Professional Talks

Inferring Parameters of Pyramidal Neuron Excitability in Mouse Models of Alzheimer's Disease Using Biophysical Modeling and Deep Learning

Bristol Myers Squibb (BMS), Immuno-Oncology Team · Princeton, NJ

Inferring Parameters of Pyramidal Neuron Excitability in Mouse Models of Alzheimer's Disease Using Biophysical Modeling and Deep Learning

Bristol Myers Squibb (BMS), Methodology Team · Princeton, NJ

Deep Hybrid Modeling of Biological Systems Using Generative Adversarial Networks

Merck & Co

Deep Hybrid Modeling of Biological Systems Using Generative Adversarial Networks

Georgia State University · Atlanta, GA

Deep Hybrid Modeling of Biological Systems Using Generative Adversarial Networks

The University of Utah · Salt Lake City, UT

Deep Hybrid Modeling of Biological Systems Using Generative Adversarial Networks

Emory University School of Medicine · Atlanta, GA

Deep Hybrid Modeling of Biological Systems Using Generative Adversarial Networks

Bristol Myers Squibb (BMS), Methodology Team · Princeton, NJ

Mathematical Modeling of Interaction Between Innate and Adaptive Immune Responses in COVID-19 and Implications for Viral Pathogenesis

IBM Journal Club

A Brief Introduction of Convolutional Neural Network (CNN) Plus a Hands-On Project in Digit Recognition

Machine Learning and Optimization Seminar, NJIT · Newark, NJ

A Markov Chain Monte Carlo Version of the Genetic Algorithm Differential Evolution: Easy Bayesian Computing for Real Parameter Spaces

Machine Learning and Optimization Seminar, NJIT · Newark, NJ

Deep Hybrid Modeling of Neuronal Dynamics Using Generative Adversarial Networks

Society for Neuroscience (SfN)

S. Saghafi, T. Rumbell, K. Wedgwood, F. Tamagnini, V. Gurev, J. Kozloski, C. O. Diekman

Deep Hybrid Modeling of Neuronal Dynamics Using Generative Adversarial Networks

NAI–NJIT Workshop on Sustainable Societies: Data Revolution

S. Saghafi, T. Rumbell, K. Wedgwood, C. O. Diekman

Deep Hybrid Modeling of Neuronal Dynamics Using Generative Adversarial Networks

Frontiers in Applied & Computational Mathematics (FACM)

S. Saghafi, T. Rumbell, K. Wedgwood, C. O. Diekman

Deep Hybrid Modeling of Neuronal Dynamics Using Generative Adversarial Networks

Dana Knox Student Research Showcase

S. Saghafi, C. O. Diekman

Generative Adversarial Networks for Parameter Inference in Populations of Neuroscience Models

Society for Neuroscience (SfN)

T. Rumbell, S. Saghafi, C. O. Diekman, J. Kozloski, V. Gurev