Metric-Constrained Optimization for Graph Clustering Algorithms Nate Veldt, David Gleich, Anthony Wirth and James Saunderson SIAM Journal on Mathematics of Data Science (accepted for publication, 2019) arXiv Preprint
Learning Resolution Parameters for Graph Clustering Nate Veldt, David Gleich, and Anthony Wirth Proceedings of the 2019 World Wide Web Conference (to appear May 2019) preprint
Flow-Based Local Graph Clustering with Better Seed Set Inclusion Nate Veldt, Christine Klymko, and David Gleich Proceedings of the 2019 SIAM International Conference on Data Mining (to appear May 2019) preprint
Correlation Clustering Generalized David Gleich, Nate Veldt, and Anthony Wirth Proceedings of the 29th International Symposium on Algorithms and Computation, December 2018, paper
A Correlation Clustering Framework for Community Detection Nate Veldt, David Gleich, and Anthony Wirth Proceedings of the 27th International World Wide Web Conference, April 2018 paper
A Simple and Strongly Local Flow-Based Method for Cut Improvement Nate Veldt, David Gleich, and Michael Mahoney Proceedings of the 33rd Annual International Conference on Machine Learning, June 2016 paperConference Talk
A Parallel Projection Method for Metric-Constrained Optimization Cameron Ruggles, Nate Veldt, and David Gleich arXiv Preprint
Recent and Upcoming Events
I'll be traveling to a few different conferences this spring and summer to present my research.
My research is focused on algorithms and complexity results for problems arising in network analysis and data science. This broadly includes work in mathematical optimization, machine learning, matrix computations, and theoretical computer science. To date much of my research has involved the theory and application of clustering algorithms.
I have specifically worked on special variants of correlation clustering for partitioning signed datasets, flow-based methods for localized community detection, and fast solvers for convex relaxations of graph clustering objectives. My motivation is to bridge the gap between the best theoretical results and the most practical algorithms for problems in network science and data mining. An overarching goal is to develop methods that are fast, satisfy strong approximation guarantees, and explicitly take into account important features of the real-world networks and datasets they operate on.
I am not teaching this semester, but here is a list of my previous teaching experience.
Instructor: Applied Calculus II (Spring, Summer, & Fall 2015)
Instructor: Precalculus (Fall 2014)
Recitation Instructor Plane Analytic Geometry and Calculus, (Fall 2013, Spring 2014)
In Fall of 2015 I received the Purdue Mathematics Department Excellence in Teaching Award:
From Fall 2016 to Spring 2017 I organized and ran the Purdue University Numerical Linear Algebra Group, a seminar series on matrix computations and network analysis. All PUNLAG talks are recorded and uploaded to YouTube. The seminar webpage is here. You can subscribe to the Purdue Numerical Linear Algebra YouTube channel at PurdueNLA.
Recorded Seminar Talks
I frequently give local seminar talks on topics in network analysis and numerical linear algebra (often as a part of the PUNLAG seminar mentioned above).
Sometimes the talks are on a specific research project I'm working on. Often though I like to take a tool or technique that has been useful in my research and present it in
a way that can be more broadly applied to other projects and problems that people might be interested in. Here are some recorded talks on optimization techniques that have been useful in my work.
Totally Unimodular Matrices in Linear Programming
Solving Low-Dimensional Optimization Problems via Zonotope Vertex Enumeration
Efficiently Solving Linear Programs with Triangle Inequality Constaints