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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Bio

Dr. 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 in Computer Science from Michigan State University in 2020 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 in Computer Science at The Pennsylvania State University in 2015 and dual BS in Computer Science and Mathematical Sciences at The Pennsylvania State University in 2013.

Tyler directs the Network and Data Science (NDS) lab, which conducts research in the areas of data mining and machine learning, with emphasis on 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 mentored his PhD students to have received numerous honors and awards including Vanderbilt's C. F. Chen Best Paper Award in 2022 and Runner-Up Award in 2023, the sole recipient of Vanderbilt's Graduate Leadership Anchor Award for Research in 2023, Finalist in Vanderbilt's Three Minute Thesis (3MT) Competition in 2023, Nvidia Academic Hardware Grant in 2022, Best Paper Award in the New Frontiers in Graph Learning (GLFrontiers) Workshop at NeurIPS’23, along with their works being selected among the top-10 Most Influential CIKM’22/WWW’23 Papers by Paper Digest. He is actively involved in top conferences in his field, both in terms of publishing and serving as an SPC/PC member, while receiving recognition such as the Best Student Poster Award at SDM’19 and Best Reviewer Awards at ICWSM’19 and ’21, as well as WSDM’22. He has contributed to the organization of numerous international conferences and workshops, including serving on the organizing committee of KDD (2021-2024), DSAA (2024), and WSDM (2022, 2024), along with co-founding the Machine Learning on Graphs (MLoG) Workshop at WSDM (2022-2024) along with at ICDM (2022-2023). Being passionate about sharing knowledge, he has delivered tutorials on Graph Neural Networks at KDD’20, AAAI’21, and SDM’24, along with given numerous invited talks, such as at the ACM Web Conference Knowledge Graph Day, Oak Ridge National Laboratory (ORNL) Core Universities AI Workshop held at Georgia Institute of Technology, the recently established Foundation Model Research Center at Tsinghua University, Max Planck Institute for Mathematics in the Sciences (MPI MiS), etc. He serves as Associate Editor for Tsinghua Science and Technology, IEEE Transactions on Big Data, Elsevier Big Data Research, and Frontiers in Big Data. Tyler has received numerous prestigious awards, such as the NSF CAREER Award in 2023. Additionally, he was honored with the Fall 2020 Teaching Innovation Award from the School of Engineering and the 2024 Provost Immersion Grant for Faculty at Vanderbilt University, highlighting his dedication to exceptional teaching and mentoring.

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 us in the NDS Lab on topics in our general interests (see NDS Lab research interests to the right).
Master's and undergraduate students within Vanderbilt University and visiting scholars are also welcome. Please feel free to email me.
Please see here for position details.

Call for Papers

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Recent News (before 2023 can be seen here)

2024

  • 2/2024: Our paper ‘‘Knowledge Graph-based Session Recommendation with Session-Adaptive Propagation’’ is accepted at WWW2024

  • 1/2024: Our paper ‘‘Can One Embedding Fit All? A Multi-Interest Learning Paradigm Towards Improving User Interest Diversity Fairness’’ is accepted at WWW2024

  • 1/2024: Gave an invited talk ‘‘Network Science for Social Good’’ in the Frist Center Salon Series

  • 1/2024: Our paper A Topological Perspective on Demystifying GNN-Based Link Prediction Performance is accepted at ICLR2024

2023