collaborators

5 papers

cs.LG2026

Does Latent Context Help? A Controlled Evaluation of Inverse Reinforcement Learning in Arctic Shipping

Vaishnav Vaidheeswaran, Dilith Jayakody, Biruk Ambaw +3

Artificial Intelligence (AI)-assisted navigation can help Arctic shipping adapt to rapidly changing sea-ice conditions, but reliable deployment requires reward models that are inte…

cs.LG2026

Transferable Dual-Stream Representations for Mesoscale-Preserving Sea Surface Temperature Downscaling

Parth Doshi, Priyanka Aravindan, Vaishnav Vaidheeswaran +2

Deep learning models for scientific spatio-temporal downscaling often minimize reconstruction error while failing to preserve physically meaningful multi-scale structure. For sea s…

cs.CE2025

A Theoretical Framework for Environmental Similarity and Vessel Mobility as Coupled Predictors of Marine Invasive Species Pathways

Gabriel Spadon, Vaishnav Vaidheeswaran, Claudio DiBacco

Marine invasive species spread through global shipping and generate substantial ecological and economic impacts. Traditional risk assessments require detailed records of ballast wa…

stat.AP2025

Modeling Maritime Transportation Behavior Using AIS Trajectories and Markovian Processes in the Gulf of St. Lawrence

Gabriel Spadon, Ruixin Song, Vaishnav Vaidheeswaran +3

Maritime transportation is central to the global economy, and analyzing its large-scale behavioral data is critical for operational planning, environmental stewardship, and governa…

cs.LG2025

Goal-Conditioned Reinforcement Learning for Data-Driven Maritime Navigation

Vaishnav Vaidheeswaran, Dilith Jayakody, Samruddhi Mulay +3

Routing vessels through narrow and dynamic waterways is challenging due to changing environmental conditions and operational constraints. Existing vessel-routing studies typically…