Researcher Kurt Keutzer

Researcher Biography
http://www.eecs.berkeley.edu/Faculty/Homepages/keutzer.html

Current Projects

Cross-Domain Self-Supervised Learning for Adaptation and Generalization
Multi-Modal Self-Supervised Pre-training for Label-Efficient 3D Perception
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
Meta Neural Architecture Search For Computer Vision

Past Projects

Data Efficiency for Point Cloud Perception Model
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
An Innovative Approach to the Dual Problems of High-Resolution Input and Video Object Detection
Enabling Valuable Training Data through Domain Adaptation
Automated Search for Neural Net Architectures
Embedded natural language processing for in-car speech commands
Efficient Neural Networks through Systematic Quantization
Real-time, Energy Efficient Video Object Detection and Tracking
Domain Adaptation for Realistic LiDAR Data Synthesis
Efficient Neural Nets for Real-Time object-recognition on High-Resolution Images
Safe and Effective Learning through Formal Simulation
Energy-Efficient Deep Neural Nets for Occupancy-Grid Mapping
Design Space Exploration for Deep Neural Nets for Advanced Driver Assistance Systems