In this talk, Hande will give an overview of some of the best practices a data scientist should know. These will include topics like virtual environments, utilizing functions, code documentation and other things that you could start incorporating in your data science projects or coding in general. She will also include some quick tips and advice...
Creator:
Tuzel, Hande (Sabre Corporation)
Created:
2022-02-11
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Value creation in private equity investment portfolios is fundamental to delivering results for PE customers. Our focus is in the energy and transportation sectors, and by having deep understanding of how these industries work, we explore applications where advanced analytics and better use of data can create more efficient operations and growth...
Creator:
Einset, Erik (Global Infrastructures Partners)
Created:
2022-04-01
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
This talk will describe a suite of physically inspired instruments we've developed to enable exploration of large-scale text data, illuminate collective behavioral patterns, and develop a science of stories. Along with our flagship efforts at http://hedonometer.org and https://storywrangling.org we show how Instagram photos reveal markers of dep...
Creator:
Kileel, Joe (The University of Texas at Austin)
Created:
2022-02-08
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Clustering algorithms based on mean shift or spectral methods on graphs are ubiquitous in data analysis. However, in practice, these two types of algorithms are treated as conceptually disjoint: mean shift clusters based on the density of a dataset, while spectral methods allow for clustering based on geometry. In joint work with Nicolás ...
Creator:
Craig, Katy (University of California, Santa Barbara)
Created:
2022-02-15
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Modern learning algorithms such as deep neural networks operate in regimes that defy the traditional statistical learning theory. Neural networks architectures often contain more parameters than training samples. Despite their huge complexity, the generalization error achieved on real data is small. In this talk, we aim to study the generalizati...
Creator:
Seroussi, Inbar (Weizmann Institute of Science)
Created:
2022-04-12
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Many diseases are complex heterogeneous conditions that affect multiple organs in the body and depend on the interplay between several factors that include molecular and environmental factors, thus requiring a holistic approach in understanding the complexity and heterogeneity. In this talk, I will present some of our current statistical and ma...
Creator:
Safo, Sandra (University of Minnesota, Twin Cities)
Created:
2022-02-22
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
In the context of numerical linear algebra algorithms, where it is natural to sacrifice accuracy in return for quicker computation of solutions whose errors are only slightly larger than optimal, the time-accuracy tradeoff of randomized sketching has been well-characterized. Algorithms such as Blendenpik and LSRN have shown that carefully design...
Creator:
Gittens, Alex (Rensselaer Polytechnic Institute)
Created:
2022-01-25
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
In this talk, I focus on the multivariate method of moments for parameter estimation. First from a theoretical standpoint, we show that in problems where the noise is high, the number of observations necessary to estimate parameters is dictated by the moments of the distribution. Second from a computational standpoint, we address the curse of di...
Creator:
Pereira, Joao (The University of Texas at Austin)
Created:
2022-04-05
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Adversarial training is a framework widely used by machine learning practitioners to enforce robustness of learning models. Despite the development of several computational strategies for adversarial training and some theoretical development in the broader distributionally robust optimization literature, there are still several theoretical quest...
Creator:
Garcia Trillos, Nicolas (University of Wisconsin, Madison)
Created:
2022-03-01
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
The assumption that high dimensional data is Gaussian is pervasive in many statistical procedures, due not only to its tail decay, but also to the level of analytic tractability this special distribution provides. We explore the relaxation of the Gaussian assumption in Single Index models and Shrinkage estimation using two tools that originate i...
Creator:
Goldstein, Larry (University of Southern California)
Created:
2022-03-29
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
In recent years, the need to accommodate non-Euclidean structures in data science has brought a boom in deep learning methods on graphs, leading to many practical applications with commercial impact. In this talk, we will review the mathematical foundations of the generalization capabilities of graph convolutional neural networks (GNNs). We will...
Creator:
Levie, Ron (Ludwig-Maximilians-Universität München)
Created:
2022-02-01
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
The game of football is undergoing a significant shift towards the quantitative. Much of the progress made in the analytics space can be attributed to play-by-play data and charting data. However, recent years have given rise to tracking data, which has opened the door for innovation that was not possible before. In this talk I will describe ho...
Creator:
Eager, Eric (ProFootballFocus
Created:
2022-02-18
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Multimorbidity, the coexistence of two or more health conditions, has become more prevalent as mortality rates in many countries have declined and their populations have aged. Multimorbidity presents significant difficulties for Clinical Decision Support Systems (CDSS), particularly in cases where recommendations from relevant clinical guideline...
Creator:
Michalowski, Martin (University of Minnesota, Twin Cities)
Created:
2022-03-22
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
The Metropolis Algorithm is an extremely useful and popular method of approximately sampling from complicated probability distributions. "Adaptive" versions automatically modify the algorithm while it runs, to improve its performance on the fly, but at the risk of destroying the Markov chain properties necessary for the algorithm to be valid. I...
Creator:
Rosenthal, Jeffrey (University of Toronto)
Created:
2021-03-23
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
In many scientific areas, a deterministic model (e.g., a differential equation) is equipped with parameters. In practice, these parameters might be uncertain or noisy, and so an honest model should provide a statistical description of the quantity of interest. Underlying this computational question is a fundamental one - If two "similar" functio...
Creator:
Sagiv, Amir (Columbia University)
Created:
2021-01-26
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Going from a scientific background into something that people haven't done comes with moments where you don't know what you're talking about... if you talk, that is. Admitting the times you don't know how your work can help and introducing your work when it may be able to help - that timing can be hard. I went from the field I was trained in...
Creator:
Oliver, Dean ( NBA's Washington Wizards)
Created:
2021-09-10
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
3D printing and design allows us to physically experience complex mathematical objects. In this talk we’ll take a 3D-printed tour of mathematical knots, tessellations, fractals, and polyhedra. Using code and generative design we can create parametric models that leverage randomness to achieve structural variety or even organic-looking behavior. ...
Creator:
Taalman, Laura (James Madison University)
Created:
2021-02-09
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
The leading eigenvalue problem of a differential operator arises in many scientific and engineering applications, in particular quantum many-body problems. Due to the curse of dimensionality conventional algorithms become impractical due to the huge computational and memory complexity. In this talk, we will discuss some of our recent works on d...
Creator:
Lu, Jianfeng (Duke University)
Created:
2021-03-02
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Securing cyber systems is paramount, but cyber defenders lack evidence-based techniques required to make high-consequence decisions. With lack of principled and rigorous measurements and models, cyber defenders resort to heuristics and expert intuitions. Cyber experimentation is commonly used in security, and we approach this problem as a new ...
Creator:
Pinar, Ali (Sandia National Laboratories)
Created:
2021-02-19
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
Widespread application of modern machine learning has increased the need for robust statistical algorithms. One fundamental geometric quantity in robust statistics is known as a data depth, which generalizes the notion of quantiles and medians to multiple dimensions. This talk will discuss recent work (in collaboration with Martin Molina-Fructuo...
Creator:
Murray, Ryan (North Carolina State University)
Created:
2021-10-19
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
