SOCR News AmStats SIM 2024 - Revision history

Dinov: /* Best Student Paper Award */

2024-05-29T14:31:38Z

‎Best Student Paper Award

Dinov: /* Conference Program */

2024-05-29T14:30:44Z

‎Conference Program

Dinov: /* Session Presenters */

2024-05-27T19:56:52Z

‎Session Presenters

Dinov: /* Conference Program */

2024-05-27T19:55:15Z

‎Conference Program

Dinov: /* Conference Program */

2024-05-27T19:51:48Z

‎Conference Program

Dinov: /* Conference Program */

2024-05-26T14:21:50Z

‎Conference Program

Dinov: /* Spacekime Analytics Tutorial */

2024-05-23T16:27:06Z

‎Spacekime Analytics Tutorial

Dinov: /* Conference Program */

2024-05-23T15:09:19Z

‎Conference Program

Dinov: /* Conference Program */

2024-05-23T15:06:59Z

‎Conference Program

Dinov: /* Best Student Paper Award */

2024-05-23T14:55:30Z

‎Best Student Paper Award

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 of the [https://medicine.iu.edu/biostatistics/news-events/statistical-methods-in-imaging-conference 2024 American Statistical Association (AmStat) - Statistical Methods in Imaging (SMI) Conference].
-This paper [https://socr.umich.edu/docs/uploads/2024/Shen_StatisticalFoundationsOfInvarianceInDNN_SMI_2024.pdf ''Statistical foundations of invariance and equivariance in deep artificial neural network learning''] was recognized as being of '''remarkable quality''' by the AmStat/SMI review committee.
+This paper [https://wiki.socr.umich.edu/images/b/bb/YueyangShen_BestPaper_AmStats_SMI_2024.pdf ''Statistical foundations of invariance and equivariance in deep artificial neural network learning''] was recognized as being of '''remarkable quality''' by the AmStat/SMI review committee.
 ==Conference Program==

@@ Line 76: / Line 76: @@
 |   10:00 || 10:10 || 10 || colspan="2" | Break
 |-
-:10 || 11:40 || 90 || Tutorial: NiChart: a software tool for building machine learning oriented neuroimaging brain chart; Instructor(s): Haochang Shou Ren Zheng; Location: Room 101 || Tutorial: Tutorial on Deep learning and generative AI; Instructor(s): Haoda Fu; Location: Room 102
+| 10:10 || 11:40 || 90 || Tutorial: NiChart: a software tool for building machine learning oriented neuroimaging brain chart; Instructor(s): Haochang Shou Ren Zheng; Location: Room 101 || Tutorial: Tutorial on Deep learning and generative AI; Instructor(s): Haoda Fu; Location: Room 102
 |-
 |  11:40 || 13:10 || 90 || colspan="2" | Lunch break

@@ Line 47: / Line 47: @@
 * [https://www.stat.iastate.edu/people/ranjan-maitra Ranjan Maitra] (Iowa State)
-: ''Title'': ToTTR: Tensor-on-Tensor Times Series Regression for Integrated One-step functional MR Imaging analysis
+: ''Title'': Title: Reduced-Rank Tensor-on-Tensor Regression and Tensor-Variate Analysis of Variance
+: ''Abstract'': Fitting regression models with many multivariate responses and covariates can be challenging, but such responses and covariates sometimes have tensor-variate structure. We extend the classical multivariate regression model to exploit such structure in two ways: first, we impose four types of low-rank tensor formats on the regression coefficients. Second, we model the errors using the tensor-variate normal distribution that imposes a Kronecker separable format on the covariance matrix. We obtain maximum likelihood estimators via block-relaxation algorithms and derive their computational complexity and asymptotic distributions. Our regression framework enables us to formulate tensor-variate analysis of variance (TANOVA) methodology. This methodology, when applied in a one-way TANOVA layout, enables us to identify cerebral regions significantly associated with the interaction of suicide attempters or non-attemptor ideators and positive-, negative- or death-connoting words in a functional Magnetic Resonance Imaging study. Another application uses three-way TANOVA on the Labeled Faces in the Wild image dataset to distinguish facial characteristics related to ethnic origin, age group and gender. A R package ''totr'' implements the methodology.
 * [https://www.marquette.edu/mathematical-and-statistical-sciences/directory/daniel-rowe.php Dan Rowe] (Marquette)

@@ Line 73: / Line 73: @@
 ! Date !! Start !! End !! Duration (minutes) !! Session 1 !! Session 2
 |-
-| Wednesday 5/29/24 || 8:30 || 10:00 || 90 || Tutorial: Complex-time representation of spatiotemporal processes and spacekime analyhtics; Instructor(s): [https://www.socr.umich.edu/people/dinov/ Ivo Dinov]; Location: Room 101 || Tutorial: Tutorial on Deep learning and generative AI; Instructor(s): Haoda Fu; Location: Room 102
+| rowspan="7" | Wednesday 5/29/24 || 8:30 || 10:00 || 90 || Tutorial: Complex-time representation of spatiotemporal processes and spacekime analyhtics; Instructor(s): [https://www.socr.umich.edu/people/dinov/ Ivo Dinov]; Location: Room 101 || Tutorial: Tutorial on Deep learning and generative AI; Instructor(s): Haoda Fu; Location: Room 102
 |-
-|  || 10:00 || 10:10 || 10 || colspan="2" | Break
+|   10:00 || 10:10 || 10 || colspan="2" | Break
 |-
-|| || 10:10 || 11:40 || 90 || Tutorial: NiChart: a software tool for building machine learning oriented neuroimaging brain chart; Instructor(s): Haochang Shou Ren Zheng; Location: Room 101 || Tutorial: Tutorial on Deep learning and generative AI; Instructor(s): Haoda Fu; Location: Room 102
+:10 || 11:40 || 90 || Tutorial: NiChart: a software tool for building machine learning oriented neuroimaging brain chart; Instructor(s): Haochang Shou Ren Zheng; Location: Room 101 || Tutorial: Tutorial on Deep learning and generative AI; Instructor(s): Haoda Fu; Location: Room 102
 |-
-|  || 11:40 || 13:10 || 90 || colspan="2" | Lunch break
+|  11:40 || 13:10 || 90 || colspan="2" | Lunch break
 |-
-|  || 13:10 || 14:40 || 90 || colspan="2" | Student Paper Competition Winners; Location: Room 101/102 <br/> Yueyang Shen & Yupeng Zhang Co-Winning (along with another student, James Buenfil/Washington) the annual "Theory and Methods Track" Best Paper Award for their work <br /> ''Statistical foundations of invariance and equivariance in deep artificial neural network learning''
+|  13:10 || 14:40 || 90 || colspan="2" | Student Paper Competition Winners; Location: Room 101/102 <br/> Yueyang Shen & Yupeng Zhang Co-Winning (along with another student, James Buenfil/Washington) the annual "Theory and Methods Track" Best Paper Award for their work <br /> ''Statistical foundations of invariance and equivariance in deep artificial neural network learning''
 |-
-|  || 14:40 || 14:50 || 10 || colspan="2" | Break
+|  14:40 || 14:50 || 10 || colspan="2" | Break
 |-
-|  || 14:50 || 15:50 || 60 || colspan="2" | Keynote - Bin Yu (Chair: Tingting Zhang); Location: Room 101/102
+|  14:50 || 15:50 || 60 || colspan="2" | Keynote - Bin Yu (Chair: Tingting Zhang); Location: Room 101/102
 |-
-|  || 15:50 || 18:00 || 130 || colspan="2" | Poster Presentations/Mixer (food and drinks offered); Location: Room 103/104
+|  15:50 || 18:00 || 130 || colspan="2" | Poster Presentations/Mixer (food and drinks offered); Location: Room 103/104
 |-
-| Thursday 5/30/24 || 8:00 || 8:30 || 30 || colspan="2" | Breakfast
+| rowspan="10" | Thursday 5/30/24 || 8:00 || 8:30 || 30 || colspan="2" | Breakfast
 |-
-|  || 8:30 || 10:10 || 100 || Longitudinal imaging and biostatistical methods; Session Organizer: [https://www.socr.umich.edu/people/dinov/ Ivo Dinov]; Speakers: Sharmistha Guha Hossein Moradi Ranjan Maitra Dan Rowe; Location: Room 103/104 || Expanding neuroimaging research: integrating insights from biomedical sciences, Session Organizer: Jun Young Park; Speakers: Sarah M. Weinstein Andrew An Chen Bingxin Zhao Jun Young Park; Location: Room 102
+|   8:30 || 10:10 || 100 || Longitudinal imaging and biostatistical methods; Session Organizer: [https://www.socr.umich.edu/people/dinov/ Ivo Dinov]; Speakers: Sharmistha Guha Hossein Moradi Ranjan Maitra Dan Rowe; Location: Room 103/104 || Expanding neuroimaging research: integrating insights from biomedical sciences, Session Organizer: Jun Young Park; Speakers: Sarah M. Weinstein Andrew An Chen Bingxin Zhao Jun Young Park; Location: Room 102
 |-
-|  || 10:10 || 10:20 || 10 || colspan="2" | Break
+|  10:10 || 10:20 || 10 || colspan="2" | Break
 |-
-|  || 10:20 || 11:20 || 60 || colspan="2" | Keynote - Andrew J. Saykin (Chair: Sujuan Gao); Location: Room 103/104
+|  10:20 || 11:20 || 60 || colspan="2" | Keynote - Andrew J. Saykin (Chair: Sujuan Gao); Location: Room 103/104
 |-
-|  || 11:20 || 13:00 || 100 || colspan="2" | Lunch break
+|  11:20 || 13:00 || 100 || colspan="2" | Lunch break
 |-
-|  || 13:00 || 14:15 || 75 || Statistical learning methods for neuroscience; Session Organizer: Shuheng Zhou; Speakers: Jian Kang Chunming Zhang Shuheng Zhou; Location: Room 103/104 || Recent developments in statistical methodology for neuroimaging data analysis; Session Organizer: Dayu Sun; Speakers: Xin Ma Shuo Chen Joshua Lukemire; Location: Room 102
+|   13:00 || 14:15 || 75 || Statistical learning methods for neuroscience; Session Organizer: Shuheng Zhou; Speakers: Jian Kang Chunming Zhang Shuheng Zhou; Location: Room 103/104 || Recent developments in statistical methodology for neuroimaging data analysis; Session Organizer: Dayu Sun; Speakers: Xin Ma Shuo Chen Joshua Lukemire; Location: Room 102
 |-
-|  || 14:15 || 14:25 || 10 || colspan="2" | Break
+|  14:15 || 14:25 || 10 || colspan="2" | Break
 |-
-|  || 14:25 || 16:05 || 100 || Statistical inference in neuroimaging; Session Organizer: Eardi Lila; Speakers: Benjamin Risk Raphiel Murden Daniel Kessler Simon Vandekar; Location: Room 103/104 || New developments for harmonization processing and modeling for imaging data; Session Organizer: Yize Zhao; Speakers: Dana Tudorascu Selena Wang Zhengwu Zhang Tsung-Hung Yao; Location: Room 102
+|  14:25 || 16:05 || 100 || Statistical inference in neuroimaging; Session Organizer: Eardi Lila; Speakers: Benjamin Risk Raphiel Murden Daniel Kessler Simon Vandekar; Location: Room 103/104 || New developments for harmonization processing and modeling for imaging data; Session Organizer: Yize Zhao; Speakers: Dana Tudorascu Selena Wang Zhengwu Zhang Tsung-Hung Yao; Location: Room 102
 |-
-|  || 16:05 || 16:15 || 10 || colspan="2" | Break
+|  16:05 || 16:15 || 10 || colspan="2" | Break
 |-
-|  || 16:15 || 17:30 || 75 || Invariance and distribution/density objects in neuroimaging studies; Session Organizer: Yi Zhao; Speakers: Bonnie Smith Changbo Zhu Yi Zhao; Location: Room 103/104 || Advances in statistical method for neuroimaging data; Session Organizer: Selena Wang; Speakers: Naomi Ding Dayu Sun Selena Wang ; Location: Room 102
+|  16:15 || 17:30 || 75 || Invariance and distribution/density objects in neuroimaging studies; Session Organizer: Yi Zhao; Speakers: Bonnie Smith Changbo Zhu Yi Zhao; Location: Room 103/104 || Advances in statistical method for neuroimaging data; Session Organizer: Selena Wang; Speakers: Naomi Ding Dayu Sun Selena Wang ; Location: Room 102
 |-
-| Friday 5/31/24 || 8:00 || 8:30 || 30 || colspan="2" | Breakfast
+| rowspan="9" | Friday 5/31/24 || 8:00 || 8:30 || 30 || colspan="2" | Breakfast
 |-
-|  || 8:30 || 10:10 || 100 || Graph-based network connectomes analysis; Session Organizer: Simon Vandekar; Speakers: Eardi Lila Sean L. Simpson Panpan Zhang Tingting Zhang; Location: Room 103/104 || Statistical methods for dissecting tumor microenvironment based on spatial proteomics datasets; Session Organizer: Souvik Seal; Speakers: Thao Vu Jiangmei Xiong Julia Wrobel Junsouk Choi; Location: Room 102
+|  8:30 || 10:10 || 100 || Graph-based network connectomes analysis; Session Organizer: Simon Vandekar; Speakers: Eardi Lila Sean L. Simpson Panpan Zhang Tingting Zhang; Location: Room 103/104 || Statistical methods for dissecting tumor microenvironment based on spatial proteomics datasets; Session Organizer: Souvik Seal; Speakers: Thao Vu Jiangmei Xiong Julia Wrobel Junsouk Choi; Location: Room 102
 |-
-|  || 10:10 || 10:20 || 10 || colspan="2" | Break
+|  10:10 || 10:20 || 10 || colspan="2" | Break
 |-
-|  || 10:20 || 11:20 || 60 || colspan="2" | Keynote - Robert E. Kass (Chair: Daniel Rowe); Location: Room 103/104
+|  10:20 || 11:20 || 60 || colspan="2" | Keynote - Robert E. Kass (Chair: Daniel Rowe); Location: Room 103/104
 |-
-|  || 11:20 || 13:00 || 100 || colspan="2" |Lunch break
+|   11:20 || 13:00 || 100 || colspan="2" |Lunch break
 |-
-|  || 13:00 || 14:40 || 100 || When machine learning and generative models meet imaging network and point cloud data; Session Organizer: Zhengwu Zhang; Speakers: Mingxia Liu Maoran Xu Yuexuan Wu Xinyi Li; Location: Room 103/104 || Novel statistical inference methods with applications; Session Organizer: Julia Fisher; Speakers: Fatma Parlak Daniel Adrian [https://socr.umich.edu/docs/uploads/2024/Shen_StatisticalFoundationsOfInvarianceInDNN_SMI_2024.pdf Yueyang Shen] Jose Rodriguez-Acosta; Location: Room 102
+|  13:00 || 14:40 || 100 || When machine learning and generative models meet imaging network and point cloud data; Session Organizer: Zhengwu Zhang; Speakers: Mingxia Liu Maoran Xu Yuexuan Wu Xinyi Li; Location: Room 103/104 || Novel statistical inference methods with applications; Session Organizer: Julia Fisher; Speakers: Fatma Parlak Daniel Adrian [https://socr.umich.edu/docs/uploads/2024/Shen_StatisticalFoundationsOfInvarianceInDNN_SMI_2024.pdf Yueyang Shen] Jose Rodriguez-Acosta; Location: Room 102
 |-
-|  || 14:40 || 14:50 || 10 || colspan="2" | Break
+|  14:40 || 14:50 || 10 || colspan="2" | Break
 |-
-|  || 14:50 || 16:30 || 100 || colspan="2" | Frontiers in medical imaging: harnessing artificial intelligence and statistical analysis for breakthrough insights; Session Organizer: Lei Liu; Speakers: Yize Zhao, Yifan Peng, Lei Liu, Haoda Fu; Location: Room 103/104
+|  14:50 || 16:30 || 100 || colspan="2" | Frontiers in medical imaging: harnessing artificial intelligence and statistical analysis for breakthrough insights; Session Organizer: Lei Liu; Speakers: Yize Zhao, Yifan Peng, Lei Liu, Haoda Fu; Location: Room 103/104
 |-
-|  || 16:30 || 16:40 || 10 || colspan="2" | Closing remarks; Location: Room 103/104
+|   16:30 || 16:40 || 10 || colspan="2" | Closing remarks; Location: Room 103/104
 |}

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 |  || 14:40 || 14:50 || 10 || colspan="2" | Break
 |-
-|  || 14:50 || 16:30 || 100 || Frontiers in medical imaging: harnessing artificial intelligence and statistical analysis for breakthrough insights; Session Organizer: Lei Liu; Speakers: Yize Zhao Yifan Peng Lei Liu Haoda Fu; Location: Room 103/104
+|  || 14:50 || 16:30 || 100 || colspan="2" | Frontiers in medical imaging: harnessing artificial intelligence and statistical analysis for breakthrough insights; Session Organizer: Lei Liu; Speakers: Yize Zhao, Yifan Peng, Lei Liu, Haoda Fu; Location: Room 103/104
 |-
 |  || 16:30 || 16:40 || 10 || colspan="2" | Closing remarks; Location: Room 103/104

@@ Line 16: / Line 16: @@
 * '''Registration''': [https://indianauniv.ungerboeck.com/prod/emc00/PublicSignIn.aspx?aat=31684847734a734c626a6e71484e376d304a2b6c4436346d334e2b464462644271737a54647347546746773d Registration]
 * '''Conference''': [https://medicine.iu.edu/biostatistics/news-events/statistical-methods-in-imaging-conference 2024 Statistical Methods in Imaging Conference, Annual Meeting] of the [https://www.statsinimaging.org/ ASA Statistics in Imaging Section - ''Statistical Methods in Imaging (SMI)'']
-* '''Format''':  A brief overview lecture followed by hands-on spacekime analytics tutorial and demonstrations.
+* '''Format''':  A [https://wiki.socr.umich.edu/images/b/b2/Dinov_Spacekime_2024_Slidedeck_V3.pdf brief overview lecture] followed by [https://www.socr.umich.edu/TCIU/HTMLs/Chapter6_TCIU_Basic_SpacekimePredictiveAnalytics.html hands-on spacekime analytics tutorial and demonstrations], [https://wiki.socr.umich.edu/images/b/b2/Dinov_Spacekime_2024_Slidedeck_V3.pdf Slidedeck].
 ===Abstract===

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 ==Conference Program==
-{| class="wikitable"
+{| class="wikitable" style="text-align: center;"
 ! Date !! Start !! End !! Duration (minutes) !! Session 1 !! Session 2
 |-

@@ Line 63: / Line 63: @@
 Congratulations to [https://www.socr.umich.edu/people/SOCR_Personnel_Yueyang.html Yueyang Shen] and [https://math.wisc.edu/graduate-students/ Yupeng Zhang]
-on Co-Winning (along with another student, [https://buenfilstats.github.io/ James Buenfil/Washington]) the [https://www.statsinimaging.org/smi_competitions_all/ annual '''“Theory and Methods Track” Best Student Paper Award''']
+on Co-Winning (along with another student, [https://buenfilstats.github.io/ James Buenfil/Washington]) the [https://www.statsinimaging.org/smi_competitions_all/ annual '''"Theory and Methods Track" Best Paper Award''']
 of the [https://medicine.iu.edu/biostatistics/news-events/statistical-methods-in-imaging-conference 2024 American Statistical Association (AmStat) - Statistical Methods in Imaging (SMI) Conference].