Tyler Derr

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Starting Fall 2020:
Assistant Professor
Computer Science, Data Science
Department of EECS, Data Science Institute
Vanderbilt University

Google Scholar
Linkedin
Email: derrtyle@msu.edu


Short Bio

Tyler Derr is a an assistant professor in computer science at Vanderbilt University since Fall 2020. His research lies in data mining and machine learning, especially in social network analysis, deep learning on graphs, and data science for social good with applications in education, health, and political science. He received his PhD (2020) in Computer Science from Michigan State University under the supervision of Dr. Jiliang Tang and was a member of the DSE Lab and TISM Project. He had completed his MS (2015) in Computer Science at The Pennsylvania State University and dual BS (2013) in Computer Science and Mathematical Sciences at The Pennsylvania State University. He was the recipient of the Best Reviewer Award at ICWSM2019, the Best Student Poster Award at SDM2019, and the “People's Choice” Award for the 3 Minute Thesis Competition at MSU. Tyler also actively serves as a reviewer/program committee member for journals/conferences in his research domain.

Research Interests

data mining, network anlaysis, graph neural networks, graph mining, machine learning, network measures and models, data science for social good (e.g., education, health, and political science)

[Open positions]
I am recruiting
PhD students to work with me on topics in my general research interests (seen below). Master's and undergraduate students within VU and visiting scholars are also welcome. Please feel free to email me.
Please see here for position details.

News

2020

2019

2018

  • Fall 2018: Invited to serve as session chair for the ICDM2018 PhD Forum.

  • Fall 2018: Awarded ICDM2018 Student Travel Award

  • Fall 2018: Invited to serve as PC member for ICWSM2019

  • Fall 2018: Invited to serve as PC member for IEEE BigData2018

  • Fall 2018: ICDM2018 PhD Forum accepted ‘‘Congressional Vote Analysis using Signed Networks’’

  • Fall 2018: Won 2nd prize for my presentation at the Southeast Michigan Postdoctoral Symposium from the University of Michigan Postdoctoral Association

  • Fall 2018: Awarded CIKM2018 Student Travel Award from SIGIR

  • Summer 2018: Our paper ‘‘Signed Graph Convolutional Network’’ is accepted by ICDM2018 (preprint here and code here)

  • Summer 2018: Our paper ‘‘Signed Network Modeling Based on Structural Balance Theory’’ is accepted by CIKM2018 (preprint here)

  • Summer 2018: Our paper ‘‘Opinions Power Opinions: Joint Link and Interaction Polarity Predictions in Signed Networks’’ is accepted by ASONAM2018
    with an extended version found here

  • Summer 2018: Our paper ‘‘Relevance Measurements in Online Signed Social Networks’’ is accepted by MLG2018

  • Summer 2018: Invited to serve as session chair for both the ‘‘Ranking & Centrality’’ and ‘‘Modeling II’’ research tracks at ASONAM2018

  • Summer 2018: Invited as a reviewer for ACM Transactions on Knowledge Discovery from Data (TKDD)

  • Summer 2018: Awarded MSU CSE Summer Fellowship 2018

2017

  • Fall 2017: Preprint Signed Node Relevance Measurements.

  • Summer 2017: Our paper ‘‘Understanding and Predicting Weight Loss with Mobile Social Networking Data’’ is accepted by CIKM2017

  • Summer 2017: Awarded KDD2017 Student Travel Award

  • Summer 2017: Awarded MSU CSE Summer Fellowship 2017

  • Spring 2017: Poster Presentation at the 2017 MSU Graduate Research Symposium
    Title: Opinions Power Opinions: Joint Link and Interaction Polarity Prediction in Signed Networks

  • Spring 2017: Awarded SDM2017 Student Travel Award from SIAM

  • Spring 2017: SDM2017 Doctoral Forum accepted ‘‘Opinions Power Opinions: Joint Link and Interaction Polarity Predictions in Signed Networks’’

  • Spring 2017: Invited lecture for the Department of Education at MSU on ‘‘Signed Network Analysis: Community Detection & Link Prediction’’

  • Spring 2017: Invited lecture for the CSE881 Data Mining course at MSU on ‘‘Similarity Search’’

  • Spring 2017: I joined the DSE Lab