SCIENTIFIC + HUMANISTIC

Integrated, or never separated?

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Hi, I am Junyi Tao.

I am a master’s student in Symbolic Systems at Stanford University, focusing on Philosophy of AI/CogSci and AI interpretability. I am especially interested in the notion of representation, abstraction, and levels of explanation. I am fortunate to be advised by Thomas Icard and also co-advised by Rosa Cao and Christopher Potts on my Master's thesis.
Before Symsys, I was an undergraduate at Duke University and Duke Kunshan University, where I studied Data Science with a customized concentration in Computational Humanities.



Scholars have long employed a divisive lens to view science and humanities, with the former rapidly developing to replace human labor while the latter ultimately oriented around human interpretations. Such division in perspective also manifests itself in the division of scholarly communities that perceive themselves as having little to do with each other.

However, my experience researching at the intersection of the two revealed that this made us miss out on an enormous potential for the two domains to co-evolute and recursively augment each other. This motivates me to pursue an integrated approach to research that combines both.

I believe that it is the questions we ask, not the methods we employ, that can positively impact the world.


I am interested in exploring connections between the (maybe) distinct perspectives of human minds and machines. To grasp these connections, an interdisciplinary approach is needed to merge computational methods with humanistic inquiries, which can be approached from two directions:

[☾˚.] In the information age where words so often give way to number crunching, how to cogitate the humanistic meaning of data science?
I am particularly interested in understanding representation and interpretability/explanatory models in both human minds and neural networks, and their connections with each other. What does it mean for the system to genuinely represent something? How can we understand the system with faithful yet interpretable explanations?
I believe that we are better positioned to answer these questions through not only empirical studies but also through philosophical and historical explorations of computation and science.

[✭] How can computational methods be used to expand and contextualize humanities research, empowering large-scale cultural analytics and historical investigations?
This line of research is commonly referred to as digital/computational humanities. (I am currently supported by the Digital Humanities Graduate Fellowship from the Center for Spatial and Textual Analysis (CESTA).)
While being open to the myriad unexploited benefits that scientific inquiries may offer, I wish to clarify that none of these approaches represent a technocratic method aiming to replace traditional humanities techniques such as close reading, or claim to be powerful enough to assume the crucial role of deep thinking. Instead, my goal is for these methods to assist thinkers in discovering new connections and perspectives, which could be better illuminated with the latest analytical tools.

[☾⋆⁺₊] These two directions may ultimately converge.
My aspiration is to develop both a theoretical framework and empirical methods that transcend simple imitation of traditional humanities methodologies and mere application of computational tools as a “technological upgrade”. Instead, I intend to propose new modes of inquiry that combine computational capabilities with humanistic interpretation. I aim for such approaches to encourage researchers to rethink and pose new questions about the mind and machine.

Visit my research page and blog for more details!


❅ Personal

Besides reading and writing, I enjoy designing, playing the Chinese zither (watch our performance here: Stanford Baipu Chinese Music Ensemble), and traveling.

I am always energized by inspiring conversations and meaningful connections. Drop me an email to say hello!

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