Georgia Institute of Technology College of Sciences

Quantum spin systems are many-body physical models where particles are bound to the sites of a lattice. These are widely used throughout condensed matter physics and quantum information theory, and are of particular interest in the classification of quantum phases of matter. By pinning down the properties of new exotic phases of matter, researchers have opened the door to developing new quantum technologies. One of the fundamental quantitites for this classification is whether or not the Hamiltonian has a spectral gap above its ground state energy in the thermodynamic limit. Mathematically, the Hamiltonian is a self-adjoint operator and the set of possible energies is given by its spectrum, which is bounded from below. While the importance of the spectral gap is well known, very few methods exist for establishing if a model is gapped, and the majority of known results are for one-dimensional systems. Moreover, the existence of a non-vanishing gap is generically undecidable which makes it necessary to develop new techniques for estimating spectral gaps. In this talk, I will discuss my work proving non-vanishing spectral gaps for key quantum spin models, and developing new techniques for producing lower bound estimates on the gap. Two important models with longstanding spectral gap questions that I recently contributed progress to are the AKLT model on the hexagonal lattice, and Haldane's pseudo-potentials for the fractional quantum Hall effect. Once a gap has been proved, a natural next question is whether it is typical of a gapped phase. This can be positively answered by showing that the gap is robust in the presence of perturbations. Ensuring the gap remains open in the presence of perturbations is also of interest, e.g., for the development of robust quantum memory. A second topic I will discuss is my research studying spectral gap stability.

URL for the talk: https://bluejeans.com/602513114/7767

Let M be a complex-hyperbolic n-manifold, i.e. a quotient of the complex-hyperbolic n-space $\mathbb{H}^n_\mathbb{C}$ by a torsion-free discrete group of isometries, $\Gamma = \pi_1(M)$. Suppose that M is  convex-cocompact, i.e. the convex core of M is a nonempty compact subset. In this talk, we will discuss a sufficient condition on $\Gamma$ in terms of the growth-rate of its orbits in $\mathbb{H}^n_\mathbb{C}$ for which M is a Stein manifold. We will also talk about some interesting questions related to this result. This is a joint work with Misha Kapovich.

https://bluejeans.com/196544719/9518

We first review the problem of the curse of dimensionality when approximating multi-dimensional functions. Several approximation results from Barron, Petrushev,  Bach, and etc . will be explained. 

Then we present two approaches to break the curse of the dimensionality: one is based on probability approach explained in Barron, 1993 and the other one is based on a deterministic approach using the Kolmogorov superposition theorem.   As the Kolmogorov superposition theorem has been used to explain the approximation of neural network computation, I will use it to explain why the deep learning algorithm works for image classification.
In addition, I will introduce the neural network approximation based on higher order ReLU functions to explain the powerful approximation of multivariate functions using  deep learning algorithms with  multiple layers.

The goal of the meeting is to decide what paper(s) we will be reading and make a rough plan going forward. The following two possibilities were suggested:

• Topological methods in graph theory and their application to the evasiveness conjecture using these lecture notes by Carl Miller.
• Furstenberg's proof of Szemeredi's theorem via ergodic theory using Yufei Zhao's lecture notes.

Other suggestions are also welcome!

In this talk, I will discuss recent advances and challenges in modelling complex dynamics of pedestrian-bridge interactions,  These challenges include a proper understanding of the biomechanics of walking on a moving structure and of the psychology of walking in crowds. I will explain the fundamental mechanism behind pedestrian-induced lateral instability of bridges due to some positive feedback from uncorrelated walkers whose foot forces do not cancel each other but create a bias. I will also present the results of our past and ongoing work that reveal the role of foot placement strategies and social force dynamics in initiating bridge instabilities. In particular, I will show that  (i)  paradoxically, depending on the human balance law (and the frequency of bridge motion), larger crowds can stabilize  bridge motions and (ii)  crowd heterogeneity can promote large vibrations of bridges.

Recording link:  https://bluejeans.com/s/h0TpdyBRatJ 

A lot of the algebraic and arithmetic information of a curve is contained in its interaction with the Galois group. This draws inspiration from topology, where given a family of curves over a base B, the fundamental group of B acts on the cohomology of the fiber. As an arithmetic analogue, given an algebraic curve C defined over a non-algebraically closed field K, the absolute Galois group of K acts on the etale cohomology of the geometric fiber and this action gives rise to various Galois cohomology classes. In this talk, we discuss how to use these classes to detect algebraic/arithmetic properties of the curve, such as the rational points (following Grothendieck​'s section conjecture), whether the curve is hyperelliptic, and the set of ``supersingular'' primes.

https://bluejeans.com/270212862/6963

Quantum Teichmüller space was first introduced by Chekhov and Fock as a version of 2+1d quantum gravity. The definition was translated over time into an algebra of curves on surfaces, which coincides with an extension of the Kauffman bracket skein algebra. In this talk, we will discuss the relation between the Teichmüller space and the Kauffman bracket, and time permitting, the quantized version of this correspondence.

Meeting URL: https://bluejeans.com/106460449/5822

Let f be a real-valued Gaussian stationary process, that is, a random function which is invariant to real shifts and whose marginals have multi-normal distribution.

What is the probability that f remains above a certain fixed line for a long period of time?

We give simple spectral(and almost tight) conditions under which this probability is asymptotically exponential, that is, that the limit of log P(f>a on [0,T])/ T, as T approaches infinity, exists.

This limit defines "the persistence exponent", and we further show it is continuous in the level a, in the spectral measure corresponding to f (in an appropriate sense), and is unaffected by the singular part of the spectral measure.

Proofs rely on tools from harmonic analysis.

Joint work with Ohad Feldheim and Sumit Mukherjee, arXiv:2112.04820.

The talk will be on Zoom via the link

https://us02web.zoom.us/j/71579248210?pwd=d2VPck1CbjltZStURWRWUUgwTFVLZz09

A central goal in modern data science is to design algorithms for statistical inference tasks such as community detection, high-dimensional clustering, sparse PCA, and many others. Ideally these algorithms would be both statistically optimal and computationally efficient. However, it often seems impossible to achieve both these goals simultaneously: for many problems, the optimal statistical procedure involves a brute force search while all known polynomial-time algorithms are statistically sub-optimal (requiring more data or higher signal strength than is information-theoretically necessary). In the quest for optimal algorithms, it is therefore important to understand the fundamental statistical limitations of computationally efficient algorithms.

I will discuss an emerging theoretical framework for understanding these questions, based on studying the class of "low-degree polynomial algorithms." This is a powerful class of algorithms that captures the best known poly-time algorithms for a wide variety of statistical tasks. This perspective has led to the discovery of many new and improved algorithms, and also many matching lower bounds: we now have tools to prove failure of all low-degree algorithms, which provides concrete evidence for inherent computational hardness of statistical problems. This line of work illustrates that low-degree polynomials provide a unifying framework for understanding the computational complexity of a wide variety of statistical tasks, encompassing hypothesis testing, estimation, and optimization.

We will introduce the machinery of hyperbolic polynomial, and see how it can help us generalize classical linear algebra theorems and inequalities on symmetric matrices, including Hadamard-Fischer inequality, Koteljanskii's inequality and Schur-Horn theorem (last one is conjectured but not proved). Joint work with Greg Blekherman, Mario Kummer, Raman Sanyal and Kevin Shu.

Team link: https://teams.microsoft.com/l/meetup-join/19%3a3a9d7f9d1fca4f5b991b4029b09c69a1%40thread.tacv2/1643388106130?context=%7b%22Tid%22%3a%22482198bb-ae7b-4b25-8b7a-6d7f32faa083%22%2c%22Oid%22%3a%2206706002-23ff-4989-8721-b078835bae91%22%7d