Getting to Know Guy Lebanon

December 10, 2015

Talent is LinkedIn’s number one operating priority and we have no shortage of talented individuals in technical roles across the company. These are the folks that create, build and maintain our platform, tools and features - as well as write the posts for this blog. We are featuring some of the people and personalities that make LinkedIn great.

Guy Lebanon, Senior Manager for Applied Machine Learning, has been leading the feed relevance team at LinkedIn since January 2015.

Prior to that, Guy was an advisor to an SVP and a senior manager at Amazon where he led the machine learning science team at Amazon's main campus in Seattle, Washington. Guy also was a tenured professor at the Georgia Institute of Technology and a scientist at Google and Yahoo. His main research areas are machine learning and data science. Guy received his PhD from Carnegie Mellon University and BA, and MS degrees from Technion - Israel Institute of Technology.

Guy has also authored over 60 refereed publications. He is an action editor of Journal of Machine Learning Research, was the program chair of the 2012 ACM CIKM Conference, and will be the conference co-chair of AI & Statistics (AISTATS 2015). He received the NSF CAREER Award, the WWW best student paper award, the ICML best paper runner-up award, the Yahoo Faculty Research and Engagement Award, and is a Siebel Scholar.

  • Guy Lebanon

What is something not found on your LinkedIn profile?
Even though my name is listed most places as Lebanon, my legal name is actually Levanon. The English letters “b” and “v” both map the same letter in Hebrew (ב). My name was originally translated to English as Lebanon, even though the Hebrew pronunciation is closer to Levanon. I later legally changed my name from Lebanon to Levanon, but since I had already published papers and books under the name Guy Lebanon, I kept it as my professional name.

What are your favorite things to do when you’re not at the office?
When I am not working, I usually spend time with my family. My wife Katharina is an engineering manager at Netflix, and we have two kids, Hannah and Eli, who are eight and six years old respectively. I also like reading books, traveling, playing squash, and swimming.

What’s your favorite thing about working at LinkedIn?
I really like LinkedIn’s mission to help people connect to economic opportunities. I feel that my work helps thousands and perhaps millions of people improve their careers by networking, being informed about their professional areas, and finding jobs that are a better fit for them, whether that means a higher salary, a shorter commute or a more rewarding experience. We are making a real positive impact in people’s lives.

What do you do best at work?
There is a gap between machine learning research and practice. I am good at bridging that gap and understanding what new techniques in machine learning may apply to concrete problems at LinkedIn. I have been working in the field of machine learning for about 20 years and am very knowledgeable about the space. I worked for eight years as a professor (Purdue University, and later Georgia Institute of Technology, where I had tenure) and four years in the tech industry (Google, Amazon, LinkedIn,) so my knowledge is both theoretical and practical.

What is the most challenging part of your job?
We get a lot of qualitative feedback from LinkedIn employees and our members. Some of this feedback is immediately useful – for example, if it points to a bug or a broken experience – but sometimes it is very hard to translate feedback to action items. For example, person A may like a certain product feature but person B may dislike it. The difficulty is incorporating this collection of sometimes contradictory comments from multiple users into the machine learning system.

What do you love most about machine learning?
I was first interested in machine learning because it is tied to philosophical questions such as “What is intelligence?” and “What is consciousness?” If we can replicate human learning in a machine, we can get closer to answering these questions. But later, I got excited about machine learning's potential to impact technology. Machine learning is currently indispensable in many industries and technologies including e-commerce, social networks, and search engines. In the near future it will bring new technologies such as digital assistants (e.g., Siri) and self-driving cars to everyday use.

Some people believe that artificial intelligence will eliminate the need for many workers. What are your thoughts?
Loss of jobs is certainly worrisome, but instead of fighting against technology, I think the right path forward is to highlight what is going on and to adapt our labor force and economy. I am also worried about using machine learning or artificial intelligence in a negative way, like an army of militarized drones, but if we are careful, AI’s impact on society will remain very positive overall.