| Gravity Spy: integrating advanced LIGO detector characterization, machine learning, and citizen science M Zevin, S Coughlin, S Bahaadini, E Besler, N Rohani, S Allen, M Cabero, ... Classical and quantum gravity 34 (6), 064003, 2017 | 384 | 2017 |
| Machine learning for Gravity Spy: Glitch classification and dataset S Bahaadini, V Noroozi, N Rohani, S Coughlin, M Zevin, JR Smith, ... Information Sciences 444, 172-186, 2018 | 122 | 2018 |
| Classifying the unknown: discovering novel gravitational-wave detector glitches using similarity learning S Coughlin, S Bahaadini, N Rohani, M Zevin, O Patane, M Harandi, ... Physical Review D 99 (8), 082002, 2019 | 54 | 2019 |
| Deep multi-view models for glitch classification S Bahaadini, N Rohani, S Coughlin, M Zevin, V Kalogera, ... 2017 ieee international conference on acoustics, speech and signal …, 2017 | 54 | 2017 |
| Innovative data reduction and visualization strategy for hyperspectral imaging datasets using t-SNE approach E Pouyet, N Rohani, AK Katsaggelos, O Cossairt, M Walton Pure and Applied Chemistry 90 (3), 493-506, 2018 | 51 | 2018 |
| Data quality up to the third observing run of advanced LIGO: Gravity Spy glitch classifications J Glanzer, S Banagiri, SB Coughlin, S Soni, M Zevin, CPL Berry, O Patane, ... Classical and Quantum Gravity 40 (6), 065004, 2023 | 44 | 2023 |
| Nonlinear unmixing of hyperspectral datasets for the study of painted works of art N Rohani, E Pouyet, M Walton, O Cossairt, AK Katsaggelos Angewandte Chemie 130 (34), 11076-11080, 2018 | 35 | 2018 |
| Direct: Deep discriminative embedding for clustering of ligo data S Bahaadini, N Rohani, AK Katsaggelos, V Noroozi, S Coughlin, M Zevin 2018 25th ieee international conference on image processing (icip), 748-752, 2018 | 24 | 2018 |
| Pigment unmixing of hyperspectral images of paintings using deep neural networks N Rohani, E Pouyet, M Walton, O Cossairt, AK Katsaggelos ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and …, 2019 | 20 | 2019 |
| Knowledge tracing to model learning in online citizen science projects K Crowston, C Řsterlund, TK Lee, C Jackson, M Harandi, S Allen, ... IEEE Transactions on Learning Technologies 13 (1), 123-134, 2019 | 19 | 2019 |
| Teaching citizen scientists to categorize glitches using machine learning guided training C Jackson, C Řsterlund, K Crowston, M Harandi, S Allen, S Bahaadini, ... Computers in Human Behavior 105, 106198, 2020 | 18 | 2020 |
| Automatic pigment identification on roman egyptian paintings by using sparse modeling of hyperspectral images N Rohani, J Salvant, S Bahaadini, O Cossairt, M Walton, A Katsaggelos 2016 24th European signal processing conference (EUSIPCO), 2111-2115, 2016 | 18 | 2016 |
| Artificial intelligence for pigment classification task in the short-wave infrared range E Pouyet, T Miteva, N Rohani, L de Viguerie Sensors 21 (18), 6150, 2021 | 15 | 2021 |
| Guess and determine attack on Trivium family N Rohani, Z Noferesti, J Mohajeri, MR Aref 2010 IEEE/IFIP International Conference on Embedded and Ubiquitous Computing …, 2010 | 11 | 2010 |
| Neural networks for modeling neural spiking in S1 cortex A Lucas, T Tomlinson, N Rohani, R Chowdhury, SA Solla, ... Frontiers in systems neuroscience 13, 13, 2019 | 9 | 2019 |
| C sterlund, JR Smith, L Trouille, and V Kalogera. Gravity spy: integrating advanced ligo detector characterization, machine learning, and citizen science M Zevin, S Coughlin, S Bahaadini, E Besler, N Rohani, S Allen, M Cabero, ... Classical and Quantum Gravity 34 (6), 064003, 2017 | 7 | 2017 |
| Graph-based identification of boundary points for unmixing and anomaly detection N Rohani, M Parente 2013 5th Workshop on Hyperspectral Image and Signal Processing: Evolution in …, 2013 | 7 | 2013 |
| Gravity Spy machine learning classifications of LIGO glitches from observing runs O1, O2, O3a, and O3b S Coughlin, M Zevin, S Bahaadini, N Rohani, S Allen, C Berry, ... Zenodo, 2021 | 6 | 2021 |
| Gravity spy machine learning classifications of LIGO glitches from observing runs O1 J Glanzer, S Banagari, S Coughlin, M Zevin, S Bahaadini, N Rohani, ... O2, O3a, and O3b (available at: https://zenodo. org/records/5649212), 2021 | 4 | 2021 |
| Matrix sparsification and non-negative factorization for task partitioning in computational sensing and imaging DG Stork, N Rohani, AK Katsaggelos Computational Imaging II 10222, 128-137, 2017 | 4 | 2017 |
Aggelos K KatsaggelosJoseph Cummings Professor of ECE (Courtesy CS and Radiology), Northwestern UniversityVerified email at eecs.northwestern.edu
