Date/Time : 11 Jan 2023 15:30
Venue : S17 #04-04 (SR3)
Speaker : Omar Kidwai, University of Birmingham

Starting from the data of a quadratic (or higher) differential, Gaiotto-Moore-Neitzke’s famous study of four-dimensional N=2 supersymmetric QFTs outputs data known as a BPS structure (the same structure describes the output of Donaldson-Thomas theory). To solve a totally different problem in the theory of matrix models, Chekhov and Eynard-Orantin introduced the topological recursion, which takes initial data called a spectral curve and recursively produces (through residue calculus and combinatorics) an infinite tower of geometric invariants, often with an enumerative interpretation.

We will describe recent work connecting these two different theories. In the simplest cases, when the spectral curve is of degree two and genus zero, we describe a simple explicit formula for the “free energies” of the topological recursion purely in terms of a corresponding BPS structure constructed from the same initial data. Time permitting, we will sketch the relation to the WKB analysis of quantum curves, and suggest a wide range of generalizations. Based on joint work with K. Iwaki and K. Osuga.

Add to calendar :

Date/Time : 09 Jan 2023 15:00
Venue : S17 #04-06 (SR1)
Speaker : Jonathan Eckstein, Rutgers University

This talk describes the solution of convex optimization problems that include uncertainty modeled by a finite but potentially very large multi-stage scenario tree.

In 1991, Rockafellar and Wets proposed the progressive hedging (PH) algorithm to solve such problems. This method has some advantages over other standard methods such as Benders decomposition, especially for problems with large numbers of decision stages. The talk will open by showing that PH is an application of the Alternating Direction Method of Multipliers (ADMM), and thus of the “Douglas-Rachford” operator splitting method. The equivalence of PH to the ADMM has long been known but not explicitly published.

Next, the talk will apply a different, more recent operator splitting method called “projective splitting” to the same problem. The resulting method is called “asynchronous projective hedging” (APH). Unlike most decomposition methods, it does not need to solve every subproblem at every iteration; instead, each iteration may solve just a single subproblem or a small subset of the available subproblems.

Finally, the talk will describe work integrating the APH algorithm into mpi-sppy, a Python package for modeling and distributed parallel solution of stochastic programming problems. Mpi-sppy uses the Pyomo Python-based optimization modeling sytem. Our experience includes using up to 2,400 processor cores to solve 2-stage and 4-stage test problem instances with as many as 1,000,000 scenarios.

Portions of the work described in this talk are joint with Patrick Combettes (North Carolina State University), Jean-Paul Watson (Lawrence Livermore National Laboratory, USA), and David Woodruff (University of California, Davis).

Add to calendar :

Date/Time : 06 Jan 2023 14:00
Venue : LT34 (S17 Level 3)
Speaker : George Lusztig, MIT

According to Chevalley, semisimple groups can be defined over any field. More recently they were defined over some structures called semifields (which include the set of strictly positive real numbers under the usual addition and multiplication). It turns out that the theory over semifields can be used to get a better understanding of the theory over fields.

Add to calendar :

Date/Time : 06 Jan 2023 14:00
Venue : S17 #05-12 (SR4)
Speaker : Tanya Veeravalli, University of Illinios, Urbana-Champaign

There has been a great deal of recent interest in learning and approximation of functions that can be expressed as expectations of a given nonlinearity with respect to its random internal parameters. Examples of such representations include “infinitely wide” neural nets, where the underlying nonlinearity is given by the activation function of an individual neuron. In this talk, we will bring this perspective to function representation by neural stochastic differential equations (SDEs). A neural SDE is an Itô diffusion process whose drift and diffusion matrix are elements of some parametric families. We will show that the ability of a neural SDE to realize nonlinear functions of its initial condition can be related to the problem of optimally steering a certain deterministic dynamical system between two given points in finite time. This auxiliary system is obtained by formally replacing the Brownian motion in the SDE by a deterministic control input. We derive upper and lower bounds on the minimum control effort needed to accomplish this steering; these bounds may be of independent interest in the context of motion planning and deterministic optimal control.

Add to calendar :

Date/Time : 04 Jan 2023 15:30
Venue : S17 #04-04 (SR3)
Speaker : Asbjorn Nordentoft, Université Sorbonne Paris Nord

Duke, Imamoḡlu, and Tóth have recently constructed a canonical hyperbolic orbifold whose boundary is equal to a given closed geodesic on the modular surface. Furthermore, they have shown that the projection of these hyperbolic orbifolds onto the modular surface equidistributes (as the discriminant of the closed geodesic tends to infinity). We will describe an extension to the case of modular surfaces of non-trivial genus relying on Morse coding with respect to special fundamental polygons as introduced by Kulkarni. We also obtain an equidistribution result which requires a detailed study of the homology of the modular curves. This is joint work with Peter Humphries.

Add to calendar :

Date/Time : 22 Dec 2022 10:00
Venue : S17 #05-12 (SR4)
Speaker : Yanni Sun, City University of Hong Kong

Known as the blueprint of life, the genomic sequence contains instructions for controlling a species’ growth, development, survival, and reproduction. From the perspective of Computer Science, the primary structure of the genomic sequences is simply a string/sequence defined on a small alphabet {A, C, G, T}. Different parts of the sequence have different biological functions and thus comprise the “language of life”.

Next-generation sequencing (NGS) technologies, which produce vast amount of sequencing data for various life forms, have provided tremendous information for tackling grand challenges in various fields. NGS enables us to obtain a large amount of data to understand more about the language of life. In this talk, I will focus on my research on using deep learning models to make sense out of the BIG microbial sequencing data obtained from host-associated (such as human gut, skin) or environmental samples (such as water, soil). By using protein clusters as the tokens (“words”), we model viral genomes as a protein-based language and thus use modern language model Transformer for feature learning. In addition, to address the issues of limited labeled biological data, we use graph convolutional network (GCN), a semi-supervised learning model for leveraging both labeled and unlabeled data for feature learning in several tasks. These research projects rendered us with deeper understanding of the advantages and limitations of using deep learning models for biological sequence classification. I will share some of our observations along this line.

Add to calendar :

Date/Time : 15 Dec 2022 10:00
Venue : S17 #04-04 (SR3)
Speaker : Ming Ha Quang, RIKEN AIP (Advanced Intelligence Project), Tokyo, Japan

Information geometry (IG) and Optimal transport (OT) have been attracting much research attention in various fields, in particular machine learning and statistics. In this talk, we present results on the generalization of IG and OT distances for finite-dimensional Gaussian measures to the setting of infinite-dimensional Gaussian measures and Gaussian processes. Our focus is on the Entropic Regularization of the 2-Wasserstein distance and the generalization of the Fisher-Rao distance and related quantities. In both settings, regularization leads to many desirable theoretical properties, including in particular dimension-independent convergence and sample complexity. All of the presented formulations admit closed form expressions that can be efficiently computed and applied practically.

Add to calendar :

Date/Time : 05 Dec 2022 10:00
Venue : S17 #04-06 (SR1)
Speaker : Soufiane Hayou, National University of Singapore

Neural networks have achieved impressive performance in many applications such as image recognition and generation, and speech recognition. State‐of‐the‐art performance is usually achieved via a series of engineered modifications to existing neural architectures and their training procedures. However, a common feature of these systems is their large‐scale nature. Indeed, modern neural networks usually contain millions ‐ if not billions ‐ of trainable parameters, and empirical evaluations (generally) support the claim that increasing the scale of neural networks (e.g. width and depth in a multi‐layer perceptron) enhances the model performance. However, given a neural network model, it is not straightforward to address the crucial question `how do we scale the network?’. In this talk, I will discuss certain properties of large‐scale neural networks and show how we can leverage different mathematical results to build robust networks with empirically confirmed benefits.

Add to calendar :

Date/Time : 01 Dec 2022 10:00
Venue : Zoom
Speaker : Yusheng Luo, Stony Brook University

Join Zoom Meeting
https://nus-sg.zoom.us/j/82637054452?pwd=ZkpEVllwN3g1YUNYRHIwMXF3TmUzdz09

Meeting ID: 826 3705 4452
Passcode: 332211

The Mandelbrot set is a fundamental object in the field of complex dynamics. It has been thoroughly studied with much success, and we know a lot about its combinatorial, topological and geometrical structures. The natural generalization of the Mandelbrot set in higher degree is the connectedness locus. It is much more challenging to understand their structures as they are of higher dimensions. Very few of the one‐dimensional tools that we use to study the Mandelbrot set carry over to this general setting.

In this talk, I will start with a gentle introduction of the Mandelbrot and its higher dimensional analogues. I will then discuss some tools that I developed to navigate these higher dimensional Mandelbrot sets. In particular, I will explain how self‐bumps can be constructed and how these tools allow us to understand polynomials with zero core entropy. I will also discuss many exciting new directions to explore motivated by the parallel theories for Teichmuller spaces.

Add to calendar :