Vermont EPSCoR Publications and Products
] . Watershed data science at the event scale: Revealing insights in watershed function through analysis of concentration-discharge relationships. In: 2020 AGU (American Geophysical Union) Fall Meeting. 2020 AGU (American Geophysical Union) Fall Meeting. Virtual: American Geophysical Union (AGU); 2020. Available from: https://agu.confex.com/agu/fm20/webprogram/Paper766889.html
. Spatiotemporal trajectories as a new approach for studying concentration-discharge relationships of hydrological events. In: 2019 UVM Student Research Conference. 2019 UVM Student Research Conference. Burlington, VT: University of Vermont (UVM); 2019. Available from: https://scholarworks.uvm.edu/src/2019/program/372/
. Spatiotemporal Trajectories as a New Approach for Studying Concentration-discharge Relationships of Hydrological Events. 2018 AGU (American Geophysical Union) Fall Meeting [Internet]. 2018 . Available from: https://agu.confex.com/agu/fm18/meetingapp.cgi/Paper/378480
. Somtimes: self organizing maps for time series clustering and its application to serious illness conversations. Data Mining and Knowledge Discovery [Internet]. 2023 . Available from: https://link.springer.com/article/10.1007/s10618-023-00979-9
. Multivariate event time series analysis using hydrological and suspended sediment data. Journal of Hydrology [Internet]. 2021 ;593:125802. Available from: https://www.sciencedirect.com/science/article/pii/S0022169420312634
. A Monitoring Tool for Analyzing Sediment Dynamics Using Water Turbidity Sensor Data. 2020 AGU (American Geophysical Union) Fall Meeting [Internet]. 2020 . Available from: https://agu.confex.com/agu/fm20/webprogram/Paper748005.html
. Hydrological Event Detection & Analysis (HEDA) tool for streamflow and water quality time series. CUAHSI Conference on Hydroinformatics [Internet]. 2019 . Available from: https://www.cuahsi.org/uploads/pages/img/2019_Conference_on_Hydroinformatics_Program.pdf
. A benchmark study on time series clustering. Machine Learning with Applications [Internet]. 2020 ;1:100001. Available from: https://www.sciencedirect.com/science/article/pii/S2666827020300013
. An Acoustical and Lexical Machine-Learning Pipeline to Identify Connectional Silences. Journal of Palliative Medicine [Internet]. 2023 ;26(12):1627 - 1633. Available from: https://www.liebertpub.com/doi/10.1089/jpm.2023.0087