Humans of SymSys: Sydney Maples

Sydney is double majoring in Public Policy and Symbolic Systems with an individually-designed concentration in Computational and Data Journalism. She spends her time thinking about everything from Japanese linguistics to Virtual Reality. Here, she offers advice on how to navigate indecisiveness and find continuity in the SymSys major.

What drew you to the SymSys major?

I'd say that about 70% of it was the community and the other 30% was the classes/skills I wanted to learn. As a freshman, SymSys was my top choice for the latter reason, but I kept my options open just in case. Over time, I noticed that the kinds of people I liked to talk to most tended to be SymSys folk, or interested in SymSys-y subjects. I decided to declare the major during the first week of my sophomore year, following the advice of a few friends in my Arts Intensive seminar. I joined the SymSys Society shortly after (thanks to the encouragement of Ian Holmes, whom I randomly met in the office hours for Intro to Syntax!), which really helped root me to the community and increased my love for the department.

What is your concentration and why did you choose it?

I have an individually designed concentration in Computational and Data Journalism. My IDC was inspired by both the Computational Journalism department and the Virtual Human Interaction Lab, both of which I discovered as a sophomore. But more generally, I wanted to have a meaningful goal to aspire towards, as this works better for me than simply taking classes that fit under a certain umbrella. My IDC helped me ground my coursework into something practical that suited my ideals. I also realized that I like being part of a field that has defined its goals to a certain degree, but also allows some level of flexibility in terms of the methods used.

What’s your favorite SymSys-related class that you’ve taken so far?

I loved everything about BIO 150, especially the complexity portion. I also liked both CS 103 and PHIL 150 for helping me see how the mathematical concepts I took for granted could fit together in intuitive and perspective-broadening ways. I also loved taking PHIL 80 with Professor Donaldson, because I appreciated his precision in defining and clarifying concepts, and the class was useful for helping me understand how to construct a philosophical argument. Honestly, this list could go on for a while!

Are you involved in research? If so, tell us about a project you are working on:

Right now I'm working on best approaches to parsing PDFs, specifically when the queries being searched for are changing on a yearly basis.

What’s the coolest (loosely) SymSys-related topic that you’re excited about right now?

I really like East Asian languages, and right now I'm (broadly) interested in Japanese linguistics.

What do you think about the perceived "STEM-humanities" divide? Does SymSys bridge this gap?

I wouldn't say that SymSys is a humanities major, as the methods used are very different than the methods I've used in the humanities courses I've taken. But I think that the interdisciplinary nature of the major gets people motivated to think about problems that concern humanity, and also attracts people who are interested in the kinds of problems that the humanities are concerned with.

As a diverse major with a lot of flexibility, many students struggle to find continuity across their coursework. (How) do you address this?

Continuity is a matter of character, not of coursework. SymSys is flexible, but it isn't forcing you to be flexible. In fact, it is doing what essentially every other non-interdisciplinary Stanford major is increasingly encouraging you to do: broaden your horizons and be willing to learn skills within different fields if they prove to be useful to what you're studying. In an abstract sense, trust your own ability to form connections and match patterns! But in a more practical sense, I would suggest dedicating yourself to either your goals or your methods, and then designing your coursework around that. If you find that your goal is to be a professor one day, for example, that's just as valuable as discovering that you want to work on neural networks without an intended purpose in mind.

What is one piece of advice you'd like to offer to younger students?

It is helpful to develop a useful relationship with indecisiveness while you're in college. I've found that the only harm in indecision is being stuck wallowing in it. You're far more likely to do something new and interesting when you're not sure what you're doing than when you have a specific path figured out. So if you can't decide what your next move should be, do anything. School alone won't teach you what you want to become; only experience (both positive and negative) will teach you that. And ultimately, you'll probably spend more time deciding what to do next than regretting any one decision you've made.

Sydney is one of many profiles featuring selected alumni, undergraduates and graduates who are involved in the Symbolic Systems community.

Symposium: Modeling the Dynamic Framing of Controversial Topics in Different Communities

For this symposium on May 12, I began by talking a little bit about how minor variations in language can index not only regional differences between speakers, but also other social, political, and ideological differences. We briefly discussed results from a paper titled “Indexing Political Persuasion: Variation in the Iraq Vowels” (Hall-Lew et al., 2010). Computer science can provide tools to investigate the connection between language, society, and politics. Specifically, I'm interested in using AI techniques to look at how controversial topics are framed (represented in order to promote a particular perspective) in online discussions, and how these framings might shift over time. During the symposium, we opened up the discussion to talk more widely about what role, if any, computer science should play in social science research. We also discussed the broader implications of computational social science research in academia and in society.

written by Julia Mendelsohn

Coffee Chat W/ John Duchi

On a beautiful Tuesday morning, we talked with Professor John Duchi over coffee. We had a high number of applicants, and I skipped my Philosophy of Neuroscience class to be there. This chat did not disappoint! We talked about optimal experiment design, Professor Duchi's experience as an undergrad at Stanford, classes at Stanford, and his view on deep learning. We were particularly inspired by his observation that in research, hard work is much more important than innate intelligence.  

Written by Allen Nie

Coffee Chat w/ Russell Poldrack

As the latest event of our faculty coffee chat series, we invited Russell Poldrack, a professor of Psychology. He runs the Poldrack Lab, which according to its website, "uses the tools of cognitive neuroscience to understand how decision making, executive control, and learning and memory are implemented in the human brain. " 

Poldrack's work sits at the intersection of informatics and cognitive neuroscience, and his background is in psychology (with some philosophy), and his computer science skills are mostly self-taught. He describes his journey to where he is now as "a serious of accidents," noting that the people he surrounded himself with at certain points in his life helped direct him to his current interests. 

Our conversation topic steered towards the issue of reproducibility in science. Poldrack believes that psychology is often the poster child for reproducibility issues because it is a field that is making an effort to actually mitigate the problem. The methods for investigating some phenomenon in psychology or neuroscience vary quite a bit from lab to lab, making it difficult to verify the robustness of someone else's results. 

Another topic that came up was how science should be conducted to properly provide the answers we seek. Poldrack mentioned "You can't play 20 questions with nature and win," an paper that criticizes scientific approaches in psychology. A nice tl;dr summary can be found here. Another good related paper is "Could a Neuroscientist Understand a Microprocessor?" where researchers applied neuroscientific methods to study a microprocessor, and fail to achieve successful understanding of a known system. 

Written by Lucy Li.

Humans of SymSys: Jon Gauthier

Jon is a Senior majoring in Symbolic Systems with a concentration in Natural Language Processing. He is currently researching computational models of human language in Chris Manning's Stanford NLP group. Here, he shares about how he got involved in research, and what he has learned about focusing on the big picture in his education. 

What is your concentration?

I'm part of the Natural Language concentration. This means I've taken a mix of classes in computer science and linguistics. In practice, though, most of my work has been in artificial intelligence. It blends topics in computer science (probabilistic modeling, machine learning) and linguistics (meaning representation, syntax-driven models, etc.).

Tell us about a research project you are working on.

My research is all about computational models of human language learning and language use. My current research project attempts to explain how language learners make successful rapid inferences about the meaning of words they haven't ever heard before, even when in situations where that meaning is highly ambiguous. This project is part of a larger attempt to explore new problems in modeling natural language dialogue. I'm thinking about new tasks that we could design to bring about interesting and robust conversational agents that talk like us (and talk with us!).

How did you get involved in research?

I started working with my advisor, Chris Manning, after applying to CURIS, the CS department's research program. They didn't mind that I was a SymSys major! After CURIS I was allowed to stick around, and I've been having a blast working with Chris' group (the NLP Group) ever since! My research is all about computational models of human language learning and language use. 

Why did you decide to major in SymSys over related majors such as CS, linguistics, etc.?

It was a pretty simple choice. The list of classes required for the SymSys major was a near perfect match with the list of classes I would want to take anyway during my time at Stanford.

What is one piece of advice you'd like to offer to younger students?

Always keep track of your interests, and take them seriously. You should always be thinking about the "big questions" that you really care about answering. For example: "How can neuroscience learn from developments in artificial intelligence?" "Is there a line we can draw between quantum physics and consciousness?" "Are there fundamental and objective moral principles underlying human behavior?" When you are choosing classes, hunting for internships, or looking for research opportunities on campus, always ask, "What could this class / opportunity do to help answer the questions I care about?" It's otherwise way too easy to get lost in the weeds of your particular field and miss the big picture. Keep imagining your education as active. It's not "what will this class teach me" — it's "what can I learn from this class."

If you could go back in time and be a Stanford student again, what would you have done differently and why?" (i.e. taking a different class, choosing a different advisor)

I would have taken philosophy classes starting in my freshman year. I left lots of the major SymSys-relevant philosophy classes for my senior year, and am only now realizing what I've missed. Philosophy classes (beginning with PHIL 80) are revealing to me the enormous amount of assumptions and ideas that lie behind artificial intelligence and cognitive science. Those classes have given me the tools to recognize these higher-level problems and, more importantly, to do really serious conceptual thinking about my own research that wasn't even conceivable to me before.

Jon is one of many profiles featuring selected alumni, undergraduates and graduates who are involved in the Symbolic Systems community.