About Us



Steve Gallant

Steve is the Founder and President of Textician, Inc., with decades of experience in machine learning, text modeling, and representations. He has developed and patented several machine learning algorithms and text representation techniques and was an early inventor of fully-distributed vector representations for text.

Steve has taught Computer Science at Northeastern University and worked with startups before founding MultiModel Research, which became Textician. He has over 40 publications, a book on neural network learning, and four patents. Steve holds a BS from MIT (math) and a Ph.D. from Stanford (Operations Research).


Dan Greenberg

Dan is the Business Executive for Textician. He has worked first as an engineer at the legendary Bell Labs. He developed broad business experience working as a market analyst, management consultant, product manager, and business development executive in tech companies from startups to Fortune 50. His background in machine learning stretches back three decades.

For the past 10 years, Dan has done “business stuff for small tech companies” – sales, business development, partnerships, marketing, and operations for startups as varied as Impossible Software (video), Midaxo (M&A workflow), and Gradison (mobile software). He holds a BSE in Computer Engineering from UPenn, an MS in Electrical Engineering from Caltech, and an MBA from MIT Sloan.
Dan came to Textician after a call from Steve which started, “You’re the only person in the MIT Sloan database with ‘neural networks’ in your profile. We should talk.”

Phil Culliton

Phil is the Head of Data Modeling and Engineering for Textician. Phil has spent the last 18 years building large scale machine learning and AI products. He built one of the first successful text understanding systems for litigation support in 2000, and one of the first fully distributed machine learning solutions for clustered text analysis in 2005. He has led teams and departments in multiple industries over the last two decades.

Phil is an internationally-recognized Master in machine learning competitions (Kaggle), a top 10 finalist for the machine learning hedge fund Numer.ai, and a prize-winning Crowdanalytix competitor. His machine learning solutions are in use across a wide variety of domains. He is currently authoring a machine learning textbook for Packt Publishing. Phil joined Textician in 2014, in part because our nascent NoNLP technology solved many problems that he had encountered during his career in text understanding.