All times below are in EST.
Day 1
2:00pm EST
in XX XXX
Speaker: Nathan Kutz
Abstract: A computationally efficient method for the automation of inverse design in science and engineering is introduced. Based on simple least-square regression, the method leverages the dynamic mode decomposition algorithm in order to construct a low-rank subspace spanning multiple geometric representations in a latent embedding space. The proposed inverse design dynamic mode composition (ID-DMD) algorithm leverages the computed low-dimensional subspace to enable fast digital design and optimization of geometry on laptop-level computing, including the potential to prescribe the dynamics themselves through reduced order models. Moreover, the method is robust to noise, physically interpretable, and can provide uncertainty quantification metrics. The architecture can also efficiently scale to large-scale design problems using randomized algorithms in the ID-DMD. The simplicity of the method and its implementation are highly attractive in practice, and the ID-DMD has been demonstrated to be an order of magnitude more accurate than competing methods while simultaneously being 3-5 orders faster on challenging engineering design problems ranging from structural vibrations to fluid dynamics. Due to its speed, robustness, interpretability, and ease-of-use, ID-DMD in comparison with other leading machine learning methods represents a significant advancement in data-driven methods for inverse design and optimization, promising a paradigm shift in how to approach inverse design in practice.
Bio: Nathan Kutz is the Director of Physics-Informed AI at Autodesk Research in London, UK. He is on-leave as the Boeing Professor of Applied Mathematics and Electrical and Computer Engineering and Director of the AI Institute in Dynamic Systems at the University of Washington, having served as chair of applied mathematics from 2007-2015. He received the BS degree in physics and mathematics from the University of Washington in 1990 and the Phd in applied mathematics from Northwestern University in 1994. He was a postdoc in the applied and computational mathematics program at Princeton University before taking his faculty position. He has a wide range of interests, including neuroscience to fluid dynamics where he integrates machine learning with dynamical systems and control.
Day 2
3:00pm EST
in XX XXX
Speakers: Kathleen Lois Foster & Alessandro Maria Selvitella
Abstract: This Workshop is aimed at students and practitioners in the biological sciences who are interested in developing coding and data science skills to solve concrete problems emerging in their biological field of study. By the end of the workshops, the participants will have gathered technical and theoretical skills in data science. They will have learned how to install R and R-studio, use the statistical software R to perform basic statistical analysis of a biological question, and visualize the biological information hidden in the data under study. Furthermore, they will have gained knowledge about the structure of different types of data, descriptive statistics, including mean, standard deviation, confidence intervals, and probability distributions, how to perform hypothesis tests, including t-test and ANOVA, and the difference between statistical and biological significance.
[In person only or specific request must be sent to the organizers for online participation]
4:30pm EST
in XX XXX
Speakers: Alessandro Maria Selvitella
Abstract: This Workshop is aimed at graduate students with some background in differential equations and machine learning. The workshop will cover topics such as Runge-Kutta methods, Physics Informed Neural Networks, and System Identification of Nonlinear Dynamics. These tools will be illustrated on a toy problem, a dynamical system modeling the central pattern generator of the lamprey.
[In person only or specific request must be sent to the organizers for online participation]
Short Talks and Posters
Day 3
XX:XX EST
in XX XXX
Speaker: TBA
Abstract: TBA
Short Talks and Posters
Day 4
3:00pm EST
in KT G83
Speakers: Kathleen Lois Foster & Alessandro Maria Selvitella
Abstract: This Workshop is aimed at students and practitioners in the biological sciences who are interested in developing coding and data science skills to solve concrete problems emerging in their biological field of study. By the end of the workshops, the participants will have gathered technical and theoretical skills in data science. They will have learned how to install R and R-studio, use the statistical software R to perform basic statistical analysis of a biological question, and visualize the biological information hidden in the data under study. Furthermore, they will have gained knowledge about the structure of different types of data, descriptive statistics, including mean, standard deviation, confidence intervals, and probability distributions, how to perform hypothesis tests, including t-test and ANOVA, and the difference between statistical and biological significance.
[In person only or specific request must be sent to the organizers for online participation]
4:30pm EST
in XX XXX
Speakers: Alessandro Maria Selvitella
Abstract: This Workshop is aimed at graduate students with some background in differential equations and machine learning. The workshop will cover topics such as Runge-Kutta methods, Physics Informed Neural Networks, and System Identification of Nonlinear Dynamics. These tools will be illustrated on a toy problem, a dynamical system modeling the central pattern generator of the lamprey.
[In person only or specific request must be sent to the organizers for online participation]
12:00pm EST
in XX XXX
Speaker: Lu Lu
Abstract: TBA
18:00 - 19:30
Virtual
Look for the documentary in the main hall of Gather "Data" Town!
Short Talks and Posters
Day 5
10:00 EST
in XX XXX
Speaker: George Haller
Abstract: TBA
noon -1:15pm EST
in XX XXX
Name | Affiliation
1:15pm EST
in XX XXX