## Seminars

## APPM Instructor Candidate - Brett Werner

Sept. 19, 2019

Brett Werner Department of Mathematics, University of Colorado Boulder On Machine Learning Machine learning (ML) is the process of making computers learn without specifically programming them to do so. ML has a variety of applications including predicting revenue, detecting credit card fraud, and self-driving cars. We will begin by discussing...

## Math Bio Seminar - Jacqui Wentz

Sept. 17, 2019

Jacqui Wentz Department of Applied Mathematics, University of Colorado Boulder Singular value decomposition of the reaction-diffusion stoichiometry matrix Flux balance analysis (FBA) is a mathematical technique used to study biochemical networks. In contrast to other methods, FBA requires limited information about kinetic parameters and metabolite concentrations. Previously, we developed a...

## APPM Instructor Candidate - Osita Onyejekwe

Sept. 17, 2019

Osita Onyejekwe Department of Mathematics, University of Colorado Boulder Feature Detection in Observed Climate Factors The detection of inflection points is an important task in science and engineering. This task is onerous with subjective results. Signals are often corrupted with noise and signal denoising is often required before feature extraction,...

## Stats, Optimization, and Machine Learning Seminar - Ashutosh Trivedi

Sept. 17, 2019

Ashutosh Trivedi Department of Computer Science, University of Colorado Boulder Reinforcement Learning and Formal Requirements Reinforcement learning is an approach to controller synthesis where agents rely on reward signals to choose actions in order to satisfy the requirements implicit in reward signals. Oftentimes non-experts have to come up with the...

## Applied Math Colloquium - Jennifer Ryan

Sept. 13, 2019

Jennifer Ryan Department of Applied Mathematics and Statistics, Colorado School of Mines Superconvergence Extraction: How to do it? When is it applicable? Many numerical simulations produce data that contains hidden information. This hidden information can be exploited to create even more accurate representations of the data by appropriately constructing convolution...

## Math Bio Seminar - Tahra Eissa

Sept. 10, 2019

Tahra Eissa Department of Applied Mathematics, University of Colorado Boulder Biased response distributions for short observation sequences of rare events In a constantly changing world, organisms should estimate the rate that their environment changes to adequately weight evidence. However, given a limited number of observations, it is easy to vastly...

## Stats, Optimization, and Machine Learning Seminar - Jorge Poveda

Sept. 10, 2019

Jorge Poveda Department of Electrical, Computer, and Energy Engineering; University of Colorado Boulder Real-Time Optimization with Robustness and Acceleration via Hybrid Dynamical Systems and Averaging Theory In this talk we will discuss robust and accelerated zero-order algorithms for the solution of black-box optimization problems in dynamical systems. In particular, we...

## Applied Math Colloquium - Henry Adams

Sept. 6, 2019

Henry Adams Department of Mathematics, Colorado State University An introduction to applied topology This talk is an introduction to computational topology, as applied to data analysis and to sensor networks. The shape of a dataset often reflects important patterns within. Two such datasets with interesting shapes are a space of...

## Math Bio Seminar - Harry Dudley

Sept. 3, 2019

Harry Dudley Department of Applied Mathematics, University of Colorado Boulder Model Selection & Bioelectrochemical Systems Microbial electrolysis cells (MECs) are devices that produce hydrogen from renewable organic matter, such as wastewater. These devices require less energy input than water electrolysis and have greater efficiency than fermentative hydrogen production. We present...

## Stats, Optimization, and Machine Learning Seminar - Bo Waggoner

Sept. 3, 2019

Bo Waggoner Department of Computer Science, University of Colorado Boulder Toward a Characterization of Loss Functions for Distribution Learning A common machine-learning task is to learn a probability distribution over a very large domain. Examples include natural language processing and generative adversarial networks. But how should the learned distribution be...