Tyler Derr

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Assistant Professor
Computer Science, Data Science
Department of CS, Data Science Institute
Vanderbilt University

Network and Data Science (NDS) Lab

Email: Tyler (dot) Derr (at) vanderbilt (dot) edu
Office: 4030 Sony Building
Mail: 400 24th Ave S Rm 254, Nashville, TN 37212

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Short Bio

Tyler Derr is an Assistant Professor in the Department of Computer Science, Teaching and Affiliate Faculty in the Data Science Institute, and Faculty Fellow in the Frist Center for Autism and Innovation at Vanderbilt University. 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 Data Science and Engineering (DSE) Lab and Teachers in Social Media (TISM) Project. He had complete 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.

Tyler directs the Network and Data Science (NDS) lab, which focuses on data mining and machine learning, especially in social network analysis, deep learning on graphs, and data science for social good with applications in drug discovery, education, political science, and autism research. He has published in and regularly serves as a SPC/PC member at the top conferences in these domains and served in organizational roles including Publicity Co-Chair of KDD’22/’23, Doctoral Consortium Co-Chair of WSDM’22, Proceedings Co-Chair of KDD’21, and co-organized the Deep Graph Learning workshop at IEEE BigData’19, Machine Learning on Graphs workshop at WSDM’22/’23 and ICDM’22, Topic Editor in Frontiers in Big Data, and Associate Editor for Elsevier Big Data Research. He was the recipient of the Best Reviewer Award at ICWSM’19/’21, the Best Student Poster Award at SDM’19, the ‘‘People's Choice’’ Award for the 3 Minute Thesis Competition at MSU, the Fall 2020 Teaching Innovation Award from the School of Engineering at Vanderbilt, and his student Yu Wang recently received Vanderbilt's C. F. Chen Best Paper Award in Computer Science in 2022.

Research Interests

data mining, machine learning, mining and learning on graphs, social network anlaysis, graph neural networks, ethical and responsible AI, recommendation systems, data science for social good (e.g., drug discovery, education, political science, and autism research)

[Open positions]
I am recruiting PhD students to work with me on topics in my general 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.

Call for Papers

  • Machine Learning on Complex Graphs - Frontiers in Big Data (Topic Editor)

    • Welcomed topics include: graph kernels/summarization/coarsening/alignment/etc, graph neural networks, network embedding, related applications, etc

    • We especially invite submissions with emphasis on complex graphs such as dynamic/hyper/heterogeneous/knowledge graphs

    • Submission Deadline: TBA (currently relaunching the topic) (link)

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News (See past news before joining VU here)

2023

2022

2021

2020

  • 12/2020: Invited to serve as PC member for ICML2021

  • 12/2020: Our paper ‘‘Stock Selection via Spatiotemporal Hypergraph Attention Network: A Learning to Rank Approach’’ is accepted at AAAI2021

  • 12/2020: Invited to serve as PC member for ACL2021

  • 11/2020: Invited to serve as PC member for KDD2021

  • 11/2020: Introduced CS and AI topics to students at Ardsley High School through Skype a Scientist

  • 11/2020: Panelist on the ‘‘Graduate School is not a Job’’ graduate recruitment event

  • 10/2020: Our paper ‘‘Node Similarity Preserving Graph Convolutional Networks’’ is accepted at WSDM2021

  • 10/2020: Our paper ‘‘CopyAttack: Attacking Black-box Recommendations via Copying Cross-domain User Profiles’’ is accepted at ICDE2021

  • 10/2020: Gave an invited talk ‘‘Navigating the Faculty Job Search’’ in Michigan State's College of Engineering Graduate Lunch & Learn seminar

  • 9/2020: Gave an invited talk ‘‘Graph Neural Networks: Social Networks and Beyond’’ in the Biomedical Engineering Department at Vanderbilt Unv.

  • 9/2020: Gave an invited talk at Change++

  • 9/2020: Our tutorial ‘‘Graph Neural Networks: Models and Applications’’ has been accepted by AAAI2021

  • 9/2020: Preprint ‘‘Road to the White House: Analyzing the Relations Between Mainstream and Social Media During the U.S. Presidential Primaries’’

  • 9/2020: Invited to serve as PC member for IJCAI2021

  • 9/2020: Invited to serve as PC member for WWW2021

  • 9/2020: Our paper ‘‘Understanding and Promoting Teacher Connections in Online Social Media: A Case Study on Pinterest.’’ is accepted at IEEE TALE2020

  • 8/2020: Invited to serve as Proceeding Chair of KDD2021

  • 8/2020: Our paper ‘‘Learning from Incomplete Labeled Data via Adversarial Data Generation’’ is accepted at ICDM2020

  • 8/2020: Invited to serve as PC member for AAAI2021

  • 8/2020: Invited to serve as PC member for GTA3@BigData2020

  • 8/2020: Invited keynote at joint workshops Deep Learning on Graphs: Methods and Applications and Mining and Learning with Graphs at KDD2020

  • 8/2020: Invited to serve as a reviewer for EAAI2021.

  • 8/2020: Awarded KDD2020 Student Registration Award (and partial KDD2021 registation credit) from NSF and SIGKDD

  • 8/2020: I joined Vanderbilt University and established the Network and Data Science (NDS) Lab