Welcome to the 23rd installment of our blog series “My Path to Google.” These are real stories from Googlers, interns, and alumni highlighting how they got to Google, what their roles are like, and even some tips on how to prepare for interviews.

Welcome to the 22nd installment of our blog series “My Path to Google.” These are real stories from Googlers, interns, and alumni highlighting how they got to Google, what their roles are like, and even some tips on how to prepare for interviews.

Today’s post is all about Christof Leng. Read on!

Left: Cathin; Right: her fellow intern

Can you tell us about yourself and your masters topic?
I’m a masters student at Stanford University, where I’m a part of the Computational

Vision and Geometry Lab — I actually just joined this October, and I’m working on projects related to semantic segmentation. I also studied at Stanford as an undergrad, and previously I worked under Sebastian Thrun with Andre Esteva and Brett Kuprel on deep learning for skin cancer detection. So I work on a lot of vision projects, and I’m especially interested in projects that lie at the intersection of machine learning and healthcare. I’m also really interested in human cognition! I loved reading books by Oliver Sacks and other neuroscientists as a kid, but when I first started in computer science, I never considered that there would be much of a direct overlap where I’d get to actually mess around in both fields. Within artificial intelligence research, though, it seems like we still have a lot to learn from actual human brains.

How did you get to work in this area?
There’s this class at Stanford, CS231N, on deep learning for computer vision. On the very first day, I remember that the professor who co-taught the class — Dr. Fei Fei Li — went through this presentation, and one of the slides was about how the initial layers of convolutional neural networks learn basic edge detecting filters that actually closely parallel the basic edge detectors found in cat and human visual cortices, suggesting that there was a deeper and more fundamental connection between these two vision systems. I thought that was insane, and also insanely cool. I joined Sebastian Thrun’s lab a little later, and have been working on AI research since then.

Why did you apply for an internship at Google and how supportive was your masters advisor?
I’d heard really great things about research at Google, and even in my classes and labs, read lots of very impressive work coming from teams in Mountain View, London, and Zurich. I was hoping to get a better sense of what research looks like outside of an academic setting, and the scope of projects and expertise was a huge draw. Also, zillions of GPUs.

My master’s advisor at Stanford is Dan Jurafsky, who is the man. He’s a computer scientist and linguist, has written a book about the language of food, and is basically the best, as far as I’m concerned. He was super supportive.

What project was your internship focused on?
I worked under Andrea Gesmundo from the Applied Machine Intelligence team on Multitask Neural Model Search, a framework to automate deep learning architecture design using reinforcement learning. This work builds off of the Neural Architecture Search research done by Barrett and Quoc from the Google Brain team - that framework was one of the first to successfully apply reinforcement learning to automatically generate convolutional neural networks.

Our project focused on extending that framework so that we could automatically design architectures for multiple different tasks, simultaneously. For example, the same framework could design a model that worked well for sentiment analysis tasks, and another that worked well for language identification, at the same time.

We then showed that it was possible to transfer learn that framework, so that knowledge learned from designing architectures for previous tasks could be reused in totally new, unseen settings. When actual humans design machine learning models, we don’t start completely from scratch every time — we can take advantage of general intuitive design patterns we’ve observed before, as well as remember what models did and didn’t work on similar tasks in the past — and this research tries to take a step closer to doing the same thing in our automated model design.

Did you publish at Google during your internship?
Yes! We submitted our work to ICML, where it’s currently under review (so fingers crossed). The pre-print is also up on Arxiv.

How closely connected was the work you did during your internship to your masters topic?
Although Andrea and I discussed a bunch of project ideas in the months before the internship, this project was actually a chance to try something fairly different from my master’s research at Stanford. For me, at least, that turned out to be one of the best things about this internship — I really loved the chance to explore a very different aspect of AI research, especially one that benefited from the guidance and computational resources available within Google, and I left with a much deeper interest in reinforcement learning that I’ve continued to explore back at Stanford.

Did you write your own code?
Heck, yeah! And then I deployed it cavalierly with enormous care across tons of GPUs. One really awesome thing about interning, though, is the chance to build off of the collaborative engineering effort of other incredibly talented engineers and researchers. I worked pretty closely with code that was being updated almost daily by researchers on the Brain team over in Mountain View, and that kind of cross-continental engineering work feels really neat.

This is your third internship at Google. What were the reasons to come back to Google Zurich?
Third time’s the charm? But actually, I’ve been lucky enough to work at a different office, on very different projects, during all three internships at Google — after my freshman year, I worked with the Glass team in Mountain View, and later I worked in New York on Google Classroom. Each time, I left with a much deeper understanding and appreciation for that particular field, and the care and expertise each of those teams brought to those particular domains. This summer, though, I wanted to come back to work on research in particular. Both of my previous internships had been very software engineering focused, and I was excited to work on AI research that more closely parallels the work I’m excited about at Stanford.

Also, Zurich! I’ve never been to Switzerland before, and this summer one of my fellow interns and I took a train out to hike past the Matterhorn. She wisely remembered to bring along a Toblerone bar for comparison. The real thing is much more breathtaking (but a lot less chocolatey.) 

Looking to grow or test your coding skills? Don’t miss two of Google’s fun and challenging programming competitions — Kickstart and Code Jam.



Registration for Kickstart and Code Jam is open! These two programming competitions are designed for programmers of all levels looking to put their coding skills to the test. All of the problems are designed by a team of Google Engineers to inspire and challenge participants. People from across the globe are invited to join the fun! We have a community of current competitors, former participants, and fans of the competitions across Twitter, YouTube, Google+, and Facebook.

Here’s everything you need to know about Kickstart and Code Jam:

Kickstart: Want to grow your coding skills?

Throughout the year, Code Jam hosts online Kickstart rounds that give participants the opportunity to grow their coding abilities, while getting a glimpse into the programming skills needed for a technical career at Google. There are 8 rounds held throughout the year, and you can participate in one or join them all! Check out a recent YouTube Live where Google engineers walk through tips on how to solve Kickstart problems. If you want to practice before the official rounds, check out previous problems from the competition and try them out for yourself.

Register here for Kickstart before Round A on March 18th. You can find the full schedule of online rounds here.



Code Jam: Want to put your coding skills to the test?

Code Jam is Google’s longest-running, global programming competition. Join programmers around the world to challenge yourself, test your coding skills, and practice in a fast-paced environment. The top 1,000 contestants receive limited edition t-shirts featuring code from the previous year’s competition. The top 25 finalists will head to Google's office in Toronto, Canada to attend the World Finals where they'll compete for a cash prize of up to $15,000. We'll livestream the whole event for fans to join in the action! If you want to begin practicing, get started by working your way through previous problems, and join us for a practice session beginning March 23rd at 18:00 UTC.


Be sure to register for Code Jam before the online Qualification Round begins on April 6th. You can see the full Code Jam schedule here.



Whether you are a student or programming professional, contestants are eligible to participate in both Kickstart and Code Jam.

We hope to see you on the scoreboard!