Bio
Dr. Dawei Zhou is an Assistant Professor at the Computer Science Department of Virginia Tech and the director of the VirginiaTech Learning on Graphs (VLOG) Lab. Zhou’s prior research on open-world machine learning, trustworthy machine learning, and risk management, with applications in financial fraud detection, cyber security, financial forecasting, social media analysis, and healthcare. He obtained his Ph.D. degree from the Computer Science Department of the University of Illinois Urbana-Champaign (UIUC). He has authored more than 40 publications in premier academic venues across AI, data mining, and information retrieval (e.g., AAAI, IJCAI, KDD, ICDM, SDM, TKDD, DMKD, WWW, CIKM) and has served as Vice Program Chair/Proceeding Chair/Session Chairs/(Senior) Program Committee Members in various top ML and AI conferences (e.g., NeurIPS, ICML, KDD, WWW, SIGIR, ICLR, AAAI, IJCAI, BigData, etc.). His research is generously supported by Virginia Tech, NSF, DARPA, DHS, Commonwealth Cyber Initiative, 4VA, Deloitte, and Cisco. His work has been recognized by CNSF Capitol Hill Science Exhibition (2018), Cisco Faculty Research Award (2023), AAAI New Faculty Highlights roster (2024), and NSF Career Award (2024).
VLOG Lab
Lab Openings
I am actively looking for self-motivated Ph.D. students (2~3 GRAs + 2 GTAs) and one postdoc (Virginia Tech Presidential Fellowship) to join my group in the Fall 2024 semester.
My current research includes but is not limited to the following topics:
Long-tail category analysis (e.g., generalization, pre-trained model)
Transfer learning across graphs (e.g., evolving domain discrepancy, data augmentation)
Symbolic reasoning and learning on knowledge graphs (e.g., question answering, recommendation)
Trustworthy learning on graphs (e.g., fairness, calibration, robustness)
Deep graph generative model for scientific domains (e.g., PPI network, ARG prediction, 3D molecular conformation)
Machine learning for finance (e.g., financial forecasting, financial fraud detection)
Students with CS, math, biostatistics, and EE background are particularly encouraged to apply! Please drop me an email (zhoud[at]vt[dot]edu) with your CV and transcripts if you are interested.
News
[6/2024]: Honored to receive the NSF Career Award as PI to support our research on Open-World Machine Learning!
[6/2024]: I am invited to serve as the Senior Program Committee Member of AAAI 2025.
[5/2024]: One paper on Long-Tail Generalization is accepted by SIGKDD 2025. Congrats to my student Haohui as the leading author!
[5/2024]: Honored to receive a new grant as PI from Department of Homeland Security (DHS) to support our research on Combating Dynamic and Networked Financial Crimes.
[5/2024]: Two papers have been accepted by ICML2024. Congrats to my student Haohui for her first leading-author paper on Dynmaic Non-iid Transfer Learning!
[4/2024]: One paper on Benchmarking 3D Molecule Learning with Functional Groups has been accepted by IJCAI2024. Congrats to my students Tingwei and Jianpeng!
[4/2024]: Congrats to my students Haohui and Shuaicheng, who have been selected as the CCI Cyber Innovation Scholar again!
[2/2024]: Honored to receive a new grant from Defense Advanced Research Projects Agency (DARPA) to support our research on 3D Metamaterial Synthesis via Large Graph-Language Foundation Model.
[1/2024]: I am invited to serve as the Editor of Elsevier Big Data Research.
[1/2024]: I am invited to serve as the Proceedings Chair for ACM SIGKDD 2024, which will be held in Barcelona, Spain.
Contact
Office: 3160 F, Torgersen Hall, 620 Drillfield Dr., Blacksburg, VA 24060
Phone: (540) 231-2642
Email: zhoud [at] vt [dot] edu