Yoyo Liu

Yoyo Liu

she/her

CS + Chemistry @ UofT. I build ML systems for protein design, RNA folding, and scientific discovery. Incoming ML Research Intern at the Acceleration Consortium.

Hey, I’m Yoyo.

I’m a Computer Science (Focus in AI and Technology Leadership) and Biological Chemistry student at the University of Toronto. I am an ML Research Intern at the Acceleration Consortium where I develop methods to incorporate chemistry reasoning agents with self-driving lab for analytical chemistry.

I recently completed an Applied ML Engineering Internship at Shopify on the Core (Search Relevance) team.

I build machine learning systems that work at the intersection of AI and the natural sciences: RNA structure prediction, drug discovery, protein design, cheminformatics, and molecular modeling.

I care about making ML useful for scientific problems: not just training models, but designing evaluation pipelines, engineering datasets, and building systems that scale. Whether it’s fine-tuning LLMs for metabolite pathway reasoning or implementing inverse-folding benchmarks for protein design, I like getting my hands dirty with the full stack of research engineering.

I’ve been interested in reasoning in the chemical space, scientific discovery agents in self-driving lab, and data-efficient learning. I currently work under Professor Alán Aspuru-Guzik and I was a research student under Professor Anatole von Lilienfeld.

What I’m working on now

  • Acceleration Consortium: Developing data-driven agentic platforms to optimize complex organic synthesis in a self-driving lab for pharmaceuticals, catalysis, and functional materials.
  • Matter Lab Contributing to the El Agente Fármaco project for early-stage drug discovery.
  • Alchemical invariant learning: machine learning the chemical space — can we use half the data to learn chemicals as effectively?
  • AutoGrader: OCR + LLM pipeline for automatic grading of calculus tests/assignments.

Research interests

  • Scalable/Agentic ML systems with reasoning — dataset pipelines, chain-of-thought prompt engineering, latent reasoning in chemical space, custom evaluation frameworks incorporating biochemistry evaluations
  • Macromolecule structure prediction and design — 3D folding of ribozymes, data-efficient deep learning approaches for tRNA structure prediction, protein inverse design
  • Scientific ML — LLM fine-tuning for bioengineering, molecular energy curve modeling, generative models for conformer prediction

A bit more about me

Outside the lab, I am the co-president of UofT Women in Computer Science (WiCS), founded the Toronto Ethics in AI Symposium (TEAS), and served as a NCWIT Campus Representative. I also sing, play double bass and make jewelry. I love watching old Sci-Fi movies :D

Feel free to reach out if you’re working on AI for science, or if you just want to chat about research.