# Author Archives: snikolov

# Generative rhythm as a self-avoiding random walk

A rhythm is just a binary vector of a certain size with some of the bits turned on. The layering of rhythms is the generation of new binary vectors (each representing a new percussive instrument) that “go well” with the

# Generative rhythm as a self-avoiding random walk

A rhythm is just a binary vector of a certain size with some of the bits turned on. The layering of rhythms is the generation of new binary vectors (each representing a new percussive instrument) that “go well” with the

# Early detection of Twitter trends explained

A couple of weeks ago on Halloween night, I was out with some friends when my advisor sent me a message to check web.mit.edu, right now. It took me a few seconds of staring to realize that an article about

# Early detection of Twitter trends explained

A couple of weeks ago on Halloween night, I was out with some friends when my advisor sent me a message to check web.mit.edu, right now. It took me a few seconds of staring to realize that an article about

# Information Diffusion on Twitter

This spring, I volunteered to teach a lecture in a new Berkeley course called “Analyzing Big Data With Twitter,” developed jointly by Twitter and Berkeley’s School of Information. I had recently done my masters thesis work on predicting the spread

# Information Diffusion on Twitter

This spring, I volunteered to teach a lecture in a new Berkeley course called “Analyzing Big Data With Twitter,” developed jointly by Twitter and Berkeley’s School of Information. I had recently done my masters thesis work on predicting the spread

# Semi-Supervised Shape Classification with Manifold Regularization

For my Statistical Learning Theory class I did a project on shape classification using manifold regularization. You can read the abstract below. You can also find the paper here and the code here. We approach the problem of semi-supervised shape classification

# Semi-Supervised Shape Classification with Manifold Regularization

For my Statistical Learning Theory class I did a project on shape classification using manifold regularization. You can read the abstract below. You can also find the paper here and the code here. We approach the problem of semi-supervised shape classification

# Underactuated Control of Vehicular Traffic

When self-driving robotic cars begin to share the road with regular cars, could we control the robotic cars to smooth out traffic jams? I did a numerical and theoretical study of vehicle traffic dynamics and control policies that smooth traffic even

# Underactuated Control of Vehicular Traffic

When self-driving robotic cars begin to share the road with regular cars, could we control the robotic cars to smooth out traffic jams? I did a numerical and theoretical study of vehicle traffic dynamics and control policies that smooth traffic even

# Stan Explains Things: RANdom SAmple Consensus

I’ve decided to start writing briefly and informally about technical things, so that I could understand them better, and so that someone else might get some insight. I’ll be writing these under the appropriately vague title of “Stan Explains Things”.

# Stan Explains Things: RANdom SAmple Consensus

I’ve decided to start writing briefly and informally about technical things, so that I could understand them better, and so that someone else might get some insight. I’ll be writing these under the appropriately vague title of “Stan Explains Things”.

# The Statistical Structure of Rhythm

I took a quick break with some random hacking last night. I wanted to see what kind of statistical structure there is in rhythm, since I am always tapping on various vaguely percussive objects. You can see the code on

# The Statistical Structure of Rhythm

I took a quick break with some random hacking last night. I wanted to see what kind of statistical structure there is in rhythm, since I am always tapping on various vaguely percussive objects. You can see the code on