Washington State University
Parastoo Alinia is an Applied Scientist at Amazon, Atlanta, GA.
Boosting Lying Posture Classification with Transfer Learning
Personalized Activity Recognition using Partially Available Target Data
TransNet: Minimally-Supervised Deep Transfer Learning for Dynamic Adaptation of Wearable Systems
ActiLabel: A Combinatorial Transfer Learning Framework for Activity Recognition
Proximity-Based Active Learning for Eating Moment Recognition in WearableSystems
Synergistic Image and Feature Adaptation: Towards Cross-Modality Domain Adaptation for Medical Image Segmentation
Transfer Learning via Learning to Transfer
Master Defense Practice III
How Accurate Your Activity Tracker Is? A Comparative Study of Step Counts in Low-Intensity Physical Activities
Defense Practice II.
Defense Practice I.
Introduction to Long Short Term Memory Networks
A Closed-loop Deep Learning Architecture for Robust Activity Recognition using Wearable Sensors
Label Propagation: An Unsupervised Similarity Based Method for Integrating New Sensors in Activity Recognition Systems
Collegial Activity Learning Between Heterogeneous Sensors
Learning from less for better: semi-supervised activity recognition via shared structure discovery
Reliable and reconfigurable framework for physical activity monitoring
Assignment Problem (Part II)
Assignment Problem (Part I)
Network-based relational knowledge transfer
NetSimile: A Scalable Approach to Size-Independent Network Similarity
Randomized 3-approximation MAX-CNF
Learn-on-the-go: Autonomous cross-subject context learning for internet-of-things applications
Multi-sensor fusion in body sensor networks: State-of-the-art and research challenges
Sequential Behavior Prediction Based on Hybrid Similarity and Cross-User Activity Transfer
Using Smart Homes to Detect and Analyze Health Events
Introduction to Transfer Learning
Estimating energy expenditure using body-worn accelerometers: a comparison of methods, sensors number and positioning.
A Reliable and Reconfigurable Signal Processing Framework for Estimation of Metabolic Equivalent of Task in Wearable Sensors
An Energy-Efficient Computational Model for Uncertainty Management in Dynamically Changing Networked Wearables
Investigating the Accuracy of Fitbit Activity Trackers
Hidden Markov Model Part II
Predicting daily activities from egocentric images using deep learning.
Stochastic methods (Simulated Annealing)
Approximation Problems (Vertex cover and TSP)
AdaBoost.R2 and Two-stage TrAdaBoost.R2 (Transfer Learning for regression models)
Boosting for Regression Transfer
Assessing Accuracy Performance of Fitbit Activity Trackers
Maximum relevancy maximum complementary feature selection for multi-sensor activity recognition
Network Flow Problem
MET Calculation using Wearable Sensors.
Impact of sensor misplacement on estimating metabolic equivalent of task with wearables
Met calculations from on-body accelerometers for exergaming movements.
Associations between Physiological Signals Captured using Wearable Sensors and Self-Reported Outcomes among Patients in AUD Recovery: Development and Usability Study