Yue Ning

Research in our group

Our group develops machine learning and AI methods that reason over complex, evolving data, with a focus on predictions that are faithful, causal, and robust. Current directions include:

LLM Reasoning and Agentic AI: agentic causal discovery, intermediate-variable reasoning, temporal reasoning over clinical narratives, and understanding the conceptual structure inside LLMs.
Causal, Interpretable, and Trustworthy ML: concept-grounded and transparent models, fairness-aware learning, and privacy-aware algorithms.
Learning on Graphs and Time Series Under Change: dynamic and continual graph learning, out-of-distribution generalization, and domain adaptation.
Use-Inspired AI: clinical prediction and diagnostic reasoning (with Yale School of Medicine, UMass Chan Medical School, and Hackensack Meridian Health), societal event forecasting, reservoir inflow prediction, and macroeconomic forecasting.

See Research for projects and Publications for recent papers (ICML, NeurIPS, KDD, ACL, EMNLP, AAAI, and others).

PhD Positions (Fall 2027)

How to apply

Step 1: Fill out this Google Form with your CV, personal statement, transcripts, and references. In your statement, tell me which of our research directions interests you and why, ideally referring to one of our recent papers. A writing sample (a first-authored paper is preferred) is welcome. No separate email is needed once you submit the form.
If you cannot access the form, email these materials to yue.ning@stevens.edu with the subject line PhD application - [Term] - [Your Name].

Step 2: Submit a formal application to the Computer Science PhD program through the Stevens graduate admissions portal and mention my name. See the admission requirements and, for international applicants, the English language requirements.

I review every form submission, but I may not be able to reply to each one individually.

What I look for

Strong programming and math background (linear algebra, probability, optimization)

Experience with machine learning, deep learning frameworks such as PyTorch, or NLP

Curiosity about real-world problems, especially in healthcare or science

Clear writing and a drive to see a research project through

Background in health informatics, causal inference, or LLMs is a plus but not required.

Visiting Scholars and Students

Candidates should have a quantitative background in computer science or a related field and a research interest that overlaps with our directions above.

Visitors typically bring their own funding.

Email your CV and a proposed project with the subject line [Visiting] Your Name.

Master's and Undergraduate Researchers at Stevens

Stevens students with a computer science or related background and strong motivation in machine learning are welcome to reach out.

Undergraduates have joined the lab through the SIAI AIRS Fellowship, the Pinnacle Scholars program, CRAFT fellowships, and REU, and several have published papers.

Master's students should have independent funding.

Email your CV, transcript, and the courses or projects most relevant to ML with the subject line [Student Research] Your Name.

About Stevens

As a private research university located in Hoboken, New Jersey, Stevens Institute of Technology is a rising star in the New York city area. Both the university and its School of Computing (launched in 2026) are growing quickly. The 55-acre campus is on the Hudson River across from midtown Manhattan within a few minutes from NYC via public transportation. Stevens' superb location offers excellent opportunities for collaboration with nearby universities and careers in both industry and academia.