The computational intelligence and machine vision laboratory (Vision Lab) aims to develop novel theory, state-of-art algorithms and architectures for learning and real-time applications in the areas of:

Human and Machine-Centric Interaction, Learning and Recognition

Expression Learning

Data-limited domain adaptation and transfer learning for learning latent expression labels of child facial expression images


Gender Classification Using Gait Kinematics


Generalized object recognition using deep recurrent models

3D face

Analysis and Recognition of 3D Facial Expressions in Pose Variations


Non-intrusive optical imaging of face to probe physiological traits in Autism Spectrum Disorder


Robot navigation and object detection/tracking


CSRN Maze Traversal with EKF


Biomedical Imaging and Signal Analysis

classification architecture

Joint Modeling of RNASEQ and Radiomics Data for Glioma Molecular Characterization and Prediction

classification architecture

Prediction of Molecular Mutations in Diffuse Low-Grade Gliomas using MR Imaging Features


Glioma Outcome Prediction And Survival Analysis


Neurophysiologic Analysis of Human Visual Processing and Interfacing for Automated Robot Navigation


Improved Brain Tumor Growth Prediction and Segmentation in Longitudinal Brain MRI

tumor growth

Modeling and Fusion of Atlas-Based Tumor Growth and Feature-Based Models for Normal and Abnormal Brain Tissue Segmentation

brain tumor segmentation

Automatic Segmentation of Brain Tumor and Lesions in MR Images


Deep Recurrent Neural Network for Seizure Detection


Environmental and Geosciences Applications

traffic segmentation

Efficient Segmentation of Real-Time Traffic Video for Gas Emissions Estimation


Sensing and Classification of Aerosols in LiDAR Scattering Plots in the Atmosphere


Shoreline Detection and Mapping Using DEMs Data and Aerial Photos