Difference between revisions of "SOCR News APS GDS ShortCourse March 2022"
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: [https://pages.stat.wisc.edu/~miaoyan/index.html Miaoyan Wang], [https://pages.stat.wisc.edu/~miaoyan/index.html Statistics, Wisconsin-Madison] | : [https://pages.stat.wisc.edu/~miaoyan/index.html Miaoyan Wang], [https://pages.stat.wisc.edu/~miaoyan/index.html Statistics, Wisconsin-Madison] | ||
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Revision as of 16:53, 6 January 2022
Contents
SOCR News & Events: APS GDS Short Course – March Meeting 2022
Logistics
- Contacts
- GDS Program Chair: Maria Longobardi, University of Naples Federico II
- Organizer: Ivo D. Dinov, University of Michigan
- APS Coordinators: Vinaya Sathyasheelappa; Cynthia Smith
- Short-Course Title: Longitudinal Data Tensor-Linear Modeling and Space-kime Analytics
- Event Date/Time: March 13, 2022, 9:00 - 17:00 (US Central Time)
- Format: Online (distance-based, virtual). Instruction will involve a blend of theoretical foundations, computational implementations, and data-driven applications
- Audience: Prerequisites: prior knowledge of college level math, physics, and statistics
- Registration Fees: Students $80, post-docs/fellows $120, regular $150
- Need-based Fee Waivers: APS Data Science Group (GDS) will select and cover the registration fee for up to 5 students and trainees. Interested trainees need to complete this additional fee-waiver request form justifying their need for a waiver. There are no guarantees for waivers, but the organizers are committed to increase participation from trainees from STEM-underrepresented communities.
Course Summary
In many scientific domains, there is a rapid increase of the volume, sampling rate, and heterogeneity of the acquired information. This amplifies the role of higher order tensors for modeling, processing, analysis and data-driven inference. The blend of repeated experiments and time dynamics of some data elements necessitates the development of novel data science methods, powerful machine learning techniques, and automated artificial intelligence tools. This short course will present the current state-of-the-art approaches for tensor-based linear modeling and space-kime analytics. We will present a generalized framework for modeling and prediction of scalar, matrix, or tensor outcomes from observed tensor inputs. In addition, we will demonstrate the complex-time (kime) representation of longitudinal data, where the temporal event order is generalized to the (unordered) complex plane. This generalization transformed classical time-series to 2D kime-surfaces. Various biomedical and health applications will be showcased.
DRAFT Agenda
Morning Session (9:00-12:00 US Central Time, GMT-5) |
Afternoon Session (13:00-17:00 US Central Time, GMT-5) | ||||
Time |
Presenter |
Topic |
Time |
Presenter |
Topic |
9:00-9:15 |
Ivo Dinov |
Welcome & Overview |
13:00-13:45 |
Presenter 3 (Talk) |
TBD |
9:15-10:00 |
Presenter 1 (Talk) |
TBD |
13:45-14:15 |
Presenter 3 (Demo) |
TBD |
10:00-10:30 |
Presenter 1 (Demo) |
TBD |
14:15-15:00 |
Presenter 4 (Talk) |
TBD |
10:30-10:45 |
Break |
15:00-15:10 |
Break | ||
10:45-11:30 |
Presenter 2 (Talk) |
TBD |
15:10-15:40 |
Presenter 4 (Demo) |
TBD |
11:30-12:00 |
Presenter 2 (Demo) |
TBD |
15:40-16:25 |
Presenter 5 (Talk) |
TBD |
12:00-13:00 |
Break (lunch recess) |
16:25-16:55 |
Presenter 5 (Demo) |
TBD | |
16:55-17:00 |
Conclusions/Adjourn | ||||
Resources
- ...
Instructors
- Maryam Bagherian, University of Michigan, Welch Lab, MIDAS
- Dr. Bagherian is a Michigan Data Science Fellow and an expert in applied and computational mathematics. Her research is focused on developing ML/AI algorithms and data science methods, e.g., multidimensional multimodal big data modeling. The primary applications of her work are in biomedical data science, health informatics, and genomic studies. Dr. Bagherian has developed new online tensor recovery and decomposition methods for couple tensors with simultaneous auxiliary information. These techniques are applied to multi-omics, spatial transcriptomics, and genomics datasets.
- Miaoyan Wang, Statistics, Wisconsin-Madison
- Dr. Wang ...
- Ivo Dinov, University of Michigan, SOCR, MIDAS.
- Dr. Dinov is a professor of Health Behavior and Biological Sciences and Computational Medicine and Bioinformatics at the University of Michigan. He is a member of the Michigan Center for Applied and Interdisciplinary Mathematics (MCAIM) and a core member of the University of Michigan Comprehensive Cancer Center. Dr. Dinov serves as Director of the Statistics Online Computational Resource, Co-Director of the Center for Complexity and Self-management of Chronic Disease (CSCD Center), Co-Director of the multi-institutional Probability Distributome Project, Associate Director of the Michigan Institute for Data Science (MIDAS), and Associate Director of the Michigan Neuroscience Graduate Program (NGP). He is a member of the American Physical Society (APS), American Statistical Association (ASA), International Association for Statistical Education (IASE), American Mathematical Society (AMS), American Association for the Advancement of Science (AAAS), and an Elected Member of the Institutional Statistical Institute (ISI).
- Raj Guhaniyogi, TAMU
- Dr. Rajarshi Guhaniyogi received his PhD in Biostatistics at the University of Minnesota, Twin Cities, under the supervision of Dr. Sudipto Banerjee. He was a Postdoctoral Researcher with Dr. David B. Dunson in the Department of Statistical Science at Duke University prior to joining the Department of Statistics at UC Santa Cruz as an Assistant Professor in 2014. In 2021, Dr. Guhaniyogi was recruited as an Associate Professor in the Department of Statistics at Texas A&M University where he is developing massive dimensional parametric and non-parametric Bayesian methods motivated by improving practical performance in real world applications in batch and online data settings, using statistical theory to justify and guide the development of new methods. Dr. Guhaniyogi research interests lie broadly in development of Bayesian parametric and non-parametric methodology in complex biomedical and machine learning applications. His ongoing research focus is on scalable and distributed Bayesian inference for big data, dimensionality reduction, functional and object data (networks, tensor) analysis. Rajarshi draws his motivation from applications primarily from neuroscience, genetics, epidemiology, environmental science, forestry and social science. Rajarshi is a recipient of the 2016 University of California Hellman Fellowship.
- Anru Zhang, Duke
- Anru Zhang is Eugene Anson Stead, Jr. M.D. Associate Professor in the Department of Biostatistics & Bioinformatics and a secondary faculty in the Departments of Computer Science, Mathematics, and Statistical Science at Duke University. He was an assistant professor of statistics at the University of Wisconsin-Madison in 2015-2021. He obtained his bachelor’s degree from Peking University in 2010 and his Ph.D. from the University of Pennsylvania in 2015. His work focuses on high-dimensional statistical inference, non-convex optimization, statistical tensor analysis, computational complexity, and applications in genomics, microbiome, electronic health records, and computational imaging. He received the ASA Gottfried E. Noether Junior Award (2021), a Bernoulli Society New Researcher Award (2021), an ICSA Outstanding Young Researcher Award (2021), and an NSF CAREER Award (2020).
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