Adam J. Baron is the Director of Big Data Quantitative Research for StarMine, a Thomson Reuters brand. He utilizes distributed machine learning techniques to build quantitative finance models that can predict events which influence stock prices, such as defaults or mergers and acquisitions.
Adam received an undergraduate degree in Computer Science from Rensselaer Polytechnic Institute and started his career at Morgan Stanley. With a growing interest in finance while working on Wall Street, Adam obtained an MBA at NYU Stern where he realized Quantitative Finance was his true passion. A series of fortunate events (mainly marriage) brought him to San Francisco where he landed at Thomson Reuters and pursued his passion at StarMine.
Just around that time, Big Data was becoming a thing, so he self-taught himself emerging technologies such as Hadoop, Hive, Mahout, Spark, Spark MLlib and Sparkling Water to apply to text mining research. Adam enjoys exploring unstructured content sets (both text and images) and big data exhaust while keeping tabs on the latest open source developments.