Past events

Lunar volatiles, especially water, hold the key to sustaining long-term human presence on the Moon and beyond. I will cover the latest discoveries in volatile stability, distribution, sources, and transport. Due to the Moon's monotonic decrease in spin axis obliquity, perennially shadowed regions near the poles have shrunk with time.   Thus, comparing the observations against theoretical models affords the opportunity to constrain the history of ice accumulation in these regions.  These constraints offer both fundamental insights and practical value.

Glucose can enter the blood following a meal, and/or can be produced by the liver and kidneys at times of need such as fasting. An elevation in blood glucose beyond steady state levels leads to secretion of the hormone insulin, leading to increase in glucose uptake into muscle and adipose tissues. Diabetes Mellitus arises when insufficient levels of insulin are secreted into the blood, manifesting as a chronic elevation in blood glucose levels. A reduction in glucose levels can lead to secretion of a large number of hormones, such as glucagon, cortisol and adrenaline, which cause endogenous glucose production and secretion into the blood,  maintaining homeostasis of glucose levels. 

In this talk we will use mathematical modeling and biochemical measurements to study the dynamics of hormone secretion in healthy individuals and Diabetes patients, and (hopefully) provide an answer to a key question: does the "body" measure glucose levels and regulates glucose levels accordingly by secreting insulin/glucose, as expected by a standard negative feedback system, or does it estimate future glucose levels and secretes hormones/glucose in a feedforward mechanism?

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The nonlinear Breit-Wheeler process — electron-positron pair creation from high-energy photons in an intense electromagnetic field — is one of the most fundamental yet experimentally elusive predictions of strong-field quantum electrodynamics. Reaching the regime where this process becomes measurable requires not only extreme light-matter interaction conditions, but also detecting technologies capable of resolving rare signatures amid complex backgrounds. Beyond its intrinsic importance for testing quantum electrodynamics in the strongest fields accessible on Earth, this process is also relevant for understanding environments such as magnetars, where similarly intense fields and abundant pair production naturally occur. I will present the ongoing international effort to realize a definitive measurement of the process and highlight how advanced particle-tracking methods, commonly used in High-Energy Physics experiments, are contributing to this goal. I will discuss the running E320 experiment at SLAC, where our tracking detector is used to characterize collisions of 10 GeV electrons and 10 TW laser pulses in unprecedented detail, and give an outlook on the upcoming LUXE experiment at DESY, which aims to operate at the intensity frontier. I will also describe new opportunities at high-power multi-PW laser facilities — including our recent all-laser campaigns at ELI-NP and APOLLON — that open complementary routes to probe strong-field physics in complementary parameter spaces. Together, these efforts bring accelerator-based, laser-based and particle physics approaches closer to a definitive measurement of the nonlinear Breit-Wheeler process.

In optics, vortices appear as phase twists of the electromagnetic field, traditionally arising from interactions between light and matter. Our lab investigates an extreme regime of optical nonlinearity in which quantum vortices arise from strong, effective interactions between individual photons. We observe extended phase singularities in the few-photon wavefunction, including vortex lines and rings, and explore their symmetry and topology. The vortex rings become warped by the underlying dispersion, and the enclosed phase flip provides a resource for deterministic quantum logic. In recent experiments moving beyond co-propagating geometries, we find that counter-propagating photons exhibit longer-range and richer vortex interactions, opening new avenues for quantum nonlinear optics.

Cells, tissues and organs can rotate spontaneously in vivo and in vitro. These motions are remarkable for their robustness and for their potential functions. However, physical mechanisms coordinating these dynamics are poorly understood. Active matter formalisms are required to understand these out-of-equilibrium phenomena with quantitative comparisons between theory and experiments.

I will present two examples of spontaneous rotation with experiments synergized with theory (1, 2). In a first study (1), we report that rings of epithelial cells can undergo spontaneous rotation below a threshold perimeter. We demonstrate that the tug-of-war between cell polarities together with the ring boundaries determine the onset to coherent motion. The principal features of these dynamics are recapitulated with a numerical simulation (Vicsek model). In a second study (2), we show that cell doublets rotate in a 3D matrix and we identify mesoscopic structures leading the movement. Our theoretical framework integrates consistently cell polarity, cell motion, and interface deformation with equations capturing the physics of cortical cell layers. We also report that the Curie principle is verified in these cellular doublets with its symmetry relations between causes and effects. Altogether both examples could set generic rules to quantify and predict generic motion of tissues and organs as well as active synthetic materials.

1- S. Lo Vecchio et al. Nature Physics 20:322–331(2024).

2- L. Lu et al. Nature Physics 20:1194–1203 (2024).

Intrinsically disordered proteins (IDPs) and disordered protein regions, which comprise over 40% of the eukaryotic proteome, exhibit complex dynamics, fluctuating between diverse conformational ensembles. Unlike structured proteins, where short-range interactions and long-range contacts dictate singular three-dimensional folding, IDPs lack a single stable structure. To understand their biological function, it is crucial to establish a correlation between the amino acid sequence and the statistical properties of their structural ensemble.

In this talk, I will present our recent work on neurofilament proteins, which are essential neuronal-specific cytoskeletal components containing large intrinsically disordered domains. Our study spans multiple length scales—from nanoscopic to macroscopic—aiming to uncover the molecular mechanisms underlying their functional behavior. By leveraging coarse-grained polymer physics models and integrating minimal parameters, we demonstrate that the structural ensemble of neurofilament proteins can be reasonably predicted. However, our findings underscore that specific sequence motifs and the surrounding context are necessary to fully capture the protein’s conformational landscape in solution.

These results highlight the power of advanced polymer theories in describing the ensemble behavior of IDPs, offering a promising avenue for modeling their function and dysfunction, particularly in neurodegenerative disease contexts. By bridging the gap between sequence specificity and polymer physics, we aim to establish a more comprehensive framework for predicting IDP behavior and its implications in health and disease.

I will describe two biologically inspired systems that can be analyzed using the same hydrodynamic Hamiltonian formalism. The first is ATP synthase proteins, which rotate in a biological membrane. The second is swimming micro-organisms such as bacteria or algae confined to a two-dimensional film. I will show that in both cases, the active systems self-assemble into distinct structural states --- the rotating proteins rearrange into a hexagonal lattice, whereas the micro-swimmers evolve into a zig-zag configuration with a particular tilt. While the two systems differ both on the microscopic, local interaction, as well as the emerging, global structure, their dynamics originate from similar geometrical conservation laws applicable to a broad class of fluid flows. I will present experiments and simulations in which the Hamiltonian is perturbed, leading to different and surprising steady-state configurations. Time permitting, I will show that higher-order force distributions lead to the aggregation of an ensemble of particles.

The development of the infant gut microbiome is primarily influenced by delivery mode (vaginal or C-section) and the infant feeding type, with breast milk serving as the optimal source of nutrition. Breast milk contains human milk oligosaccharides (HMOs) that act as nourishment for the developing gut microbiome, potentially conferring advantages to specific bacterial species. Previous studies have demonstrated the ability of certain Bifidobacterium species to utilize individual HMOs, yet it is unclear whether the HMO composition impacts the gut bacteria community. 

In this seminar I will introduce the field of the gut microbiome and infant gut specifically, I will dig deeper into bacteria from the Bifidobacterium genus and their ability to utilize HMOs. From computational tool development to estimate their abundance, and our identification of a novel subspecies in the infant gut, to the experimental follow-ups of validation and examining the functional potential of the bacteria. 

 "No previous knowledge of the field is needed, just a critical and open mind."

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How cancer arises from a single normal cell is still the subject of active debate, affecting intervention strategies. While many cells may harbor oncogenic mutations, only a few unpredictably end-up developing a full-blown tumour. Various theories have been proposed to explain that transition, but none has been tested in vivo at the single cell level. Here using an optogenetic approach we permanently turn on an oncogene (KRASG12V) in a single cell of a zebrafish brain that, only in synergy with the transient co-activation of a reprogramming factor (VENTX/NANOG/OCT4), undergoes a deterministic malignant transition and robustly and reproducibly develops within 6 days into a full-blown cancer. The controlled way in which a single cell can thus be manipulated to give rise to cancer lends support to the “ground state theory of cancer initiation” through “short-range dispersal” of the first malignant cells preceding tumour growth.

P. Scerbo B. Tisserand, M. Delagrange , H.Debare,   B.Ducos, D. Bensimon

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From electrically responsive neuronal networks to immune repertoires, biological systems can learn to perform complex tasks. In this seminar, we explore physical learning, a framework inspired by computational learning theory and biological systems, where networks physically adapt to applied forces to adopt desired functions. Unlike traditional engineering approaches or artificial intelligence, physical learning is facilitated by physically realizable learning rules, requiring only local responses and no explicit information about the desired functionality. Our research shows that such local learning rules can be derived for broad classes of physical networks and that physical learning is indeed physically realizable, without computer aid, through laboratory experiments. We take further inspiration from learning in the brain to demonstrate the success of physical learning beyond the quasi-equilibrium regime, leading to faster learning with little penalty. By leveraging the advances of statistical learning theory in physical machines, we propose physical learning as a promising bridge between computational machine learning and biology, with the potential to enable the development of new classes of smart metamaterials that adapt in-situ to users’ needs.

For diffusive systems (i.e., systems which satisfy Fourier's law or Fick's law) maintained in a non-equiilibrium steady state by contact with two heat baths or two reservoirs, the macroscopic fluctuation theory developed over the last 25 years has become a major tool to calculate the fluctuations  and the large deviation function of the heat or of the particle currents. 

After a review of the main achievements of the theory, I will try to list some open issues. In particular,  although the theory predicts the same large deviation
function of the current in all dimensions, numerical calculations exhibit some small discrepancy in space dimension d > 2.
This talk will try to explain the origin of these discrepancies.

work in collaboration with Thierry Bodineau

Explaining life in terms of the jiggling and wiggling of atoms is a central goal in modern biophysics. The dynamics of folded proteins include concerted motions of thousands of atoms, thus clearly exceeding the capabilities of analytical theories. On the other hand, intrinsically disordered proteins (IDPs) are well described by analytic polymer models of different flavors. Yet, these models are not applicable if disorder and order mix, e.g., for IDPs that form partially ordered complexes or for highly compact IDPs. Using single-molecule spectroscopy, we studied the dynamics of such ‘mixed’ cases and found that even weak interactions can tremendously slow down the IDP-dynamics. In the second part of the talk, I will demonstrate that such protein disorder is key for transmitting allosteric signals across many nanometers in DNA. An intrinsically disordered tail of a DNA-binding protein amplifies microsecond fluctuations in DNA and increases the chance of binding proteins at a distant site. These findings have implications for our understanding of transcription activation in gene expression and suggest a new functional role for IDPs in transcription factors.

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Explaining life in terms of the jiggling and wiggling of atoms is a central goal in modern biophysics. The dynamics of folded proteins include concerted motions of thousands of atoms, thus clearly exceeding the capabilities of analytical theories. On the other hand, intrinsically disordered proteins (IDPs) are well described by analytic polymer models of different flavors. Yet, these models are not applicable if disorder and order mix, e.g., for IDPs that form partially ordered complexes or for highly compact IDPs. Using single-molecule spectroscopy, we studied the dynamics of such ‘mixed’ cases and found that even weak interactions can tremendously slow down the IDP-dynamics. In the second part of the talk, I will demonstrate that such protein disorder is key for transmitting allosteric signals across many nanometers in DNA. An intrinsically disordered tail of a DNA-binding protein amplifies microsecond fluctuations in DNA and increases the chance of binding proteins at a distant site. These findings have implications for our understanding of transcription activation in gene expression and suggest a new functional role for IDPs in transcription factors.

Since the groundbreaking discovery of 51 Pegasi b in 1995, a few thousands extra-solar planets have been identified, marking the beginning of a rapidly evolving new field in Astronomy. This talk will trace the history of exoplanet research and explore how these discoveries have reshaped our understanding of planetary formation and opening new frontiers in the study of planetary systems.

Generative AI models, including those behind image creation tools, have shown remarkable capabilities in transforming random inputs into coherent outputs. Inspired by these advancements, we've developed Parnassus, a deep-learning model designed for particle physics. Parnassus processes point clouds representing particles interacting with a detector and outputs reconstructed particle data.  Parnassus accurately replicates the particle flow algorithm and generalizes well beyond its training set. This approach exemplifies how techniques from image generation can be adapted to accelerate simulations in high-energy physics.

Materials that are constantly driven out of thermodynamic equilibrium, such as active and living systems, typically violate the Einstein relation. This may arise from active contributions to particle fluctuations which are unrelated to the dissipative resistance of the surrounding medium. In this talk I will show that in these cases the widely used relation between informatic entropy production and heat dissipation does not hold. Consequently, fluctuation relations for the mechanical work, such as the Jarzynski and Crooks theorems, are invalid. The breaking of the correspondence between informatic entropy production and heat dissipation will then be related to the departure from the fluctuation-dissipation theorem. I will finally propose a temperaturelike variable that restores the correspondence between information and thermodynamics and gives rise to a generalized second law of thermodynamics. The Clausius inequality, Carnot maximum efficiency theorem, and relation between the extractable work and the change of free energy are recovered as well.

Chromosomes are long polymers of genomic DNA decorated with proteins. We are interested in understanding how cells fold chromosomes to read, write, and process genetic and epigenetic information. Could the way chromosomes are folded carry information itself?

Recent works from my group and others have shown that chromosomes function as active polymers. First, we discovered that chromosomes are folded by the ATP-dependent process of loop extrusion, where molecular motors form progressively larger loops. This collective action of nanometer-sized motors shapes micrometer-sized chromosomes. We demonstrated how this mechanism can also establish complex long-range communication between regulatory elements and genes.

Second, we found that chromosome folding plays a key role in storing "epigenetic memory, " which refers to patterns of chemical marks along the genome. Although these marks are subject to loss and spreading by enzymes, when genome folding is influenced by the marks, the pattern can be preserved for hundreds of cell divisions. We also identified a parallel between this mechanism of epigenetic memory and associative memory in neural networks, suggesting that this system may be capable of performing more complex information-processing tasks.

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One-dimensional edge states arising from a system of non-trivial bulk topology are potential quantum information carriers and platforms to explore the physics of topology and interactions. In this talk, I will discuss our effort in realizing the quantum valley Hall effect and the properties of its edge state, the kink states. Using van der Waals stacking and precision lithography, we create valley-momentum locked kink states in bilayer graphene and demonstrate its precise resistance quantization, a hallmark of ballistic edge state transport. The quantization is robust to temperatures of tens of Kelvin, which bolds well for potential applications. The all-electrical construction of the kink states gives us the ability to realize a variety of electron quantum optics operations, now in an edge state platform. I will show the workings of a reconfigurable ballistic waveguide, a topological valley valve, and a continuously tunable electron beam splitter. The cleanness and controllability of the kink states enable future experiments in helical Luttinger liquid and its use as quantum information highways.

1. Li, J. et al. Gate-controlled topological conducting channels in bilayer graphene. Nature Nanotechnology 11, 1060, doi:10.1038/Nnano.2016.158 (2016).

2. Li, J. et al. A valley valve and electron beam splitter. Science 362, 1149, doi:10.1126/science.aao5989 (2018).

3. Huang, K et al.  High-temperature quantum valley Hall effect with quantized resistance and a topological switch. Science 385, 657, doi:10.1126/science.adj3742 (2024).

I will explain the conjectured duality between double-scaled SYK

at infinite temperature and the static patch of a particular

version of JT-de Sitter space. The conjectured duality raises the

question of what particle physics is like in the bulk of the static

patch.

The answer: I will give it in the lecture.

In my talk I will introduce a novel framework that applies a principle from information theory, that of Minimum Description Length (MDL), to understand how regulation of human gene expression across organs, tissues is shaped by regulatory architecture.

Examination of expression patterns of human genes across the body reveals an intriguing duality: While many genes are expressed in only one tissue, others, known as “housekeeping genes”, are ubiquitously expressed in essentially every tissue. Yet, interestingly, a considerable portion of the genes are on the mid-range, deliberately expressed in many tissues but are also absent in many others.

Intuitively, in human language terms, specifying the expression program of the genes on the two ends of the spectrum requires a short description – e.g. “expressed in all tissues”, or “expressed only in brain”. Yet specifying the expression of genes in the middle of the scale requires longer description, or a longer MDL, having to specify in each tissue if the gene is expressed or not, and at what level. We sought to measure regulatory complexity of each human gene and examine if the MDL principle predicts and explains regulatory complexity. Our findings lend support to the MDL principle’s prediction. Our measure of regulatory complexity of a gene’s expression pattern can be predicted by quantifying its regulatory information content. In the talk we shall discuss evolutionary implications to the development of multi-cellularity.

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Living systems from individual cells to entire tissues adopt diverse curved shapes, appearing on many length scales and commonly driven by active contractile stresses generated in the cell cytoskeleton. Yet, how these forces generate specific 3D forms remains unclear. By recreating the cell cytoskeleton from basic components, with precisely controlled composition and initial geometry, we demonstrate that the spontaneous buildup of stress gradients generated by these molecular motors drive shape deformation. We identify the shape selection rules that determine the final adopted configurations. These are encoded in the initial radius to thickness aspect ratio, likely indicating shaping scalability. These results provide insights on the mechanically induced spontaneous shape transitions in contractile active matter, revealing potential shared mechanisms with living systems across scales.

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Ordered mechanical systems typically have one or only a few stable rest configurations, and hence are not considered useful for encoding memory. Multistable and history-dependent responses usually emerge from quenched disorder, for example in amorphous solids or crumpled sheets. Inspired by the topological structure of frustrated artificial spin ices, we introduce an approach to design ordered, periodic mechanical metamaterials that exhibit an extensive set of spatially disordered states. We show how such systems exhibit non-Abelian and history-dependent responses, as their state can depend on the order in which external manipulations were applied. We demonstrate how this richness of the dynamics enables to recognize, from a static measurement of the final state, the sequence of operations that an extended system underwent. Thus, multistability and potential to perform computation emerge from geometric frustration in ordered mechanical lattices that create their own disorder.

The word “learning” often conjures images of school or other human endeavors. Neuroscientists have used the word for a wide range of phenomena in the animal kingdom. For those engulfed in python code, perhaps learning is also associated with gradient descent or other technical terms from computer science. What do we gain from using the same name for all these cases?
In this talk, I will argue that systems that learn can be useful models of one another. This is because of general principles that seem to transcend specific instances, such as multiplicity of solutions, low-rank perturbations and more. I will demonstrate these properties using several examples. These include representational drift, the connection (or lack thereof) between neural activity and behavior, and more.
Throughout the talk I will try to highlight the benefits, dangers and challenges of this approach.

Transcription factors (TFs) bind genomic DNA regulating gene expression and developmental programs in embryonic stem cells (ESCs). Even though comprehensive genome-wide molecular maps for TF-DNA binding are experimentally available for key pluripotency-associated TFs, the understanding of molecular design principles responsible for TF-DNA recognition remains incomplete. In this talk, I will show that binding preferences of key pluripotency TFs exhibit bimodality in the local GC-content distribution. Sequence-dependent binding specificity of these TFs is distributed across three major contributions. First, local GCcontent is dominant in high-GC-content regions. Second, recognition of specific k-mers is predominant in low-GC-content regions. Third, short tandem repeats (STRs) are highly predictive in both low- and high-GC-content regions. In sharp contrast, binding preferences of a key oncogenic protein, c-Myc, are exclusively dominated by local GC-content and STRs in high-GC-content genomic regions. I will propose that the transition in the TF-DNA binding landscape upon ESC differentiation is solely regulated by the concentration of c-Myc, which forms a bivalent c-Myc-Max heterotetramer upon promoter binding, competing with key pluripotency factors. Taken together, these findings point out that c-Myc may significantly affect the genome-wide TF-DNA binding landscape, chromatin structure, and enhancerpromoter interactions.

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The study of dilute, strongly interacting quantum gases reveals

universal properties that transcend the specifics of individual

systems. These features arise from their short-range behavior

and are encapsulated in a key quantity called the “contact”, which

quantifies the probability of two particles being in close proximity.

In this talk, I will introduce the contact theory and its extension to

nuclear and molecular systems beyond the zero-range limit. I will

demonstrate its applicability in analyzing nuclear electron

scattering and photo absorption reactions.

Additionally, I will discuss how mean-field approximations, such as

the nuclear shell model, can effectively estimate the contact,

offering valuable insights into the underlying physics.

Non-equilibrium active matter systems often exhibit self-organized, collective motion that can give rise to the emergence of coherent spatial structures. Prime examples covering many length scales range from mammal herds, fish schools and bird flocks, to insect and robot swarms. Despite significant advances in understanding the behavior of homogeneous systems in the last decades, little is known about the self-organization and dynamics of heterogeneous active matter. I will present results of bioconvection experiments with multispecies suspensions of wild-type bacteria from the hyper-diverse bacterial communities of Cuatro Ciénegas, Coahuila, whose origin dates back to the pre-Cambrian. Under oxygen gradients, these bacteria swim in auto-organized, directional flows, whose spatial scales exceed the cell size by orders of magnitude, demonstrating a plethora of amazing dynamical behaviors, including segregation. I will present evidence supporting the notion that the mechanisms giving rise to these complex behaviors are predominantly physical, and not a result of biological interactions. This research significantly advances our understanding of both heterogeneity in active matter, as well as in the dynamics of complex microbial ecological communities, bringing profound insights into their spatial organization and collective behavior.

Cryo-EM has famously revolutionized structural biology with atomic-scale resolution of macromolecules in 3D. The conventional protocol is based on wide-field imaging with phase contrast introduced by defocus, followed by extensive image processing and averaging from a great number of identical objects. Key assumptions break down in the extension toward 3D imaging of thicker specimens such as cells, however, and especially for interpretation of unique features.

The talk will be in two parts (by Michael and Shahar). The first will introduce an alternative imaging modality by scanning a focused probe, i.e., scanning transmission electron microscopy, or STEM, which circumvents some of these constraints. New camera technologies enable recording of the entire pattern of diffraction at every pixel, called 4D STEM. Combining the imaging with tomography, we explore new methods to exploit the wave coherence for 3D reconstruction with optimal contrast and resolution. Examples include crystals of heme, intact cells and sections of cell multilayers, and bacteriophage for the latest advances. The second part will center on the physical mechanisms of electron scattering relevant to cryo-EM. Combined with an energy loss spectrometer, a 4D STEM measurement provides atomic differential cross-sections, both elastic and inelastic. The elastic part relates to de Broglie phase delay by the average electric potential. The inelastic part is mainly due to generation of plasmons and scattering by the resulting polarization. The cross-sections provide data to test new modeling approaches, as well as to develop characterization tools for biological and other organic materials.

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About a century after Albert Einstein's presentation of General Relativity and Karl Schwarzschild's first solution, have three experimental techniques made remarkable progress in proving the existence of the Schwarzschild/Kerr black hole solution. I will describe the impressive progress of high resolution near-infrared and radio imaging and interferometry, and of precision measurements of gravitational waves in the Galactic Center and other galaxies. I will then discuss what we now know about the cosmic co-evolution and growth of galaxies and black holes, and finish with the riddle of massive black holes detected by JWST only a few hundred Myrs after the Big Bang.

For the overwhelming majority of organisms, effective communication and coordination are critical in the quest to survive and reproduce. A better understanding of these processes can benefit from physics, mathematics, and computer science – via the application of concepts like energetic cost, compression (minimization of bits to represent information), and detectability (high signal-to-noise-ratio). My lab's goal is to formulate and test phenomenological theories about natural signal design principles and their emergent spatiotemporal patterns. To that end, we adopted insect swarms as a model system for identifying how organisms harness the dynamics of communication signals, perform spatiotemporal integration of these signals, and propagate those signals to neighboring organisms. In this talk, I will focus on two types of communication in insect swarms: visual communication, in which fireflies communicate over long distances using light signals, and chemical communication, in which bees serve as signal amplifiers to propagate pheromone-based information about the queen's location. Through a combination of behavioral assays and computational techniques, we develop and test model-driven hypotheses to gain a deeper understanding of these communication processes and contribute to the broader understanding of animal communication.

Capillary networks are prevalent in nature and biology, playing a crucial role in systems like animal vasculature and plant capillaries, with broad applications in medicine and science. However, many aspects of how these networks regulate and control flow remain unresolved. While the basic principles of capillary networks and their functions are well understood, ongoing research seeks to uncover how these systems dynamically respond to environmental changes, adapt to varying conditions, and whether they retain a memory of past states. Developing a model system for capillary networks allows us to pose exciting new questions, such as: "Can capillary networks store memory?"

Building such a model presents two key challenges. First, the need to dynamically modify the nature of bonds within the networks and understand its impact on transport. Second, designing networks capable of evolving in response to external stimuli. Successfully addressing these challenges could transform our ability to actively control macroscale flow by manipulating local bonds within the networks.

Here, a novel experimental model of capillary networks is proposed, consisting of hundreds of interconnected liquid diodes. Like electrical diodes, these microscale surface structures direct liquid flow in specific directions while preventing reverse flow. However, under certain conditions, liquid diodes may fail, permitting bidirectional flow and introducing bonds of varying properties within the capillary network.

This system will allow us to investigate whether the wetting state of liquids in the network depends on its actuation history—essentially exploring whether capillary networks can exhibit memory. This question opens up new possibilities, including the potential to encode information within these networks, analyze how transport responds to external stimuli, study the interplay between global actuation and local fluid dynamics, explore the coupling between mechanics and flow, and better understand how information propagates through capillary systems.

Capillary networks are prevalent in nature and biology, playing a crucial role in systems like animal vasculature and plant capillaries, with broad applications in medicine and science. However, many aspects of how these networks regulate and control flow remain unresolved. While the basic principles of capillary networks and their functions are well understood, ongoing research seeks to uncover how these systems dynamically respond to environmental changes, adapt to varying conditions, and whether they retain a memory of past states. Developing a model system for capillary networks allows us to pose exciting new questions, such as: "Can capillary networks store memory?"

Building such a model presents two key challenges. First, the need to dynamically modify the nature of bonds within the networks and understand its impact on transport. Second, designing networks capable of evolving in response to external stimuli. Successfully addressing these challenges could transform our ability to actively control macroscale flow by manipulating local bonds within the networks.

Here, a novel experimental model of capillary networks is proposed, consisting of hundreds of interconnected liquid diodes. Like electrical diodes, these microscale surface structures direct liquid flow in specific directions while preventing reverse flow. However, under certain conditions, liquid diodes may fail, permitting bidirectional flow and introducing bonds of varying properties within the capillary network.

This system will allow us to investigate whether the wetting state of liquids in the network depends on its actuation history—essentially exploring whether capillary networks can exhibit memory. This question opens up new possibilities, including the potential to encode information within these networks, analyze how transport responds to external stimuli, study the interplay between global actuation and local fluid dynamics, explore the coupling between mechanics and flow, and better understand how information propagates through capillary systems.