WiSDM 2019 is a research collaboration workshop targeted toward people working in data science and mathematics. This program will bring together researchers at all stages of their careers, from graduate students to senior researchers, to collaborate on problems in data science.

Data science is typically characterized as work at the intersection of mathematics, computer science, statistics, and an application domain. The scientific focus will be on cutting-edge problems in network analysis for gene detection, group dynamics, graph clustering, novel statistical and topological learning algorithms, tensor product decompositions, reconciliation of assurance of anonymity and privacy with utility measures for data transfer and analytics, as well as efficient and accurate completion, inference and fusion methods for large data and correlations.

Applications are now open. Applicants should rank their top 3 choices of projects in their personal statement. Project descriptions can be found... (more)

The Women in Symplectic and Contact Geometry and Topology workshop (WiSCon) is a Research Collaboration Conference for Women (RCCW) in the fields of contact and symplectic geometry/topology and related areas of low-dimensional topology. The goal of this workshop is to bring together researchers at various career stages in these mathematical areas to collaborate in groups on projects designed and led by leaders in the field.

The mathematical fields of symplectic and contact geometry/topology, rooted in concepts from classical physics, have experienced huge growth in the past few decades. This growth has come in many forms, including multiple flavors of homology theories, symplectic embedding problems, techniques for regularizing spaces of pseudoholomorphic curves, and examples of mirror symmetry, to name a few. This workshop aims to generate research collaborations which build on the growing momentum in these topics, while fostering a network for the traditionally underrepresented... (more)

Dehn surgery has played a central role in the development of low-dimensional topology since it was first introduced by Max Dehn in 1910. Its study has stimulated several fascinating techniques that incorporate ideas from across mathematics: hyperbolic geometry, representation varieties, combinatorics, sutured manifold theory, and Floer homology, to name a few. These tools have led to sensational progress in understanding problems about Dehn surgery and low-dimensional topology at large. Furthermore, they seem well-suited to attack the major open problems in the area, such as the Berge conjecture and the L-space conjecture.

The workshop will function as a graduate summer school. At its core, the school will feature a sequence of mini-courses delivered by a cast of leading experts and distinguished expositors. The courses will unveil Dehn surgery and this suite of techniques to the next generation of researchers in the area. The school will additionally feature guided problem sessions... (more)

Designing, planning, and operating many systems is challenging due to the possibility of high-impact rare events. A motivating application is the electricity power grid, whose operation can be significantly disrupted by rare weather events such as a severe storm or a polar vortex. This workshop will explore optimization and simulation approaches to designing, planning, and operating systems impacted by such events. Stochastic optimization is one approach for optimizing such systems, in which the uncertain outcomes are modeled with random variables. Rare and high-impact events provide a challenge for stochastic optimization because (1) it is difficult to estimate the likelihood of rare events, (2) estimates of expected values with outcomes that have very low probability but high cost are inherently unstable, and (3) the actual distribution of the random events is often not known. Alternatively, robust and distributionally robust optimization models attempt to identify a solution that is... (more)

This workshop, a formal collaboration between ICERM and the American Institute of Mathematics (AIM), is one in a series of annual REUF workshops. These workshops bring together leading research mathematicians and faculty based at primarily undergraduate institutions to investigate open questions in the mathematical sciences and to equip participants with tools to engage in research with undergraduate students. REUF also serves to jump-start faculty who want to re-engage in research or who are considering a change in their research area.

The goals of this workshop are to promote undergraduate research and to forge research collaborations among the participating faculty. The majority of the workshop will be spent working on problems in small research groups, reporting on progress, and formulating plans for future work. Note that there are opportunities for participants to continue research activities beyond the workshop week, which will be discussed during the workshop.

Preference will... (more)

Imagine spending eight-weeks on the beautiful Brown University campus in historic Providence, RI, working in a small team setting to solve mathematical research problems developed by faculty experts in their fields.

Imagine creating career-building connections between peers, near peers (graduate students and postdocs), and academic professionals.

Imagine spending your summer in a fun, memorable, and intellectually stimulating environment.

Now, imagine having this experience with support for travel within the U.S., room and board paid, plus a $3,570 stipend*.

The 2019 Summer@ICERM program at Brown University is an eight-week residential program designed for a select group of 18-22 undergraduate scholars.

The faculty advisers will present a variety of interdisciplinary research... (more)

The area of encrypted search focuses on the design and cryptanalysis of practical algorithms and systems that can search on end-to-end encrypted data. With encrypted search algorithms, data can remain encrypted even in use. As such, encrypted search algorithms have a wide array of applications including in data management, healthcare, cloud computing, mobile security, blockchains, and censorship- and surveillance-resistant systems.

In this workshop, we will explore a number of themes in the arithmetic of abelian varieties of low dimension (typically dimension 2–4), with a focus on computational aspects. Topics will include the study of torsion points, Galois representations, endomorphism rings, Sato-Tate distributions, Mumford-Tate groups, complex and p-adic analytic aspects, L-functions, rational points, and so on. We also seek to classify and tabulate these objects, in particular to understand explicitly the underlying moduli spaces (with specified polarization, endomorphism, and torsion structure), and to find examples of abelian varieties exhibiting special behavior. Finally, we will pursue connections with related areas, including the theory of modular forms, related algebraic varieties (e.g., K3 surfaces), and special values of L-functions.

Our goal is for the workshop to bring together researchers working on abelian varieties in a number of facets to establish collaborations, develop algorithms, and... (more)

Partial differential equations (PDEs) have long played crucial roles in the field of fluid dynamics. These PDE models, including Euler and Navier-Stokes equations for incompressible and compressible flows, kinetic equations for rarefied flows, and equations for more complex flows such as magneto-hydrodynamics flows, have motivated numerous studies from the theory of PDEs to the design and analysis of computational algorithms, and their implementation and application in computational fluid dynamics (CFD). This discipline is continually and dynamically evolving, constantly bringing forward new results in PDE theory, computation, and application to CFD, and also setting up the ground for generalizations to other related applications including electro-magnetics, fluid-structure interactions, cosmology, and computational electronics.

The aim of this workshop is to review the recent progress in the type of PDEs arising from fluid dynamics and other related physical areas, in terms of their... (more)

Building Community in the Foundations of Data Science

Brown's NSF TRIPODS grant is sponsoring a two-day informal networking workshop for the greater New England Foundations of Data Science community. In a series of informal discussions and short talks, we would like to draw attention to the opportunities to collaborate in foundational questions that lie at the focus of our TRIPODS program:

• structure of large and complex networks
• causal inference
• geometry and topology of data

We invite short talks on how these or other foundational or methodological data science themes appear in ongoing research projects in your work. We will spend the afternoon of the second day engaging in brainstorming for how collaborative structures across institutions can build and strengthen data science activity in the region.

GirlsGetMath is a weeklong mathematics summer day-program for 9th and 10th grade high school girls in the Providence, RI area.

GirlsGetMath occurs in an encouraging environment that builds young women's confidence in math and science.

GirlsGetMath expands participants' understanding and knowledge of mathematics through computations and experimentations.

GirlsGetMath provides expert mathematical training and mentoring.

GirlsGetMath will become a replicable national model of mathematical outreach for high school girls, with an emphasis on mathematical experimentation.

This five-day non-residential mathematics program is open to high school girls who live in greater Rhode Island and who will be entering the 10th or 11th grade in the fall of 2018.

GirlsGetMath@ICERM encourages 20-25 young women to explore,... (more)

Modern data analysis presents a variety of challenges, including the size, the dimensionality, the complexity, and the multiple-modality of the data. In an attempt to keep pace with these growing challenges, data scientists combine tools inspired from mathematics, from computer science, and from statistics. This TRIPODS Summer Bootcamp will provide attendees a hands-on introduction to emerging techniques for using topology with machine learning for the purpose of data analysis.

Topological and machine learning techniques potentially play complimentary roles for analyzing data. In topological data analysis, one leverages the fact that the shape of the data often reflects important and interpretable patterns within, although topological techniques alone typically cannot match the predictive power of machine learning. By contrast, machine learning algorithms provide state-of-the-art accuracies on predictive tasks, but the manner by which they arrive at a prediction is often difficult... (more)