-Physical Systems
- Course (with recorded lectures) if you're interested in programming drones/space shuttles/robots/cars and other things that cannot fail from avoidable errors.
- Self contained, will teach you differential eq but assumes you already have some calculus background.

## 12 Algorithms

### 12.1 Introduction to Parallel and Sequential Algorithms

Unlike a traditional introduction to algorithms and data structures, this puts an emphasis on thinking about how algorithms can do multiple things at once instead of one at a time and ways to design to exploit these properties. It uses a language based analysis model to estimate complexity for abstractions such as garbage collection. Most of the self learning and discovery is done through the labs, try them.

### 12.2 Advanced Algorithms (Purely Functional Data Structures)

The typical text for a senior undergrad or graduate course on algorithm analysis is *The Design and Analysis of Algorithms* by Kozen (1992). This is an excellent book to study with difficult problems well presented and clearly analyzed by Kozen. The Okasaki book (1999), *Purely Functional Data Structures* gives examples in Standard ML and Haskell.

- (Full Course) CS224 Advanced Algorithms
- Pair relevant Harvard Advanced Algorithms analysis lectures with Purely Functional Data Structures book by Chris Okasaki (the full book, not the thesis .pdf it's missing a lot of material)
- Splay trees/heaps/binomial heaps/hashing ect are all in the Okasaki book as well as CS224

- Alternatively this free 2017 Functional Data Structures book from University of Waterloo with exercises done in OCaml.

- Pair relevant Harvard Advanced Algorithms analysis lectures with Purely Functional Data Structures book by Chris Okasaki (the full book, not the thesis .pdf it's missing a lot of material)

## 13 Complexity Theory

### 13.1 Undergraduate Complexity Theory

Expands on the lectures in 15-251.

### 13.2 Graduate Complexity Theory

You may want to try solving some of the problems in this domain.

### 13.3 Useful Math for Theoretical CS

These scribed lecture notes give you a diverse background in math useful for theoretical CS, from the excellent book The Nature of Computation such as these slides from *A Theorist's ToolKit* which describe how to find research, how to write math in LaTeX, how to give a talk, resources on math.overflow ect.

## 14 Introduction to Quantum Computing

This is a graduate introduction to quantum computation/information theory, from the perspective of theoretical computer science. You'll learn that designing algorithms for a quantum computer is within the realm of your expertise despite not knowing anything about Physics.

- (Full Course) 15-859BB: Quantum Computation
- Recorded lectures on YouTube.
- The prereqs are an undergrad background in complexity theory (15-251 or 15-455), linear algebra, and discrete probability.

## 15 Jobs

This service matches your skills to people who want to pay you. Jane Street Capital is a finance tech company that hires functional programmers worldwide, you may want to apply there. There are numerous opportunities to apprentice as a security researcher or get paid to prove business logic programs like 'smart contracts'. Check your local university employment listings as well, often students do not take these jobs as they are off chasing that startup stock options payday or doing industry internships, plenty of opportunities to work with post-doc researchers writing algorithms or analyzing data. For web programming there exists free programming notes for software like NodeJS, which you can write software for in OCaml by compiling it into JS with bucklescript or compile to web assembly.

## 16 Graduate Research in Type Theory

Read these slides from *A Theorist's ToolKit* on how to find research, how to write math in LaTeX, how to give a talk, where to ask on stackexchange ect.

### 16.1 Basic Proof Theory

- Start with the lectures on
*Basic Proof Theory*by Frank Pfenning- These 15-317 Constructive Logic notes go with the seminar, there is another set here
- Read Martin-Löf's lecture notes

### 16.2 Intro to Category Theory

- (Book) Category Theory - Steve Awodey
- The most accessible introduction see errata, and recorded lectures by Steve Awodey here(HQ mp4) or here(YouTube)
- There is also this seminar
*Basic Category Theory: Semantics of Proof Theory*by Ed Morehouse- Also see this post here explaining Category Theory and as types in SML

### 16.3 Type Theory Foundations

- Read Chapters 1-12, 23 and 24 from TAPL
- Watch seminar on
*Type Theory Foundations*by Robert Harper- Pair with Programming in Martin-Löf's Type Theory and the rest of the suggested reading

### 16.4 Higher Dimensional Type Theory

Start with this talk *A Functional Programmer's Guide to Homotopy Type Theory* with intro to Cubical Type Theory

- (Full Course) Homotopy Type Theory
- Self-contained grad seminar with recorded lectures
- Assigns readings from self-contained HoTT book https://homotopytypetheory.org/book/
- Try the homework, see some of the open problems in HoTT research

- Self-contained grad seminar with recorded lectures

### 16.5 Further Research

- Use the Sci-Hub proxy to get access to journalslike Functional Programming or search Google Scholar for topics that interest you and click "Cited by"
- Follow the research on Robert Harper's CMU page or see CMU's Principles of Programming Group for more graduate course offerings
- See the POPL 2018 tutorial on Computational Higher Type Theory

## 17 Graduate Research in Machine Learning/AI

Read these slides from *A Theorist's ToolKit* on how to find research, how to write math in LaTeX, how to give a talk, where to ask on stackexchange ect.

### 17.1 Graduate Introduction to General AI

These notes here cover more topics, such as Computer Vision/NLP/AGI and follows *AI: A Modern Approach* book by Norvig. The 15-780 course is specific to some topics in AI providing them 2-3 lectures each.

- (Full Course) 15-780 Graduate Introduction to AI
- Recorded lectures here and here
- No formal pre-requisites except grad level standing so familiar with some undergrad calculus and linear algebra/probability
- MIT's Artificial General Intelligence free class explores possible research paths for creating AGI
- Sussman had a great 2017 grad course reviewing older paper with unsolved problems or deep ideas, like his own
*Art of the Propagator*paper and implementation - AI readings to explain intelligence from a computational point of view

### 17.2 Math Background for ML

### 17.3 Statistics Theory

- (Full Course) 36-705 Intermediate Statistics
- Recorded lectures and reading cover the fundamentals of theoretical statistics
- The probability prereqs can be found here or from any undergrad text on probability
- Additional lecture notes on the intersection of stats theory and ML
- An advanced statistics theory book relevant to ML:
*High-dimensional statistics: A non-asymptotic viewpoint*by M. Wainwright (no preprint draft, though some copies are floating around if you search)

- An advanced statistics theory book relevant to ML:

### 17.4 Graduate Introduction to ML

- (Full Course) 10-601 Masters Introduction to ML
- Recorded lectures and recitations
- Self contained, assumes you are grad level standing so have familiarity with basic probability and algebra
- Doesn't cover as much theory as 10-701/715

### 17.5 Advanced Introduction to ML

- (Full Course) 10-715 Advanced Introduction to ML

### 17.6 Convex Optimization

### 17.7 Deep Learning

If you're interested in parallel GPU programming for training see these lectures and notes.

### 17.8 Algorithms for Big Data

- (Full Course) CS229r Algorithms for Big Data
- A grad level course in designing/analyzing algorithms for massive data

### 17.9 Further Research

- The Journal of Machine Learning Research and other AI journals you can access with Sci-Hub
- Open challenges in ML
- Algorithmia.com an open marketplace for AI algorithms

## 18 Graduate Research Elective: Cryptography

Read these slides from *A Theorist's ToolKit* on how to find research, how to write math in LaTeX, how to give a talk, where to ask on stackexchange ect.

### 18.1 Graduate Cryptography Intro

This course covers post-quantum crypto, elliptic curve crypto, and is taught by premiere researcher Tanja Lange (TU/e)

- (Full Course) Masters Level Cryptology
- Recorded lectures here
- See djb's talks and papers from his personal site, and post-quantum crypto site

### 18.2 Applied Cryptography

- Draft book
*A Graduate Course in Applied Cryptography*- Dan Boneh and Victor Shoup - Lecture notes from 18-733 Applied Cryptography
- Read all Daniel J. Bernstein's (and Peter Gutmann's) posts on the IETF Crypto Forum Research Group [Cfrg] archive, it's a master class in modern cryptanalysis and he rips apart bad standards/protocol designs.
- Read
*The Art of Computer Programming - Seminumerical Algorithms*by Knuth (Vol 2) chapter on Random Numbers. These same tests are still used in crypto grad courses. Try it on every library you can find that supposedly generates random numbers - Read about the proof of the Wireguard protocol, a VPN that uses
`AEAD_CHACHA20_POLY1305`

### 18.3 Future Research

- Follow whatever the PhD students of djb and Tanja Lange are working on
- Watch the lectures from the 2017 Post-Quantum Crypto Summer School
- Read the journal of Crypto Engineering (use SciHub proxy)
- Read a book on Random Graphs there is a connection between Graph Theory and Cryptography
- Try the Cryptopals challenges
- Read some cryptocurrency papers, such as Stellar's Consensus algorithm (soon to be used by mobilecoin.com), Fail-Safe Network or the protocol specification for Zcash.