Qingyun Wang

Qingyun Wang

Ph.D. student, Siebel School of Computing and Data Science, University of Illinois at Urbana-Champaign

About me

Email: qwang16 [at] wm [dot] edu

Hello!

I am Qingyun Wang, an incoming Assistant Professor of the Data Science Department at William & Mary, starting in August 2025.

I am a Ph.D. student in the Siebel School of Computing and Data Science at the University of Illinois at Urbana-Champaign. I have been a member of the BLENDER Lab since 2017, supervised by Prof. Heng Ji. Previously, I graduated a summa cum laude from Rensselaer Polytechnic Institute with a dual B.S. degree in Computer Science and Mathematics.

I am among the first researchers to develop a virtual scientific research assistant (i.e., PaperRobot [ACL 2019]) for literature-based discovery by extracting and synthesizing insights from papers. My research interest lies in Automated Literature Understanding and Scientific Discovery. My long-term vision is to develop AI for Scientists (AI4Scientist) tools to effectively accelerate and democratize the entire research lifecycle for scientists, from knowledge acquisition [(NAACL ‘21 Best Demo🏆)I,II,III], hypothesis generation [IV], multimedia procedure planning for experiment design [V], experiment execution[VI], conduction to writing[VII,VIII], and evaluating the paper draft[IX].

Research Interests

  1. Scientific Multimodal Foundation Models with Critical Thinking: Build a new multimodal scientific LLM to understand formulas, tables, figures, and charts; Design model can dynamically extract and integrate new multimodal knowledge elements without additional training
  2. Few-Shot Scientific Knowledge Acquisition: Investigate methods for extracting knowledge from scientific corpora with limited annotation
  3. Planning and Reasoning in Scientific Domain: Utilize both structured and unstructured knowledge as well as logic rules among knowledge elements to produce trustworthy and explainable results
  4. Scientific Research Agents with Physical World Interactions: Train a new human-in-the-loop reinforcement learning framework with human, experimental, and literature feedback, which can leverage small datasets in closed-loop discovery platforms; Develop a human-in-the-loop self-driving laboratory that can complete the scientific research lifecycle through interactions with the physical world, such as a robotic laboratory

Prospective students

I am constantly looking for highly motivated PhD students (as fully-funded RAs) and interns to join my lab! If you are interested in working with me, please fill this form. After completing the form, you are also welcome to reach out via email (qwang16 [at] wm [dot] edu). I will read all submitted forms and emails but I do apologize for not being able to respond to each of them. Prospective Students English, Prospective Students Chinese

I’m happy to collaborate and answer questions about my research. I especially encourage students from underrepresented groups to reach out, as I commited to foster diversity, equity and inclusion in our community.

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