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DTSTART;TZID=America/New_York:20220602T161300
DTEND;TZID=America/New_York:20220602T171300
DTSTAMP:20240301T102323Z
CREATED:20240214T090758Z
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UID:10002608-1654186380-1654189980@cmsa.fas.harvard.edu
SUMMARY:Fast Point Transformer
DESCRIPTION:Abstract: The recent success of neural networks enables a better interpretation of 3D point clouds\, but processing a large-scale 3D scene remains a challenging problem. Most current approaches divide a large-scale scene into small regions and combine the local predictions together. However\, this scheme inevitably involves additional stages for pre- and post-processing and may also degrade the final output due to predictions in a local perspective. This talk introduces Fast Point Transformer that consists of a new lightweight self-attention layer. Our approach encodes continuous 3D coordinates\, and the voxel hashing-based architecture boosts computational efficiency. The proposed method is demonstrated with 3D semantic segmentation and 3D detection. The accuracy of our approach is competitive to the best voxel-based method\, and our network achieves 129 times faster inference time than the state-of-the-art\, Point Transformer\, with a reasonable accuracy trade-off in 3D semantic segmentation on S3DIS dataset. \nBio: Jaesik Park is an Assistant Professor at POSTECH. He received his Bachelor’s degree from Hanyang University in 2009\, and he received his Master’s degree and Ph.D. degree from KAIST in 2011 and 2015\, respectively. Before joining POSTECH\, He worked at Intel as a research scientist\, where he co-created the Open3D library. His research interests include image synthesis\, scene understanding\, and 3D reconstruction. He serves as a program committee at prestigious computer vision conferences\, such as Area Chair for ICCV\, CVPR\, and ECCV.
URL:https://cmsa.fas.harvard.edu/event/6-2-2022-interdisciplinary-science-seminar/
CATEGORIES:Interdisciplinary Science Seminar
ATTACH;FMTTYPE=image/jpeg:https://cmsa.fas.harvard.edu/media/CMSA-Interdisciplinary-Science-Seminar-06.02.2022-1583x2048-1.jpg
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DTSTART;TZID=America/New_York:20220616T090000
DTEND;TZID=America/New_York:20220616T100000
DTSTAMP:20240229T084329Z
CREATED:20240215T094047Z
LAST-MODIFIED:20240229T084329Z
UID:10002724-1655370000-1655373600@cmsa.fas.harvard.edu
SUMMARY:Surface hopping algorithms for non-adiabatic quantum systems
DESCRIPTION:Interdisciplinary Science Seminar\n\n\n\n\nSpeaker: Jianfeng Lu\, Duke UniversityTitle: Surface hopping algorithms for non-adiabatic quantum systems \nAbstract: Surface hopping algorithm is widely used in chemistry for mixed quantum-classical dynamics. In this talk\, we will discuss some of our recent works in mathematical understanding and algorithm development for surface hopping methods. These methods are based on stochastic approximations of semiclassical path-integral representation to the solution of multi-level Schrodinger equations; such methodology also extends to other high-dimensional transport systems.
URL:https://cmsa.fas.harvard.edu/event/iss_61622/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Interdisciplinary Science Seminar
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DTSTART;TZID=America/New_York:20220623T090000
DTEND;TZID=America/New_York:20220623T100000
DTSTAMP:20240301T101920Z
CREATED:20240214T091046Z
LAST-MODIFIED:20240301T101920Z
UID:10002611-1655974800-1655978400@cmsa.fas.harvard.edu
SUMMARY:Some new algorithms in statistical genomics
DESCRIPTION:Abstract: The statistical analysis of genomic data has incubated many innovations for computational method development. This talk will discuss some simple algorithms that may be useful in analyzing such data. Examples include algorithms for efficient resampling-based hypothesis testing\, minimizing the sum of truncated convex functions\, and fitting equality-constrained lasso problems. These algorithms have the potential to be used in other applications beyond statistical genomics. \nBio: Hui Jiang is an Associate Professor in the Department of Biostatistics at the University of Michigan. He received his Ph.D. in Computational and Mathematical Engineering from Stanford University. Before joining the University of Michigan\, he was a postdoc in the Department of Statistics and Stanford Genome Technology Center at Stanford University. He is interested in developing statistical and computational methods for analyzing large-scale biological data generated using modern high-throughput technologies.
URL:https://cmsa.fas.harvard.edu/event/6-23-2022-interdisciplinary-science-seminar/
CATEGORIES:Interdisciplinary Science Seminar
ATTACH;FMTTYPE=image/jpeg:https://cmsa.fas.harvard.edu/media/CMSA-Interdisciplinary-Science-Seminar-06.23.2022-1583x2048-1.jpg
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20220630T162300
DTEND;TZID=America/New_York:20220630T172300
DTSTAMP:20240301T101730Z
CREATED:20240214T091304Z
LAST-MODIFIED:20240301T101730Z
UID:10002613-1656606180-1656609780@cmsa.fas.harvard.edu
SUMMARY:Entanglement and its key role in quantum information
DESCRIPTION:Abstract: Entanglement is a type of correlation found in composite quantum systems\, connected with various non-classical phenomena. Currently\, entanglement plays a key role in quantum information applications such as quantum computing\, quantum communication\, and quantum sensing. In this talk the concept of entanglement will be introduced along with various methods that have been proposed to detect and quantify it. The fundamental role of entanglement in both quantum theory and quantum technology will also be discussed. \nBio: Spyros Tserkis is a postdoctoral researcher at Harvard University\, working on quantum information theory. Before joining Harvard in Fall 2021\, he was a postdoctoral researcher at MIT and the Australian National University. He received his PhD from the University of Queensland.
URL:https://cmsa.fas.harvard.edu/event/6-30-2022-interdisciplinary-science-seminar/
CATEGORIES:Interdisciplinary Science Seminar
ATTACH;FMTTYPE=image/jpeg:https://cmsa.fas.harvard.edu/media/CMSA-Interdisciplinary-Science-Seminar-06.30.22-1583x2048-1.jpg
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