Researcher Amir Gholaminejad

Current Projects

Systematic Quantization on Vision Models for Real-time and Accurate Inference in ADAS/AV

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