About

Michael (Stu) Stewart is a highly skilled Staff Deep Learning Engineer at Opendoor with over 10 years of experience in the field of deep learning and machine learning. He has previously worked with Uber and Federal Reserve Bank of New York Research and Statistics Function, where he held the positions of Software Engineer - Data and Researcher, respectively. Michael is an expert in deep learning and optimization, and has given workshops and invited talks at various conferences such as ODSC East, PyBay, PyGotham, QConSF, PyData LA, and GOTOconf. He has also been a grant awardee of the National Science Foundation Graduate Research Fellowship in Economic Sciences. At Opendoor, Michael is currently working as a Manager of Algorithmic Valuations, where he leads a team that designs and deploys algorithms to accurately value and make offers on hundreds of thousands of homes each year. He has also contributed to the Opendoor Engineering blog, where he has shared insights on adapting novel algorithms to the real estate domain. Michael is proficient in various data and modeling tools such as Scala, Spark, Hadoop/Hive, Presto, MemSQL, Pandas+Dask, Flink, Postgres, Redis, PyTorch, Jax, Tensorflow, and Keras. He is also experienced in data engineering and data science, and has a strong background in infrastructural development. Overall, Michael is a highly skilled and experienced deep learning engineer with a strong background in data science and infrastructural development. He is passionate about developing novel algorithms and models to solve complex problems, and is always seeking new challenges to further his expertise in the field.