Researcher Amir Gholaminejad

Current Projects

In-Car AI Assistant: Efficient End-to-End Conversational AI System
Real-time and Accurate Object Detection through Systematic Quantization of Transformer and MLP-based Computer Vision Models
Measuring Prediction Trustworthiness and Safety Through Neural Network Loss Landscape Analysis

Past Projects

Efficient Deep Learning for ADAS/AV Through Systematic Pruning and Quantization
Hessian Aware Neural Cleansing: Searching for Optimal Trade-offs between Adversarial Robustness, Accuracy, and Speed
Embedded In-Car AI Assistant: Efficient End-to-End Speech Recognition and Natural Language Understanding for Command Recognition at the Edge
Unified Neural Architecture Search Framework for Computer Vision
Using High-Level Structure and Context for Object Recognition
Training Data Augmentation for Autonomous Driving
Efficient Model for Large-Scale Point Cloud Perception
Systematic Study of Neural Network Robustness Against Adversarial Attacks
Trust-Region Based Robustness of Neural Networks in the Face of Adversarial Attacks
Embedded natural language processing for in-car speech commands
Efficient Neural Networks through Systematic Quantization