Teaching & Mentorship

Math 227, Mathematical Modeling

Co-Instructor

An introduction to the mathematical modeling process, covering dynamical modeling with difference equations, stochastic modeling with Markov chains, statistical modeling with regression, and optimization and agent-based models.

Math 451-H, Methods of Applied Mathematics II (Capstone II)

Co-Instructor

A capstone course on data assimilation—combining dynamical models with data to understand and predict complex nonlinear systems—applied to neurophysiological data. Students worked on two semester-long projects:

• Dynamical modeling of pyramidal neuron excitability using deep learning — building conductance-based ODE models of pyramidal neuron excitability from voltage- and current-clamp data, then using deep learning to refine parameter estimates.
• Dynamical modeling of cardiac excitability using deep learning — implementing ODE/PDE models of ventricular cardiomyocytes and using deep learning on ECG data to infer how cardiac excitability parameters shift across the circadian cycle, with implications for the timing of sudden cardiac death.

Math 451-H, Methods of Applied Mathematics II (Capstone II)

Co-Instructor

A capstone course on data assimilation—combining dynamical models with data to understand and predict complex nonlinear systems—applied to neurophysiological data. Students learned sequential and variational data assimilation algorithms and computational neuroscience models (FitzHugh–Nagumo, Hodgkin–Huxley), then applied them in semester-long projects such as inferring pyramidal neuron and cardiac excitability parameters from experimental data using deep learning.

Machine Learning and Optimization Seminar

Co-Organizer · New Jersey Institute of Technology, Newark, NJ

Lawan Wijayasooriya

Georgia State University · Dynamic Entrainment (Fall 2025)

Emeka Mazi

Georgia State University · Dynamic Entrainment (Fall 2022)

Elizabeth Epstein

Thesis: Using Deep Hybrid Modeling to Determine Treatment Strategies for COVID-19 Patients (Fall 2019 – Present)

Research Symposium — Summer 2022

David Alonge, Chinonye Uzowuru (Essex County College)

Using Deep Hybrid Modeling to Identify Biophysical Mechanisms Underlying Circadian Rhythms in Cardiac Arrhythmias.

Research Symposium — Summer 2021

Kevin Yotongyos (NJIT undergrad)

SEIAQRVn Model of Spread of Covid-19 with cGAN Parameter Estimation.

 

Francis Kanwaya, Karolina Kowal, David Alonge, Chinonye Uzowuru (Essex County College)

Using Deep Hybrid Modeling to Determine Treatment Strategies for COVID-19 Patients.

 

Fritz Huggins (Governor Livingston High School)

Feature Extraction and Parameter Estimation in HH Model.