Nate Veldt


I am a sixth year Mathematics PhD student at Purdue University. My advisor is Professor David F. Gleich of Purdue's computer science department.

My research interests lie in network analysis, optimization, and machine learning. In particular I've done a lot of work recently on algorithms for graph clustering.

I will be graduating after this year and am actively applying for academic positions starting in Fall 2019. Please feel free to reach out to me if your university is seeking candidates in data science, algorithms, and network analysis.

Research Statement

Teaching Statement

Curriculum vitae

Email: lveldt--at--purdue--dot--edu

Githubhttps://github.com/nveldt

Twitterhttps://twitter.com/n_veldt

 

     Publications

    1. Correlation Clustering Generalized
      David Gleich, Nate Veldt, and Anthony Wirth
      (To appear) Proceedings of the 29th International Symposium on Algorithms and Computation
    2. 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
    3. full version
    4. Low-Rank Spectral Network Alignment
      Huda Nassar, Nate Veldt, Shahin Mohammadi, Ananth Grama, David Gleich
      Proceedings of the 27th International World Wide Web Conference, April 2018
      paper
    5. Correlation Clustering with Low-Rank Matrices
      Nate Veldt, Anthony Wirth, and David Gleich
      Proceedings of the 26th International World Wide Web Conference, April 2017
      paper
    6. full version
    7. 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
      paper Conference Talk

     Preprints and Submitted Manuscripts

    1. A Projection Method for Metric-Constrained Optimization
      Nate Veldt, David Gleich, Anthony Wirth and James Saunderson
      arXiv Preprint
    2. Flow-Based Local Graph Clustering with Better Seed Set Inclusion
      Nate Veldt, Christine Klymko, and David Gleich (recently submitted)

    Recent and Upcoming Events

    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.

    EAPSI Fellowship: Low-Rank Correlation Clustering

    During summer 2016 I was funded as an East Asia and Pacific Summer Institute Fellow. For this program I traveled to the University of Melbourne during the months of June and July 2016 to work with Associate Professor Anthony Wirth on a project involving correlation clustering on low-rank matrices. The results of our summer work and subsequent follow-up work appeared in last year's World Wide Web Conference.

    Teaching

    I am not teaching this semester, but here is a list of my previous teaching experience.

    In Fall of 2015 I received the Purdue Mathematics Department Excellence in Teaching Award:

    Purdue Math Department Excellence in Teaching Award 2015

    Purdue University Numerical Linear Algebra Group

    Since Fall of 2016 I have been the organizer for 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