Barna Saha - Efficient Fine-Grained Algorithms

3108 Etcheverry Hall
  • Barna Saha, University of Massachusetts Amherst
Abstract: One of the greatest successes of computational complexity theory is the classification of countless fundamental computational problems into polynomial-time and NP-hard ones, two classes that are often referred to as tractable and intractable, respectively. However, this crude distinction of algorithmic efficiency is clearly insufficient when handling today's large scale of data. We need...

Dr. Mingfu Shao, Department of Computational Biology, Carnegie Mellon University

HP Auditorium 306 Soda Hall
Title: Abstract: I will present modeling and algorithmic designs for two challenging problems in biology and argue that efficient computational methods enable significant advances in our understanding of cell machinery and genome evolution. The first problem is the assembly of full-length transcripts -- the collection of expressed gene products in cells -- from noisy and highly...

GraphXD Seminar: Vector Representations of Graphs and the Maximum Cut Problem

1011 Evans Hall
  • David P. Williamson, Operations Research and Information Engineering, Cornell University
In this talk, I will look at a classical problem from graph theory of finding a large cut in a graph. We’ll start with a 1967 result of Erdős that showed that picking a random partition of the graph finds a cut that is at least half the largest possible cut. We’ll then describe a result due to Goemans and myself from 1995 that shows that by representing the graph as a set of vectors, one per...

Splunk Info-Session

Wozniak Lounge (430) Soda Hall

Design Field Notes: Paula Te

220 Jacobs Hall
Paula Te, an interaction designer who is driven to make technology accessible in the widest possible sense, will speak at Jacobs Hall.

Oracle Info-Session

Wozniak Lounge (430) Soda Hall

Algorithmic Regularization in Over-parameterized Matrix Recovery and Neural Networks with Quadratic Activations

1011 Evans Hall
  • Tengyu Ma, Facebook AI Research
Over-parameterized models are widely and successfully used in deep learning, but their workings are far from understood. In many practical scenarios, the learned model generalizes to the test data, even though the hypothesis class contains a model that completely overfits the training data and no regularization is applied. In this talk, we will show that such phenomenon occurs in...

Dr. Julia Fukuyama, Fred Hutchinson Cancer Research Institute

306, HP Auditorium Soda Hall
Abstract: Transcription, the fundamental cellular process by which DNA is copied to RNA, is tightly regulated in healthy human development but frequently dysregulated in disease. During or shortly after transcription, regions known as “introns” are spliced out of the RNA to produce mature “messenger” RNA. Massively parallel sequencing of RNA (RNA-seq) has become a ubiquitous technology in...

Currents and Phases in Quantum Rings

60 Evans Hall
  • Prof. Kathryn Moler, Stanford University, Physics & Applied Physics
Emergent phenomena in quantum systems often exhibit magnetic signatures. In this talk, I will describe how to use the current in a ring to access fundamental and topological properties of quantum states of charge-carrying particles. Applying a magnetic flux through a ring creates a phase gradient, in response to which a current flows, creating magnetic fields that we measure with a scanning...

Statistics and Data Science: the Prediction and Modeling Cultures

102 Moffitt Undergraduate Library
  • Roderick Little, University of Michigan
I recently taught a course entitled "Seminal Papers and Controversies in Statistics", and Leo Breiman's (2001) article "Statistical Modeling: The Two Cultures" was a very popular paper with students. The paper contrasts the machine learning culture, with it's focus on prediction, with more classical parametric modeling approach to statistics. I am more in the parametric modeling camp, but...