Assembling and Disassembling Solids using Collective Action

Monday August 4th 2025 at 14:00 

Wolfson Building of Mechanical Engineering, Room 206

Abstract:

Swarm robotics is a promising methodology for accomplishing complicated tasks through the collective behavior of multiple active units. Interactions between active individuals in animal collectives (like fire ants and worms) lead to emergent responses that remain elusive in synthetic systems. In this talk, I present shape-morphing polymers as a framework to create bio-inspired transient swarms that can self-assemble into a stable solid structure, modulate their mechanical properties, and disassemble on demand. The solids are composed of aggregates of many magnetic, heat-responsive liquid crystal elastomer ribbons. Dilute-suspensions of curved and moving ribbons mechanically interlock, inducing reversible aggregation. The degree of bend and twist of the ribbon and the motion of the ribbon in a rotating external field control how ribbons interact with one another. A mathematical model is developed that sheds light on the role of topological mechanisms in aggregation. The ribbon suspensions reversibly transition between fluid- and solid-like states, exhibiting up to 6 orders-of-magnitude increase in the storage moduli of the entangled aggregates compared with the liquid dispersions. Subsequent heating resulted in a 2-fold increase in both stiffness and yield stress. Controlled dissociation is induced by imparting kinetic energy to the individual ribbons at high magnetic field rotation speeds. Study results provide insights that can lead to advancements in control and task programming of such swarming systems, specifically, by designing mechanical and chemo-mechanical switches for system manipulation. Imparting dynamic collective behaviors into synthetic systems may enable a range of potential applications from autonomous bio-inspired soft robotics to injectable biomaterials.

Bio:

Dr. Asaf Dana is an incoming assistant professor in the Department of Mechanical and Materials Engineering at the University of Nebraska – Lincoln. Until recently, he was a postdoctoral researcher in the Departments of Biomedical Engineering and Materials Science and Engineering at Texas A&M University. His current research focuses on developing soft stimuli-responsive material platforms for applications in tissue engineering, soft robotics and materials processing. Dr. Dana received his Ph.D. (2022) from the Department of Mechanical Engineering at the Technion – Israel Institute of Technology, focusing on the design of a previously unaccessed high-rate actuation mode of shape memory alloys. His research interests include the mechanics of responsive materials with emphasis on processes involving phase transitions in fabrication and actuation. In his research, he develops new experimental systems and methods to study the fundamental relations between microstructural evolution and macro-scale response and employs this knowledge for the development of new engineering design tools for smart and responsive systems.

https://tau-ac-il.zoom.us/j/83662553844

 Electrical Engineering Systems ZOOM Seminar

Speaker: Yotam Erel

Ph.D. student under the supervision of Prof. Amit H. Bermano

Sunday, 22^nd June 2025, at 16:00

PractiLight: Practical Light Control Using Foundational Diffusion Models

Abstract

Light control in generated images is a difficult task, posing specific challenges, spanning over the entire image and frequency spectrum. Most approaches tackle this problem by training on extensive yet domain-specific datasets, limiting the inherent generalization and applicability of the foundational backbones used. Instead, PractiLight is a practical approach, effectively leveraging foundational understanding of recent generative models for the task. Our key insight is that lighting relationships in an image are similar in nature to token interaction in self-attention layers, and hence are best represented there. Based on this and other analyses regarding the importance of early diffusion iterations, PractiLight trains a lightweight LoRA regressor to produce the direct light map for a given image, using a small set of training images. We then employ this regressor to incorporate the desired lighting into the generation process of another image using Classifier Guidance. This careful design generalizes well to diverse conditions and image domains. We demonstrate state-of-the-art performance in terms of quality and control with proven parameter and data efficiency compared to leading works over a wide variety of scene types. We hope this work affirms that image lighting can feasibly be controlled by tapping into foundational knowledge, enabling practical and general relighting.

הרישום לסמינר יבוצע באמצעות סריקת הברקוד למודל (יש להיכנס לפני כן למודל,  לא באמצעות האפליקציה) - )- הרישום מסתיים ב- 13:10

Registration to the seminar will be done by scanning the barcode for the Moodle (Please enter ahead to the Moodle, NOT by application)- Registration ends at 13:10

(The talk will be given in English)

Speaker:     Dr. Yair Carmon

                        Blavatnik School of Computer Science and AI, Tel Aviv University

Monday, June 9^th, 2025

13:00 - 14:00

The Price of Adaptivity in Stochastic Convex Optimization

Abstract

While stochastic optimization methods drive continual improvements in machine learning, choosing the optimization parameters—particularly the learning rate (LR)—remains challenging. In this talk, I will present our work on designing tuning-free algorithms, and characterizing the fundamental costs of not knowing key problem parameters in advance. Inspired by the Price of Anarchy in algorithmic game theory, we define the Price of Adaptivity which measures the multiplicative performance drop due to uncertainty in problem parameters, and - in a number of settings - establish nearly matching upper and lower bounds.

The talk is based on joint work with Maor Ivgi, Itai Kreisler, and Oliver Hinder.

Short Bio

Yair Carmon is an assistant professor of computer science at Tel Aviv university (currently on leave).  He works on the foundations of optimization and machine learning, focusing on questions about fundamental limits and robustness. Yair received a PhD from Stanford University, advised by John Duchi and Aaron Sidford, and M.Sc. and B.Sc. degrees from the Technion.