Trajectory Flow – pariser.me/tflow

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.

Itinerary Builder – pariser.me/itinerary

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.

Network Analysis

The Two Dimensional Anisotropic Ising Model as Paradigm for Repeat Protein Folding Behavior

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 folding.

Statistical Prediction

What Makes a Nobel Prize Winner in Physics? Classification on the Citation Network:

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.

Cluster Analysis

Skeletonization Via a Biologically Motivated Data-Driven Process in Digital Binary Shapes:

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.

Sociological Modeling

Urban Sprawl - Modeling the Morphology of US Cities:

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 limied 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 real results.

Travel

View my travel photos from my trip to Hong Kong and China, Summer 2011.

• China I - Hong Kong
• China II - Sichuan Province
• China III - Shanghai
• China IV - Beijing (and the Great Wall)

View my travel photos from my trip to Vietnam, Cambodia & Thailand, Summer 2009.

• Southeast Asia I - Southern & Central Vietnam
• Southeast Asia II - Northern Vietnam
• Southeast Asia III - Kuala Lumpur, Angkor Wat, Angkor Thom
• Southeast Asia IV - Angkor Thom, Ta Prohm, Banteay Srei
• Southeast Asia V - Cambodia to Thailand
• Southeast Asia VI - Chang Mai and Sukhothai

View my travel photos from my trip to Morocco, Autumn 2008.

• Morocco I - Casablanca and Marrakech
• Morocco II - High Atlas Mountains, Ait-ben-Haddou, Todra Gorge, The Sahara
• Morocco III - Middle Atlas Mountains, Fes
• Morocco IV - Rif Mountains, Chefchaouen, Volubilis
• Morocco V - Meknes, Rabat
• Morocco VI - Coastal towns on the Atlantic

View my travel photos from my trip to Japan, Summer 2008.

• Japan I - Sendai, Hiraizumi, Aizu Wakamitsu, Nagoya and Takayama
• Japan II - Tokyo
• Japan III - Kyoto

View my travel photos from Summer 2007, with destinations in Eastern Europe, Greece, and the UK.

• Coming soon.

Food & Cooking

Eating In

• I really like the DIY aspects of cooking. One of my dreams is to become this man.
• 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.]

Eating Out

• I've been very fortunate to eat at Thomas Keller's The French Laundry. I took pictures. [Coming soon.]
• Read a friend's reflections on our Burger Tour of San Francsicso.
• 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.)

Ultimate Frisbee

• Playing for FL-ICME, my department's intramural team, since 2007.
• Playing for Stanford Prison Experiment since 2007. 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.

Work Experience

LEARNUP, San Francisco, CA (current)

LEXITY, Mountain View, CA

YAHOO! INC., Sunnyvale, CA

BEAR, STEARNS & CO. INC., New York, NY

YALECOOKIES, New Haven, CT

SIEMENS TRANSPORTATION SYSTEMS, New York, NY

Education

STANFORD UNIVERSITY, Stanford, CA

Completed Classes:

• 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

Teaching Experience:

• 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

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