| Synthetic Sensor Data Generation for Health Applications: A Supervised Deep Learning Approach S Norgaard, R Saeedi, K Sasani, AH Gebremedhin | 46 | 2018 |
| Personalized Human Activity Recognition using Wearables: A Manifold Learning-based Knowledge Transfer R Saeedi, K Sasani, S Norgaard, AH Gebremedhin | 37 | 2018 |
| Gait speed and survival of older surgical patient with cancer: prediction after machine learning K Sasani, HN Catanese, A Ghods, SA Rokni, H Ghasemzadeh, ... Journal of geriatric oncology 10 (1), 120-125, 2019 | 23 | 2019 |
| Co-MEAL: Cost-optimal multi-expert active learning architecture for mobile health monitoring R Saeedi, K Sasani, AH Gebremedhin Proceedings of the 8th ACM International Conference on Bioinformatics …, 2017 | 16 | 2017 |
| BEAMS: bounded event detection in graph streams MH Namaki, K Sasani, Y Wu, T Ge 2017 IEEE 33rd International Conference on Data Engineering (ICDE), 1387-1388, 2017 | 13 | 2017 |
| Performance Prediction for Graph Queries MH Namaki, K Sasani, Y Wu, AH Gebremedhin Proceedings of the 2nd International Workshop on Network Data Analytics, 9, 2017 | 11 | 2017 |
| Collaborative multi-expert active learning for mobile health monitoring: architecture, algorithms, and evaluation R Saeedi, K Sasani, AH Gebremedhin Sensors 20 (7), 1932, 2020 | 8 | 2020 |
| Multi-metric graph query performance prediction K Sasani, MH Namaki, Y Wu, AH Gebremedhin Database Systems for Advanced Applications: 23rd International Conference …, 2018 | 7 | 2018 |
| Network Similarity Prediction in Time-evolving Graphs: A Machine Learning Approach K Sasani, MH Namaki, AH Gebremedhin | 3 | 2018 |
| MACHINE LEARNING FOR PREDICTING PERFORMANCE OF GRAPH ALGORITHMS WITH APPLICATIONS IN GRAPH DATABASES K Sasani Washington State University, 2018 | | 2018 |
Assefaw GebremedhinWashington State University, School of EECSVerified email at eecs.wsu.edu
