Teaching

My teaching at the University of Nevada, Reno is mainly focused on artificial intelligence and machine learning, visual computing, digital interactive gaming, and robotics. 

CS 687/487: Fundamentals of Deep Learning

Semester: Spring

Offered: 2026

Principles, design and implementation of deep learning systems. Topics include statistical machine learning, Multi-layer perceptron (MLP) and neural networks, deep neural networks, optimization and learning, convolutional neural networks (CNN), CNN architectures,CNN applications in classification, detection, segmentation, and advanced topics in recurrent networks and generative adversarial networks (GAN). This course requires some basic programming skills and a very strong background in basic math and linear algebra. Enough background on the required mathematical theories and applications will be provided.                                   

CS 680/480: Computer Graphics

Semester: Spring

Offered: 2025

Software, hardware and mathematical tools for the representation, manipulation and display of two- and three dimensional objects: applications of these tools to specific problems. 

CS 791: Stochastic Deep Learning

Semester: Spring

Offered: 2024

Study of stochastic fundamentals of deep learning systems. Topics include statistical machine learning, graphical models of learning and inference, Boltzmann machines, variational autoencoders, generative adversarial networks (GAN), diffusion models, and applications of stochastic deep learning. Stochastic Deep learning focuses on the development of a theoretic and algorithmic foundation by which higher order statistics from a population of samples is utilized to perform tasks such as sampling, inference, and representation learning.