| A deep hybrid model for recommendation by jointly leveraging ratings, reviews and metadata information ZY Khan, Z Niu, AS Nyamawe, I ul Haq Engineering Applications of Artificial Intelligence 97, 104066, 2021 | 19 | 2021 |
| CAMELS-Chem: Augmenting CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) with atmospheric and stream water chemistry data G Sterle, J Perdrial, DW Kincaid, KL Underwood, DM Rizzo, IU Haq, L Li, ... Hydrology and Earth System Sciences Discussions 2022, 1-23, 2022 | 12 | 2022 |
| An automated machine learning approach for detecting anomalous peak patterns in time series data from a research watershed in the Northeastern United States critical zone IU Haq, BS Lee, DM Rizzo, JN Perdrial Machine Learning with Applications 16, 100543, 2024 | 3 | 2024 |
| Diverse misinformation: impacts of human biases on detection of deepfakes on networks J Lovato, J St-Onge, R Harp, G Salazar Lopez, SP Rogers, IU Haq, ... npj Complexity 1 (1), 5, 2024 | 3 | 2024 |
| Peak Anomaly Detection from Environmental Sensor-Generated Watershed Time Series Data BS Lee, JC Kaufmann, DM Rizzo, IU Haq Annual International Conference on Information Management and Big Data, 142-157, 2022 | 3 | 2022 |
| Evaluating and Enhancing the Robustness of Sustainable Neural Relationship Classifiers Using Query-Efficient Black-Box Adversarial Attacks IU Haq, ZY Khan, A Ahmad, B Hayat, A Khan, YE Lee, KI Kim Sustainability 13 (11), 5892, 2021 | 3 | 2021 |
| TransNAS-TSAD: Harnessing Transformers for Multi-Objective Neural Architecture Search in Time Series Anomaly Detection IU Haq, BS Lee arXiv preprint arXiv:2311.18061, 2023 | 2 | 2023 |
| CAMELS-Chem: augmenting CAMELS (Catchment Attributes and Meteorology for Large-sample Studies) with atmospheric and stream water chemistry data G Sterle, J Perdrial, DW Kincaid, KL Underwood, DM Rizzo, IU Haq, L Li, ... Hydrology and Earth System Sciences 28 (3), 611-630, 2024 | 1 | 2024 |
| From Ashes to Insights: Dissecting Ecosystem Dynamics Before and After Wildfire in Illilouette Creek Basin UL Ijaz, G Boisrame, BS Lee, K Underwood, JN Perdrial AGU23, 2023 | | 2023 |
| Impact of changes in water availability on water quality: a data-driven investigation of Critical Zone subsurface and vegetation interactions N Hicks, L Li, B Stewart, K Underwood, UL Ijaz, DW Kincaid, L Lowman, ... AGU23, 2023 | | 2023 |
| Peak Anomaly Detection using Critical Zone Time Series Data: Knowledge-Engineering and Deep-Learning BS Lee, JC Kaufmann, JB Shanley, DM Rizzo, JN Perdrial, IU Haq AGU Fall Meeting Abstracts 2022, H31E-06, 2022 | | 2022 |
| Leveraging Catchment Attributes to Explain Patterns of Concentration-Discharge Relationships Across the Contiguous United States DW Kincaid, K Underwood, SD Hamshaw, I Ul Haq, L Li, DM Rizzo, ... AGU Fall Meeting Abstracts 2022, H32S-1150, 2022 | | 2022 |
| Automated Machine Learning Approach to Supervised Anomaly Detection from Critical Zone Watershed Sensor-Generated Time Series Data IU Haq, BS Lee, DM Rizzo, JN Perdrial, JB Shanley AGU Fall Meeting Abstracts 2022, H22P-1031, 2022 | | 2022 |
| From pattern to process and process to pattern: insights on data-driven Critical Zone research from the Big Data collaborative network cluster JN Perdrial, K Underwood, S Swami, BS Lee, IU Haq, D Kincaid, ... 2022 Goldschmidt Conference, 2022 | | 2022 |
| Lifelikeness is in the eye of the beholder: demographics of deepfake detection and their impacts on online social networks J Lovato, L Hébert-Dufresne, J St-Onge, GS Lopez, SP Rogers, R Harp, ... | | 2022 |
| Why Critical Zone (CZ) science needs team science: insights from the big data CZ network cluster J Perdrial, D Kincaid, D Wheaton, L Walls, I Ul Haq, D Rizzo, S Hamshaw, ... AGU Fall Meeting Abstracts 2021, EP45H-1597, 2021 | | 2021 |
