Summer 2016

Microsoft New England

Azure Machine Learning Team

Fall 2015

Research Semester at CERN

Monte Carlo simulation of Higgs Radiation.

Summer 2014

Oceanography REU at Oregon State

Nearshore physics and video feature extraction

Winter 2013

Internship at BigML

Machine Learning for image classification.

2013

Dartmouth College

Major in Physics.

I am one of the captains of the Dartmouth Endurance Racing Team and Vice President of the Dartmouth Physics Society. In my free time, I like climbing, fishing, and being outdoors. Before Dartmouth, I raised pigs in the countryside around Corvallis, Oregon.

Learning the Enigma with Recurrent Neural Networks

Recurrent Neural Networks (RNNs) are Turing-complete. In other words, they can approximate any function. As a tip of the hat to Alan Turing, I formulate the Enigma's decryption function as a sequence-to-sequence translation task and learn it with a large RNN.

Sam Greydanus

Approximating Matrix Product States with Neural Networks

The wave function is essential to most calculations in quantum mechanics and yet itâ€™s a difficult beast to tame. In this project, I trained a neural network to approximate the ground state wave function of a many-body quantum system. This was my senior honors thesis.

Sam Greydanus, James Whitfield

Machine Learning for fMRI data

Traditionally, neuroscientists have used a simple (but computationally demanding) technique called searchlight analysis to understand fMRI data. New machine learning approaches have yielded great success as well. The goal of this project was to compare the performances of the two approaches.

Sam Greydanus, Luke Chang

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Higgs Electroweak Calibration

My CERN research project. Particle physicists use a theory called the Standard Model to predict physics in the Large Hadron Collider. I used two Monte Carlo physics simulators to see what happens to numerical models of Higgs physics when we leave out the electroweak force.

Sam Greydanus, Andre Mendes

Image Classification project at BigML

BigML is a machine learning startup which aims to make machine learning accessible to people without specialized backgrounds. As an intern, I tested the feasibility of using their online interface to detect cat heads from the Microsoft cat head database

Sam Greydanus, Poul Peterson

Nearshore physics and automated ship wake detection

There are waves between layers of water that have slightly different densities called internal waves. In this project I wrote an algorithm that automatically recognized and tracked internal waves in time-lapse footage from the Columbia River Estuary.

Sam Greydanus, Robert Holman

AGU 2014 (Oral Presentation)

Fractal Tree

The physics of a tree blowing in the wind is very difficult to model because the system is so nonlinear. I made a numerical model of the system by expressing it as a fractal.

Baby A3C

It's amazing that deep RL agents can master complex environments using just pixels and a few rewards. While learning about these agents, I built a high-performance Atari A3C agent in just 180 lines of PyTorch.

Friendly qLearning

What happens when qLearning agents interact with one another? The goal of this toy JavaScript model is to create emergent social behavior. I am especially interested in discovering "social neurons" in the model's network.

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Pythonic OCR

I trained a deep Convolutional Neural Network in Keras, then rewrote it in numpy to run as a web demo.

Full Tilt

When I was first teaching myself to program, I wrote a few simple apps for Android. My best one is a spinoff of the popular iOS game Tilt to Live.

Pong

I used a policy gradient method written in TensorFlow to beat the Atari Pong AI.

Cartpole

I also solved the Cartpole control problem using Policy Gradients.

Dartbook

While working for the Digital Arts Lab at Dartmouth, I wrote an iOS textbook exchange app. We launched it on the App Store but disbanded soon afterwards. It is a cool little project!

Stereograms

When I discovered stereograms, I got so excited that I wrote code to make my own.

MrLondon

While learning about recurrent neural networks I trained a deep character-level model to write in the style of one of my favorite authors, Jack London.

I wrote a math + code introduction to neural networks and backpropagation. Uses pure numpy and a Jupyter notebook.

An article I wrote about depth perception (page 27) for the Dartmouth Undergraduate Journal of Science

A class project about numerically modeling quantum systems in MATLAB

A gist for dynamic plotting with matplotlib. Useful for updating loss functions within a training loop.

I wrote a numpy model of synthetic gradients for a neural network MNIST classifier. Inspired by this DeepMind paper.

An Jupyter notebook about Mixture Density Networks implemented in Google's TensorFlow library

Some other Android apps I wrote a few years ago are on Google Play

I wrote a Generative Adversarial Network repo for MNIST in PyTorch