Center for Mathematical Sciences Research
Arup K Chakraborty – MIT
Wednesday, September 9, 2026
Zoom opens: 10:30AM EDT
Seminar begins: 10:45AM EDT
How the Immune System Learns
The humoral immune system, comprised of B cells and their antibody and memory B cell products, plays an important role in protecting us from infection. This system is also a learning algorithm. Antibodies and memory B cells are produced by a Darwinian evolutionary process. This is a non-equilibrium stochastic dynamic process that allows the immune system to learn about a new antigen (pathogen or vaccine component). I will first describe results obtained from statistical physics-based models of these processes and complementary data from animals and humans. These studies show that the human immune system has a remarkable ability to learn to develop responses that can respond to previously unseen variant antigens upon “training” with only a few exposures to the same antigen. The mechanism underlying this ability to generalize will be discussed. I will then discuss how exploring and exploiting analogies between how the immune system learns and how machines learn is now enabling us to address basic scientific questions that can potentially guide better strategies to cure and prevent disease. Finally, I will comment on similarities and differences between learning in the immune system and deep learning networks.
Eduardo Fradkin – University of Illinois at Urbana Champaign
Wednesday, September 2, 2026
The Quantum Roughening Problem revisited
In 1978 Andreev and Parshin conjectured that the surface of a quantum crystal would become rough due to effects of quantum fluctuations. Two papers published in 1983, one by Daniel Fisher and John Weeks and another one by me, showed that quantum fluctuations make these surfaces smooth and are not rough. In my talk I will revisit these arguments and sinus some interesting connections between this problem and other problems of interest such as quantum dimer models and Josephson junction arrays. In particular I will discuss the case of a model with a global conservation law in its quantum dynamics and the existence of two types of smooth phases in this case.
Karin Dahmen – University of Illinois at Urbana Champaign
Wednesday, August 26, 2026
Universal Avalanches and Prediction: from Nanoindentation and the Brain to Earthquakes and Stars?
Slow nanoindentation, or slow compression of nano-crystals, bulk metallic glasses, rocks, granular materials, and the earth all show intermittent slips or “quakes”. We find that although these systems span 12 decades in length scale, they all show the same scaling behavior for their slip size distributions and other statistical properties. Remarkably, the size distributions follow the same power law multiplied with a stress-dependent cutoff, indicating an underlying nonequilibrium phase transition. A simple model for avalanches of slipping weak spots explains the agreement across scales. It predicts the observed slip-size distributions and the observed stress-dependent cutoff function. The analysis draws on tools from statistical physics and the renormalization group. The results enable extrapolations from one scale to another, and from one force to another, across different materials and structures, from nanocrystals to earthquakes. Connections to neuron avalanches in the brain and recent observations on stars will also be discussed, extending the range of scales to 16 decades in length.
Cris Moore– Santa Fe Institute
Wednesday, August 19, 2026
Which links matter most? Sparsifying network dynamics with effective resistance
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“Sparsification” is the act of reducing a network to a subset of its edges while approximately preserving its properties: either to reduce the computational cost of solving problems about it, or to identify which edges are the most important in some sense. Computer scientists have developed beautiful techniques for sparsifying a graph using physics-related ideas like the effective resistance. However, while these methods preserve the spectral properties of the Laplacian, it is not obvious to what extent they preserve the behavior of nonlinear dynamical systems. Using a mobility network from the United States as an example, I’ll show that they do very well for the SIR epidemic model, including the probability each node becomes infected and its distribution of arrival times, even when the sparse network includes less than 10% of the original edges. Choosing edges using purely topological methods, or by thresholding edge weights, does not perform nearly as well. I will end by discussing the possibility of using sparsification to “denoise” networks from bioinformatics, and present some preliminary results on the Kuramoto model of coupled oscillators.
This is joint work with Alexander Mercier (Harvard School of Public Health), Emmie Fitz-Gibbons (Brown), and Sam Scarpino (Northeastern).
Christopher Jarzynski – University of Maryland
Wednesday, July 29, 2026
Estimating free energy differences with virtually escorted trajectories
The convergence of numerical free energy estimation methods can be accelerated using artificial fields that “escort” simulated trajectories along near-equilibrium paths. Unfortunately, designing such fields is not easy. Taking a cue from the mathematics behind diffusion models – a class of generative models in machine learning – we introduce a method based on virtual escorting. This method adopts a post-processing approach. Given a fixed set of nonequilibrium trajectories, a parameter-dependent virtual escorting field is constructed, possibly using a neural network. This field is used in combination with the trajectories to produce an estimate of the desired free energy difference. The parameters are then adjusted to optimize the convergence of the estimate. I will describe the method, and will discuss conditions under which it produces a zero-variance estimator of the free energy difference.
Uri Alon - Weizmann Institute
Wednesday, July 22, 2026
Why is the upper tail of human lifespan so rigid? Insights from Langevin threshold-crossing models of aging
Human life expectancy has doubled over the last two centuries, yet the upper tail of human lifespan has barely moved. Why is median lifespan so plastic while extreme longevity remains so rigid? I will present a stochastic threshold-crossing view of aging, in which physiological damage follows time-dependent Langevin dynamics and death occurs as a first-passage event across a critical threshold. In this framework, the exponential increase in mortality with age corresponds to escape from a linearly declining barrier. The model separates two classes of parameters: **robustness parameters**, such as noise amplitude and threshold height, which affect the probability of crossing the barrier, and **senogenic parameters**, which govern the deterministic damage production and removal. Changes in robustness can improve median survival while preserving extreme-lifespan distributions, whereas changes in senogenic parameters shift extreme longevity. I will discuss how this distinction helps interpret historical mortality improvements, lifestyle and socioeconomic effects, which all seem to only affect robustness parameters, as well as familial longevity, and progeroid diseases.
Larry Abbott - Columbia University
Wednesday, July 15, 2026
Biological Systems for Spatial Orientation
I will discuss several topics related to how animals represent and update their sense of orientation in the world. In flies, this is done with a set of neurons that collectively form a ring-attractor network. Activity in this system indicates the azimuthal orientation of the fly. I will discuss how the polarization pattern of the sky is used as keep this encoded angle aligned with the physical world. Vertebrates have an analogous system for head orientation, but it is more complex. I will discuss various ways that a ring-attractor could be embedded in a large population of neurons, with results relevant for studies of the synaptic connections in these circuits. Finally, in both flies and vertebrates, these systems have been studied primarily in two dimension (that is, considering only yaw rotations). I will discuss different ideas about how they might respond to three-dimensional rotations.
Itamar Procaccia - Weizmann Insitute Wednesday, July 8, 2026 Zoom opens: 10:30AM EDT Seminar begins: 10:45AM EDT Amorphous Solids: More than Meets the Eye Understanding the mechanical response of amorphous solids, like glasses and granular media, requires considerations that go
Michael Shelley - Flatiron Institute & NYU Wednesday, July 1, 2026 Zoom opens: 10:30AM EDT Seminar begins: 10:45AM EDT Modeling chromatin as an active polymer melt In this talk I'll discuss a line of work wherein chromatin in the nucleus
Robert McCann – University of Toronto Wednesday, June 24, 2026 Zoom opens: 10:30AM EDT Seminar begins: 10:45AM EDT Trading linearity for ellipticity: a low regularity Lorentzian splitting theorem While Einstein's theory of gravity is formulated in a smooth setting, the
Doyne Farmer – Oxford University Wednesday, June 17, 2026 Zoom opens: 10:30AM EDT Seminar begins: 10:45AM EDT Quantitative complexity economics: A revolution in the making Mainstream economic theory follows a standard template that I will review in this talk. Complexity
130th Statistical Mechanics Conference Sunday May 10 2026 - Tuesday May 12 2026 at 0800am - 0500pm nbspHill Center 100 Frelinghuysen Road Room 116 Piscataway NJ nbsp Celebrating the achievements of our guests of honor Michael Loss Marc Mzardnbsp Subir
