Most recently, I was director of engineering—and a founding software engineer—at
a startup whose mission was to enable job seekers to learn the skills they need in
order to get hired. I am glad to announce that LearnUp has been acquired by Manpower Group.
Prior to LearnUp, I was a software engineer at Lexity (since acquired by Yahoo!), helping small to medium sized businesses
manage the complications of online advertising. While at Lexity I worked on
automated advertising algorithms, built large portions of the lexity.com website,
coded the foundations of the Commerce Central API and co-wrote the Live Sales app
which was featured at LAUNCH 2013.
I nearly got my Ph.D. in Stanford's ICME
department, but decided to leave with my M.S. and an all-but-dissertation status. My academic interests have
included interactive data analysis, complex network analysis, statistical
prediction, pattern recognition, and mathematical modeling of social behavior.
A Chrome Extension/Firefox Add-on for Socially Conscious Consumers
Personal Project, released March 2017.
Taking inspiration from the Grab Your Wallet Boycott, I decided to make a
browser extension that would help consumers identify when they were visiting
websites of Trump-affiliated companies. Users of the extension are given
options to take actions on said websites: contacting the companies, shopping
elsewhere, buying Trump-offset credits, or dismissing the alert.
A Tool for Exploratory Data Analysis of Trajectory Data
Trajectory Flow is an interactive tool for exploratory data analysis of space-time
trajectory data, where each location and timestamp is associated with a sensor id.
Trajectory Flow breaks the cycle of creating ad-hoc scripts to visualize different
cuts of the data, providing tools for filtering and analyzing the data while guided
by a user; furthermore, its import and export tools allow for simple collaboration
between multiple data analysts. The author details a potential use case, wherein
Trajectory Flow is able to identify locations in the vicinity of Stanford University
where people spend the majority of their time around the holiday season. User studies
are necessary to evaluate the effectiveness of certain design decisions as well as to
determine which tools should next be added to the system.
Building an itinerary is a difficult task. One has to research information about all
destinations in consideration, determine which of this information is relevant to their
personal preferences, and then use this information to decide on whether each location
makes the final cut. Furthermore, the decision on whether to visit a given city or
region involves information that is disparate, including at least point of interest
descriptions, transportation schedules and weather information, and involves a
considerable amount of work in the planning stage.
The goal of this project is to create an interactive visualization that helps users
explore the space of all possible travel itineraries to better inform their vacations.
In some sense, Itinerary Builder is a heuristic human-guided solver for the Traveling
Salesman problem. The user has access to an interactive map and a representation of
their current planned itinerary. Controls are provided that allow the user to add and
subtract destinations from their itinerary, and as they change the details about their
trip the map dynamically updates with a geographically embedded representation of the
path of the cities that will be visited along this itinerary.
The Two Dimensional Anisotropic Ising Model as Paradigm for Repeat Protein Folding Behavior
Yale Univ., Fall - Spring 2007, Physics Intensive Senior Thesis,
advised by Simon Mochrie (Group Information
TPR protein structure is only a function of
nearest-neighbor helix interaction. Accordingly, we look to
the analogous nearest-neighbor Ising model to quantify the
folding behavior of TPR proteins. Previous research has
demonstrated the efficacy of a one-dimensional model in which
the spin state (±1) corresponds with the folding
behavior, where spin-up corresponds to a folded helix and
spin-down to an unfolded helix.
This paper explores the possibility of extending previous
work into a second dimension. Currently, the one-dimensional
model cannot take into account the fact that each helix in the
repeat structure can be simultaneously folded and unfolded. By
allowing each amino acid comprising the helices to assume its
own spin state, then it is easy to account for this. A two
dimensional model would also extend the biological relevance
of this computational paradigm for studying protein
What Makes a Nobel Prize Winner in Physics? Classification on the Citation Network:
Yale Univ., Spring 2007
The Physics citation network indicates the relationship
between scientific publications. The bibliographic data can
be viewed as a graph, with papers as nodes, and citations as
directed edges from one paper to another. Given a chunk of
the citation network, we can use measurements (number of
publications, number of citations, h-index, etc.) to define an
author's status in the scientific community. Classification
techniques are then used in an attempt to identify the Nobel
Prize winning authors. Out-of-sample cross validation error
indicates that the most successful learning method, a linear
kernel support vector machine, will misclassify approximately
37% of the authors.
Skeletonization Via a Biologically Motivated Data-Driven Process in Digital Binary Shapes:
Yale Univ., Spring 2007
The recognition of object skeletons allow complicated
object recognition algorithms to work on smaller input data.
This paper proposes a novel technique that uses a
self-organizing feature map in order to find these object
skeletons. Because self-organizing feature maps preserve
topology, we train the network with object coordinates. By
imposing links between neurons, which we selectively delete
over the network convergence phase, we show how to devise a
skeleton from a self-organizing feature map. This technique
requires no a priori object-identification, and may be
performed on noisy image data. This new model is both
biologically relevant and computationally efficient.
Urban Sprawl - Modeling the Morphology of US Cities:
Yale Univ., Spring 2007, Applied Mathematics Senior Thesis,
advised by Daniel Spielman
This thesis extends a long tradition of research within the
urban studies and economics communities. The belief that some
simple forms may underlie the very nature of a city's
structure inherits directly from this discourse, whose
standard urban model suggests that cities are generally radial
with an exponential decay in population density from the city
center. Though this model is not always accurate, it has been
shown to hold for many cities. More importantly, even the
existence of this model allows for investigations into which
kinds of cities follow the observed patterns (and which do
not) and why.
We explore the vast effort that has been put forth both to
challenge and to defend the standard urban model, and
reconstitute the work of some other theorists to put forth
alternative measures that might characterize city
morphology. We take these concepts to derive a set of metrics
for a given city, using these metrics to analyze over 150 of
the cities of largest population in the United States, with
geodata provided by the 2000 Census.
Three stochastic models of city morphology are discussed at
length and then analyzed according to the defined metrics.
The first two originate in the literature: diffusion limited
aggregation assumes that households settle at the location
where a Brownian motion walk started from a distance reaches
the city's frontier; correlated site percolation meanwhile
assumes that deposit according to fluid flow on a regular
lattice with some autocorrelation. The third method proposed
by the the authors is to define a city as the connected
component of a slightly sub-critical bond percolation process.
What may be the most important feature of our bond percolation
model is the ease by which it may altered to incorporate
geographical constraints upon the city generation process, a
consideration not well explored previously.
To answer the question of which model is best, we take as
training observations the simulated cities that each model has
produced, and we perform a data classification example on the
test set of cities as given by US Census data. If a sizeable
majority of actual cities are classified to one the group of
cities constructed by a given model, then we can say (albeit
with some reservations) that this model is better at producing
Chile, December 2016.
Utah's National Parks, May 2016.
South Africa, November 2015.
Italy, Summer 2014.
Ireland and England, Summer 2013.
Bangladesh, India and U.A.E. (Dubai), Summer 2012.
Hong Kong and China, Summer 2011.
Vietnam, Cambodia & Thailand, Summer 2009.
Morocco, Autumn 2008.
Japan, Summer 2008.
"Mega-Trip" Summer 2007, with destinations in Eastern Europe, Greece, and the UK.
Food & Cooking
- I really like the DIY aspects of cooking. One of my dreams is to become
- I built a DIY BBQ smoker and made pulled pork and brisket. Photos and documentation here. [Coming soon.]
- Inspired by my trip to Japan, I tried my hand at making ramen from first principles. [Coming soon.]
- I've been very fortunate to eat at Thomas Keller's The French Laundry. I took pictures. [Coming soon.]
- View photos from my Barbecue Tour of the Carolinas and Memphis.
- When living in the New York area, I had a mission to
eat at all of New York Magazine's "Cheap Eats." To aid me in
my quest, I created these Google Maps (see the 2007 Edition
or the 2006 Edition—I've been
to the restaurants with green place markers.)
- Played for FL-ICME, my Stanford department's intramural team, from 2007 to 2012.
- Playing for Stanford Prison Experiment from 2007 to 2010. See their website.
- Played for Yale Superfly from 2004 to 2007. See their website.
- To find out more about ultimate frisbee, please visit the UPA website.
LearnUp (acquired by Manpower Group), San Francisco, CA
Director of Engineering; March 2016 to February 2017
Founding Software Engineer; May 2013 to February 2017
Lexity (acquired by Yahoo!, Inc.), Mountain View, CA
Front End Engineer; Fall 2012 to April 2013
Back End Engineer; July 2011 to Fall 2012
Intern and part-time employee; April 2010 to July 2011
Yahoo! Inc., Sunnyvale, CA
Advertisement Systems Development Group, Intern; Summer, 2008
Bear, Stearns & Co., Inc., New York, NY
Mortgage Backed Securities Research, Analyst; Summer 2006
Financial Analytics & Structured Transactions, Analyst, Summer 2004 & Summer 2005
YaleCookies, New Haven, CT
Co-founder, Co-president 2004-2007
Siemens Transportation Systems, New York, NY
Research Intern, Summer 2003
STANFORD UNIVERSITY, Stanford, CA
MS in Institute for Computational and Mathematical Engineering
PhD in Institute for Computational and Mathematical Engineering
- Numerical Linear Algebra; Numerical Optimization
- Partial Differential Equations; Mathematical Methods for Fluids, Solids and Interfaces
- Discrete Mathematics and Algorithms; Information Networks; Molecular Algorithms
- Multi-Agent Systems; Decision Analysis
- Probability Theory
- Stochastic Methods in Engineering; Simulation; Stochastic Simulation
- Statistical Modeling; Applied Multivariate Statistics
- Spatial Statistics
- Data Visualization; Research Topics in Interactive Data Analysis
- ICME Excellence in Teaching Award, 2010-2011
- CME200/ME300A (Linear Algebra) Autumn 2008-2009 - TA
- CME204/ME300B (Partial Differential Equations) Winter 2008-2009 - TA
- CME200/ME300A (Linear Algebra) Autumn 2009-2010 - Head TA
- CME204/ME300B (Partial Differential Equations) Winter 2009-2010 - Head TA
- CME212/ENERGY212 (Large-Scale Computing in Engineering) Spring 2009-2010 - TA
- CME200/ME300A (Linear Algebra) Autumn 2010-2011 - TA
- CME204/ME300B (Partial Differential Equations) Winter 2010-2011 - Head TA
YALE UNIVERSITY, New Haven, CT
Graduated May 2007 Cum Laude
BS in Physics and Applied Mathematics, both with Honors
Selection of Completed Classes:
- Probability Theory; Statistics
- Quantum Mechanics; Statistical Mechanics; Classical Mechanics
- Design and Analysis of Algorithms; Graphs and Networks
- Stochastic Processes
- Neural Networks; Computational Vision
- Data Mining and Machine Learning
PAUL D. SCHREIBER HIGH SCHOOL, Port Washington, NY
Graduated June 2003 Top 1%