Dr. Jindong Wang is currently an assistant professor at William & Mary, one of America’s “Public Ivies,” since January 2025, and also serves as an affiliated faculty member at the renowned non-profit Future of Life Institute. From 2019 to 2024, he was a Senior Researcher at Microsoft Research Asia. He plans to recruit fully funded PhD students to start in Fall 2026. Research areas: foundation models and machine learning, the philosophy of language models, and the intersection of AI and the social sciences.
He has published 60+ papers in top venues such as ICML and NeurIPS, with 23,000+ citations (h-index 54). He has been named among Stanford University’s global top 2% scientists for four consecutive years, and his work has been covered by MIT Technology Review and Forbes. He maintains close ties with leading universities and companies. Within less than a year of joining W&M, he has received research awards and funding from Google, Amazon, Microsoft, AMD, Cohere, and others. The lab has ample GPU and API resources. He has supervised 10+ undergraduate, master’s, and PhD students to publish top-tier papers starting from scratch. He is an Associate Editor of IEEE TNNLS and JCST, and serves as Area Chair for ICML, NeurIPS, ICLR, KDD, and ACL. Personal site: http://jd92.wang. Application form: https://forms.gle/uKTAz3n9ySPeemMBA.
Founded in 1693, William & Mary is the second-oldest university in the United States after Harvard and is known as the “Alma Mater of the Nation.” As a “Public Ivy,” it consistently ranks among the top institutions nationwide and is an R1 research university. It has produced many distinguished alumni, including three U.S. Presidents: Thomas Jefferson, James Monroe, and John Tyler. The university is known for its small size and excellence, favorable student–faculty ratio, and rich academic atmosphere. In the 2026 U.S. News rankings it is 21st among public universities and 51st overall, and it is one of the public universities most recognized by alumni and parents.
Mentoring: During his time at Microsoft, Prof. Wang supervised 10+ interns who published their first top-tier conference papers, averaging roughly one top-tier paper per person per six months. Several students had already surpassed 3,000 Google Scholar citations before graduating. He respects students, avoids micromanagement, and is committed to helping them grow. Under his guidance, some students pursued PhDs at UW and UCSB, or joined top companies like Google DeepMind and Microsoft. PhD projects in the group are conducted in communication and collaboration with industry partners and senior PhD students. He is active on social media, sharing insights on research and life over many years.
Representative work: Before the LLM era, he published widely in core machine learning areas such as transfer learning, OOD generalization, semi-supervised learning, and federated learning. Representative works include transfer learning algorithms MEDA (800 citations) and DSAN (1,200 citations in 5 years), semi-supervised algorithms FlexMatch (1,300 citations in 4 years) and FreeMatch (500 citations in 2 years), and the federated learning algorithm FedHealth (1,100 citations in 5 years). In the era of large models, he has actively embraced new technologies and made solid progress in evaluation, alignment, fine-tuning, and agents. Representative works include the dynamic evaluation protocol DyVal and the evaluation framework PromptBench, the agent framework CompeteAI, the new direction of noisy model learning, and the psychology-inspired EmotionPrompt. Below are areas and outputs from the past two years.
The group currently has 2 PhD students and 3 remote interns, with the following resources: