Tyler Derr

Tyler Derr is an assistant professor in computer science at Vanderbilt University since Fall 2020. His research lies in social computing, 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, political science, and autism research. 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 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 ICWSM 2019 & 2021, the Best Student Poster Award at SDM 2019, the “People's Choice” Award for the 3 Minute Thesis Competition at MSU, and the Fall 2020 Teaching Innovation Award from the School of Engineering at Vanderbilt. Tyler also actively serves as a reviewer/program committee member for journals/conferences in his research domain.

Research Interests

[Open positions]
I am recruiting a postdoc to work with me on topics in my general research interests (seen below). Similarly, I am also recruiting PhD students to work with me on these topics. 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.

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

Call for Papers

News (See past news before joining VU here)

2021

11/2021: Invited to serve as SPC member for KDD2022
11/2021: Our workshop ‘‘Machine Learning on Graphs (MLoG)’’ is accepted at WSDM2022
10/2021: Prepring ‘‘Distance-wise Prototypical Graph Neural Network for Imbalanced Node Classification’’
9/2021: Preprint of our review on ‘‘Graph Neural Networks: Self-supervised Learning’’
9/2021: Invited to be a judge for VandyHacks 2021 (VU's premier student hackathon)
9/2021: Invited to serve as PC member for The Web Conference 2022
8/2021: Invited to serve as Social Media and Publicity Chair of KDD2022
8/2021: Invited to serve as PC member for GTA3@BigData2021
8/2021: Invited to serve as SPC member for WSDM2022
8/2021: My PhD student Yu Wang's first paper since joining NDS ‘Tree Decomposed Graph Neural Network’ is accepted at CIKM2021
8/2021: Our paper ‘Graph Feature Gating Networks’ is accepted at CIKM2021
8/2021: Our paper ‘Deep Adversarial Network Alignment’ is accepted at CIKM2021
7/2021: Our paper ‘Road to the White House: Analyzing the Relations Between Mainstream and Social Media During the U.S. Presidential Primaries’ is accepted at ACM Hypertext2021
6/2021: Awarded Best Reviewer Award at ICWSM2021
6/2021: Our paper ‘Interpretable Visual Understanding with Cognitive Attention Network’ is accepted at ICANN2021
6/2021: Invited to serve as PC member for ICLR2021
5/2021: Out paper ‘‘Graph Adversarial Attack via Rewiring’’ is accepted at KDD2021
5/2021: Invited to serve as PC member for IEEE BigData2021 and CIKM2021
4/2021: Invited to serve as PC member for EMNLP2021, IRI2021, and NeurIPS2021
4/2021: Awarded SIAM Early Career Travel Award for SDM2021 supported by NSF
4/2021: Our paper ‘‘Self-supervised Learning on Graphs: Deep Insights and New Directions’’ is accepted at SSL@WWW2021
3/2021: Invited to serve as PC member for GNNSys’21 Workshop @ MLSys’21
2/2021: Invited to serve as Doctoral Consortium Chair of WSDM2022
2/2021: I received the Fall 2020 Innovative Teaching Award from the School of Engineering
1/2021: Invited to serve as PC member for WebSci2021
2021: Invited to serve (twice) as a grant proposal panelist for NSF during 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: Gave a talk through Skype a Scientist in Ardsley High School's Science Research class
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