Rachel_v1_800sq

Rachel Lomasky

Director of Machine Learning

Dr. Rachel Lomasky is Director of Machine Learning at Manifold. In this capacity, she helps our clients train and productionalize their machine learning algorithms.

Prior to Manifold, she was co-founder and Chief Data Officer of WEVO Conversion, a platform for digital marketers that uses AI to improve websites and search results websites. She was responsible for growing the company—from building the engineering team, to defining product strategy, to investor meetings.

Before her time at WEVO Conversion, she was Director of Analytics at Opera Solutions, an AI analytics company. There, she led a 10-person data team, as well as a machine learning and advanced analytics platform. Rachel has also served as an adjunct professor at Northeastern University. Her course Parallel Processing in MapReduce covered theory and real-world applications of cloud computing, Hadoop, MapReduce, Hive, Pig, HBase, Spark, as well as other technologies.

In addition to her time with WEVO, Rachel has further early company experience—she was CTO and Founding Engineer at Relay Technology Management, a patent commodity firm. She led the design and development of the Relay Innovation Engine, which aggregates both proprietary and public data sources, and suggests promising new technologies for licensing by pharmaceuticals, or any patent-based industry.

Rachel is well versed in helping executives understand their data, and what features should be engineered from that data. Her work with large amounts of data (from industries spanning airlines to banks) sets her up perfectly to create real value for Manifold's clients. Her extensive startup experience ensures she knows how to be agile and deliver quickly.

That delivery step is part of what excites Rachel about her role: “There's this attitude of working together to deliver a tangible product, along with a focus on life-long learning and exploring new ways to solve problems.”

When she's not working on machine learning, Rachel can be found biking to work, writing Perl poetry, and chasing her two sons down the mountain (on skis).

Rachel received her PhD in Computer Science from Tufts University, specializing in research on acquiring informative training examples. She has a BA in Computer Science from Wellesley College.