As a yet minuscule startup, each member holds a significant power over the overall atmosphere of the team. And in our ultimate quest to make big waves in the data world, we need to make sure that the people at the helm are at least kind of cool. We think we’ve done a pretty good job so far in assembling a society of unique but equally driven members.

So we bring you this seven-part series, one of each devoted to interviewing each of our members in detail, to give you an in-depth glimpse into the people responsible for bringing you the future of machine learning with Daria. Plus, we peppered the interviews with questions from Dr. Aron’s “The 36 Questions that Lead to Love”*, cherry picked to make work appropriate and concise, but interesting.

(*actually falling in love with our members highly discouraged)

Jungkap, the most recent addition to our team, made the move from sunny Santa Clara to Seoul, a city that is slowly freezing over as you read this. But he is used to the cold, Jungkap assures us, having spent his doctorate years in the apocalyptic winters of Michigan. When he’s not busy helping build Daria’s machine learning engine, Jungkap devotes his time to re-exploring Korea and taking care of his cats Jolie and Brad (named so before the tragic dissolve of Brangelina). Learn more about him here!

• Tell us about your role at XBrain.
• JP: I joined the team as a machine learning engineer, and my main task is to develop our machine learning engine. I have the task of researching and finding solutions to obstacles that hinder people from using automated machine learning technology with ease.
• What does a typical work day look like for you, morning to evening?
• JP: I get to work at about 9:15 AM (*the earliest, we note, out of any of the members), and check the Slack messages and emails I got overnight. Since I concentrate the best in the morning, I take a look at relevant articles and dissertations then. Since I didn’t major in machine learning at school, there’s a lot I still have quite a bit to learn, learning’s still a big part of my work process. After I’ve warmed up a bit, I study the code that’s already been written, and develop the parts that need to be developed. Then I have lunch with the team, which is a part of our culture that I really enjoy — a set meal time and a chance to have a conversation with other members. Today I did investigation into an issue we had with the machine learning engine, and worked on how to solve that problem based on my discoveries. I think I’ll be working on constructing that idea into actuality, with a lot of validation, tests, trial and error.
• What are the parts of your job that you enjoy the most?
• JP:I enjoy enhancing and optimizing processes, and actually seeing improvement after I’ve worked on something. I’m working on improving the system that we have right now, but a long-term project we have in mind is developing technology of XBrain’s own, and figuring out the needs of our customers. In order to do that, I’m spending a lot of time looking into any issues that we have with our current technology, hoping to get insight from the process.
• What are the least enjoyable/most challenging parts of your job?
• JP:The most challenging, rather than the least enjoyable, is issue definition. There are four types of situations to what can happen: either I find a problem that’s already been found, or something that’s so insignificant that no one cares, something that’s unsolvable, and, finally, an issue that’s both important and solvable. The fourth is what we’re going after, and the process is long and arduous, but I do enjoy it to a certain extent.
• Pick one item on your desk that tells us something about you.
• JP:I don’t have much stuff on my desk, which is something I also noticed about some of the Silicon Valley companies I visited while I was working in the States, like Twitter or LinkedIn. Most engineers’ desks just had computers on them, and I appreciate that sort of simplicity.

• What made you go into machine learning?
• JP:I was on the user end of machine learning technology in grad school and at work thereafter, and felt that the process of utilizing and understanding tools was too complex and difficult. I thought that I might find it fulfilling to optimize this process myself by becoming a machine learning engineer myself.
• Why XBrain?
• JP:First off, I really liked how the team was set up, people-wise. I was also struck by the competency of the members and the company culture, which suited me well. The values that XBrain pursues, and the ideas that it had about the future of machine learning technology was in line with my own. Not to see it simply as a source of profit, but as something that could potentially bring a lot of people a great deal of help.
• As our most recent member, what’s a vision you have for our team?
• JP:It’s not so much a vision as a direction we should be heading in — despite how machine learning is such a huge buzzword now, I think it’s still in the process of research and development. A lot of work needs to be done before it can start having a real impact in the world. What our role is, then, is to look far ahead and start with the basics.
• Recommend a movie for our next Cinema Society, please.
• JP:Downsizing, which hasn’t come out in Korean theaters yet, but I think it presents a lot of points for discussion.
• If you could sum up XBrain in three words or less?
• Serious, but quirky.
• If you could have dinner with any XBrain member, who would it be and why?
• JP: JY — we haven’t really gotten a chance to share a meal, and I feel like he’d have some interesting stories
• What can you tell us about the JP 10 years from now?
• JP:He will probably be a more seasoned machine learning engineer, from his 10 years of research and studying. I’m a novice engineer now, but I’d like to be in a more senior position then, mentoring younger engineers.
• Given the choice of anyone in the world, whom would you want as a dinner guest?
• JP:Carl Sagan, who first got me interested in science and technology. In my head, he’s this benevolent father figure who would offer to mentor me.
• Would you like to be famous? In what way?
• JP:No…
• What would constitute a “perfect” day for you?
• JP:I think a “perfect” day is a day that’s yet to come. Is that too weird to publish?
• If you were able to live to the age of 90 and retain either the mind or body of a 30-year-old for the last 60 years of your life, which would you want?
• JP:The body, definitely. Minds can mature — bodies not so much.
• For what in your life do you feel most grateful?
• JP:Probably soundness of mind and body.
• If you could wake up tomorrow having gained any one quality or ability, what would it be?
• JP:Speedier comprehension upon reading something?
• What is the greatest accomplishment of your life?
• JP: Forging strong relationships with good people.
• What, if anything, is too serious to be joked about?
• JP:It depends on the audience, I think. Anything that they might consider offensive, or a weak spot, is off limits.
• Your house, containing everything you own, catches fire. After saving your loved ones and pets, you have time to safely make a final dash to save any one item. What would it be? Why?
• JP: My hard drive — it has everything on it.

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