PeopleGraduate Students
People
My research involves models of nonlocal interactions in population dynamics.
My research interests are in the fields of Nonlinear Optimization and Machine Learning. More specifically, on Stochastic Newton and Stochastic Quasi-Newton methods. Currently, I am involved in one main project in the subfield of Derivative-Free Optimization (DFO).
Using a chimera grid scheme to study particles confined to fluid interfaces.
Noise-induced tipping between metastable states in periodically forced systems
Non-equilibrium fluctuations in (biological) membrane stuctures under electric fields, Quincke rotors under AC electric fields, and applying machine learning to condensed and active matter physics.
Gradient detection and planar cell polarity in the Drosophila imaginal eye disc.
I use High Performance Computing and Level Set Methods to model the growth of biofilm.
I am interested in developing algorithms for anomaly detection in time series data using deep neural networks.
Deep Learning focused on representation on the data, layer, and model levels.
Simulations of particle structuring driven by electric fields on spherical drop surface
Using Multi-Objective Optimization to Agnostically Develop Genetic-Neuronal Models of Drosophila Clock Neurons.
A minimal-model perpective of emergent phenomena in a network of coupled mixed-mode oscillators motived by a neural network in the mammalian olfactory system.
Analysis of temperature processing and behavioral response in Drosophila
Impact of non-orthogonal axes on the mixing of a hemispherical piecewise isometry representing the infinitely thin flowing layer approximation of a granular mixing system.
Model study on how neurongenesis and synapse fluctuation improve distinguishing different odors in mice.
I use experimental data from fruit fly development to understand how DNA moves and how to extract transcription rates in real time.
I study symmetry in complex networks.
Gene network reconstruction methods, graph theory
Studying dynamics of cells/vesicles with deformable membranes using phase field methods. Specifically substrate-based eukaryotic cell motility (autotaxis and galvanotaxis) and microrotors in electric field.
Decomposing biomedical data into components and groups for analysis. More specificly, using Bayesian statistics in order to compute the Tucker decomposition of tensors.
I study errors and large deviations in nonlinear optical systems with stochastic perturbations. My current project is studying a mode-locked laser.
I work on a computational model of the CA1 pyramidal cell, featuring calcium buffering dynamics and simulated GCaMP6 fluorescence throughout dendrites and spines.
Adaptive Parallel (Domain Decomposition) Finite Differences Scheme for the Cahn Hilliard Equations.
Network-Based Identification of Driver Genes in Expression Data
Exploring multi-physical fluidic phenomenon at the nano-scale
recurrent neural networks
A computational model of how reward-based training and granule cell inhibition enhance sensory decorrelation in the olfactory bulb.
The compound vesicle problem: Introduced a type of repulsion forces into the free energy of system, where the repulsion force can vary according to distance between each part of vesicle surface.
Model of matching of orientation preference in the mouse visual cortex
Chuqiao (Vicky) Yang
Graduate StudentPhone: 847-467-8783
Office: Tech M410
Email Chuqiao (Vicky)
Chuqiao (Vicky)'s Website
Mathematical models applied to social systems. Applications include crime and patent rates in cities, and political elections.
Jia Yu
Graduate Student
