2024
Nivron, O., Wischik, DJ., Vrac, M. and Shuckburgh, E., 2024. A Temporal Bias Correction using a Machine Learning Attention model. CoRR, v. abs/2402.14169
Wilkins, G., Keshav, S. and Mortier, R., 2024. Offline Energy-Optimal LLM Serving: Workload-Based Energy Models for LLM Inference on Heterogeneous Systems ACM SIGEnergy Energy Informatics Review, v. 4
Doi: 10.1145/3727200.3727217
Rau, E-P., Gross, J., Coomes, DA., Swinfield, T., Madhavapeddy, A., Balmford, A. and Keshav, S., 2024. Mitigating risk of credit reversal in nature-based climate solutions by optimally anticipating carbon release Carbon Management, v. 15
Doi: 10.1080/17583004.2024.2390854
Rau, E-P., Gross, J., Coomes, DA., Swinfield, T., Madhavapeddy, A., Balmford, A. and Keshav, S., 2024. Research data supporting “Mitigating risk of credit reversal in nature-based climate solutions by optimally anticipating carbon release”
Doi: http://doi.org/10.17863/CAM.110933
Swinfield, T., Shrikanth, S., Bull, JW., Madhavapeddy, A. and zu Ermgassen, SOSE., 2024. Nature-based credit markets at a crossroads Nature Sustainability, v. 7
Doi: 10.1038/s41893-024-01403-w
Millar, J., Sethi, S., Haddadi, H. and Madhavapeddy, A., 2024. Poster: Towards Low-Power Comprehensive Biodiversity Monitoring Sensys 2024 Proceedings of the 2024 ACM Conference on Embedded Networked Sensor Systems,
Doi: 10.1145/3666025.3699400
Vijayakumar, S., Madhavapeddy, A. and Kalyvianaki, E., 2024. Scheduling for Reduced Tail Task Latencies in Highly Utilized Datacenters Socc 2024 Proceedings of the 2024 ACM Symposium on Cloud Computing,
Doi: 10.1145/3698038.3698522
Rau, EP., Gross, J., Coomes, DA., Swinfield, T., Madhavapeddy, A., Balmford, A. and Keshav, S., 2024. Mitigating risk of credit reversal in nature-based climate solutions by optimally anticipating carbon release Carbon Management, v. 15
Doi: 10.1080/17583004.2024.2390854
Rau, E-P., Gross, J., Coomes, DA., Swinfield, T., Madhavapeddy, A., Balmford, A. and Keshav, S., 2024. Mitigating risk of credit reversal in nature-based climate solutions by optimally anticipating carbon release Carbon Management, v. 15
Doi: 10.1080/17583004.2024.2390854
Berkes, A. and Keshav, S., 2024. SPAGHETTI: a synthetic data generator for post-Covid electric vehicle usage Energy Informatics, v. 7
Doi: 10.1186/s42162-024-00314-6
Millar, J., Sethi, S., Haddadi, H. and Madhavapeddy, A., 2024. Terracorder: Sense Long and Prosper. CoRR, v. abs/2408.02407
Holcomb, A., Burns, P., Keshav, S. and Coomes, DA., 2024. Repeat GEDI footprints measure the effects of tropical forest disturbances Remote Sensing of Environment, v. 308
Doi: 10.1016/j.rse.2024.114174
Berkes, A. and Keshav, S., 2024. SOPEVS: Sizing and Operation of PV-EV-Integrated Modern Homes The 15th ACM International Conference on Future and Sustainable Energy Systems,
Doi: 10.1145/3632775.3661941
Wilkins, G., Keshav, S. and Mortier, R., 2024. Hybrid Heterogeneous Clusters Can Lower the Energy Consumption of LLM Inference Workloads The 15th ACM International Conference on Future and Sustainable Energy Systems,
Doi: 10.1145/3632775.3662830
Lisaius, MC., Blake, A., Keshav, S. and Atzberger, C., 2024. Using Barlow Twins to Create Representations From Cloud-Corrupted Remote Sensing Time Series IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, v. 17
Doi: 10.1109/JSTARS.2024.3426044
Rau, EP., Gross, J., Coomes, DA., Swinfield, T., Madhavapeddy, A., Balmford, A. and Keshav, S., 2024. Mitigating risk of credit reversal in nature-based climate solutions by optimally anticipating carbon release Carbon Management, v. 15
Doi: 10.1080/17583004.2024.2390854
2023 (No publication date)
2023
Meijers, AJS., Meredith, MP., Shuckburgh, EF., Kent, EC., Munday, DR., Firing, YL., King, B., Smyth, TJ., Leng, MJ., George Nurser, AJ., Hewitt, HT., Povl Abrahamsen, E., Weiss, A., Yang, M., Bell, TG., Alexander Brearley, J., Boland, EJD., Jones, DC., Josey, SA., Owen, RP., Grist, JP., Blaker, AT., Biri, S., Yelland, MJ., Pimm, C., Zhou, S., Harle, J. and Cornes, RC., 2023. Finale: impact of the ORCHESTRA/ENCORE programmes on Southern Ocean heat and carbon understanding. Philos Trans A Math Phys Eng Sci, v. 381
Doi: 10.1098/rsta.2022.0070
Ghasemitaheri, S., Holcomb, A., Golab, L. and Keshav, S., 2023. On the Data Quality of Remotely Sensed Forest Maps Ceur Workshop Proceedings, v. 3462
Holcomb, A., Mathis, SV., Coomes, DA. and Keshav, S., 2023. Computational tools for assessing forest recovery with GEDI shots and forest change maps Science of Remote Sensing, v. 8
Doi: 10.1016/j.srs.2023.100106
