Bio coming soon.
I started my MS in CS studies here at Northeastern University, during which time I started working with Professor Chris Amato on sequential decision-making problems and finally shifted my interests towards cooperative multi-agent systems.
My interest in reinforcement learning techniques was immediately formed when I was first introduced to Artificial Intelligence, as it represents a journey of demystifying optimal design-making and planning in the physical world. Upon further exploration in the field, multi-agent cooperation and emergent behaviors hooked my interest: not only because they hold great difficulty and application promises, but also because solving those problems provides insights into human organizations, languages, and social structures around us.
I want to uncover tractable ways through which agents learn and evolve effective cooperation techniques that are actually generalizable to a wide range of problems including in the real-world.
It often astonishes me that humans can process information in a much more sophisticated manner. Yet despite apparent gaps, we find current autonomous machines and processes already incredibly valuable in our society. I am almost sure that as automation becomes more efficient, robust, and more at scale, it could completely alter our current lifestyles and values.
I want to eventually become an expert in the domain and contribute to this vibrant field with inspiring theoretical research and potential real-world applications.
I grew up in Tianjin, a Chinese city with a mindbogglingly optimistic culture. The most defining years might have been my undergraduate days, opening to a great variety of disciplines and beliefs.
I started my undergrad at UC Irvine majored in “psychology and social behavior”. I was keen to analyze social problems and its human aspects. By accident, I find myself interested in information theories and applications, hence here I am.
Bio coming soon.
I started my MS in CS studies here at Northeastern University, during which time I started working with Professor Chris Amato on sequential decision-making problems and finally shifted my interests towards cooperative multi-agent systems.
My interest in reinforcement learning techniques was immediately formed when I was first introduced to Artificial Intelligence, as it represents a journey of demystifying optimal design-making and planning in the physical world. Upon further exploration in the field, multi-agent cooperation and emergent behaviors hooked my interest: not only because they hold great difficulty and application promises, but also because solving those problems provides insights into human organizations, languages, and social structures around us.
I want to uncover tractable ways through which agents learn and evolve effective cooperation techniques that are actually generalizable to a wide range of problems including in the real-world.
It often astonishes me that humans can process information in a much more sophisticated manner. Yet despite apparent gaps, we find current autonomous machines and processes already incredibly valuable in our society. I am almost sure that as automation becomes more efficient, robust, and more at scale, it could completely alter our current lifestyles and values.
I want to eventually become an expert in the domain and contribute to this vibrant field with inspiring theoretical research and potential real-world applications.
I grew up in Tianjin, a Chinese city with a mindbogglingly optimistic culture. The most defining years might have been my undergraduate days, opening to a great variety of disciplines and beliefs.
I started my undergrad at UC Irvine majored in “psychology and social behavior”. I was keen to analyze social problems and its human aspects. By accident, I find myself interested in information theories and applications, hence here I am.
