Publications

iHARP -> Research -> Publications
last updated 2024 April 19

iHARP Publications and accepted research papers.

 

2024

  1. Tolulope Ale, Nicole-Jeanne Schlegel, and Vandana Janeja. “Harnessing Feature Clustering for Enhanced Anomaly Detection with VAE and Dynamic Threshold.” 2024. In 2024 IEEE International Geoscience and Remote Sensing Symposium in Athens, Greece.
  2. Bayu Adhi Tama, Vandana Janeja, and Sanjay Purushotham. “Assessing Annotation Accuracy in Ice Sheet Using Quantitative Metrics,” 2024. In 2024 IEEE International Geoscience and Remote Sensing Symposium in Athens, Greece
  3. Maruthi P. Chellatore and Sharad Sharma. “Mobile Application for Identifying Anomalous Behavior and Conducting Time Series Analysis Using Heterogeneous Data.” In 26th International Conference on Human-Computer Interaction (HCI International 2024), June 29 – July 4, 2024, Washington, DC. HCI International.
  4. Sharad Sharma, Sri Chandra Dronavalli, Maruthi P. Chellatore, and Rishitha Pesaladinne. “Interactive Visualizations for Crime Data Analysis by Mixed Reality.” In 26th International Conference on Human-Computer Interaction (HCI International 2024), June 29 – July 4, 2024, Washington, DC. HCI International.
  5. Bayu Adhi Tama, Sanjay Purushotham, and Vandana Janeja. “A Pilot Study on the Challenges in Ice Layer Annotations,” 2024. In 13th International Conference on Climate Informatics in London, UK.
  6. Ziqi Yin, Adam R Herrington, Rajashree Datta, Aneesh C. Subramanian, Jan Thérèse Maria Lenaerts, and Andrew Gettelman. 2024. Improved Understanding of Multicentury Greenland Ice Sheet Response to Strong Warming in the Coupled CESM2‐CISM2 with Regional Grid Refinement. https://doi.org/10.22541/au.170967825.56554188/v1 [pre-print March 2024]
  7. Gong Cheng, Mathieu Morlighem, and Sade Francis. 2024. A unified framework for forward and inverse modeling of ice sheet flow using physics-informed neural networks. https://doi.org/10.22541/essoar.170965007.78479393/v1 [pre-print]
  8. Devon Dunmire, Nander Wever, Alison F. Banwell, and Jan T. M. Lenaerts. 2024. Antarctic-wide ice-shelf firn emulation reveals robust future firn air depletion signal for the Antarctic Peninsula. Commun Earth Environ 5, 1 (February 2024), 100. https://doi.org/10.1038/s43247-024-01255-4
  9. Sahara Ali, Omar Faruque, Yiyi Huang, Md Osman Gani, Aneesh Subramanian, Nicole-Jeanne Schlegel, and Jianwu Wang. “Estimating Causal Effects of Greenland Blocking on Arctic Sea Ice Melt Using Deep Learning Technique,” 2023. In American Meteorological Society’s 23rd Conference on Artificial Intelligence for Environmental Science 2024. Poster at AMS, 668. [Conference Paper/Poster]
  10. Tolulope Ale, Nicole-Jeanne Schlegel, Vandana Janeja. “Enhanced Multivariate Anomaly Detection and Feature Attribution in Climate Data.” 2024. In the 30th SIGKDD Conference on Knowledge Discovery and Data Mining – Applied Data Science Track. KDD 2024 – A.D.S. [Under peer review]
  11. Sharad Sharma and Sri Chandra Dronavalli. “Data Analysis and Visualization of Crime Data.” Electronic Imaging 36, no. 1 (January 21, 2024): 364-1-364–66. https://doi.org/10.2352/EI.2024.36.1.VDA-364.

2023

  1. Sharad Sharma, Sri Chandra Dronavalli, Maruthi P. Chellatore, and Rishitha Pesaladinne. “Situational Awareness and Feature Extraction for Indoor Building Navigation Using Mixed Reality.” In IEEE International Conference on Computational Science and Computational Intelligence, December 13-15, 2023, Las Vegas, Nevada. IEEE-CSCI-RTSC. Vol. 2023.
  2. Sharad Sharma, Rishitha Pesaladinne, and Sri Chandra Dronavalli. “Crime Data Visualization Using Virtual Reality and Augmented Reality.” In the IEEE International Conference on Computational Science and Computational Intelligence, December 13-15, 2023, Las Vegas, Nevada. IEEE-CSCI-RTSC. Vol. 2023.
  3. Naomi Tack, Nicholas Holschuh, Sharad Sharma, Rebecca Williams, and Don Engel. 2023. “Initial Development of a WebXR Platform for Ice Penetrating Radar Data, to Improve Our Understanding of Polar Ice Sheets,” December 2023. Poster at the AGU23 meeting, IN43B-0627. [Poster]
  4. Christopher Shuman and Mark A. Fahnestock. “Last of the Big Thwaites Bergs – Iceberg B22A Modulates Fast Ice and Ice Front Stability, as It Departs the Amundsen Sea Embayment (2017-2023),” December 2023. Poster at the AGU23 meeting, C21D-1264. [Poster]
  5. Sudip Chakraborty, Chhaya Kulkarni, Atefeh Jebeli, Jianwu Wang, and Vandana Janeja. “Extreme Slash and Burn Practices over the Amazon Rainforest in 2019 Wreaked Havoc on Sea Ice Extent Over the Antarctic,” December 2023. Poster at the AGU23 meeting, C51D-0975. [Poster]
  6. Tolulope Ale and Vandana Janeja. 2023. Multi-domain Anomalous Relationships in Heterogeneous Temporal Data. In Review. https://doi.org/10.21203/rs.3.rs-3705904/v1 [Journal (Under Review)]
  7. Atefeh Jebeli, Bayu Adhi Tama, Sanjay Purushotham, and Vandana P. Janeja. 2023. Tracing Englacial Layers in Radargram via Semi-supervised Method: A Preliminary Result. AAAI-SS 2, (2023), 85–88. https://doi.org/10.1609/aaaiss.v2i1.27653 [Symposium Paper]
  8. Nidhin Harilal, Bri-Mathias Hodge, Aneesh Subramanian, and Claire Monteleoni. 2023. STint: Self-supervised Temporal Interpolation for Geospatial Data. (2023). https://doi.org/10.48550/ARXIV.2309.00059 [pre-print]
  9. Katherine Yi, Angelina Dewar, Tartela Tabassum, Jason Lu, Ray Chen, Homayra Alam, Omar Faruque, Sikan Li, Mathieu Morlighem, and Jianwu Wang. “Evaluating Machine Learning and Statistical Models for Greenland Subglacial Bed Topography.” In 2023 International Conference on Machine Learning and Applications (ICMLA), 659–66. Jacksonville, FL, USA: IEEE, 2023. https://doi.org/10.1109/ICMLA58977.2023.00097. [Conference Paper]
  10. Sharad Sharma and Rishitha Reddy Pesaladinne. “Spatial Analysis and Visual Communication of Emergency Information through Augmented Reality.” Journal of Imaging Science and Technology 67, no. 6 (November 1, 2023): 1–9. https://doi.org/10.2352/J.ImagingSci.Technol.2023.67.6.060401.
  11. Emam Hossain, Sahara Ali, Yiyi Huang, Nicole J Shchlegel, Jianwu Wang, Aneesh C. Subramanian, & Md Osman Gani. “Incorporating Causality with Deep Learning in Predicting Short-term and Seasonal Sea Ice”. Abstract in 23rd Conference on Artificial Intelligence for Environmental Science, 104th AMS Annual Meeting, 2024, November 2023. [Abstract]
  12. Subhankar Ghosh, Jayant Gupta, Arun Sharma, Shuai An, and Shashi Shekhar. 2023. Reducing False Discoveries in Statistically-Significant Regional-Colocation Mining: A Summary of Results. (2023), 18 pages, 3877579 bytes. https://doi.org/10.4230/LIPICS.GISCIENCE.2023.3 [Conference Paper]
  13. Subhankar Ghosh, Shuai An, Arun Sharma, Jayant Gupta, Shashi Shekhar, and Aneesh Subramanian. 2023. Reducing Uncertainty in Sea-level Rise Prediction: A Spatial-Variability-Aware Approach. Purdue. https://doi.org/10.5703/1288284317665 [HDR Forum Paper (iGUIDE)]
  14. Prathamesh Walkikar, Lei Shi, Bayu Adhi Tama, and Vandana P. Janeja. 2023. Discovery of multi-domain spatiotemporal associations. Geoinformatica (October 2023). DOI:https://doi.org/10.1007/s10707-023-00506-4 [Journal]
  15. Uzma Hasan, Emam Hossain, and Md Osman Gani. “A Survey on Causal Discovery Methods for I.I.D. and Time Series Data,” 2023. https://doi.org/10.48550/ARXIV.2303.15027. [Journal]
  16. Sudip Chakraborty, Chhaya Kulkarni, Atefeh Jabeli, Akila Sampath, Gehan Boteju, Jianwu Wang, and Vandana Janeja. 2023. Understanding the Role of 2019 Amazon Wildfires on Antarctic Ice Sheet Melting Using Data Science Approaches. In Submitted to 2023 ACM SIGKDD International Conference on Knowledge Discovery and Data Mining Workshop: Fragile Earth: AI for Climate Sustainability – from Wildfire Disaster Management to Public Health and Beyond, 2023 [Conference/Workshop Paper]
  17. Subhankar Ghosh, Jayant Gupta, Arun Sharma, Shuai An, and Shashi Shekhar. 2023. Reducing False Discoveries in Statistically-Significant Regional-Colocation Mining: A Summary of Results. In 12th International Conference on Geographic Information Science (GIScience 2023) (Leibniz International Proceedings in Informatics (LIPIcs)), Schloss Dagstuhl – Leibniz-Zentrum für Informatik, Dagstuhl, Germany, 3:1-3:18. DOI: https://doi.org/10.4230/LIPIcs.GIScience.2023.3 [Conference Paper]
  18. Naomi Tack, Rebecca Williams, Nicholas Holschuh, Sharad Sharma, and Don Engel. 2023. Visualizing the Greenland Ice Sheet in VR using Immersive Fence Diagrams. In Practice and Experience in Advanced Research Computing, ACM, Portland OR USA, 429–432. DOI: https://doi.org/10.1145/3569951.3603635 [Conference Paper]
  19. Naomi Tack, Bayu Adhi Tama, Atefeh Jebeli, Vandana P. Janeja, Don Engel, and Rebecca Williams. 2023. Metrics for the Quality and Consistency of Ice Layer Annotations. In IGARSS 2023 – 2023 IEEE International Geoscience and Remote Sensing Symposium, July 16, 2023, Pasadena, CA, USA. IEEE, Pasadena, CA, USA, 4935–4938. https://doi.org/10.1109/IGARSS52108.2023.10283420 [Symposium Paper]
  20. Chhaya Kulkarni, Vandana Janeja, and Nicole-Jeanne Schlegel. 2023. Multi-Contextual Learning: Analyzing Melt Over the Greenland Ice Sheet. In IGARSS 2023 – 2023 IEEE International Geoscience and Remote Sensing Symposium, July 16, 2023, Pasadena, CA, USA. IEEE, Pasadena, CA, USA, 6736–6739. https://doi.org/10.1109/IGARSS52108.2023.10281954 [Symposium Paper]
  21. Naomi Tack, Nicholas Holschuh, Sharad Sharma, Rebecca Williams, and Don Engel. 2023. Development and Initial Testing of XR-Based Fence Diagrams for Polar Science. In IGARSS 2023 – 2023 IEEE International Geoscience and Remote Sensing Symposium, July 16, 2023, Pasadena, CA, USA. IEEE, Pasadena, CA, USA, 1541–1544. https://doi.org/10.1109/IGARSS52108.2023.10281776 [Symposium Paper]
  22. Atefeh Jebeli, Bayu Adhi Tama, Vandana P Janeja, Nicholas Holschuh, Claire Jensen, Mathieu Morlighem, Joseph A Macgregor, and Mark A Fahnestock. 2023. TSSA: TWO-STEP SEMI-SUPERVISED ANNOTATION FOR RADARGRAMS ON THE GREENLAND ICE SHEET. (2023). DOI:https://doi.org/10.13140/RG.2.2.23219.20007 [Symposium Paper]
  23. Sharad Sharma. 2023. Mobile Augmented Reality System for Emergency Response. In 2023 IEEE/ACIS 21st International Conference on Software Engineering Research, Management and Applications (SERA), IEEE, Orlando, FL, USA, 402–406. DOI:https://doi.org/10.1109/SERA57763.2023.10197820 [Conference Paper]
  24. Muhammad Hasan Ferdous, Uzma Hasan, and Md Osman Gani. 2023. eCDANs: Efficient Temporal Causal Discovery from Autocorrelated and Non-stationary Data (Student Abstract). (2023). DOI:https://doi.org/10.48550/ARXIV.2303.02833 [Abstract]
  25. Sahara Ali, Omar Faruque, Yiyi Huang, Md. Osman Gani, Aneesh Subramanian, Nicole-Jienne Shchlegel, and Jianwu Wang. 2023. Quantifying Causes of Arctic Amplification via Deep Learning based Time-series Causal Inference. (2023). DOI:https://doi.org/10.48550/ARXIV.2303.07122 [Conference Paper]

2022

  1. Subhankar Ghosh, Jayant Gupta, Arun Sharma, Shuai An, and Shashi Shekhar. 2022. Towards geographically robust statistically significant regional colocation pattern detection. In Proceedings of the 5th ACM SIGSPATIAL International Workshop on GeoSpatial Simulation, ACM, Seattle Washington, 11–20. DOI:https://doi.org/10.1145/3557989.3566158 [Conference/Workshop Paper]
  2. Sahara Ali, Seraj A.M. Mostafa, Xingyan Li, Sara Khanjani, Jianwu Wang, James Foulds, and Vandana Janeja. 2022. Benchmarking Probabilistic Machine Learning Models for Arctic Sea Ice Forecasting. In IGARSS 2022 – 2022 IEEE International Geoscience and Remote Sensing Symposium, IEEE, Kuala Lumpur, Malaysia, 4654–4657. DOI:https://doi.org/10.1109/IGARSS46834.2022.9883505 [Symposium Paper]
  3. Sahara Ali and Jianwu Wang. 2022. MT-IceNet – A Spatial and Multi-Temporal Deep Learning Model for Arctic Sea Ice Forecasting. In 2022 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT), IEEE, Vancouver, WA, USA, 1–10. DOI:https://doi.org/10.1109/BDCAT56447.2022.00009 [Best Paper Award] [Conference Paper]
  4. Uzma Hasan and Md Osman Gani. 2022. KCRL: A Prior Knowledge Based Causal Discovery Framework with Reinforcement Learning. In Machine Learning in Health Care. Retrieved from https://api.semanticscholar.org/CorpusID:256943569 [Conference Paper]
  5. Xingyan Li, Jian Li, Zachary Williams, Xin Huang, Mark Carroll, and Jianwu Wang. 2022. Enhanced Deep Learning Super-Resolution for Bathymetry Data. In 2022 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT), IEEE, Vancouver, WA, USA, 49–57. DOI:https://doi.org/10.1109/BDCAT56447.2022.00014 [Conference Paper]