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\n  \n 2023\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n 3D Vision System with Automatically Calibrated Stereo Vision Sensors and Lidar Sensor.\n \n \n \n\n\n \n Swierczynski, P.; Jiang, L. A.; and Beveridge, M.\n\n\n \n\n\n\n 2023.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@patent{swierczynski20233D,\n  title={3D Vision System with Automatically Calibrated Stereo Vision Sensors and Lidar Sensor},\n  author={Swierczynski, Piotr and Jiang, Leaf Alden and Beveridge, Matthew},\n  year={2023},\n  number={U.S. Patent Pending}\n}
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\n  \n 2022\n \n \n (5)\n \n \n
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\n \n\n \n \n \n \n \n \n A Machine Learning and Computer Vision Approach to Rapidly Optimize Multiscale Droplet Generation.\n \n \n \n \n\n\n \n Siemenn, A. E; Shaulsky, E.; Beveridge, M.; Buonassisi, T.; Hashmi, S. M; and Drori, I.\n\n\n \n\n\n\n ACS Applied Materials & Interfaces, 14(3): 4668–4679. 2022.\n \n\n\n\n
\n\n\n\n \n \n \"A paper\n  \n \n \n \"A code\n  \n \n \n \"A arxiv\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{siemenn2022machine,\n  title={A Machine Learning and Computer Vision Approach to Rapidly Optimize Multiscale Droplet Generation},\n  author={Siemenn, Alexander E and Shaulsky, Evyatar and Beveridge, Matthew and Buonassisi, Tonio and Hashmi, Sara M and Drori, Iddo},\n  journal={ACS Applied Materials \\& Interfaces},\n  volume={14},\n  number={3},\n  pages={4668--4679},\n  year={2022},\n  publisher={American Chemical Society},\n  url_paper={https://pubs.acs.org/doi/abs/10.1021/acsami.1c19276},\n  url_code={https://github.com/PV-Lab/ML-Multiscale-Droplets},\n  url_arXiv={https://arxiv.org/abs/2105.13553}\n}\n\n
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\n \n\n \n \n \n \n \n \n Generalizing imaging through scattering media with uncertainty estimates.\n \n \n \n \n\n\n \n Cochrane, J. M; Beveridge, M.; and Drori, I.\n\n\n \n\n\n\n In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pages 760–766, 2022. \n \n\n\n\n
\n\n\n\n \n \n \"Generalizing paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 4 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{cochrane2022generalizing,\n  title={Generalizing imaging through scattering media with uncertainty estimates},\n  author={Cochrane, Jared M and Beveridge, Matthew and Drori, Iddo},\n  booktitle={Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision},\n  pages={760--766},\n  year={2022},\n  url_paper={https://openaccess.thecvf.com/content/WACV2022W/WACI/papers/Cochrane_Generalizing_Imaging_Through_Scattering_Media_With_Uncertainty_Estimates_WACVW_2022_paper.pdf}\n}\n\n
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\n \n\n \n \n \n \n \n Tracking Blobs with Machine Learning in the Turbulent Edge Plasma of a Tokamak.\n \n \n \n\n\n \n Han, W.; Pietersen, R.; Villamor-Lora, R.; Beveridge, M.; Offeddu, N.; Ahmed, S.; Decristoforo, G.; Golfinopoulos, T.; Theiler, C.; Terry, J.; Marmar, E.; and Drori, I.\n\n\n \n\n\n\n Bulletin of the American Physical Society. 2022.\n \n\n\n\n
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@article{han2022tracking,\n  title={Tracking Blobs with Machine Learning in the Turbulent Edge Plasma of a Tokamak},\n  author={Han, Woonghee and Pietersen, Randall and Villamor-Lora, Rafael and Beveridge, Matthew and Offeddu, Nicola and Ahmed, Sajidah and Decristoforo, Gregor and Golfinopoulos, Theodore and Theiler, Christian and Terry, James and Marmar, Earl and Drori, Iddo},\n  journal={Bulletin of the American Physical Society},\n  year={2022},\n  publisher={American Physical Society}\n}\n\n
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\n \n\n \n \n \n \n \n \n Tracking Blobs in the Turbulent Edge Plasma of a Tokamak Fusion Device.\n \n \n \n \n\n\n \n Han, W.; Pietersen, R. A; Villamor-Lora, R.; Beveridge, M.; Offeddu, N.; Golfinopoulos, T.; Theiler, C.; Terry, J. L; Marmar, E. S; and Drori, I.\n\n\n \n\n\n\n Scientific Reports, 12(1): 18142. 2022.\n Top 100 in Physics (ranked #37) for 2022.\n\n\n\n
\n\n\n\n \n \n \"Tracking paper\n  \n \n \n \"Tracking code\n  \n \n \n \"Tracking arxiv\n  \n \n \n \"Tracking press\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{han2022tracking,\n  title={Tracking Blobs in the Turbulent Edge Plasma of a Tokamak Fusion Device},\n  author={Han, Woonghee and Pietersen, Randall A and Villamor-Lora, Rafael and Beveridge, Matthew and Offeddu, Nicola and Golfinopoulos, Theodore and Theiler, Christian and Terry, James L and Marmar, Earl S and Drori, Iddo},\n  journal={Scientific Reports},\n  volume={12},\n  number={1},\n  pages={18142},\n  year={2022},\n  publisher={Nature Publishing Group UK London},\n  url_paper={https://www.nature.com/articles/s41598-022-21671-w},\n  url_code={https://github.com/harryh5427/GPI-blob-tracking},\n  url_arXiv={https://arxiv.org/abs/2111.08570},\n  url_press={https://news.mit.edu/2022/fusion-machine-learning-turbulence-1101},\n  note={Top 100 in Physics (ranked #37) for 2022.}\n}\n\n
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\n \n\n \n \n \n \n \n \n Interpretable Spatiotemporal Forecasting of Arctic Sea Ice Concentration at Seasonal Lead Times.\n \n \n \n \n\n\n \n Beveridge, M.; and Pereira, L.\n\n\n \n\n\n\n In NeurIPS Workshop on Tackling Climate Change with Machine Learning, 2022. \n \n\n\n\n
\n\n\n\n \n \n \"Interpretable project\n  \n \n \n \"Interpretable paper\n  \n \n \n \"Interpretable slides\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 7 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{beveridge2022interpretable,\n  title={Interpretable Spatiotemporal Forecasting of Arctic Sea Ice Concentration at Seasonal Lead Times},\n  author={Beveridge, Matthew and Pereira, Lucas},\n  booktitle={NeurIPS Workshop on Tackling Climate Change with Machine Learning},\n  year={2022},\n  url_project={https://www.climatechange.ai/papers/neurips2022/109},\n  url_paper={https://s3.us-east-1.amazonaws.com/climate-change-ai/papers/neurips2022/109/paper.pdf},\n  url_slides={https://s3.us-east-1.amazonaws.com/climate-change-ai/papers/neurips2022/109/slides.pdf}\n}\n\n
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\n  \n 2021\n \n \n (11)\n \n \n
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\n \n\n \n \n \n \n \n \n Image2Reverb: Cross-Modal Reverb Impulse Response Synthesis.\n \n \n \n \n\n\n \n Singh, N.; Mentch, J.; Ng, J.; Beveridge, M.; and Drori, I.\n\n\n \n\n\n\n In IEEE/CVF International Conference on Computer Vision (ICCV), 2021. \n \n\n\n\n
\n\n\n\n \n \n \"Image2Reverb: project\n  \n \n \n \"Image2Reverb: code\n  \n \n \n \"Image2Reverb: arxiv\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{singh2021image2reverb,\n  title={Image2Reverb: Cross-Modal Reverb Impulse Response Synthesis},\n  author={Singh, Nikhil and Mentch, Jeff and Ng, Jerry and Beveridge, Matthew and Drori, Iddo},\n  booktitle={IEEE/CVF International Conference on Computer Vision (ICCV)},\n  year={2021},\n  url_project={https://web.media.mit.edu/~nsingh1/image2reverb/},\n  url_code={https://github.com/nikhilsinghmus/image2reverb},\n  url_arXiv={https://arxiv.org/abs/2103.14201}\n}\n\n
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\n \n\n \n \n \n \n \n \n Online Preconditioning of Experimental Inkjet Hardware by Bayesian Optimization in Loop.\n \n \n \n \n\n\n \n Siemenn, A. E; Beveridge, M.; Buonassisi, T.; and Drori, I.\n\n\n \n\n\n\n arXiv preprint arXiv:2105.02858. 2021.\n \n\n\n\n
\n\n\n\n \n \n \"Online arxiv\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{siemenn2021online,\n  title={Online Preconditioning of Experimental Inkjet Hardware by Bayesian Optimization in Loop},\n  author={Siemenn, Alexander E and Beveridge, Matthew and Buonassisi, Tonio and Drori, Iddo},\n  journal={arXiv preprint arXiv:2105.02858},\n  year={2021},\n  url_arXiv={https://arxiv.org/abs/2105.02858}\n}\n\n
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\n \n\n \n \n \n \n \n Autonomous Optimization of Fluid Systems at Varying Length Scales.\n \n \n \n\n\n \n Siemenn, A. E; Shaulsky, E.; Beveridge, M.; Buonassisi, T.; Hashmi, S. M; and Drori, I.\n\n\n \n\n\n\n arXiv, 2105: 13553. 2021.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{siemenn2021autonomous,\n  title={Autonomous Optimization of Fluid Systems at Varying Length Scales},\n  author={Siemenn, Alexander E and Shaulsky, Evyatar and Beveridge, Matthew and Buonassisi, Tonio and Hashmi, Sara M and Drori, Iddo},\n  journal={arXiv},\n  volume={2105},\n  pages={13553},\n  year={2021}\n}\n\n
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\n \n\n \n \n \n \n \n Artificial Intelligence Enhances Control Parameter Space Investigation in Flow-Focusing Droplet Generation.\n \n \n \n\n\n \n Shaulsky, E.; Siemenn, A.; Beveridge, M.; Buonassisi, T.; Drori, I.; and Hashmi, S.\n\n\n \n\n\n\n In ACS Colloids and Surface Symposium, 2021. \n \n\n\n\n
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@inproceedings{shaulsky2021artificial,\n  title={Artificial Intelligence Enhances Control Parameter Space Investigation in Flow-Focusing Droplet Generation},\n  author={Shaulsky, Evyatar and Siemenn, Alexander and Beveridge, Matthew and Buonassisi, Tonio and Drori, Iddo and Hashmi, Sara},\n  booktitle={ACS Colloids and Surface Symposium},\n  year={2021}\n}\n\n
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\n \n\n \n \n \n \n \n Exploring the Edge/SOL Fluctuations in Negative Triangularity Plasmas on TCV.\n \n \n \n\n\n \n Han, W.; Offeddu, N.; Golfinopoulos, T.; Theiler, C.; Tsui, C.; Boedo, J.; Terry, J.; Marmar, E.; Pietersen, R.; Villamor Lora, R.; Beveridge, M.; and Drori, I.\n\n\n \n\n\n\n In American Physical Society Division of Plasma Physics, 2021. \n \n\n\n\n
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@inproceedings{han2021exploring,\n  title={Exploring the Edge/SOL Fluctuations in Negative Triangularity Plasmas on TCV},\n  author={Han, Woonghee and Offeddu, Nicola and Golfinopoulos, Theodore and Theiler, Christian and Tsui, Cedric and Boedo, Jose and Terry, Jim and Marmar, Earl and Pietersen, Randall and Villamor Lora, Rafael and Beveridge, Matthew and Drori, Iddo},\n  booktitle={American Physical Society Division of Plasma Physics},\n  year={2021}\n}\n\n
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\n \n\n \n \n \n \n \n \n Consistent Depth Estimation in Data-Driven Simulation for Autonomous Driving.\n \n \n \n \n\n\n \n Beveridge, M.\n\n\n \n\n\n\n Master's thesis, Massachusetts Institute of Technology, 2021.\n \n\n\n\n
\n\n\n\n \n \n \"Consistent paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 3 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@mastersthesis{beveridge2021consistent,\n  title={Consistent Depth Estimation in Data-Driven Simulation for Autonomous Driving},\n  author={Beveridge, Matthew},\n  year={2021},\n  school={Massachusetts Institute of Technology},\n  url_paper={https://dspace.mit.edu/handle/1721.1/139136}\n}\n\n
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\n \n\n \n \n \n \n \n \n Image2lego: Customized lego set generation from images.\n \n \n \n \n\n\n \n Lennon, K.; Fransen, K.; O'Brien, A.; Cao, Y.; Beveridge, M.; Arefeen, Y.; Singh, N.; and Drori, I.\n\n\n \n\n\n\n arXiv preprint arXiv:2108.08477. 2021.\n \n\n\n\n
\n\n\n\n \n \n \"Image2lego: project\n  \n \n \n \"Image2lego: arxiv\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 2 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{lennon2021image2lego,\n  title={Image2lego: Customized lego set generation from images},\n  author={Lennon, Kyle and Fransen, Katharina and O'Brien, Alexander and Cao, Yumeng and Beveridge, Matthew and Arefeen, Yamin and Singh, Nikhil and Drori, Iddo},\n  journal={arXiv preprint arXiv:2108.08477},\n  year={2021},\n  url_project={https://krlennon.github.io/image2lego/},\n  url_arXiv={https://arxiv.org/abs/2108.08477}\n}\n\n
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\n \n\n \n \n \n \n \n \n Pedestrian Wind Factor Estimation in Complex Urban Environments.\n \n \n \n \n\n\n \n Mokhtar, S.; Beveridge, M.; Cao, M.; and Drori, I.\n\n\n \n\n\n\n In Asian Conference on Machine Learning (ACML), 2021. \n Oral spotlight.\n\n\n\n
\n\n\n\n \n \n \"Pedestrian project\n  \n \n \n \"Pedestrian arxiv\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{mokhtar2021pedestrian,\n  title={Pedestrian Wind Factor Estimation in Complex Urban Environments},\n  author={Mokhtar, Sarah and Beveridge, Matthew and Cao, Melody and Drori, Iddo},\n  booktitle={Asian Conference on Machine Learning (ACML)},\n  year={2021},\n  url_project={https://samokhtar.github.io/UrbanPedestrianWindEstimation/index.html},\n  url_arXiv={https://arxiv.org/abs/2110.02443},\n  note={Oral spotlight.}\n}\n\n
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\n \n\n \n \n \n \n \n Supplementary Material for Image2Reverb: Cross-Modal Reverb Impulse Response Synthesis.\n \n \n \n\n\n \n Singh, N.; Mentch, J.; Ng, J.; Beveridge, M.; and Drori, I.\n\n\n \n\n\n\n In IEEE/CVF International Conference on Computer Vision (ICCV), 2021. \n \n\n\n\n
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@inproceedings{singhsupplementary,\n  title={Supplementary Material for Image2Reverb: Cross-Modal Reverb Impulse Response Synthesis},\n  author={Singh, Nikhil and Mentch, Jeff and Ng, Jerry and Beveridge, Matthew and Drori, Iddo},\n  booktitle={IEEE/CVF International Conference on Computer Vision (ICCV)},\n  year={2021},\n}\n\n
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\n \n\n \n \n \n \n \n \n Predicting Atlantic Multidecadal Variability.\n \n \n \n \n\n\n \n Liu, G.; Wang, P.; Beveridge, M.; Kwon, Y.; and Drori, I.\n\n\n \n\n\n\n In NeurIPS Workshop on Tackling Climate Change with Machine Learning, 2021. \n Best paper, oral spotlight.\n\n\n\n
\n\n\n\n \n \n \"Predicting project\n  \n \n \n \"Predicting paper\n  \n \n \n \"Predicting slides\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{liu2021predicting,\n  title={Predicting Atlantic Multidecadal Variability},\n  author={Liu, Glenn and Wang, Peidong and Beveridge, Matthew and Kwon, Young-Oh and Drori, Iddo},\n  booktitle={NeurIPS Workshop on Tackling Climate Change with Machine Learning},\n  year={2021},\n  url_project={https://www.climatechange.ai/papers/neurips2021/35},\n  url_paper={https://www.climatechange.ai/papers/neurips2021/35/paper.pdf},\n  url_slides={https://www.climatechange.ai/papers/neurips2021/35/slides.pdf},\n  note={Best paper, oral spotlight.}\n}\n\n
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\n \n\n \n \n \n \n \n \n Predicting Critical Biogeochemistry of the Southern Ocean for Climate Monitoring.\n \n \n \n \n\n\n \n Park, E.; Kim, J. D.; Aoki, N.; Cao, Y. M.; Arefeen, Y.; Beveridge, M.; Nicholson, D.; and Drori, I.\n\n\n \n\n\n\n In NeurIPS Workshop on Tackling Climate Change with Machine Learning, 2021. \n \n\n\n\n
\n\n\n\n \n \n \"Predicting project\n  \n \n \n \"Predicting paper\n  \n \n \n \"Predicting slides\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{park2021predicting,\n  title={Predicting Critical Biogeochemistry of the Southern Ocean for Climate Monitoring},\n  author={Park, Ellen and Kim, Jae Deok and Aoki, Nadege and Cao, Yumeng Melody and Arefeen, Yamin and Beveridge, Matthew and Nicholson, David and Drori, Iddo},\n  booktitle={NeurIPS Workshop on Tackling Climate Change with Machine Learning},\n  year={2021},\n  url_project={https://www.climatechange.ai/papers/neurips2021/13},\n  url_paper={https://www.climatechange.ai/papers/neurips2021/13/paper.pdf},\n  url_slides={https://www.climatechange.ai/papers/neurips2021/13/slides.pdf},\n}\n\n
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