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Dawei ZhouView profile

Assistant Professor

• Open-World Machine Learning (OpenML)
• AI for Material Science
• AI for Finance
• +8 more

Dr. Dawei Zhou is an Assistant Professor at the Department of Computer Science, Virginia Tech, and the Director of the Virginia Tech Learning on Graphs (VLOG) Lab. His research focuses on Open-World Machine Learning (OpenML), with applications in AI for Material Science, Finance, and Healthcare. He holds a Ph.D. from the University of Illinois Urbana-Champaign (UIUC). Education: Ph.D., Computer Science, UIUC (2021) M.S., Electrical and Computer Engineering, University of Rochester (2014) B.S., Electrical Engineering, Nanjing University of Posts and Telecommunications (2012) Research Interests: Long-tail category analysis, transfer learning across graphs, symbolic reasoning on knowledge graphs, trustworthy learning on graphs, graph generative models for scientific domains, and AI for finance. Grants & Awards: NSF Career Award (2024) AAAI New Faculty Highlights Roster (2024) Cisco Faculty Research Award (2023) Virginia Tech Outstanding Assistant Professor Award (2025) Amazon and Google Cloud Computing Grants (2024-2025) Advising & Lab: Directs the VLOG Lab, actively recruiting Ph.D. students and postdocs. Notable advisees include Shuaicheng, Haohui, Longfeng, and others. The lab focuses on cutting-edge research in graph learning and AI-driven scientific discovery. Service: Serves as Proceedings Chair for ACM SIGKDD 2026, Workshop Chair for IEEE BigData 2025, and on program committees for top conferences (NeurIPS, ICML, KDD).

• Open-World Machine Learning
• Artificial Intelligence
• Machine Learning for Material Science
• +6 more

Dr. Dawei Zhou is an Assistant Professor in the Computer Science Department at Virginia Tech and Director of the VirginiaTech Learning on Graphs (VLOG) Lab. He holds a Ph.D. from the University of Illinois Urbana-Champaign (UIUC) and focuses on Open-World Machine Learning (OpenML), with applications in material science, finance, healthcare, and AI safety. His research has been supported by NSF, DARPA, and industry partners like Amazon and Google. His work addresses challenges in long-tailed learning, graph neural networks, and agentic AI for scientific discovery. Notable contributions include benchmarks like MetamatBench and DISPROTBENCH, and frameworks like MentorGNN and UnifiedGT. Awards include the NSF Career Award (2024) and Virginia Tech’s Outstanding Assistant Professor Award (2025). Education: Ph.D., Computer Science, UIUC Grants: NSF, DARPA, Commonwealth Cyber Initiative Service Roles: Vice Program Chair (KDD 2023), Proceedings Chair (SIGKDD 2024) Lab: VLOG Lab focuses on hypothesis generation/validation, LLM safety, and open-world learning Recent articles emphasize scientific hypothesis validation, LLM generalization, and fair graph learning. Students with CS, statistics, or physics backgrounds are recruited for Ph.D. and postdoc roles.