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 looking for academic positions starting in Fall 2019.

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

Githubhttps://github.com/nveldt

Twitterhttps://twitter.com/n_veldt

Curriculum vitae

 

     Publications

    1. A Correlation Clustering-Based Framework for Community Detection
      Nate Veldt, David Gleich, and Anthony Wirth
      Proceedings of the 27th International World Wide Web Conference, April 2018
      paper
    2. full version
    3. 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
    4. 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
    5. full version
    6. 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. Correlation Clustering Generalized
      David Gleich, Nate Veldt, and Anthony Wirth

    Recent and Upcoming Events

    Research

    I've been working as a research assistant in the computer science department at Purdue University for my advisor David Gleich since Spring 2015. My research focuses on how to develop better algorithms with rigorous guarantees (in terms of runtime or approximation) for commonly-studied problems in data science and network analysis, typically by exploiting special features in the underlying data and considering specific structural properties often exhibited by real-world networks. To date, much of my research has focused on clustering in graphs, which includes the study of correlation clustering for partitioning signed graphs, using flow-based methods for localized community detection, and developing fast solvers for linear programming relaxations of NP-hard clustering objectives. My goal is to bridge the gap between theory and practice in algorithms for graph optimization problems, in order to provide data science practitioners with methods that are fast, satisfy strong approximation guarantees, and explicitly take into account important features of the datasets that are analyzed.

    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