Rate My Professor Jacq Christmas

Her research contributions are evidenced in key publications such as 'VTOL UAS Auto-recovery Using A Tested Long-Term Motion Prediction Method to define the Deck Environment' (2024), 'Changing Definitions of Digital Twins In Ship Design' (2023), 'Launch and Recovery of Traditional VTOL UAVs by Quiescent Period Prediction (QPP), the development of QPP for All Weather Operations (AWOPS)' (2023), 'Emergent Technology to Forecast Flight Deck Motions for Vehicle Auto-Launch and Recovery' (2022), 'Sea state from ocean video with singular spectrum analysis and extended Kalman filter' (2022), 'Automating RTI: Automatic light direction detection and improved shape recovery' (2020), and 'Genetic C Programming with Probabilistic Evaluation' (2015). Christmas has garnered 470 citations on ResearchGate. She has participated in significant projects including Project ADA focused on machine learning, computer vision, and optimisation; the Defence Data, Machine Learning and AI network; IDSAI seed-corn funded Roman Workshop in digital humanities; automated detection of hillforts in South West England; and GW4 alliance collaborations. Additionally, she contributed to an open innovation grant with HM Land Registry exploring machine learning applications. Her expertise has been shared through seminars on RTI revealing hidden details in heritage objects and presentations at evolutionary computation conferences, influencing advancements in AI-driven imaging and prediction technologies.