All projects in this section are the final projects from graduate-level classes I've taken. I have been particularly interested in probabilistic modeling, inference, and machine learning.
Author: Jack Hensley
This project simulates rumor spreading. It builds a Barabasi-Albert scale free network to represent a social network, and initializes one "person" with a "fact". Over a period of time steps, this fact gets spread to everyone else, with some chance to mutate into a "rumor". The model used for spreading and mutating rumors relies on the entropy within each person's memory. This project investigates different parameters and initialization configurations, and visualizes them through gifs.
Author: Jack Hensley
While being able to predict the stock market's future would be a profitable ability, it is impossible to account for every factor that influences the market's movement. With a clear goal of earning money and no clear model of the market's actions, this problem is ideally suited for model-free reinforcement learning. In particular, this project applies Q-learning, a form of model-free reinforcement learning, to the stock prices from Google for the past six years. It treats the stock prices and the number of shares owned as the state, and every day, it decides whether to buy, sell, or do nothing, having learned from five years of Google's stock prices.
Author: Jack Hensley
This project recreates a paper that suggests a Bayesian model for predicting the outcome of a presidential election using polling data. The implementation uses Gibbs sampling, a form of Markov chain Monte Carlo, to predict the results.
Authors: Jack Hensley, Botong Ma, Hongyi Shi
We compare three machine learning techniques' ability to classify instruments and pitches based on short samples of notes. In particular, we look at the results from using k-nearest-neighbor, multi-class SVMs, and neural nets.
Author: Jack Hensley
This project deciphers text that has been encrypted with a simple substitution cipher, which maps each character in "abcdefghijklmnopqrstuvwxyz ." to another character. It does this by modeling text as a Markov chain, in which each character has some probability of following the previous character. It then uses the Metropolis-Hastings algorithm, a Markov chain Monte Carlo method, to repeatedly generate possible ciphers until it finds one that gives a deciphered piece of text with a high likelihood - typically the original input text!
Author: Jack Hensley
This project modeled the height (z position) of a small commercial drone as a hidden Markov model (HMM) with noisy observations. Under this model, I applied causal Kalman filtering to the height sensor on the drone, and achieved better flying performance with the drone as a result!
Author: Sarah Hensley
Abstract: Because the vast majority of monitoring alarms in the adult intensive care unit (ICU) do not require intervention, care providers are slow to respond to all alarms, endangering patients. We collect, characterize, and analyze alarms, alarm annotations provided by clinical staff while responding to alarms, and physiological data from a community hospital ICU. In order to suggest opportunities for suppressing irrelevant alarms, we examine monitoring device coverage across patients and analyze the alarms observed by device, priority, and type. On average, we observe 196.3 alarms per patient-day, for a total of 23,057 alarms. From these, the electrocardiogram and pulse plethysmogram produce 86.1% of all alarms. The lowest priority alarms represent 81.1% of all alarms, while the highest priority alarms compose just 5.5% of the total. While the rate of annotations is low, also just 5.5% of possible alarms, it is comparable to the rate of care provider interactions with alarms, as measured by alarm silencing, at 9.6%. Using these annotations, we find -- surprisingly -- that the annotated nuisance threshold-violation alarms tend to have higher excursions than actionable and advisory alarms, offering a statistic for separation. When focusing on threshold-crossing alarms, we find that 22.5% of Heart Rate Low alarms may actually indicate device error. Among ST segment alarms, 44.4% occur simultaneously with at least one other ST segment alarm, producing redundant alarms. Addressing these issues represent strategies for reducing excessive alarms in this community hospital cohort of ICU patients.
Authors: Philip C. Michael; Sarah Hensley; Christopher Galea; Edward Chen; Haran Karmaker; Leslie Bromberg
Abstract: We present the design of a noncontact high-torque magnetic coupler in the context of a high-speed (> 3600 r/min) fully superconducting rotating electric machine. The magnetic coupler is an essential component for machines where both stator and rotor are enclosed within a common cryostat. The magnetic coupler eliminates the need for rotating shaft vacuum seals, which would otherwise be needed if a conventional torque tube were used. The rotor is cooled by natural convection from the stator, similarly eliminating the need for the rotating cryogen transfer coupling used for conventional superconducting rotors. The magnetic coupler satisfies the need to transmit torque from the prime mover to the generator across a stationary cryostat boundary. We rely to an array of multiple radially spaced stages to limit axial extent and improve the torque rating of the design. We demonstrate that, while it is feasible to transmit several kilonewton meter of torque through a magnetic coupler for medium-size machines (> 10 MW), the magnetic coupler is nearly as complex as the rest of the machine. This option should be considered if known reliability issues with high-speed rotating vacuum seals and cryogen transfer couplings cannot be resolved.
Presented at the Medical Electronic Device Realization Center Workshop and at EECS Masterworks
Authors: Sarah Hensley; Minoru Matsushima; Thomas Heldt
Abstract: The vast majority of alarms from bedside monitoring systems in intensive care units do not require intervention from care providers. As a result, care providers are slow to respond or ignore alarms altogether, endangering patients with time-sensitive need for intervention. To prevent this, it is important to understand which types of alarms are excessively non-actionable. This research seeks to characterize alarms from the ICU of a local community hospital. Unlike previous studies, we developed a system for care providers to annotate alarms by actions taken. Most monitor alarm studies have been conducted at academic hospitals, so collecting alarms from a community hospital offers insights that maybe be more representative of most hospitals.
Presented at EECSCon and to the EECS Visiting Committee
Authors: Sarah Hensley; Michael Posa; Russ Tedrake
Abstract: Valkyrie is a humanoid robot designed to work in environments designed for humans, so it needs manual dexterity comparable to that of a human. The current controller operating on the arm of Valkyrie restricts the flow of sensor data between actuators in the arm, impairing performance. We examine three different information restriction policies to evaluate the performance each attains. We designed controllers that enforce these policies and implemented them on a simulation of the arm. Our results show that the controller with the least restrictive information sharing policy significantly improves performance of the arm over the other controllers.
from work at LeafLabs, LLC
This work reconstructed high-resolution 3D images of neuron activity from raw 2D images taken by a light- field microscope. In particular, this work implemented the Richardson-Lucy deconvolution algorithm from Wave Optics Theory and 3-D Deconvolution for the Light Field Microscope to generate 3D images. The processing steps were computationally intensive, requiring several calculations on tens of thousands of data points. Without leveraging computational tricks, these steps would require half a year of computation. Through physical symmetry and parallel processing, I reduced the runtime to eleven minutes.