Department Seminar of Itay Griniasty - Emergent complex functionality in microscopic machines and computational models

15 January 2024, 14:00 - 15:00 
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Department Seminar of Itay Griniasty - Emergent complex functionality in microscopic machines and computational models

 

SCHOOL OF MECHANICAL ENGINEERING SEMINAR
Monday January 15.1.2024 at 14:00

Wolfson Building of Mechanical Engineering, Room 206

 

Emergent complex functionality in microscopic machines and computational models

Itay Griniasty

Itay Griniasty is a Schmidt AI in science postdoc fellow at Cornell university

 

Systems composed of many interacting elements that collaboratively generate a function, such as meta-material robots, proteins, and neural networks are notoriously difficult to design.

Such systems elude traditional explicit design methodologies, which rely on composing individual components with specific subfunctions, such as cogs, springs and shafts, to achieve complex functionality. In part the problem stems from the fact that there are few principled approaches to the design of emergent functionality.  In this talk I will describe progress towards creating such paradigms for two canonical systems: I will first describe how bifurcations of the system dynamics can be used as an organizing principle for the design of functionality in protein like machines with magnetic interactions. I will then introduce a computational microscope that we have developed to analyze emergent functionality, and its application to machine learning. There we uncovered compelling evidence that the training of neural networks is inherently low dimensional, suggesting new paradigms for their design.

References

1. T. Yang et al. Bifurcation instructed design of multistate machines. Proceedings of the National Academy of Sciences, 120(34):e2300081120, 2023

2. J. Mao et al. The training process of many deep networks explores the same low-dimensional manifold. arXiv preprint arXiv:2305.01604, 2023.

3. R. Ramesh, et al. A picture of the space of typical learnable tasks. Proc. of International Conference of Machine Learning (ICML), 2023.

A diagram of a geodesic system

Description automatically generated

 

 

Short bio

Itay Griniasty is a Schmidt AI in science postdoc fellow at Cornell university, studying the design of microscopic and soft machines, non newtonian fluids and computational tools to analyze deep neural networks and multiphysics simulations.

 

Itay was trained as a mechanical designer in the technological unit in the IDF intelligence corps.

He studied mathematics and physics at the Hebrew university for his BSc, where his minor thesis led to a long collaboration on developing novel mathematical tools for the integration of non linear partial differential equations. He went on to a PhD in physics at the Weizmann institute, studying the propagation of waves in inhomogeneous media.

 

Itay has been awarded an Amirim merit scholarship for his BSc, an Azrieli excellence scholarhship for his PhD,  the Chaim Mida Prize for an excellent PhD student, a Fulbright postdoctoral fellowship (which he declined) and a Schmidt AI in science fellowship towards his postdoc

 

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