Here is the list of courses to be offered at cSplash 2014, which will take place on April 26th.
Mathematics of the Multiverse
The first part of the talk will be a general introduction to
inflation, eternal inflation, and the multiverse. The
second part will describe some of the mathematics behind the
multiverse: exponential growth, the measure problem, binary
trees, and conformally invariant stochastic models.
Prerequisites: I will structure the talk so that all but the last few
minutes are accessible to high-school students. In the last
few minutes I will connect what I have said to current research.
Teacher info: Matthew Kleban (kleban [at] nyu [dot] edu), Physics Department, Associate Professor
Class Size: 50 (enrolled: 27)
Introduction to Machine Learning
Machine learning is used all around us: in Microsoft's
Kinect, Apple's Siri, Google's self-driving cars, Facebook's
face recognition, Amazon.com's recommendations. Come and
discover what it takes to create an algorithm that learns
Prerequisites: No prerequisites.
Teacher info: David Sontag (dsontag [at] cs.nyu [dot] edu), Courant Computer Science, Professor
Class Size: 70 (enrolled: 34)
A probability paradox
Throw a stick at a circle. What is the length of the
inscribed chord, on average? There are three ways to answer
this (at least), and all of them give a *different* answer.
Yet, we could also just do the experiment, and obtain only
one answer. Or would we?
In this class we'll talk about this paradox, do an
experiment, and learn a little about the philosophy of
Prerequisites: A bit of exposure to probability will help, but is not
Enthusiasm to do an experiment is required.
Teacher info: Miranda Holmes-Cerfon (holmes [at] cims.nyu [dot] edu), Courant, Math, Assistant professor
Class Size: 15 (enrolled: 15)
The cSplash! Calculus Crash Course: An Introduction to
In this course, we will explore the basic yet ubiquitous
idea of optimization.
Starting with unconstrained problems in a single variable,
we will discuss local and
global optima before expanding into functions of two
variables (twice the fun!).
Then we will dive into the subtle area of constrained
optimization, where we will
introduce ideas including duality and sensitivity analysis.
Finally, we will touch
upon the numerical algorithms used for optimization in
Prerequisites: familiarity with basic functions, algebra, and pre-calculus.
calculus itself is not
assumed and is not be required.
Teacher info: Scott Yang (yangs [at] cims.nyu [dot] edu), Courant Math, 2nd Year PhD
Class Size: 10 (enrolled: 9)
Git into GitHub
Knowing how to use Git is essential to being a modern
software developer. Git is a form of version control, a way
of managing changes to source code. GitHub hosts some of the
largest open source projects in the world using Git -- from
Linux to Rails to Bootstrap. In this talk, we will cover the
basics of Git, GitHub, and why they should matter to you.
Prerequisites: Basic programming ability would be very helpful.
Teacher info: Tyler Palsulich (tpalsulich [at] nyu [dot] edu), Courant Computer Science, First Year Master's
Class Size: 35 (enrolled: 3)
Hey! Are We There Yet? Strategies for Finding the Most
Efficient Route between Two Locations
How does Google Maps plan your road trip? The shortest
distance between any two points is, theoretically, a
straight line. In practice, however, we must carefully
consider how roads are already laid out to account for
rivers, mountains and other changes in terrain, like towns
and cities. A number of well-established search algorithms
are available to help evaluate the costs and benefits of
choosing different possible paths. We will discuss how
uninformed searches can be improved to assess alternative
courses in addressing a route-finding problem, and the
effectiveness of different algorithms will be compared.
Additional applications ranging from robotics to biology
will also be introduced.
Teacher info: Loretta Au (loretta.au [at] stonybrook [dot] edu), Stony Brook University, Department of Applied Mathematics
and Statistics, 6th year PhD
Class Size: 50 (enrolled: 26)
Pearls in Graph Theory
Graph theory is an important branch of Mathematics today.
Euler wrote a paper in 1736 on the Seven Bridges of
Königsberg and the theory of Graphs was born. What is a
graph? Why are they so important? We will use Euler's paper
as a starting point and see what we can get from there.
Topics should include(but are not limited to): definition of
a graph and related terminologies, planar graphs,
Kuratowski's theorem, Euler's formula, vertex coloring,
six/five color theorems, and a brief discussion of the
famous four color theorem.
Note: the title is dedicated to N. Hartsfield and G.
Ringel's book of the same name.
Prerequisites: We won't be using anything beyond high school algebra, but
some mathematical maturity is required. Familiarity with
proofs and mathematical induction is recommended. We will be
moving rather quickly because we want to eventually discuss
the four color theorem!
Teacher info: Junichi Koganemaru (jk3967 [at] nyu [dot] edu), Courant Math, 2nd Year Undergrad
Class Size: 35 (enrolled: 13)
Edge Detection: How Computers See
Edge detection is a basic yet fundamental problem in image
processing and computer vision. In this course, we will talk
about some basic tools and concepts in image processing,
then use this foundation to study the Canny Edge Detection
algorithm and some of its optimizations. It is my goal for
everyone in the class to be understand the Canny edge
detector well enough to implement it on their own, although
we will not do this during the class.
If time permits, we will also take a brief high level look
at some more modern approaches to edge detection, especially
those involving machine learning.
Prerequisites: If you know what a derivative is and have some programming
knowledge (any language), you should understand everything
in this course. Experience with MATLAB is a plus.
Teacher info: Vyassa Baratham (vyassa.baratham [at] stonybrook [dot] edu), Stony Brook University Physics, 2nd year undergrad
Class Size: 35 (enrolled: 11)
Cryptography: Real World Magic
It may have started with sending secret instructions to
Generals across enemy territory during the expansion of the
Roman Empire, but Cryptography is now far more than just
keeping sensitive messages from prying eyes. How do you play
a multi-player version of Where is Waldo? How do you get
Amazon servers to compute functions on your DNA without
compromising your privacy? How do you ensure fairness in a
network-play version of Rock-Paper-Scissors? Believe it or
not, with cryptography.
Prerequisites: I'll try to make it as self-contained as possible - and make
it more of a qualitative and anecdotal talk than a technical
Teacher info: Siddharth Krishna (siddharth [at] cs.nyu [dot] edu), Courant Computer Science, 1st Year PhD
Class Size: 35 (enrolled: 35)
The cSplash! Calculus Crash Course: Biology and Medicine
In this course, we will explore some fundamental concepts in
single-variable calculus by considering real-life problems
in biology and medicine. As we investigate these biological
problems, we will introduce and analyze relevant topics in
calculus and see how they can help us model and solve the
Prerequisites: Familiarity with basic functions. Precalculus would be
helpful. Knowledge of calculus is not required; we will
build the necessary machinery together.
Teacher info: Olivia Chu (olivia.chu [at] nyu [dot] edu), Courant Math, 3rd Year Undergrad
Class Size: 10 (enrolled: 10)
Practical Tractor Beams
Beams of light can exert forces on illuminated objects. How to
control those forces has been worked out only recently. After
introducing the theory of photokinetic effects, we will put
practice with a new experimental technique called holographic
optical trapping, in which computer-generated holograms are
used to exert total control over the dynamics of
objects. This interplay of theory and technology has
first experimental demonstration of a knotted force field and,
more recently, the first practical realization of a tractor
propagation-invariant traveling wave
that can transport illuminated objects back to its source.
Prerequisites: High-school algebra will be very helpful for understanding the
theory. Any level of mathematical preparation is fine for
appreciating the experiments, which are carried out with
holograms and are visually appealing.
Teacher info: David Grier (david.grier [at] nyu [dot] edu), FAS Physics, Professor
Class Size: 50 (enrolled: 12)
MapReduce, The Big Data Workhorse
An Intel Core i7 980 XE processor can run 100 billion
floating point operations every second. But some data
processing jobs require astronomically huge computing
resources, which require tasks to be distributed over
several machines. Often, this means using an algorithm
called MapReduce, which deals with the fact that two pieces
of data sent to two different machines may, in fact, depend
on each other. In this course, we will explore some basics
of distributed computing, and then talk about the MapReduce
algorithm conceptually, before seeing a basic example and
discussing some practical aspects of the algorithm and its
open source implementation, Hadoop, and Amazon's MapReduce
Prerequisites: Some programming knowledge (small amounts of pseudocode will
be shown, so any language should be fine).
Teacher info: Vyassa Baratham (vyassa.baratham [at] stonybrook [dot] edu), Stony Brook Physics, 2nd year undergrad
Class Size: 35 (enrolled: 4)
The Fibonacci Numbers: Counting with Algebra
The Fibonacci numbers are a famous sequence of numbers where
each number is given by the sum of the previous two numbers
in the sequence. Because of this recursive definition, it is
not clear how to calculate the 1000th Fibonacci number
without first calculating the first 999.
In this talk, we will use algebra to tackle this problem and
learn more about the Fibonacci numbers. We will also explain
a neat trick that lets one convert miles to kilometers using
only the Fibonacci numbers.
Prerequisites: Basic algebra and manipulating algebraic expressions.
Knowing how to sum an infinite geometric series is a bonus,
but is not required and will be explained in the talk!
Teacher info: Mihai Nica (nica [at] cims.nyu [dot] edu), Courant Math, 3rd year PhD
Class Size: 70 (enrolled: 21)
Information Theory in Biology
Time is money, but information is priceless! Or is it? In
this course, we'll discuss quantitative measures of
information and mutual information, discovered by Claude
Shannon, and consider several examples to build intuition.
Information theory clarifies what it means to communicate
efficiently and relates to biology via DNA, which contains
instructions for assembling the proteins required to sustain
life. We will compare the efficiencies of DNA coding for
different organisms, and also apply information theory to
study transcription factor binding, which is thought to
explain the majority of variations between species. As we
know from everyday experience, messages can become distorted
as they travel from sender to recipient, and we will
consider such noisy channels as well. Time permitting,
fundamental connections with the concept of entropy in
physics will be explored.
Though widely accessible, this course will require deep
thinking. Welcome to the world of information!
Prerequisites: Familiarity with logarithms and basic probability
Teacher info: Thomas Fai (tfai [at] cims.nyu [dot] edu), Courant Math, 6th Year PhD
Class Size: 50 (enrolled: 23)
Quantum Information: The Surprisingly Simple Math of the
New quantum technologies are bringing us face to face with
seemingly impossible realizations about the world.
This talk will include some examples of real-life paradoxes
that happen in the lab all the time, as well as one or two
of the (simplest) algorithms that are in use in quantum
technologies today and in the near future.
Prerequisites: algebra, basic probability
Teacher info: Seth Cottrell (cottrell [at] cims.nyu [dot] edu), Courant, venerable PhD student
Class Size: 35 (enrolled: 30)
What gives the sky its colors
I will derive and solve the equations pertaining to driven
simple harmonic oscillators and resonance, and demonstrate
using the example of a pendulum. I will follow with a simple
description of the phenomenology of electromagnetic
radiation, connect this description to resonant systems, and
derive a simple model for Rayleigh scattering. Rayleigh
scattering explains the color of the sky.
Prerequisites: single variable calculus, basic knowledge of E&M.
Teacher info: Arya Tafvizi (arya [at] math.nyu [dot] edu), Courant Math, 1st year PhD
Class Size: 50 (enrolled: 9)
Programming in an Eggshell
In this lecture, you will learn a simple puzzle game called
Alligators and Eggs. What does this have to do with
programming, you might ask? Quite a lot! Surprisingly,
every computer program can be translated into this very
simple game. To understand how this works, we will study
Church encodings (no praying involved - I promise) and
fixpoint combinators. Along the way, we will touch on
fundamental questions in computer science such as "What
is computable?". I will end by giving you a glimpse of
a real programming language. You will see that programming
is actually like playing alligators and eggs in disguise.
Prerequisites: basic algebra; induction
Teacher info: Thomas Wies (wies [at] cs.nyu [dot] edu), Courant Computer Science, Assistant Professor
Class Size: 70 (enrolled: 16)
All kinds of pi
You've learned by now that the length of the circumference
of a circle is 2 pi times the radius. You've also learned
that pi is some fixed constant, a fundamental property of
the university, and certainly never changing. But is it? In
this talk we will see how the traditional value of
pi=3.14... is intricately linked with how we measure
distance; and that once we decide to measure distance
differently, the idea of pi as a static constant disappears.
Prerequisites: High-school geometry and trigonometry.
Teacher info: James Fennell (fennell [at] cims.nyu [dot] edu), Courant Mathematics, 1st year PhD
Class Size: 35 (enrolled: 3)
The projective plane is an amazing place. Every pair of
in a point. Circles, ellipses, parabolas, and hyperbolas are
all the same shape; and all quadrilaterals are the same
shape. You can turn all the
points into lines and all the lines into points, and nobody
The price you pay is that you can't compare two distances or
and, if you have three points on a line, you can't say which
one is in the
This lecture will give an introduction to projective
geometry, one of the
most important examples of a non-Euclidean geometry. We will
explain how projective geometry relates to perspective in
images and how you can use projective geometry to prove the
astonishing and important Pappus' theorem.
Pappus' theorem: Draw two lines L and L'. Choose three
points A, B, C on
L and D, E, F on L'. Let X be the intersection of AE with
BD; let Y
be the intersection of AF with CD; and let Z be the
intersection of BF
with CE. Then X, Y, and Z are collinear.
Prerequisites: Geometry and algebra
Teacher info: Ernest Davis (davise [at] cs.nyu [dot] edu), Courant Computer Science, Professor
Class Size: 50 (enrolled: 12)
Randomness and Computation
It turns out that if we allow computers to have access to
random numbers, we can come up with faster and more elegant
algorithms for some problems. However, there are some
algorithms for which we can eliminate the need for access to
random numbers. In this class we will see a couple of my
favorite results that explore the relationship between
randomness and computation.
Prerequisites: Basic probability. Experience with algorithms, graph
theory, and modular arithmetic would be helpful, but not
Teacher info: Shravas Rao (rao [at] cs.nyu [dot] edu), Courant, Computer Science, 1st Year PhD
Class Size: 35 (enrolled: 9)
Vision by Humans and by Machines
We will introduce Vision, the amazing abilities needed to
process images. We will analyze a few optical illusions. We
will discuss why mathematics and computers are need to model
it. Then, we will illustrate such modeling with an
introduction to Haar Wavelets, a multiscale representation
of images (and the base of JPEG 2000 compression.) We will
show the link of wavelets to neuroscience.
Prerequisites: basic algebra
Teacher info: Davi Geiger (geiger [at] cims.nyu [dot] edu), Computer Science and Neural Science, professor
Class Size: 50 (enrolled: 15)
The Mathematics of Pleasure and Reward
I will discuss how neurons in the dopaminergic system in the
brain fire as a function of presentation of unexpected
reward. A relatively simple equation can capture the
properties of the firing of these dopaminergic cells with
profound accuracy. The insights gleaned from this simple
relationship can describe how we learn associations between
stimuli and reward and how we reinforce behaviors that are
pleasurable to us.
Prerequisites: Basic algebra
Teacher info: Stephen Keeley (stephenlkeeley [at] gmail [dot] com), Computational Biology at Courant, Home Dept: Center for
Neural Science , 3rd Year PhD Student
Class Size: 70 (enrolled: 17)
Electronic Privacy and Security: Introductory Cryptography
In a world where the scope of electronic surveillance is
becoming more known, questions about just what security is
and how it works are becoming increasingly prevalent. In
this talk, we will discuss the basic tenants of cryptography
and what it means for something to be “secret” or “secure”.
We will look at some of the cryptographic methods used from
ancient times up to the present and we will consider various
Prerequisites: Standard algebra is necessary. Basic knowledge of
probability and binary are highly suggested.
Teacher info: George Wong (gwong [at] nyu [dot] edu), Courant Mathematics, 3rd Year Undergraduate
Class Size: 35 (enrolled: 6)
Your Brain on Bayes: Understanding How Your Brain Uses
Bayesian Inference to Influence Perception and Guide Action
Bayesian Inference is a statistical method that optimally
combines prior knowledge with current evidence. Behavioral
studies have shown that many human actions can be fit with
Bayesian models. Recent work suggests that neurons in the
brain can compute the components of these Bayesian models,
and thus provide the mechanism for Bayesian behavior.
In this class, we will go over basic notions of probability
and how probability distributions can be updated by Bayes'
theorem. Then, we will look at how Bayesian inference can
explain several behavioral phenomenon (e.g., optical illusions).
Prerequisites: Knowledge of basic probability (probability spaces,
distribution functions, etc.).
Teacher info: Ryan Shewcraft (ryan.shewcraft [at] nyu [dot] edu), Center for Neural Science, 3rd Year PhD
Class Size: 35 (enrolled: 14)
This Machine Kills Cancer
Cancer is a terrible disease in which the cells of your body
rebel against their normal function and multiply out of
control. Despite decades of research on cancer treatments,
most cancers are still treated with medieval methods:
cutting and poison. There has been a promising new trend
toward analyzing each cancer's DNA in order to make
personalized treatments for every patient. How do you
sequence DNA? How do you look at DNA on a computer? What
mathematical models help you build a personalized cancer
treatment from a DNA sequence? All these questions, and
more, will be answered!
Prerequisites: Biology (should know what DNA is)
A little bit of experience with programming
Teacher info: Alex Rubinsteyn (alex.rubinsteyn [at] gmail [dot] com), Mount Sinai School of Medicine, Bioinformatics Researcher (just finished my CS PhD @ NYU)
Class Size: 50 (enrolled: 20)
The M Stands for Magic
The M in mathematics stands for magic. Join us for a brief
survey of the most fantastic results in mathematics. From
Euler's formula to Euclid's proof, to the mathematical
algorithms that hold internet banking together, math can go
from the theoretically beautiful to the surprisingly
practical. We will go through results in geometry, calculus,
and number theory. And if time allows, cryptography and
Prerequisites: Some familiarity in calculus and trigonometry would be helpful.
Teacher info: Imran Qureshi (iq214 [at] nyu [dot] edu), Courant Math, Bsc. Alumni
Class Size: 35 (enrolled: 24)
Killing the Hydra
In Greek mythology, Hercules was ordered to kill the
Lernaean Hydra as a part of his Twelve Labors. Many years
have passed since then, and the modern-day Hydra that we now
face has evolved to be much more vicious. In this
mini-course, we will model the kill-the-Hydra game by a
simple numeric problem. We will then tackle the problem via
the technique of transfinite induction, which will produce
Prerequisites: It helps to know what it means to compute the limit of a
sequence, but this isn't strictly necessary.
Teacher info: Mark Kim (markhkim [at] math.nyu [dot] edu), Courant Math, 2nd Year PhD
Class Size: 70 (enrolled: 13)
Hyperplanes, unfortunately, don't describe aircraft that are
super excited and giddy. But they do describe spaces that
are of the utmost importance in linear algebra,
multivariable calculus, and even convex analysis. In this
talk, I will develop an elementary introduction to the
geometry of hyperplanes and explain some different types
(e.g. supporting, separating). Then I'll jump into a
description of the applications, which span from linear
approximations and tangent hyperplanes to describing convex
Prerequisites: Basic geometry, basic set theory, some exposure to calculus
could be helpful
Teacher info: Mohammad Manzari (mtm430 [at] cims.nyu [dot] edu), Courant Math, 1st Year Masters
Class Size: 35 (enrolled: 3)
Paradoxes of the Continuum
The real numbers is the main example of a continuum.
Intuitively, it is a continuous medium with no gaps any where.
For instance, the integers clearly has big gaps,
but even the rational numbers has many gaps (like square
root of 2).
In Calculus, you learn about continuous functions: this is
defined using Cauchy's idea of epsilon-delta continuity.
But the concept of a continuum is rife with paradoxes.
Does Cauchy's idea capture all the properties of the real
It turns out that we can introduce new kinds of numbers
(infinitesimal) into the real line!
We live in the space-time continuum. So is
the physical world a continuum? Newtonian physics assumes so,
but quantum physics denies it.
Continuum mechanics is a field that studies the ``mechanical
of fluids and continuous material. But surely, the name is
Come join us as we explore this paradox.
Prerequisites: A bit of calculus.
Teacher info: Chee Yap (yap [at] cs.nyu [dot] edu), Courant Computer Sciene Dept, Professor
Class Size: 50 (enrolled: 8)
The story of Hilbert's hotel, a hotel with an infinite
number of rooms, reveals some unintuitive facts about
infinity. The hotel is full, and yet it has vacancies.
We'll explore the example of Hilbert's hotel and some of the
mathematical ideas behind it. We'll discuss the notion of
countably infinite, and learn how to determine if an
infinite set is countable. Finally, we'll see an example of
an infinite set that is not countable, and if time allows we
will discuss how to prove it is not countable.
Prerequisites: Familiarity with sets will be helpful.
Teacher info: Steven Delong (delong [at] cims.nyu [dot] edu), Courant Math, Fourth-year PhD
Class Size: 35 (enrolled: 11)
Large-Scale Structure of the Universe
Over the last few decades cosmology has developed a very
detailed picture of how the large-scale structure we see in the
universe (galaxies, clusters of galaxies, and so on) arose from
very small ripples in the distribution of matter and energy
early times (as early as 10^-34 seconds after the Big Bang).
discuss how small fluctuations in the early universe are
generated and then amplified as they evolve until the present
time and how cosmologists tests these ideas with telescopes
and satellite observations.
Prerequisites: calculus for the most technical bit, but for the most part
and trigonometry is enough.
Teacher info: Roman Scoccimarro (rs123 [at] nyu [dot] edu), Center for Cosmology and Particle Physics, NYU Physics, Professor
Class Size: 50 (enrolled: 11)
The Halting Problem, Incompleteness, and the Limits of
Anyone who's spent much time programming knows that it's
surprisingly easy (and very frustrating!) to accidentally
write programs that never stop running. To save ourselves
some time and embarrassment, we'll ask a very simple
question: Given some computer program, how can we tell
whether it will run forever?
Unfortunately, this basic question will prove to be
maddeningly difficult to answer. We'll quickly find that
computer programs can go into infinite loops in dizzyingly
complex ways. Without intending to do so, we'll somehow find
ourselves talking about some seemingly unrelated problems in
mathematics that have stumped mathematicians for centuries.
We'll actually learn that we were trying to solve them
without realizing it. Oops...
This will lead us, by way of Alan Turing's famous Halting
Problem, to a simple proof of one of the most profound and
humbling results in the philosophy of mathematics: Kurt
Godel's First Incompleteness Theorem. Namely, we will show
that, in a sense, NO formal system can encompass all of
mathematics. We'll ponder the philosophical meaning of this
statement and, if we have time, conclude by considering the
statement "Nothing that is interesting can be defined
Prerequisites: Students should have a very basic understanding of computer
programming: If you know what an If statement is, then
you're fine. Other than that, this talk will be completely
self-contained. However, it will be advanced and move
relatively quickly, so students should be very comfortable
with mathematics in general.
Students who have already heard about the halting problem or
Godel's Incompleteness Theorem(s) are encouraged to attend,
as this presentation will likely be quite different from
what they've seen.
Teacher info: Noah Stephens-Davidowitz (noahsd [at] gmail [dot] com), Courant Computer Science, 2nd Year PhD Student
Class Size: 35 (enrolled: 17)
Introduction to Quantum Mechanics
Although quantum mechanics is fundamental to our
understanding of the universe, conceptually, it is entirely
foreign to our everyday experience. We will use mathematics
to develop some of the framework for quantum mechanics (in
one dimension). Along the way, we will consider and discuss
particle-wave duality, wavefunctions, the Schrödinger
Equation, the square well and harmonic oscillator
potentials, and quantum tunneling. Questions are welcome and
expected and will hopefully play an important part in the
Prerequisites: Knowledge of graphs and certain types of functions
(trigonometric and piecewise) is necessary. A general
understanding of linear algebra, complex numbers, and
calculus (differentiation / integration in one variable) is
highly suggested but not necessary. We will functionally
develop necessary concepts over the course of the lecture.
Teacher info: George Wong (gwong [at] nyu [dot] edu), NYU Physics, 3rd Year Undergraduate
Class Size: 35 (enrolled: 18)
The Hardware of a Computer
Ever wonder how a computer works? This talk will cover the
basics of the internal structure and design of a computer.
It will cover the basics including CPUs, the memory
hierarchy, pipelining and multicore processors.
Teacher info: Mike Jaoudi (mjaoudi [at] nyu [dot] edu), Courant Computer Science, 3rd Year Undergrad
Class Size: 35 (enrolled: 35)
Nuclear fusion: the fuel of stars, and energy for the future?
Using elementary math tools, we will look at the physics of
nuclear fusion. We will explain how the Sun has been able to
shine and heat us for so long, and talk about the progress
we are making in bringing nuclear fusion on Earth for a
carbon-free, quasi-infinite energy source. In this class,
you will see a few cool formulas, curves, and pictures, and
no long boring derivations. We will also make sure to keep
plenty of time for all the questions you want to ask!
Prerequisites: Elementary algebra and the ability to read/interpret graphs!
Teacher info: Antoine Cerfon (cerfon [at] cims.nyu [dot] edu), Courant Math, Assistant Professor of Mathematics
Class Size: 50 (enrolled: 28)
The cSplash! Calculus Crash Course: Length, Area, and Volume
A basic question in mathematics is to compute the length of
a curve. In this course, we will learn how to compute the
lengths, areas, and volumes of various objects by the ideas
of calculus. Some examples will include a surface with
infinite area but which encloses a finite volume, along with
a formula for pi involving only the number 2.
Prerequisites: knowledge of calculus is not required
Teacher info: Ian Tobasco (tobasco [at] cims.nyu [dot] edu), Courant Math, 3rd Year PhD
Class Size: 35 (enrolled: 25)
Puzzles and Invention
The writer of puzzles often invents puzzles to illustrate a
The puzzles, however, sometimes have other ideas.
They speak up and say that they would be so
much prettier as slight variants of their original selves.
The dilemma is that the puzzle inventor sometimes can't
solve those variants.
Sometimes he finds out that his colleagues can't solve them
because there is no existing theory for solving them.
Sometimes entire new fields ensue. We present and challenge
students to solve several such puzzles having inspired
advances in cryptography, graph theory, and even diplomacy.
Prerequisites: Basic algebra
Teacher info: Dennis Shasha (shasha [at] cims.nyu [dot] edu), Courant Computer Science, Prof
Class Size: 70 (enrolled: 15)
Coloring planar graphs
The four-color theorem states that any map in a plane can be
colored using four colors in such a way that no regions
sharing a common boundary have the same color. This theorem
is very well known because it was the first major result to
require a computer program to solve it, and there was
controversy in the mathematical community on whether such a
proof should be accepted.
In this talk we will discover that with a little bit of
combinatorics, we can prove the six-color theorem. In the
process we will derive the beautiful Euler's formula. If
there is enough time, we will also prove the five-color
theorem as a bonus.
Prerequisites: Students should be comfortable with combinatorial reasoning
and also should know how to color!
Teacher info: Igor Balla (balla [at] cims.nyu [dot] edu), Courant Math, 2nd Year PhD
Class Size: 35 (enrolled: 2)
The 15-puzzle, bubble sort, and symmetric groups
The 15-puzzle is a famous 4-by-4 sliding puzzle from the
19th century, consisting of 15 numbered tiles in a 4-by-4
grid. In this mini-course, we will tackle Sam Loyd's
one-thousand-dollar problem of swapping 14 and 15 while
keeping all the other tiles in place. Along the way, we will
devise a simple sorting algorithm and use it to study an
abstract mathematical object called the symmetric group.
This mini-course will provide you with sufficient
mathematical background to study the mathematics of Rubik's
cube, which we unfortunately will not have time to discuss.
Prerequisites: Some familiarity with mathematical proofs. For example, a
course in Euclidean geometry is sufficient (though we won't
use any geometric reasoning).
Teacher info: Mark Kim (markhkim [at] math.nyu [dot] edu), Courant Math, 2nd Year PhD
Class Size: 35 (enrolled: 14)
An easy introduction to difficult probability
This class will introduce the concepts of probability limit
theorems and their applications through an interesting
example. We will analyze a random experiment that can
estimate the value of pi to any desired accuracy!
In order to analyze this convergence result I will introduce
powerful probability tools such as Hoeffding's concentration
inequality as well as the law of iterated logarithm and the
central limit theorem.
Prerequisites: Multivariable calculus, basic notions of probability such as
independence, joint probability distributions, expectation ...
Teacher info: Andres Munoz Medina (amunoz88 [at] gmail [dot] com), Courant Math, 4th year PhD
Class Size: 50 (enrolled: 13)
About the difficulty icons:
We have developed a color grading system in an attempt to indicate the overall difficulty of each talk. A green icon indicates that anyone with a standard high-school mathematics background should be able to follow. A black icon indicates that the talk will be fast-paced, and that students without extra-curriculuar exposure to more advanced mathematics---through math camps, college courses, competition preparations, and so on---are likely to find the talk challenging. These are the two extremes, and blue and purple icons indicate somewhere-in-between points of the difficulty spectrum. It is, of course, impossible to determine the objective difficulty of a talk, and the icons should only be taken as a crude approximation. The best way to figure out whether the talk is at the right level for you is to talk to the lecturer. Instructors' emails are listed on this page, so ask away!