George Sivulka

Graduate Student in Applied Physics and EE· Stanford University · gsivulka (at) stanford.edu

I care deeply about solving interesting and impactful problems at the intersection of machine learning, theoretical neuroscience, and engineering.


Experience

The Boston Consulting Group

Summer Associate

Helped streamline demand generation efforts for an international technology conglomorate; developed artificial intelligence solutions.

June 2018 - August 2018

SLAC National Accelerator Laboratory

Scientific Researcher

Worked on optimization efforts for the LUX-Zepplin experiment for the direct detection of dark matter

June 2017 - September 2017

NASA Goddard Institute for Space Studies

Research Intern

Developed computational simulations for satelite based remote sensing of landmines

June 2014 - December 2016

Education

Stanford University

Ph.D. Student
Electrical Engineering — Computational Neuroscience

Stanford Graduate Fellowship Recipient

Currently on leave

Stanford University

Master of Science
Applied Physics — Machine Learning Engineering Depth

Threshold Venture Fellow 2019-2020

Class of 2020

Stanford University

Bachelor of Science
Mathematics

GPA: 4.0, Graduated with Distinction in less than 3 years.

Class of 2019

Projects

Machine Learning inspired by Neuroscience

Wu Tsai Neurosciences Institute
"Neurobiologically Inspired Encoding and Transfer Learning"

In the Baccus Lab at Stanford, developed machine learning systems that leverage biology--the computations in the retina--for improved efficiency. Specifically, we applied transfer learning methods that employ natural visual processing pathways as an encoder for changing visual stimuli--analyzing their efficacy in video classification tasks.

[Poster]

Mathematical Principles for RNN Behavior

Max Plank Institute for Mathematics in the Sciences
"State Space Narrowing for the Optimization of Recurent Neural Networks"

Analyzed the dynamics of general artificial neural networks by combining tools from physics, dynamical systems and network science at the Max Planck Institute for Mathematics in the Sciences in Leipzig. Spectral analysis of the adjacency matrix, neural state space trajectories, and other mathematical tools were employed to offer insights into network behavior and state space narrowing in a Spiking Neural Network trained with Hebbian Learning.

Probing the Earliest Evolved Neural Systems

Stanford University & The Palau International Coral Reef Center
"The Neural Dynamics and Nerve Networks of Coral Polyps: Analyzing Anthozoan Action Potentials"

Designed a suction electrode experiment to study coral nerve nets--biophysically interesting due to their simple 2D topology and their early position on the evolutionary timescale--exhibiting some of the first neural dynamics. Analyzed spike train experimental results in lieu of theory and modeling in an attempt to better understand the neural code of these animals.

[Paper] [Presentation]

Neural Interferogram Classification

Stanford Physics Department
"Classification of Neural Interferometric Imaging: Machine Learning for Non-invasive Neurosensing"

Designed and implemented machine learning techniques for the classification of neural interferograms from Daniel Palanker’s lab at Stanford--detecting action potentials from a new type of non-invasive optical biosensing technology.

[Poster] [Paper]

Optimization Engineering for a Dark Matter Detector

SLAC National Accelerator Laboratory
“Direct Detection of Dark Matter: Developing an Experimental Test System for the LZ Experiment”

Worked on the LUX-Zepplin experiment for the direct detection of dark matter, developing an experimental test vessel for the optimization of detector components used by the international collaboration. Focused on overarching test system design and implementation, thermosiphon head efficiency engineering, and the layout of various test detector components.

[Poster]

Landmine Detection Multiphysics Simulations

NASA Goddard Institute for Space Studies
“Subsurface Imaging of Electromagnetically Semi-Penetrable Hazardous Objects"

As a Research Intern hired by NASA I spent two years developing multiphysics simulations in COMSOL Multiphysics to predict electromagnetic wave propagation and scattering. My particular task involved generating a computer template for the subsurface imaging of landmines and other underground hazards that pose a threat to millions of lives across the globe.

[Poster] [Conference Coverage] [Award Coverage] [Presentation]

Awards:
  • Received Best Poster Award by Popular Choice out of professional projects from around the world at the Boston COMSOL Conference - (October 2015)
  • NASA’s New York City Research Initiative (NYCRI) Achievement Award
  • Best Poster Award - NASA/NOAA Research Symposium at CCNY - (August 2015)



Epidemiology Visualizations

Mount Sinai Medical System
"Bioinformatics Analysis: Visualizing Transmission Patterns of Clostridium Difficile Within an Urban Hospital"

Programmed an interactive web visualization to lend insight into the transmission patterns of the potentially fatal Clostridium Difficile bacteria between patients and caregivers in a hospital. This allowed for an analysis of caretaker groups with the least successful sanitation techniques and specific medical technologies leading to higher rates of infection.

[Poster] [Presentation]


Interests

Outside of science, I enjoy surfing, photography, web development, mountain climbing, motorcycle touring, and free diving.

I’ve climbed above Mount Everest Base Camp, have spent a month motorcycling through Vietnam, and practiced silent Vipassana meditation for 10 days in Sri Lanka.

Some photos from these and other places can be found on my 500px account.


Additionally, here are some of my takes on: mankind's stellar origins (cited twice on Wikipedia [5][4]), a proof for the atom, group theory and the Platonic solids, American anti-science movements, and recent European Union history and politics.