SOCR News APS GDS ShortCourse March 2022
Contents
SOCR News & Events: APS GDS Short Course – March Meeting 2022
Logistics
- Contacts
- GDS Chair Program: 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, 9am-5pm (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
(20+ participants should bring us close to 3K baseline, I’m hoping for 30-50, but who knows what to expect) 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 an additional fee-waiver request form justifying their need. 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-surface. Various biomedical and health applications will be showcased.
DRAFT Agenda
Morning Session (9:00-12:00 US CT, GMT-5) |
Afternoon Session (13:00-17:00 US CT, 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
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Instructors
- 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).
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