Our team of top engineers has direct experience building state-of-the-art personalization systems at scale. We make it easy for publishers & e-commerce to harness the same techniques as Google, Amazon, and other consumer web pioneers. Born out of the top machine learning personalization teams at Google, LiftIgniter’s mission to enable better, more engaging user experiences by enabling content creators to put the best content and items in front of each individual user at each click. Websites & apps should react in real-time to their users.
Part of YCombinator’s winter 2014 class, we raised funding from some of Silicon Valley’s best investors. LiftIgniter is dynamic. LiftIgniter learns. LiftIgniter adapts to your changing users and changing content. LiftIgniter optimizes for the highest click-through-rate, engagement, reduced bounced, sharing, and conversion. Create brand affinity. Personalization is revolutionizing the Internet. Be part of the change.
Indraneel loves to apply mathematical insights to solve hard real life problems. Prior to LiftIgniter, he worked in a small team at Google that pushed the boundaries of machine learning research and infrastructure by building one of the world’s largest personalization engines. Ever the geek, he participated in major programming competitions in high school, and went on to get a PhD in theoretical machine learning from Princeton. His research in boosting can be found here. He won the “Outstanding Student Paper” award at NIPS 2010 and had the high honor of an invitation to lecture at the same conference. In his spare time, he enjoys running and playing soccer.
Excited about building & creating, Adam joins LiftIgniter from Henge Docks where he was Chief Operating Officer. Adam previously ran his own social data science company, and did business development at Clearwell Systems, an e-discovery company that sold to Symantec for $400m. Before moving to California, Adam worked at Neustar, a trusted, neutral provider of real-time information. Adam holds a JD & MBA plus dual-majored with minor in undergraduate from the University of Miami and Vanderbilt. In his free time, Adam can be found scuba diving (he’s a board member of the Coral Restoration Foundation), reading, and skiing.
Passionate about math since childhood, Vipul has won top medals, multiple times, at the prestigious International Math Olympiad, and holds a PhD in Math from the University of Chicago where his thesis was in group theory. His work in machine learning involves applying powerful math tools to achieve immediate real world impact. At LiftIgniter, he is developing cutting edge algorithms for massive datasets that provide dramatic improvements in cost as well as performance. In his spare time, he enjoys contributing to Wikipedia, reading about economics and psychology, and composing music.
Eric enjoys the challenges that come when merging the mathematics of machine learning with the technical problems of building high performance, distributed systems. At Stanford, he double majored in physics and electrical engineering before focusing on machine learning for his masters degree. Prior to working at LiftIgniter, Eric built out a distributed platform for analyzing and classifying massive numbers of malicious files at FireEye. Outside of work, he enjoys rock climbing and biking and trying not to twist his ankle.
Boris likes math. He gets to do a lot of it at LiftIgniter. Prior to joining, Boris was a researcher at a financial firm in Berkeley where he worked on statistical problems, coding in R and Python. Boris received a PhD in mathematics from Princeton University where his advisor was John Horton Conway. Outside of work, Boris likes to ride his bike and play with cats.
Eugene likes math…enough to finish BSc. in Applied Math and BA in Pure Math at Brown University. He started programming in high school at a University lab in Korea Advanced Institute of Science and Technology, where he developed Android applications related to Ubiquitous Computing. Eugene lived throughout Denmark, Korea, and Rhode Island until he came to San Francisco Bay area and found an awesome home at LiftIgniter. Outside of work, Eugene likes playing video games and watching animations. He plans on going outdoors in the near future.
Yonathan likes to make things more efficient and dive in to get to the bottom of problems. He finds infrastructures (and their speed & scalability) fascinating. Prior to Liftigniter, Yonathan made data capture programs at Acuitus and worked on book search at Google. He holds a BS in Electrical Engineering and Computer Science from the University of California, Berkeley. He loves coupons and deals and cooks a fair amount when time allows.
David Blei is a Professor of Statistics and Computer Science at Columbia University. His research is in statistical machine learning, involving probabilistic topic models, Bayesian nonparametric methods, and approximate posterior inference algorithms for massive data. He works on a variety of applications, including text, images, music, social networks, user behavior, and scientific data.
David earned his Bachelor’s degree in Computer Science and Mathematics from Brown University (1997) and his PhD in Computer Science from the University of California, Berkeley (2004). Before arriving to Columbia, he was an Associate Professor of Computer Science at Princeton University. He has received several awards for his research, including a Sloan Fellowship (2010), Office of Naval Research Young Investigator Award (2011), Presidential Early Career Award for Scientists and Engineers (2011), Blavatnik Faculty Award (2013), and ACM-Infosys Foundation Award (2013).
Tony Jebara is Associate Professor of Computer Science at Columbia University and Director of Machine Learning Research at Netflix. His research intersects computer science and statistics to develop new frameworks for learning from data with applications in social networks, recommendation, spatio-temporal data, vision and text. Jebara has founded and advised several startups including Sense Networks (acquired by yp.com), Evidation Health, Agolo, Ufora, MagikEye, and Bookt (acquired by RealPage NASDAQ:RP). He has published over 100 peer-reviewed papers in conferences, workshops and journals including NIPS, ICML, UAI, COLT, JMLR, CVPR, ICCV, and AISTAT. He is the author of the book Machine Learning: Discriminative and Generative and co-inventor on multiple patents in vision, learning and spatio temporal modeling. In 2004, Jebara was the recipient of the Career award from the National Science Foundation. His work was recognized with a best paper award at the 26th International Conference on Machine Learning, a best student paper award at the 20th International Conference on Machine Learning as well as an outstanding contribution award from the Pattern Recognition Society in 2001. Jebara’s research has been featured on television (ABC, BBC, New York One, TechTV, etc.) as well as in the popular press (New York Times, Slash Dot, Wired, Businessweek, IEEE Spectrum, etc.). Esquire magazine named him one of their Best and Brightest of 2008. Jebara will be General Chair for the 34th International Conference on Machine Learning (ICML) in 2017and was a Program Chair for the 31st International Conference on Machine Learning (ICML) in 2014. In 2006, he co-founded the NYAS /Machine Learning Symposium / and has served on its steering committee since then. He obtained his PhD in 2002 from MIT.
I’ll Be Back: The Return of Artificial Intelligence: “Behind much of the proliferation of AI startups are large companies such as Google, Microsoft Corp., and Amazon, which have quietly built up AI capabilities over the past decade to handle enormous sets of data and make predictions, like which ad someone is more likely to click on.”
First and foremost – that means a group of people who enjoy spending time together. We work hard but we’re also friends. Second – brains. It’s almost cliched to say but we truly only want to work with the best. We push each other and expect you, as our new teammate, to push us too.
LiftIgniter delivers billions of personalized recommendations and experiences every month, on some of the largest websites across the world. Building machine learning products at this scale is extremely hard, and not well solved by existing academic literature. At the same time, a few extra points in improvement can mean millions in incremental revenue, and the difference between success and failure. So we innovate, at the cutting edge. Our current team of ex-IMO, IOI, Phds from MIT, Stanford, Berkeley, Princeton love the challenges. If you’re excited by the problem and team, we should talk!