Mahmoud Lababidi
Machine Learning Engineer


Photo by Isabelle Le Normand

"I don't see machine learning taking over our life. I see machine learning more as a useful tool. Machine learning is an optimization. It's a simplification of a process."

Mahmoud Lababidi is a machine learning engineer at Square based in New York City. He specializes in machine learning for social media, satellite imagery, and banking. After receiving his B.S. in Computer Engineering, Lababidi went on to get his PhD in theoretical condensed matter physics from George Mason University. Prior to joining Square, he worked for a number of startups in Silicon Valley as well as Google and Capital One. Lababidi is also a conceptual photographer.

TGL: How was your childhood?

ML: I had a complicated childhood. Not only am I the child of Syrian immigrants, but we also lived as immigrants in Kuwait when I was young. It's actually common for Syrians and Palestinians to go to Dubai, Qatar, and Kuwait to have a better life. We moved to the United States when I was seven due to the Kuwait Gulf War. The change in environment affected me, I was confused about my identity growing up. 

Another piece of my childhood, which affected me was my father’s mental health issues. He believed that in order to prevent my mom from divorcing him, he should kidnap my brother and me to convince her to come to Syria. So he kidnapped us. That added some trauma to my upbringing. 

As a young child, my mom could take me anywhere, and I could effectively keep myself occupied with whatever was around me. When I was three or four years old, I used to take luggage suitcases and learn how to take apart the coded locks by trying out different numbers. 

TGL: Where in the United States did you move to? 

ML: I bounced between Michigan and Washington, DC. We eventually settled in Washington DC and I went to McLean High School.

TGL: In high school, were you already attracted to science and technology?

ML: Yes. Both of my uncles got me into computers at a young age. I had my first computer when I was 11 years old. I started programming when I was 12. One summer, I walked into an office building near my house and said to the receptionist, "I'd like a job here." She brought out their HR person who took me around so I could meet some of the engineers. I showed them a webpage I had made. It was a terrible webpage, but it was a webpage where I put together different pictures of things I liked. I knew how to write the HTML and all this stuff. They were impressed enough to say, "We'll bring him on for a summer internship," as a 12 year old. They didn't give me any serious work. It was mostly for me to just see the culture and how things are done, and so on. It was a good learning experience.

TGL: How did your coding skills evolve?

ML: At first, I was coding mostly webpages and things like that. When I got to high school, I took a course in computer science. That's where I started learning how to really do programming. I took two courses that counted for university credit, so when I entered university, I was already what some people would consider the second or third year level of programming.

TGL: Which university did you go to?

ML: I started at George Mason and finished my bachelor’s degree in Computer Engineering at the University of Florida. Afterwards I spent one year in Silicon Valley and decided I wanted to go to grad school. After that, I went back to George Mason and did a PhD in physics. 

TGL: Why did you get your PhD in physics instead of computer engineering?

ML: I lived in Mountain View in 2007 and my roommate David Fattal was a French physicist that studied at X and Stanford. He was very encouraging in showing me physics and quantum computing, which he was an expert in. I was pretty scared of physics to tell you the truth. Because it was a fear, I decided to jump into it. It seems like an apt challenge. David recently launched a 3D display that doesn't require glasses. He's a real pioneer and inspiration.

TGL: What was your job in Silicon Valley between the undergrad and grad school?

ML: I worked in a data center for Google. I didn't really like that for a couple reasons, so then I went and helped a guy put together a startup. I built a technology for him. This guy already had a successful startup, which he sold to Hollywood Video, a video store many years ago. He wanted to build a startup for personal movie recommendations. Less algorithmic, and more specific. You tell them a movie you like and based on that movie, they’ll tell you which ones you should see. 

TGL: Did this project succeed?

ML: The timeline of this project was a little tricky. It was right around the time of the 2008 financial crisis. Overall it didn't succeed because the only company who was interested was Netflix. They bought the data, but Netflix is more interested in algorithmic style recommendations. This guy hired actual movie people to make these recommendations.

TGL: When you went to get your PhD in physics, did you feel like you were on a specific track?

ML: I felt like the stars aligned. I thought, wow, I'm doing things that I really enjoy. I'm solving problems the way I would solve them. I'm using mathematics the way I would enjoy using that mathematics. Science is different from engineering. Engineering is incredibly applied and business focused. Science is much more conceptual. 

TGL: What was your first job after your PhD?

ML: I worked for this startup that did social media called Zoomph. I did data science for them and machine learning. I built a sentiment tracker to determine whether people are happy or unhappy about an event when they tweet. They're actually still using it.

TGL: After that, did you go to a financial institution?

ML: No, I spent four years in defense. The National Geospatial Intelligence Agency (NGA) was my biggest funder and paid my salary indirectly. Their job is to do national geospatial type, which is mostly satellite based. Trevor Paglen's work is interesting to me, because I almost did everything that Paglen did.

TGL: What was your role at NGA?

ML: I was focused on social media, capturing social media and understanding any information about it that we can. I applied machine learning to do some of this work. Later on, I started using satellite imagery to detect objects on the pictures like cars or special vehicles. Those were the big highlights that we had.

TGL: I imagine a lot of this work is confidential?

ML: In a way I was lucky. I didn't have any security clearance. I tried to go through the CIA and I was denied one. I think it's because I have relatives in Syria and so on.

TGL: After this experience, did you work for Capital One?

ML: Yes, I decided to grow a little differently and try Capital One after four years in defense. I was a machine learning engineer and it was an interesting role, but I found that big corporate banking is incredibly slow and conservative, and very hierarchical in their decision-making. So I spent two years there and then I left to be a machine learning engineer at Square.

TGL: What is your role as a machine learning engineer at Square?

ML: I pivoted a little differently. I don't build the algorithms anymore, I build tools to help build the algorithms faster and easier. They say it's more infrastructure, I build the factory that makes the process easier.

TGL: What kind of tools do you build?

ML: Tools to get data, tools to explore data, tools to build the algorithm and take the algorithm to production, so it's live and running. 

TGL: What do you think the future of machine learning is?

ML: Machine learning will start to mature a little. There will be tried and true processes that they will determine to be consistent. It will become much easier to build a machine learning model algorithm and get it deployed. There will be more pushes to do experimental work, but the tricky thing is always asking, can this be useful in the real world? 

In your day to day, how have you found machine learning to be useful? If you have an Amazon Echo, it uses machine learning to decipher what you're saying, which is important, but it doesn't change the fact that you could type the same thing into Google. We see machine learning from the outside, but these companies working on it have very targeted goals. 

I don't see machine learning taking over our life. I see machine learning more as a useful tool. Machine learning is an optimization. It's a simplification of a process.

TGL: Where do you see your career in 10 years?

ML: I sincerely do not know. I actually made the prediction in 2006 that machine learning will be a big deal. And I was right. I think cryptocurrency will be a big player. I don't think it will be as big as machine learning, but I think it will play a pivotal role. There's an emphasis on machine learning that helps companies make a lot of money. Without machine learning, companies cannot make the amount of money that they want to make. They are dependent on it. Cryptocurrency is going to open up new fields that we haven't seen before.

TGL: What’s an example of a new field in Cryptocurrency?

ML: We're going to start seeing intricate ways of money laundering with cryptocurrency. We're going to start seeing Venezuela and other countries that are getting hit with currency crises start using cryptocurrencies more. 

TGL: Do you also believe in blockchain?

ML: Blockchain and crypto are the same. I believe that these currencies will help some people escape their problems. If I can pay someone in Venezuela to do a job for me, I can pay them in dollars or in cryptocurrency. If the neighborhood market accepted cryptocurrency, then they can buy their fruits and vegetables from that person. Now you have a reputable currency floating around that country.

TGL: You are also an artist and photographer. When did you start taking photos?

ML: I probably took my first photograph when I was eight or nine years old. I remember having a roll of film and taking a variety of photos. In general, my art has helped me find my place in the world and make peace with the world. It has helped me understand what I see and what I have connections with, because each person has their own relationship with what they see and why it's important to them. I'm a very sensitive person, and I tend to maybe be a little over sensitive. When I see something, I interact with it internally a lot. Photography allows me to refine that interaction in some way. 

I make my art for myself. I'd be happy to share it, but it's not a top priority, because I don't need to make money on it. I don't think anyone is producing their art only to make money, but I think it helps. Since I already have a career, I've come to peace with the role that I have with art in my life.

I'm moving to New York in a month, and I'll be two blocks away from a photo development store. I'm very excited only for the sole fact that I can continue producing work for myself. Photography will help me understand my relationship with life, with New York, with my job, with my partners, if I have a family, and so on. Our world is so bizarre, that we need a way to help organize all the visual symbols, that for me is in photography.

TGL: Who would you like to have dinner with that you don’t already know?

ML: Wes Anderson would be one. I'd be interested to understand what makes him tick and what he thinks about when he makes his work. This is a person who has extreme control over his work and has produced things that have caught the eye of so many people in the world. I really resonate with his work, because he presents a reality that takes extreme focus and dedication.

TGL: What advice would you like to give to The Genius List’s readers?

ML: Soak in the journey and the process of what you are doing, whether it's art, whether it's science, whether it's math, whether it's meetings, whether it's personal relationships.

I would advise young people that it's okay to be anxious. It's okay to be nervous. It's okay to be worried. We created a shame in our culture for having these things. And I think it's okay to have shame too. Be mindful of your own feelings. You don't need to remove them. 

I may not be perfect in my own practicing of this, but if you can't do that, you're going to carry a big burden. I just lost a friend three weeks ago to suicide, because he put a lot of pressure, blame, and negativity on himself. If you're unhappy, it's okay. You're allowed to be unhappy. Don't let the world make you feel like it's unacceptable for you to be unhappy.