Graduate Students

Doctoral Candidates

NickAllgaier Nick Allgaier PhD Candidate
Department of Mathematics and Statistics
website
Advisor, Christopher Danforth
Research Interests: Weather & Climate Prediction; Reverse Engineering the Brain
Degrees Received: Graduate Certificate in Complex Systems
catherinebliss Cathy Bliss PhD Candidate
Department of Mathematics and Statistics
website
Advisor, Peter Dodds
Research Interests: Inference on Social Networks with Missing Data
Degrees Received: Graduate Certificate in Complex Systems
eitanpechenick Eitan Pechenick PhD Candidate
Department of Mathematics and Statistics
website
Advisor, Peter Dodds
Research Interests: Computational Linguistics
Degree Received: Graduate Certificate in Complex Systems
AndyReagan Andy Reagan PhD Candidate
Department of Mathematics and Statistics
website
Advisors, Christopher Danforth and Yves Dubief
Data Assimilation and Uncertainty Quantification
Masters in Mathematics, 2013
Title: Predicting Flow Reversals in a Computational Fluid Dynamics Simulated Thermosyphon Using Data Assimilation
Abstract: A thermal convection loop is a circular chamber filled with water, heated on the bottom half and cooled on the top half. with sufficiently large forcing of heat, the direction of fluid flow in the loop oscillates chaotically, forming an analog to the Earth’s weather. As is the case for state-of-the-art weather models, we only observe the statistics over a small region of state space, making prediction difficult. To overcome this challenge, data assimilation (da) methods, and specifically ensemble methods, use the computational model itself to estimate the uncertainty of the model to optimally combine these observations into an initial condition for predicting the future state. First, we build and verify four distinct d.a. methods. Then, a computational fluid dynamics simulation of the loop and a reduced order model are both used by these d.a. methods to predict flow reversals. The results contribute to a testbed for algorithm development.
JakeWilliams Jake Williams PhD Candidate
Department of Mathematics and Statistics
Advisor, Peter Dodds
Research Interests: Computational Linguistics; Quantifying Stories
EricClark Eric Clark, PhD Student
Department of Mathematics and Statistics
Advisor, Peter Dodds
Research Interests: Computational Linguistics
Degree Received: Graduate Certificate in Complex Systems; Masters in Mathematics
BrandonTries Brandon Tries PhD Candidate
Department of Mathematics and Statistics
TomMcAndrew Tom Mcandrew PhD Candidate
Department of Mathematics and Statistics
Advisor, Peter Dodds
Research Interests: Computational Linguistics; Quantifying Stories
markWagy Mark Wagy PhD Candidate
Department of Computer Science
Degree Received: Graduate Certificate in Complex Systems
RobertSwain Bob Swain PhD Candidate
Department of Computer Science
Degree Received: Graduate Certificate in Complex Systems
EmilyCody Emily Cody, PhD Candidate
Department of Mathematics and Statistics
Advisor, Chris Danforth
Research Interests: NSF Igert; Small Grid & Human Behavior
PooyaRezaei Pooya Rezaei, PhD Candidate
Department of Chemical Engineering
Advisor, Paul Hines
Research Interests: Cascading failure risk estimation and mitigation in power networks; Plug-in Electric Vehicle (PEV) charging impacts on distribution system infrastructure and smart charging methods; Reconfiguration and capacitor placement in radial distribution systems for loss reduction and reliability enhancement.
JeffSprenger Jeff Sprenger PhD Candidate
Department of Computer Science
Advisor, Maggie Eppstein
Currently taking a year-long leave of absence to work with start-up company “Xemery, LLC” to develop educational software based on evolutionary robotics, funded by an SBIR.
GoodarzGhanavati Goodarz Ghanavati, PhD Candidate
Department of Electrical Engineering
Advisor, Paul Hines
Research Interests: Power system dynamic stability
RomanPopov Roman Popov PhD Candidate
Department of Neuroscience
Anton Bernatskiy PhD Candidate

Masters Students

SharonAlajajian Sharon Alajajian, Masters Student
Department of Mathematics and Statistics
Advisor, Peter Dodds
Research Interests: Food Systems & Eating Behavior Via Social Media
MikeFoley Mike Foley, Masters Student
Department of Mathematics and Statistics
Advisor, Brian Tivnan
Research Interests: Computational Finance
MorganFrank Morgan Frank, Masters Student
Department of Mathematics and Statistics
Advisors, Peter Dodds and Chris Danforth
Research Interests: Human Mobility and Nonlinear Dynamics
Degrees Received: Graduate Certificate in Complex Systems
kayla-horak Kayla Horak, Masters Student
Department of Mathematics and Statistics
Advisor, Chris Danforth
Research Interests: Statistical Hedonometrics
LindsayVanLeir Lindsay Van Leir, Masters Student
Department of Mathematics and Statistics
Advisor, Chris Danforth
Research Interests: Synchronization in Chaotic Systems
Degrees Received: Graduate Certificate in Complex Systems
Photo on 6-23-14 at 1.43 PM Dilan Kiley, Masters Student
Department of Mathematics and Statistics
Advisors, Peter Dodds
David Buckingham, Masters Student
Department of Computer Science
Research Interests: Cellular Communication with the Snowcloud Wireless Sensor Network

Students Earning Graduate Certificates in Complex Systems

ChristopherClement Christopher Clement
Currently a PhD candidate in Ecological Sciences and Complex Systems
DanFredman Dan Fredman
Currently a Graduate Fellow at the Gund Institute; IGERT Fellow, Smart Grids
TianxinMiao Tianxin Miao
Currently a PhD candidate in Tissue Engineering
Javier Garcia-Bernardo
Currently a Computer Science Masters Student
Scott Hamshaw
Owner of Hamshaw Design; Research Assistant
Christopher Pierce
Currently an employee at PharMerica Corporation
Curtis Saunders
Currently a PhD candidate in Mechanical Engineering

Alumni

SumaDesu Suma Desu, Alumna
Currently at the Massachusetts Institute of Technology
AmandaCasari Amanda Casari, Alumna
Degree Earned: Graduate Certificate in Complex Systems
Currently Data Scientist at Mantis Technology Group (A ProKarma Company)
DSCN0091 Kameron D. Harris, Alumnus
Received Masters in Mathematics from University of Vermont, 2012
Currently at the University of Washington
Title of Masters Thesis: On-off Threshold Models of Social Behavior
Abstract: We study binary state dynamics on a social network, where nodes act according to individual response functions of the average state of their neighborhood. These response functions model the competing tendencies of imitation and non-conformity by incorporating an “off-threshold” into standard threshold models of behavior. In this way, we attempt to capture important aspects of fashions and general societal trends allowing varying amounts of stochasticity in both the network and response functions, we find different outcomes in the random and deterministic versions of the model. In the limit of a large, dense network, however, these dynamics coincide. The dynamical behavior of the system ranges from steady state to chaotic depending on network connectivity and update synchronicity. A mean field theory is laid out in detail for general random networks. In the undirected case, the mean field theory predicts that the dynamics on the network are a smoothed version of the response function dynamics. The theory is compared to simulations on Poisson random graphs with response functions that average to the chaotic tent map.
Received Undergraduate Honors in Mathematics & Physics from University of Vermont, 2009
Title of Undergraduate Honors Thesis: Predicting Climate Regime Change in Chaotic Convection
Abstract: A toy climate analogous to the famous lorenz system is derived and compared to computational fluid dynamics simulations in order to test new modeling techniques. In particular, methods of data assimilation and ensemble forecasting are used to predict regime changes and residencies in this toy climate. A climate “truth” is created using a finite-element simulation of a thermal convection loop, a physical apparatus designed to be the simplest model of convection in the Earth’s atmosphere. Forecasts of the climate are made using low-dimensional lorenz-like models and synchronized to noisy observations of the truth using various Kalman filters. Forecasting of regime changes has been successfully demonstrated when the same model is used to create both the observations and the forecast, but never for realistic chaotic convection.
IsabelKloumann Isabel Kloumann, Alumna 2011
Currently at Cornell University
AhmedRagabNabhan Ahmed Ragab Nabhan
Degree Earned: Graduate Certificate in Complex Systems
Currently a Senior Software Engineer at Sears Holdings Corporation
PaulLessard Paul Lessard, Alumnus 2012
Currently at the University of Colorado
RossLiebLappen Ross Lieb-Lappen, Alumnus 2011
Received a Masters in Mathematics & Statistics from University of Vermont, 2010
Currently at the Dartmouth College in the Thayer School of Engineering
Title of Masters Thesis: Aggressive Shadowing of a Low-Dimensional Model of Atmospheric Dynamics
Abstract:
Modeling Earth’s atmospheric conditions is difficult due to the size of the system, and predictions of its future state suffer from the consequences of chaos. As a result, current weather forecast models quickly diverge from observations as uncertainty in the initial state is amplified by nonlinearity. One measure of the strength of a forecast is its shadowing time, the period for which the forecast is a reasonable description of reality. The present work uses the Lorenz ’96 coupled system, a simplified nonlinear model of atmospheric conditions, to extend a recently developed technique for lengthening the shadowing time of a dynamical system. An ensemble of initial states, systematically perturbed using knowledge of the local dynamics, is used to make a forecast. The experiment is then repeated using inflation, whereby the ensemble is regularly expanded along dimensions whose uncertainty is contracting. The first goal of this work is to compare the two forecasts to reality, chosen to be an imperfect version of the same model, and determine whether variance inflation succeeds. The second goal is to establish whether inflation can increase the maximum shadowing time for a single member of the ensemble. In the second experiment the trajectory of reality is known a priori, and only the closest ensemble members are considered at each time step. When inflation is introduced to this technique, it is called stalking. Variance inflation was shown to have the potential to be successful, with the extent dependent upon algorithm parameters (e.g. size of state space, inflation amount). Under idealized conditions, the technique was shown to improve forecasts over 50% of the time. Under these same conditions, stalking also exhibited the potential to be useful. When only the best ensemble members were considered at each time step, the known trajectory could be shadowed for an entire 50-day forecast 50-75% of the time. However, if inflation occurs in directions incommensurate with the true trajectory, inflation can actually reduce stalking times. Thus, utilized appropriately, inflation has the potential to improve predictions of the future state of atmospheric conditions, and possibly other physical systems.
NatineManukyan Narine Manukyan, PhD
Department of Computer Science
Advisor, Margaret Eppstein
Research Interests: Computational Data Engineer interested in Machine Learning, Evolutionary Computation, Artificial Neural Networks and Agent Based Modeling of Complex Systems. Her research focuses on data mining and predictions in healthcare using new evolutionary fitness landscapes and more.
Degrees Received: Graduate Certificate in Complex Systems; PhD in Computer Science
Currently doing a 3-month internship at IBM Watson in Data Analytics
EduardoCotilla-Sanchez Eduardo Cotilla-Sanchez, Alumnus
Department of Computer Science
Advisor, Paul Hines
Research Interests: Energy Systems
Currently Assistant Professor at the School of Electrical Engineering and Computer Science at Oregon State University
JoshAuerbach Josh Auerbach, PhD
Department of Computer Science
Currently a Post Doctoral Fellow at the Laboratory of Intelligent Systems at EPFL
PeterFroncek Peter Froncek
Degree Received: Graduate Certificate in Complex Systems
Currently a Graduate Teaching Assistant at the University of Vermont
KarimChichakly Karim Chichakly, PhD
Department of Computer Science
Advisor, Maggie Eppstein
Currently Co-Owner of ieee Systems in Lebanon, NH and Adjunct Faculty at WPI
Jo Krupa, Masters of Computer Science
Advisors, Maggie Eppstein and Donna Rizzo
Currently City Engineer for the city of New Haven, CT
Shreya Mukherjee, Masters of Computer Science
Advisors, Maggie Eppstein
Research Interest: Genetic Programming
Currently a Software Engineer at Data Innovations LLC, S
Joshua Payne, PhD
Department of Computer Science
Advisors, Maggie Eppstein
Currently a Post Doctoral Fellow with Andrea Wagner at the Institute of Evolutionary Biology and Environmental Studies at the University of Zurich
Paul Haake, Masters of Computer Science
Advisors, Maggie Eppstein
Currently a Software Development Engineer II at Amazon.com
Paul Beliveau
Degree Earned: Graduate Certificate in Computer Science
Adjunct Faculty at Champlain College