Y. Helena Liu

Computational principles of learning

Y. Helena Liu

NeuroAIMachine LearningComputational NeuroscienceMathematical Modeling

Incoming Assistant Professor, University of Toronto January 2027

CV Google Scholar Twitter/X

Research Interests

How do brains and artificial neural networks learn from experience, connect outcomes to relevant past events, and generalize to new situations?

We study the computational principles of learning in biological and artificial neural systems. We develop theory-driven and data-driven models that connect synaptic plasticity, neural circuit dynamics, and trial-by-trial behavior. Our approach combines neural-network modeling, machine learning, mathematical analysis, and the analysis of high-dimensional neural and behavioral data. By studying these systems together, we aim to uncover how neural circuits learn and make decisions, and which learning principles are shared across brains and AI.

Ongoing projects

  1. Biologically grounded neural-network learning
  2. Mathematical theory of learning and generalization
  3. Characterizing learning rules from neural and behavioral data
KeywordsNeuroAImachine learningcomputational neurosciencebiologically grounded AIlearning dynamicsdecision-makingneural networksdynamical systemsneural data science

About

Y. Helena Liu

My full name is Yuhan Helena Liu, but I go by Helena. In January 2027, I will return to the University of Toronto, my alma mater, as an Assistant Professor in the Department of Psychology, specializing in AI for Psychological Sciences. I am currently a postdoctoral researcher at the Princeton Neuroscience Institute and Princeton's Center for Statistics and Machine Learning, working with Jonathan Pillow.

I received my PhD in Applied Mathematics from the University of Washington, where I was advised by Eric Shea-Brown and focused on computational neuroscience and NeuroAI. Before that, I completed my undergraduate training in Engineering Science and my master's degree at the University of Toronto. I have also held visiting research roles at Mila, MIT, and the Allen Institute.

My research has been recognized through national fellowships, invited talks, and four Rising Stars distinctions spanning EECS, data science, computational science, and engineering in health. My first-author work has appeared in PNAS, NeurIPS, ICLR, and ICML. I am also deeply committed to teaching and mentorship and received a departmental teaching award during my PhD. Outside research, I enjoy strength training, calisthenics, hiking, traveling, and birdwatching.

Recruiting graduate and undergraduate students

Join Us

Interested in machine learning and computational neuroscience?

Whether your background is in computer science, engineering, statistics, mathematics, physics, neuroscience, or psychology, you are welcome to apply.

I will be recruiting students who are curious, creative, and motivated, with the persistence to carry projects through. Some experience with scientific programming (ideally in Python) is expected, while familiarity with linear algebra, probability, or statistics would be helpful. Prior training in neuroscience or psychology is not required.

Interested students are encouraged to email me with their CV and a brief description of their research experience and interests. To be considered for graduate admission, students must also submit a formal application to the University of Toronto Psychology graduate program by the program’s application deadline.

Email Helena Application information ↗

Former Mentees

  • Hanson Mo: Physics undergraduate; now a PhD student at Brown University. Paper: H. H. Mo, Y. H. Liu, E. Shea-Brown, and S. Mihalas (manuscript in preparation, 2026).
  • Weixuan Liu: Computer Science undergraduate; now a graduate student at Carnegie Mellon University. Paper: W. Liu, X. Zhang, and Y. H. Liu (AAAI AI2ASE, 2025).
  • Xinyue Zhang: Mathematics undergraduate; now a graduate student at Columbia University. Paper: W. Liu, X. Zhang, and Y. H. Liu (AAAI AI2ASE, 2025).