Zhiting Hu¶
Biography¶
My primary research interests lie in the general areas of Machine Learning, Artificial Intelligence, Natural Language Processing, as well as ML systems, computer vision, healthcare, and other application domains.
In particular, I'm interested in principles and methodologies of Panoramic Learning (HDSR)—building AI agents with ALL types of experience, ranging from data instances (NeurIPS), embodied experiences (NeurIPS), structured knowledge (ACL, NeurIPS), constraints, to rewards (EMNLP), adversaries (NeurIPS), lifelong interplay, etc. To this end, we've been studying a standardized ML formalism ("Standard Model" of ML) for systematic understanding, unifying, and generalizing a wide range of ML paradigms (e.g., supervised, unsupervised, active, reinforcement, adversarial, meta, lifelong learning). We're also building World Models (EMNLP, NeurIPS) to enable next-generation machine reasoning beyond large language models.
On this basis, I develop methods and tools for Composable ML that enable easy composition of ML solutions (LLM Reasoners, and Texar, ASYML as part of the open-source consortium CASL); and rich applications for controllable text generation (ICML) and others.
Selected PhDs¶
- Bowen Tan (@CMU, with Eric Xing)
Selected Masters¶
Collaborators¶
Recruitment Information¶
I am taking on new students and postdocs from HDSI, CSE, and related departments. We also have research positions for MS/undergrad students (UCSD or external). See more details here.
Visiting scholars / interns: We usually have openings for visiting scholars (graduate/undergrad students, etc) with external funding. Visitors are expected to stay for at least 3 months and preferably 6-12 months. Please make sure you indicate the time range in your email.