Coffee chats: Jeannette Bohg

Jeannette Bohg, Assistant Professor of Robotics, chatted with us on Friday about her research, her advice for aspiring academics, and what keeps her going in her work as a professor.

Professor Bohg’s current research focuses on perception for autonomous robotic manipulation and grasping. Although she just arrived at Stanford two months ago, she is already fond of the collaborative culture she sees in the computer science department, as well as in the rest of the School of Engineering. She says she has been an academic “her whole life,” so although she has considered working in industry, she prefers the excitement of figuring out how things work by implementing the underlying principles on robots rather than building a robot that works for a specific task.

For those who are considering pursuing a career in academia, Bohg described the differences in titles such as “Assistant Professor,” “Associate Professor,” and “Lecturer.” She talked about her day-to-day life as an assistant professor and how her daily work might differ compared to that of, say, a grad student.

Bohg stressed the importance of keeping an multidisciplinary perspective throughout one’s academic journey. She studied computer science as an undergrad, but she took a detour for her master’s to explore the intersection of art and technology, because she was always interested in art but did not study it previously due to financial considerations. Bohg emphasized sticking with “what you like”, even when a clear path for the future does not materialize immediately. Without an intrinsic drive to learn more about what you are studying, she said, it’s really hard to excel in any field.

Written by Marika Buccholz

Coffee chats: James Landay

Our most recent faculty coffee chat was on Tuesday, November 1st, with James Landay, a computer science professor at Stanford who specializes in human-computer interaction. As the student attendees sipped their Coupa drinks, Landay began to recount his journey: “I started by stealing video games.” 

Not literally, of course; video games served as his avenue into the world of computer science. Landay continued to enter the human-computer science niche, first studying at UC Berkeley and Carnegie Mellon before serving as faculty for UC Berkeley, University of Washington, Cornell Tech, and, finally, Stanford University. 

During the engaging coffee chat (it exceeded the scheduled end by 23 minutes), Landay described his research in the Smart Primer, a story-based tutor that grows with the student, as well as the future he envisioned of designing for behavior change with interactive environments. In between, conversation shifted from research to education to speech recognition to an objective comparison of everywhere Landay has lived: Bay Area, Seattle, New York City, and Beijing. 

To learn more about James Landay, visit his website at www.landay.org.

Written by Jenny Zhi

Humans of SymSys: Blue Sheffer

“Fight off academic intimidation of others as much as you can -- it's entirely unhelpful. Learn from the brilliant peers that surround you, and befriend those who you look up to.”

Blue is a recent graduate of the Symbolic Systems Program (B.S. ’17). He concentrated in neuroscience, completed an honors thesis on brain-machine interfaces, and was an advising fellow. Here, he shares some thoughts on SymSys, research, and gives academic advice.

What drew you to the SymSys major?

Once I found out about Symbolic Systems, I didn’t really even consider any other majors. When I came to Stanford, I knew that I wanted to study neuroscience, but I also had budding interests in computer science and philosophy. Studying SymSys would allow me to study each of these fields in depth, as well as their intersections, so it was a perfect match.

What is your concentration and why did you choose it?

I chose the Neuroscience concentration because understanding how the brain processes information is at the forefront of my academic interests. The concentration puts emphasis on technical skills which are increasingly necessary for a career as a neuroscientist.

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

I have many, many "favorites", but here are two:

1) Mathematical Foundations of Computing (CS 103). Depending on what you do, the material may or may not be super applicable, but Keith's quality of instruction is so unbelievably good that you should take it regardless.
3) High-level Vision: From Neurons to Deep Neural Networks (CS 431). I took this as a kind of "capstone" course my senior spring with Kalanit Grill-Spector and Dan Yamins. It was an awesome class that analyzed and compared neuroscience/deep learning approaches to vision.

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

Yes! I currently work at the Stanford Neuroscience and Artificial Intelligence Laboratory. My project focuses on bridging sensory representations with decision making. We've built a model capable of solving complex visual tasks that reflect typical neuroscience experiments, and we are collaborating with experimentalists to compare our model to both behavioral and neural data.

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

1) Fight off academic intimidation of others as much as you can -- it's entirely unhelpful. Learn from the brilliant peers that surround you, and befriend those who you look up to.
2) Get involved in research! Even if you don't want to go to grad school/have a career in research, it's a really fun way to deepen your knowledge of a subject and be at the forefront of development in your field.

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

Humans of SymSys: Marika Buchholz

“SymSys is all about learning a way of thinking (within the core) that you can then apply to many different subjects (inside or outside of your concentration).”

Marika is a junior majoring in Symbolic Systems with a Learning concentration. She’s interested in learning from many different angles: how/whether we can use technology to help people learn, whether musical ability/experience plays a role in tonal language acquisition... Here, she talks about the importance of the growth mindset, reaching out to others for advice and insight, and perseverance. Marika exemplifies using a SymSys education to learn how to think about and address the issues that are important to her!

What drew you to the SymSys major?

Freshman year I tried lots of majors out like CS, MCS, linguistics, and Econ, but I think I always had a gut feeling that I wanted to do SymSys. I'd always been interested in language, and I wanted to explore fields like computer science and philosophy, both of which I had little to no background in before coming to Stanford. Now, my favorite part of SymSys is that my classes each quarter span many areas of interest, and slowly the connections among them are growing!

What is your concentration and why did you choose it?

Although I’ve been declared SymSys for awhile now, I’ve only recently decided to concentrate in Learning. I’m broadly interested in how people learn, as well as how (or whether!) we can use technology to help people learn. I haven’t taken many classes in my concentration yet, but I’m excited to use the concepts I’ve learned in my CS, psych, and linguistics classes to explore topics like educational neuroscience, learning/design/technology, and applied sociolinguistics/language education.

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

Two classes stand out for me: Ed101 (Introduction to Teaching and Learning) and CS103 (Mathematical Foundations of Computing). Taking Ed101 has been an absolutely fantastic way to get a broad picture of the field of education and reflect on my own experiences as a student for the past ~15 years. I also loved CS103, because I learned techniques and mindsets for learning (such as growth mindset and directed practice) that have helped me so much in other classes.

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

I grew up speaking Japanese, and I’ve taken two years of Chinese at Stanford. I love looking at the similarities between the two languages, as well as how the character systems in both languages have changed over time. I’m also fascinated by second language acquisition, specifically for tonal languages, and whether musical ability or experience plays a role in how well we can learn those tones!

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

In my view, thinking of STEM and humanities as two irreconcilable entities is dangerous, because working on technical problems without a knowledge of society, especially here in Silicon Valley, can lead to doing harm. I would say, though, that if you are intentional about it, SymSys can bridge the “STEM-humanities” gap. SymSys is all about learning a way of thinking (within the core) that you can then apply to many different subjects (inside or outside of your concentration). Your focus topic within SymSys can be about so many things that aren’t touched upon specifically in the core, from education to digital democracy. For me, learning more about people and society is just as important as exploring the questions addressed in SymSys, and I feel lucky to have found a way to do both congruously.

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

If you’re considering SymSys, there’s a chance that you have something specific you really want to explore (like AI or NLP). If you’re anything like me, though, you’ll spend a fair bit of time being wildly indecisive about what classes to take, what to major in, what to do over the summer, or what to do after graduation(?!). My advice is to go talk to as many people as you can who can help you make your decision. If you talk to enough people, you will reach insights you hadn’t even thought about before. Also, do SymSys at your own pace. If your grades in a class are lower than you wanted, or if you need to drop classes and take them another quarter, don’t immediately consider switching majors. I almost did switch out of SymSys (twice!) due to feeling behind academically, but I’m so glad I stuck with it, because now I found a niche within the major that I really love.

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

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.