Rami Khushaba

PhD, M.Sc, B.Sc, SM-IEEE

Received his PhD in Engineering degree from the University of Technology, Sydney (UTS) in 2010, with a focus on myoelectric signal processing. He is currently holding the position of Senior Manager, Operation Analysis and Modelling with Transport for NSW. His research interests include myoelectric control, algorithms development, machine/deep learning theory and applications, and sensor signal processing.

Interests

Human-robot interaction
Myoelectric control
Modeling & Data Analysis
Artificial intelligence
Feature extraction and reduction
Advanced algorithm design
Signal processing

Education

PhD in Engineering - Myoelectric Pattern Recognition - University of Technology, Sydney (UTS), 2010.
Master of Science in Electronics Engineering - VLSI of Artificial Neural Networks - University of Technology, 2001.
Bachelor of Science - Control and Computers Engineering - University of Technology, 1999.

Machine/Deep learning control of powered prosthetics using brain/muscles/IMUs/force sensors. EMG/EEG/FORCE/ACC/MAG/GYRO/AMG signals processing, feature engineering and Deep Learning.
Radar-Based Materials Identification with centimetre and millimetre wave units Wavelet Scattering application on Walabot-3D (6.3-8 GHz) and IMAGEVK-74 (62-69 GHz) imaging units.
Developing real-time quality control/assurance analytics for blast-hole inspection in a mine site Sensors signal processing for blast-hole inspection to guide subsequent blasting processes.
Automated materials/assays estimation using measure-while-drilling (MWD) data of autonomous drills. Developing novel machine learning algorithms to map drill-rig parameters to minerals contained in a mine site.
Object detection & visual analytics design based on digger-mounted cameras in a mine site. Deep learning (YOLO)-based digger operation analysis for scoring operation cycle time & other parameters.
Causal Impact and Root Cause Analysis in smart buildings’ HVAC faults-prediction and tracking. Machine learning and time-series analysis to predict and track faults in large commercial buildings' HVACs.
Predictive HVAC energy optimization/modelling and analytics design Analytics (descriptive/diagnostic/predictive), modelling and prediction and large-scale optimization.
Energy-savings measurement and verification/baseline modelling with Random Forests, SVM, ELM, and LSTM.
Doppler radar-based prediction of symptoms of worsening health in COPD/HF patients. Statistical signal processing, machine learning, time-series analysis, analytics, and feature extraction ·
AI-based sleep scoring/staging algorithms development in Type III/IV home sleep testing devices. Using machine learning to identify patterns of sleep and score these into the different stages. ·
Noncontact sleep monitoring and activity/heart/respiratory rates tracking using Doppler radars. Machine learning and Radar signals to track periodic leg movements (PLM) & vital signs remotely.
Driver drowsiness, fatigue, and cognitive impairment detection (EEG/ECG/EOG signal processing). Machine learning, signal processing analysis, vision, and eye-tracking to identify onsets of fatigue.
Consumer neuroscience, neuromarketing, and decision-making (EEG signal processing/eye-trackers). Using signal processing, statistical analysis, and machine learning to infer user decision of purchase.
Towards speed-independent road-type classification Wavelet analysis, Fourier-Transform, Time-series analysis, SVM classification to classify road types.

Projects

Membership

Senior Member, IEEE (2015 onwards) - a member of several IEEE societies, most importantly including:

IEEE Engineering in Medicine and Biology Society
IEEE Signal Processing Society
IEEE Computational Intelligence Society

I have also been an active reviewer for many IEEE Transactions Journals, including IEEE Transactions on Biomedical Engineering, IEEE Journal of Biomedical and Health Informatics (JBHI), IEEE Transactions on Neural Systems and Rehabilitation Engineering, just to mention a few.

Contact

Use the form to reach out to me.

Rami Khushaba

PhD, M.Sc, B.Sc, SM-IEEE

Human-robot interaction

Artificial intelligence

Feature extraction and reduction

Advanced algorithm design

Signal processing

PhD in Engineering - Myoelectric Pattern Recognition - University of Technology, Sydney (UTS), 2010.

Bachelor of Science - Control and Computers Engineering - University of Technology, 1999.

Machine/Deep learning control of powered prosthetics using brain/muscles/IMUs/force sensors. EMG/EEG/FORCE/ACC/MAG/GYRO/AMG signals processing, feature engineering and Deep Learning.

Radar-Based Materials Identification with centimetre and millimetre wave units Wavelet Scattering application on Walabot-3D (6.3-8 GHz) and IMAGEVK-74 (62-69 GHz) imaging units.

Developing real-time quality control/assurance analytics for blast-hole inspection in a mine site Sensors signal processing for blast-hole inspection to guide subsequent blasting processes.

Automated materials/assays estimation using measure-while-drilling (MWD) data of autonomous drills. Developing novel machine learning algorithms to map drill-rig parameters to minerals contained in a mine site.

Object detection & visual analytics design based on digger-mounted cameras in a mine site. Deep learning (YOLO)-based digger operation analysis for scoring operation cycle time & other parameters.

Causal Impact and Root Cause Analysis in smart buildings’ HVAC faults-prediction and tracking. Machine learning and time-series analysis to predict and track faults in large commercial buildings' HVACs.

Predictive HVAC energy optimization/modelling and analytics design Analytics (descriptive/diagnostic/predictive), modelling and prediction and large-scale optimization.

Energy-savings measurement and verification/baseline modelling with Random Forests, SVM, ELM, and LSTM.

Doppler radar-based prediction of symptoms of worsening health in COPD/HF patients. Statistical signal processing, machine learning, time-series analysis, analytics, and feature extraction ·

AI-based sleep scoring/staging algorithms development in Type III/IV home sleep testing devices. Using machine learning to identify patterns of sleep and score these into the different stages. ·

Noncontact sleep monitoring and activity/heart/respiratory rates tracking using Doppler radars. Machine learning and Radar signals to track periodic leg movements (PLM) & vital signs remotely.

Driver drowsiness, fatigue, and cognitive impairment detection (EEG/ECG/EOG signal processing). Machine learning, signal processing analysis, vision, and eye-tracking to identify onsets of fatigue.

Consumer neuroscience, neuromarketing, and decision-making (EEG signal processing/eye-trackers). Using signal processing, statistical analysis, and machine learning to infer user decision of purchase.

Towards speed-independent road-type classification Wavelet analysis, Fourier-Transform, Time-series analysis, SVM classification to classify road types.

Projects

Membership

Senior Member, IEEE (2015 onwards) - a member of several IEEE societies, most importantly including:

IEEE Engineering in Medicine and Biology Society

IEEE Signal Processing Society

IEEE Computational Intelligence Society

I have also been an active reviewer for many IEEE Transactions Journals, including IEEE Transactions on Biomedical Engineering, IEEE Journal of Biomedical and Health Informatics (JBHI), IEEE Transactions on Neural Systems and Rehabilitation Engineering, just to mention a few.

Contact