Group Alumni

Research Area: Dynamical Systems and Control, Collective Behaviors

PhD 2022 - Mechanical and Aerospace Engineering - Princeton University (Advisor - Naomi Leonard)
Thesis - Nonlinear dynamics of multi-agent multi-opinion belief and opinion formation

Research Area: Data-Driven Modeling and Machine Learning

PhD 2023 - Applied Mathematics - University of Washington (Advisor - J. Nathan Kutz and Sasha Aravkin)
Thesis - Robust Approaches for Unsupervised Learning

Research Area: Data-Driven Discrepancy Modeling and Bio-Mechanics

PhD 2023 - Mechanical Engineering - University of Washington (Advisor - J. Nathan Kutz and Kat Steele)
Thesis - Machine Learning for Dynamical Models of Human Movement

Research Area: Dynamical Systems, Data-driven modeling

PhD 2022 - Mechanical and Aerospace Engineering - Princeton University (Advisor - Clancy Rowley)
Thesis - Advances in data-driven modeling and sensing for high-dimensional nonlinear systems

Research Area: Dynamical Systems and Quantum Analog Systems

PhD 2017 - Mathematics - New Jersey Institute of Technology (Advisor - Denis Blackmore)
Thesis - Qualitative modeling and analysis of chaotic logical circuits and walking droplets: A dynamical systems approach

Research Area: Computational Neuroscience

PhD 2021 - Neuroscience - Washington University (Advisor - Marcus Raichle)
Thesis - On arousal and the internal regulation of brain function: theory and evidence across modalities and species

Research Area: Stochastic and Multiscale Modeling

PhD 2020 - Mechanical Engineering - Stanford University (Advisor - Daniel Tartakovsky)
Thesis - Stochastic multiscale modeling of complex materials

Research Area: Data-Driven Modeling

PhD 2020 - Mechanical Engineering - ETH Zurich (Advisor - P. Ermanni)
Thesis - Reduced-order aeroelastic modeling of morphing wings for optimization and control

Research Area: Deep Learning and Reduced Order Models

PhD 2020 - Aerospace Engineering - Michigan (Advisor - Karthik Duraisamy)
Thesis - Robust and interpretable learning for operator-theoretic modeling of nonlinear dynamics

Research Area: Machine Learning and Dynamical Systems

PhD 2019 - Civil & Environmental Engineering - Carnegie Mellon University (Advisor - Mario Berges)
Thesis - Variational Methods for Energy Systems

Research Area: Dynamical Systems

PhD 2017 - Electrical Engineering - University of Ottawa (Advisors - Victor Gilles Leblanc & Benoit Dionne)
Thesis - On the existence and stability of rotating wave solutions to lattice dynamical systems

Research Area: Machine Learning and Neural Networks, Computational Neuroscience

PhD 2017 - Electrical Engineering - Washington (Advisor - Nathan Kutz)
Thesis - Smart as a Bug: A Computational Model of Learning in the Moth Olfactory Network, with Applications to Neural Nets

Research Area: Networks and Dynamical Systems

PhD 2019 - Mathematics - University of California, Davis (Advisor - Raissa D'Souza)
Thesis - Collective Behavior in Dynamics on Networks

Research Area: Reduced Order Modeling, Machine learning, Fluid Dynamics

PhD 2015 - Fluid Dynamics - Universite de Poitiers (Advisor - Bernd Noack)
Thesis - Cluster-based reduced order models

Research Area: Rotating Detonation Engines and Combustion

PhD 2020 - Aeronautics and Aerospace Engineering - Washington (Advisor - Nathan Kutz and Carl Knowlen)
Thesis - Nonlinear Dynamics of Rotating Detonation Engine

Research Area: Neural Networks and Model Reduction

PhD 2020 - Applied Mathematics - Washington (Advisor - Nathan Kutz and Steven Brunton)
Thesis - Data-driven discovery and model reduction of complex systems

Research Area: Data-Driven Dynamical Systems & Discovery

PhD 2019 - Applied Mathematics - Washington (Advisor - Nathan Kutz and Steven Brunton)
Thesis - From data to dynamics: discovering governing equations from data

Research Area: Atmospheric Sciences, Machine Learning, Scientific Computing

PhD 2017 - Mathematics - Courant Institute, NYU (Advisor - Andrew Majda)

Research Area: Scientific Computing, Numerical Methods, Fast Algorithms

PhD 2015 - Mathematics - Courant Institute, NYU (Advisor - Leslie Greengard)
Thesis - Integral equation methods for inhomogeneous elliptic partial differential equations in complex geometry

Research Area: Machine learning, Tensors, Deep Neural Networks, Sensors

PhD 2016 - Applied Mathematics - Washington (Advisor - J. Nathan Kutz)
Thesis - Machine learning and data decompositions for complex networked dynamical systems

Research Area: Computational Neuroscience, Flight Dynamics, Insect Behavior

PhD 2017 - Control and Dynamical Systems - California Institute of Technology (Advisor - Michael Dickinson)

Research Area: Machine learning, randomized linear algebra, computer vision, data science

PhD 2017 - Statistics - St. Andrews (Advisor - Carl Donovan)
Thesis - Randomness as a computational strategy: On matrix and tensor decompositions

Research Area: Numerical Methods, Nonlinear Optics, Nonlinear Evolution Equations

PhD 2016 - Mathematical Engineering - Istanbul Technical University (Advisors - Nalan Antar & Ilkay Bakirtas)
Thesis - Fundamental Lattice Solitons in Davey Stewartson Equations

Research Area: Systems Biology, Energy Systems, Photovoltaics

PhD 2013 - Systems Biology - Harvard (Advisor - Michael Brenner)
Thesis - Organization and diffusion in biological and material fabrication problems

Research Area: Computational Neuroscience, Traumatic Brain Injuries, Neurodegenerative Diseases, Decision Making

PhD 2014 - Applied Mathematics - Washington (Advisor - J. Nathan Kutz)
Thesis - Mathematical modeling of focal axonal swellings arising in traumatic brain injuries and neurodegenerative diseases

Research Area: Computational Fluid Dynamics, Finance, Data Science & Methods

PhD 2014 - Aerospace Engineering - California Institute of Technology (Advisor - Dale Pullin)
Thesis - Large-eddy simulations of fully developed turbulent channel and pipe flows with smooth and rough walls

Research Area: Computational Neuroscience, Data Science & Methods

PhD 2012 - Neuroscience - Princeton (Advisor - Carlos Brody)
Thesis - Optimal accumulation of evidence for decision-making in rats and humans

Research Area: Adaptive Control, Fluid Dynamics, Data Science & Methods

PhD 2012 - Mechanical & Aerospace Engineering - Princeton (Advisor - Clarence Rowley)
Thesis - Unsteady aerodynamic models for agile flight at low Reynolds numbers

Research Area: Compressive Sensing, Data Science & Methods

PhD 2011 - Mathematics - Weizman Institute of Science (Advisor - Zvi Artstein)
Thesis - Averaging Ordinary Differential Equations

Research Area: Data Science & Methods, Computational Neuroscience

PhD 2009 - Mathematics - Weizman Institute of Science (Advisor - Vered Rom-Kedar)
Thesis - Classification of Chaos in Near-integrable Infinite Dimensional Hamiltonian Systems

Thesis — Data-driven methods for sparse sensor problems in spatio-temporal systems

Thesis — Sparse neural networks for modeling physical and biological systems

Thesis — Robust Approaches for Unsupervised Learning

Thesis — Machine Learning for Dynamical Models of Human Movement

Thesis — Machine Learning for Aero-Optical Wavefront Characterization and Forecasting

Thesis — Optimization Methods for Parameter Identification in Settings with Only Partial Knowledge

Thesis — Reduced Order Model for Global Atmospheric Chemistry Data

Thesis — Mathematical methods for magnetoencephalography forward and inverse modeling with enhanced spatial resolutions

Thesis - Data driven modeling and control of quantum systems

Thesis — Robust Sparse Identification and Saddle Mediated Transport of Nonlinear Dynamics

Thesis — Life Together: Modeling the Collective Behavior of Cellular Communities

Thesis — Neural Networks for Nonlinear Dynamical Systems

Thesis — Data-driven methods for time series forecasting, classification and uncertainty quantification

Thesis - Machine learning for nonlinear materials characterization and modeling

Thesis — From worms to wars, modeling and controlling networked dynamical systems

Thesis — Data-driven reduced-order modeling for symmetrical systems

Thesis - Numerical Modeling of Frequency Combs: Functionality, Stability and Control

Thesis - Dimensionality Reduction and Sparsity Promotion for Complex Dynamical Systems

Thesis - Greedy algorithms for sparse, physics-informed sensor placement

Thesis - Data-driven methods for physics-constrained dynamical systems

Thesis - Data-driven discovery and model reduction of complex systems

Thesis - Control Theory for C. Elegans Calcium Imaging Dynamics

Thesis - Nonlinear Dynamics of Rotating Detonation Engine

Thesis - From data to dynamics: discovering governing equations from data

Thesis - A machine learning and computer vision framework for damage characterisation and structural behavior prediction

Thesis - Computational methods for system identification and data-driven forecasting

Thesis - Data-driven sensor placement methods

Thesis - Smart as a Bug: A Computational Model of Learning in the Moth Olfactory Network, with Applications to Neural Nets

Thesis - Functionality, Robustness and Control of Nonlinear Network Dynamics: Modeling and Understanding the C. elegans Connectome

Thesis - Machine learning and data decompositions for complex networked dynamical systems

Thesis - Dimensionality hyper-reduction and machine learning for dynamical systems with varying parameters

Thesis - Integrating Data-Driven Methods in Nonlinear Dynamical Systems: Control, Sparsity and Machine Learning

Thesis - Self-Optimizing Metamaterial Antennas

Thesis - Mathematical modeling of focal axonal swellings arising in traumatic brain injuries and neurodegenerative diseases

Thesis - Robust Real-Time Image Processing Through Dynamic Mode Decomposition

Thesis - Exploiting Low Dimensionality in Nonlinear Optics and Other Physical Systems

Thesis - Modeling high-energy temporal and spatial mode-locking

Thesis - Modeling the stability and dynamics in mode-locked fiber lasers

Thesis - Stability and Dynamics of Solitary Waves in Nonlinear Optical Materials

Thesis - Dynamics and Stability of Periodic Spatial Patterns in the Optical Parametric Oscillator

PhD 2017 (Expected)
Research Area: Statistics, Machine Learning, Computer Vision
Visit: Spring 2016 - Fall 2016

PhD 2017 (Expected)
Research Area: Power grids, dynamic mode decomposition, dynamical systems, machine learning
Visit: Spring 2016 - Fall 2016

PhD 2017 (Expected)
Research Area: Epidemiology, data methods, dynamical systems
Visit: Fall 2016

Research Area: Scientific Computing, Machine Learning
Visit: Spring 2017 - Fall 2017

Research Area: Mode-Locked Lasers and Frequency Combs
Visit: Spring 2018 - Summer 2018

Research Area: Data Science and Machine Learning
Visit: Fall 2017 - Summer 2018

Research Area: Machine Learning and Aerodynamics
Visit: Spring 2022 — Summer 2022

Research Area: Reduced Order Modeling
Visit: Summer 2022

Fall 2022

Fall 2022

University of Stuttgart

Milan Polytenic

Spring 2015 - Winter 2016

Winter 2015 - Summer 2015

Summer 2015 - Summer 2016

Winter 2015 - Summer 2016

Spring 2016 - Summer 2016

Spring 2016 - Summer 2017

Spring 2017 - Fall 2017

Fall 2017 - Spring 2019

Fall 2017 - Spring 2019

Fall 2021 - Spring 2022

Fall 2021-Fall 2022

Fall 2023 - Spring 2024