Jeff Palmucci has been writing code professionally since he was 11 years old. A serial entrepreneur, Jeff has started several companies. He was a Founder and the VP of Software Development for Optimax Systems, a developer of scheduling systems for manufacturing operations. Optimax was acquired by i2 Technologies where he continued on as an i2 Fellow and Lead Architect for scheduling products doing extensive research into production scheduling. As a Founder and CTO of Percipio Capital Management, he helped lead the company to an acquisition by Link Ventures. Percipo Capital ran a programmatic hedge fund, trading in commodity futures and equities.
Jeff is currently leading the Machine Learning group at Tripadvisor, which does various machine learning projects across the company including natural language processing, review fraud detection, personalization, click fraud detection, and machine vision.
Jeff has publications in natural language processing, machine learning, genetic algorithms, expert systems, and programming languages.
When Jeff is not writing code, he enjoys spending time with his family and going to innumerable rock concerts as a professional photographer.
We caught up with Jeff at the Yandex Data Factory Conference and here’s our interview with him.
What do you think about the conference?
The conference was very interesting. A lot of times people in the industry will be heads down, trying to solve problems, dealing with stuff that is immediately in front of them and the conference was a great chance for me to set back and take a look at some of the emerging technologies that are out there. There was some very interesting stuff. Some stuff that is very applicable to what we are doing at TripAdvisor, so I’ll take it back and try it.
What are some major milestones or landmark events that stood out during your time in the industry?
Well, my industry, I guess you would say is artificial intelligence and machine learning because my first startup was in operations research and scheduling. I think it is kind of a common theme. When you see industries turning over, capabilities with computer are growing and now storage and big data growing, things that were not possible before become possible. My initial startup was for manufacturing scheduling, we used optimization techniques to schedule plant floors and we just did way better than a human could because a human can’t keep track of all the stuff that needs to be kept track of and that boost, that application of compute power to a very specific problem at a very immediate payoff was very successful. And I think that is what you are seeing with data science and machine learning these days, especially is in the company like TripAdvisor, where our main product is information. We are providing our users with information. That is why they keep coming back to the site. Being able to organize this information, categorize it, figure out what they what and put it in front of them – that is what moves our bottom line. Machine learning and data science is right at the point where you can very directly affect those numbers. I think this is the same kind of thing when you have a size make move in the industry like over the past ten years with the machine learning and data science. I mean Google for example, one of the biggest companies in the world was born on machine learning, organizing information in better ways. It is kind of exciting.
What would be the next thing in the industry? What do you see coming?
If I knew that I’d have another startup already, wouldn’t I? (laughter). What we find useful is being able to categorize information when human decision making is used and we can augment that with machine learning models. For example, we have banks of people who look at the reviews and try to find fraud, inappropriate content or something like that. When we can put in a machine learning model and make that process more efficient by auto-publishing or auto-rejecting something, I am pretty sure humans deal with more difficult classified stuff. Making stuff more efficient, I think with both advances in algorithms and the better collection of data is just going to keep happening and making people efficient. The amount of data that you have online, like TripAdvisor has millions and millions of reviews and other companies have lots of data too. Organizing that stuff and turning it from just data on to information that people can consume, I think that is going to be another big component of machine learning. Or coordinating stuff, in TripAdvisor we try to match hotels to people. Behind the scenes using clever filtering and personalization type of stuff.
You have been angel investor for the last 19 years. As an investor what technology development most excite you?
Ok. Let me qualify my angle investor experience. When investors I know come across a good opportunity and they need somebody very technical to go in and make sure it is on the up and up, that’s where my angel experience comes in. So all of my investments have been co-investments where I have done technical due diligence on companies.I talked about where machine learning is going but I am not an active angel investor.
What are some of your achievements that make you most proud of?
We are actually making a lot of progress at TripAdvisor in machine learning. We have made some pretty big strides using machine learning in review fraud detection, personalization and we are starting to work on site search now too. All areas directly affect how people use TripAdvisor, increase their engagement and increase TripAdvisor’s revenue. So it is great to work at a company like TripAdvisor where we have so much traffic and so much data which we could monetize by using all that data, getting traffic to be more profitable. I have worked with a lot of startups and one of the most successful ones is the one I was talking about before, the factory floor scheduling, at one point our software was scheduling over 500 billion dollars worth of manufacturing equipment. It really excites me when you are able to take a computer and make it do something that nobody thought you could make it do before. So the whole field of Artificial intelligence as intelligence is a kind of term like magic that goes on and implementing that magic is always exciting.
Building on that, who are some individuals who inspire you?
Individuals that inspire me. You know it is inspiring to see people put together solutions to problems that haven’t been thought of before like the Google founders. I really appreciate people that can be smart about providing value to the world. You know, Google is a company that has done more to accelerate science in the last decade than any scientist that has actually been out there.
What advice would you give to young professionals looking into finding their feet in data science?
That is interesting. Because one of the biggest problems right now, I think in machine learning practices is finding good people. Hiring takes a significant part of my day and it is very hard to find good people especially people that know the techniques and understand them well enough to apply them. So if you want to get into this area, machine learning or data science, now is the great time to do it because the demand is high and the supply is low. And what you need to do is prepare yourself and educate yourself – do more than a couple competitions but take the courses and make sure that you work hard. Because one thing about interviewing in this field is that it is very quantitative. You can tell pretty much off the bat if somebody knows what they are talking about. My best advice to somebody going into this area is, first of all I’d say you are going into a great area, you are going to be in high demand but study hard, make sure you know the techniques.
Thank you for your time.
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