photo taken in Vancouver, Canada

I'm interested in solving fundamental AI problems through theoretical research that leads to real-world solutions.

Currently, my research focuses on learning in systems composed of dynamically and stochastically interacting components, particularly how such systems can give rise to autonomous machine intelligence. Pease see my selected works for more details.

• PhD Student - Department of Computer Science (present)

• M.S. in Computer Science (2022)

• B.E. in Computer Science (2019)

• An important open problem in the science of artificial intelligence is how to build autonomous agents that learns continuously in unknown environments using local observations.
• This paper explores this problem through dynamical stochastic neural networks, where each neuron acts as a simple agent that continuously adapts through local learning rules. The learning requires no resets, replay buffers, context windows, or backpropagation.

• It is challenging to describe complex systems composed of interacting and dynamic components. In particular, it becomes more challenging as these components in the system exhibit objective-driven behaviors.
• This paper develops a formal and elegant description of such systems using a field-theoretic language inspired by physics. The proposed theoretical framework is referred to as intelligent fields.

• Compound AI systems, comprising multiple interacting components such as LLMs and diffusion models, have demonstrated improvements compared to single models. However, aligning compound AI systems to human preferences is challenging.
• We propose a principled framework (SysDPO) for aligning all components in a compound AI system as a cohesive whole.